IBIS-AMI: AMI Modeling Using Scripts and Spice Models

Preface:

This blog post is written in preparation for the upcoming IBIS summits at EPEPS (San Jose, Oct/18/2017), Shanghai (Nov/13/2017) and Taipei (Nov/15/2017), where I will present paper of the same topic. Slides, example models and audio recording will be made available below:

Motivation:

Many years ago when I entered the signal integrity field, we analyzed the channel by performing spice-like simulation for several hundred nano-seconds at most. Post-process were then done to get FOM metrics. At that time, the bus speed was barely around 1Gbps. These days, high speed-IO SERDEs are common among various computing devices and their speed reaches multi-Gbs or higher easily. Not to mention the several new 802.3 network protocols which have even higher speed (50G~ >100GB). With such high data rate, one needs to “simulate” number of bits at 1E12 level to reach certain bit-error-rate (BER, say 1E-12) with certain confidence level (CI, say 99%) . As such, traditional SI analysis method is no longer valid because it is simply inpossible to simulate so many bits in reasonable time using spice-based simulator. A new channel analysis methodology, like link analysis, is thus needed and invented around year 2003 to address this problem (e.g. StatEye). For link analysis, traditional buffer models such as IBIS are not much useful as they mostly time-domain based. Algorithmic modeling interface (AMI, a subset of IBIS) models are used mostly instead.

AMI modeling is very technically challenging, it requires cross domain expertise such as simulation, modeling and C/C++ programming across different OSes and platforms. Thus it usually takes much longer for an engineer to ramp up to be able to develop and deliver a model when comparing to traditional IBIS. Two of the big hurdles which cause this slow ramp-up for AMI modeling are the requirements to express the circuit’s behaviors in C/C++ language and then be compiled according to Spec’s API requirements. To lower such barriers, we are asked often: 1. Can we create an AMI model using scripting languages? 2. Can I simulate existing spice models using link simulator before committing to develop a full blown C/C++ version?

We propose approaches to meet these two common requests in this presentation.

Background:

Channel analysis: Nowadays the high speed link analysis most definitely includes stages such as Tx/Rx EQ, which are beyond traditional IBIS. Equalization is needed to compensate channel noise such as inter-symbol interference (self-channel interference) and crosstalk (co-channel interference). These EQ stages can open “eye” from a closed one of a noisy channel, represented by S-parameter interconnect.

There are two analysis methodologies for modern link analysis:

  • Statistical: If the circuit is linear time-invariant (LTI), one can obtain many information about channel’s limit by using a single pulse or impulse response. In this flow, a channel is assumed LTI (s-parameter may need to be enforced/fixed in terms of causality, passivity etc first). Its impulse response is then “fed” into Tx/Rx circuitry to obtain the response. By using superimpose (or superposition), response of the channel + circuitry of  different UIs are added together and probability of various BER level can be computed from there.
  • Bit-by-bit: If a circuit is non-linear time variant (NLTV), then such superposition is not allowed. In that case, a bit-stream may be fed into Tx/Rx circuitry by link tool/simulator to obtain their continuous time-domain response. These outputs are then “convoluted” with LTI channel to obtain overall channel response. In order to do this for many millions of bits, some assumption needs to be made (high-z, to be discussed later) in order to gain speed performance when comparing to same time-domain spice-like nodal analysis. Also, the link tool may break bits to several chunks and feed to Tx/Rx separately before combining them together, with “aliasing” of adjacent chunks of bits being taken care of properly at the link tool level.

AMI models support both of these two channel analysis methodologies.

AMI Model: an IBIS-AMI model contains several parts:

  •  .ibs file: In the .ibs file, there is a section called “Algorithmic Model” which points to the paths where the .ami and .dll/.so files reside. This keyword block also provides info such as bit, OS platform and the compiler used to generate the .dll/.so files. Other than these info, the .ibs file and AMI model it points to are basically independent. Further more, in the link analysis, traditional IBIS part are often considered “analog front end” and is “absorbed” into either the AMI portion or the channel portion of the data.
  • .dll/.so file: This is compiled binary format. The language MUST be plain old C and it must be compatible defined AMI API spec. in order to be able to loaded by the link simulator.
  • .ami file: This is the plain text file which contains “config. settings” for the binary .dll/.so files. As shown in the picture below, it has two main sections:

  • Reserved Parameters: This block is usually at the top of the .ami file. These settings are for link simulator only. Depending on part of the settings in this block, such as “Resolve_Exists” or “GetWave_Exists” set to true or false, the associated API functions are invoked by the link tool.
  • Model Specific: This block is usually at the bottom of the .ami file. It contains AMI model developed defined parameters which link tool and API spec does not interfere with. While there are many “text” in this block, when the .dll/.so portion of the AMI model received their info. passed by the link tool, they have been “filtered” and converted to simple “name-value” pair as shown above. So while the depth of the “tree structure” for this model specific section can have many levels, the parameters received inside the model can be just two levels at its simplest form with model name (RX_model above) at the root and the other name-value pairs as “leaves”.

AMI-API functions: As of IBIS Spec. V6.1, there are up to five API functions can be used in a compiled AMI model:

Among which, AMI_Resolve and AMI_Resolve_Close are for string parameter pre-processing which can be ingored in a lot of cases. AMI_Close is like garbage collection/clean-up to release allocated memory, so is trivial is most cases as well. Modern OS may reclaim memory space back even one does not do any “Free” or “Delete” there in AMI_Close. Two most important ones, AMI_Init and AMI_GetWave, are marked in red.  In particular, they participate in the aforementioned “Statistical (for LTI)”  and “Time-domain bit-by-bit (for NLTV)” models. That is, for a LTI model, its AMI model must/should implement the AMI_Init function call. A LTI model can also implement AMI_GetWave function call but this is optional. On the other hand, a NLTV model must implement AMI_GetWave function while implementing AMI_Init as “initialization” rather than “computing” portion of the codes. The bit-by-bit convolution part of a NLTV model should be implemented in the AMI_GetWave part of the codes.

When looking at the function declaration part of the spec, as boxed in red in right part of the image above, one should also realized that the first arguments (an array represented by double pointer) serves as both input and output purpose. These are “pass by reference” arguments as they are pointer. So at the beginning of the AMI_Init/AMI_GetWave calls, the model can obtain either impulse response of the channel or digital bit sequence from the simulator via this pointer. Then the model perform necessary computing using info from the rest of the arguments (some of them also serve as output purpose, such as char **msg, but is not that important in this context). At the end of the computation of this function call, the modified response must be filled in back to the address where the first arguments points to, so that the link simulator will retrieve the values and carry on the rest of the analysis.

AMI Modeling Flow:

A typical AMI modeling flow involves the following steps:

  1. Identify the behaviors of circuits being modeled. As we are going to use a computer language to describe the model’s behavior, we must know how it works first. These behaviors can be obtained via either mathematical derivation, simulation results or measurements. In the last two cases, a look-up table may be used inside the model.
  2. Code the behavior and IBIS-AMI API: There are two parts of this section. The API part MUST be implemented in C (not even C++). The other part of the codes can be in any language, shape or form as long as the developed C codes know how to communicate and exchange the data. That is, the actual computing part, can be in language other than C/C++ if you like or even be completed via circuit simulation.
  3. Compile and link as .dll/.so: This is the compilation of the strict C portion of the API. In windows, one usually needs to compile for both 32 and 64-bit. On Linux, on top of different bits, one should also test on various distros (debian or red-hat based) as they may use different version of GNU C (and thus support different version of C spec. e.g. C99 (1999) or C11 (2011)

Now let’s talk more about item 2 above. There are many considerations on how you should code this part. A specifically C/C++ coded model for one circuit will mostly run very fast. However, it may requires frequent re-compilation/re-testing when new design comes. If we can make this part as simple as possible and non design specific, such as calling external scripts, then this work may only need to be done once as all the variation are now external to the compiled .dll/.so.

Modeling with Scripts:

Knowing the requirements of the AMI API, we can now propose a flow to create an AMI model using user’s favorite scripting language:

Flow:

To support script based AMI modeling, we need to have a thin API implementation (in C, as required by AMI Spec) whose sole task is to translate all the received arguments into a text format and write as a text file. It will then pass the path of the text file to user defined scripts or batch file via system call. Location and type of the script is defined in advance in plain-text .ami file. The script needs to retrieve the argument information first by parsing the plain text file, perform necessary computing, then write into another or same text file which this AMI model with thin layer knows where to find. So when the script completes, the AMI model will parse the text file generated by the script, fill the information back to the aforementioned “passed by reference” pointer array, then complete this step. As discussed, whether the scripts is needed for either AMI_Init or AMI_GetWvae or both are pre-defined based on circuit behavior. And since this is developer chosen favorite language, parsing and writing to text file should not be an issue when comparing to say C language. Lastly, should there be any information need to be passed between API calls (such as model member’s values between AMI_Init and AMI_GetWave), they can also be file based. To summarize, the AMI model with thin layer completes the API calls with upper simulator like other regular AMI models. However, its “transactions” with underlying user’s scripts are all file based.

Example:

A matlab example of the AMI_Init is shown above. In the matlab codes, it first calles parseInput function, which is a text file prepared by the thin AMI model and contains input waveform. It then performs computation such as convolve with FFE, then the result matrix is written back to the text file via storeOutput function call which thin AMI model know where to find. Since matlab’s “conv” function is used directly, the model developer does not need to deal with c-based implementation details such as memory allocation FFT/iFFT in some cases or other math library linking/compilation.

Considerations:

While script based AMI development is simple and handy, there are several considerations before deciding to release such models:

  • Performance and distributional: Since all communications between thin AMI model and user’s script are file based, it inevitably will suffer some performance issue. If this is AMI_Init, it’s only called once by simulator during analysis and such performance penalty is less of a concern. Next, one must also consider the how the mode can be distributed? If the script is in matlab .m file, then model clients need to have matlab environment installed as well. If it’s in compiled matlab, then client needs to install matlab compiled runtime (MCR). If the scripting language is in perl, then perl interpreter, which is usually installed by default on linux but not Windows, is needed. To distribute such interpreter, one must also check the license terms and then also think about the elegance of such model release.
  • Consider Python!: Python is a very worthy candidate here because it has rich math or matlab like libraries such as SciPy or NumPy. More importantly, there is a mechanism called “embedded python” in which the whole python interpreters together with math libraries used can be bundled and distributed in a single zip file. That is, the end user does not need to install python environment first as the thin AMI model already linked with C-Python and can find all required functions in either user’s scripts or bundled zip file.

Modeling with Spice circuits:

Now that we know a thin layer can call external script either directly or via its interpreter, we may also come to the conclusion that it can also call external program such as a circuit simulator. This is of course true!.

An assumption we mentioned at the beginning of the post is the “High-Z” condition. In a typical spice-like nodal analysis, there are many “Newton-Raphson” iteration going on within same time step. At the beginning of each Newton iteration, tentative voltages are given at each node. Each components then compute the drawing or output current into these nodes based on these voltages and its own behaviors. At the end of the iteration, circuit simulator solves the system matrix to see whether KCL/KVL reaches balance and then determine either another Newton iteration is needed or it can march into next time step.

In channel simulation, such iteration is not needed as there is a “High-Z” assumption… and that’s why it can run much faster than nodal spice simulation. In High-Z assumption, each blocks is assume to have high input impedance and output impedance so it will not draw any current at the input and the output is set once determined. Since the thin layer AMI model can obtain the inputs from simulator via API call, if it can perform another task… such as convert this inputs input PWL source with time step equal to UI/number_sample_per_UI (both are know and passed by the simulator), then it can theoretically call a simulator to drive user provided spice subckt like above. Note that the input waveform is just voltage which represents potential difference of two nodes. So there is no reference to GND at all. It’s subject to user’s spice circuit to determine what the reference is and provide GND reference if needed.

Flow:

The picture above shows the flow to simulate/model AMI with spice subcircuit. It is very similar to the flow for AMi with scripting language. First the thin layer AMI model need to generate a PWL source dynamically based on the provided inputs. Then form (either write out as a file or internally in  memory) a netlist as a driving circuit and probe at the output. This driving circuit will use user provided spice subckt with possible value overrides defined at the .ami file. Then thin layer AMI will call external spice simulator (or internal API) to perform nodal based simulation. The output (like .tr0 for HSpice) is then processed and its value is again filled back to the initial API pointer array to return back to upper circuit simulator.

Example:

An example is shown above. The template is pre-defined with the PWL source and path to user’s spice circuit being left to be filled-in. The thin AMI generates the netlist with all values filled properly upon being called by simulator. It then call external simulator to do nodal simulation. Resulting waveform are post-processed and filled-in to the API argument memory address and complete this API call.

Considerations:

Similar to the AMI modeling with script language case, there are several considerations when adopting the spice-based AMI modeling approach. First is the performance… as each AMI API call involves nodal simulator initialization (allocate matrix, solve for DC, Newton iteration between each time sample etc), it will be significantly slower than pure C implementation. However, this is less of a concern if only AMI_Init is needed as it’s called only once. More over, one does not need derive any equation or do any coding at all and can get accurate link performance using this Tx/Rx circuitry directly… so development time is saved significantly there. If one decides to implement such block in C/C++, then simulation results obtained during this process can serve as a very good reference or correlation data for C/C++ based model to be developed.

Another consideration is again the distribuability: If this spice model has particular MOSFET model and requires say HSpice, then the AMI model recipient also needs to have HSpice in their environment in order to run. While commercial simulator like HSpice may not provide API or serve as shared library, many open source ones do. Examples are NgSpice and/or QUCS. In these case, the compiled thin AMI model is basically a simulator in .dll/.so form and can perform simulation all by itself. The binary size is around 8MB larger then without as it also needs to link with all simulator supported device models as well.

Summary:

Using either scripts or existing spice circuits for AMI modeling is actually doable. The presentation I give here is not just talk on paper. The implemented “thin-layer AMI” and examples are also provided together with the slides. These flows can be considered as part of the AMI development process as they can shorten the modeling cycle significantly while providing data for correlation should one decide to go full C/C++ implementation at the later stage. The consideration points includes performance, elegance of released models and distributability of either the script’s interpreter or simulator. Also a thin layer AMI models is needed. This thin layer API is called Proxy model in computing science terms. As a matter of fact, SPISim has implemented such proxy models and made them available for public to use free of charge. [Link Here] Having that said, this API model only needs to be done once as all the model variation are located externally in user scripts/spice models. and thus require no re-compilation when design changes. Nevertheless, these two possible modeling approaches provide an AMI model developer alternative ways to decide on how a model can be developed more efficiently and effectively.

IBIS-AMI: A case study

IBIS-AMI modeling is a task usually executed at the end of IP development process. That is, hardward IPs are created first, then associated AMI models are developed and released to be accompany with this hardware IP since the IPs vendor usually does not want to expose the design details to the their customers. On the other hand, it is also likely the case that a user may have received behavioral or encrypted spice models first, or even obtain measurement data from the lab, then would like to create corresponding AMI models for channel analysis. Such needs arise usually because either original IP vendor does not have AMI model or may charge too much. It is also possible that end user would like to explore different design parameters before determining which IP/part to use.

In this post, we would like to discuss how such an usage demands can be accomplished with SPISim’s AMI flow without any C/C++ coding or compilation for AMI. Here are the topics for this case study:

  • Collateral
  • Design spec. and modeling goals
  • Modeling process
  • Model release
  • Other thoughts

Collateral:

For this design, we received a testbench for a typical SERDES channel using this IP. The schematic is shown below:

Both Tx and Rx are encrypted hspice models with adjustable parameters. When picking one set of parameters to test run the channel test bench, we get valid simulation results.

As this is a hspice netlist, we have the freedom to probe different points and change the simulation options as needed (for example, from transient to ac or transfer-function).

Design Spec. and modeling goals:

The original IP vendor only provides simple descriptions for various blocks and design parameters. The Tx and Rx packages are spice sub-circuits and channel is a s-parameter file. Both Tx and Rx model has typ/min/max corners. On the Tx side, there are settings for voltage supply which is independent with the corner settings, a 7-bit resolutions for a multiplier to work with the swing amplitude, a 6-bit resolution for a de-emphasis and a flag for turning-on/off the boost.

With these IP design spec, the modeling goal is to create associate AMI models which will allow end users to fine tune performance with similar parameters like original IP. Further more, SPIPro is used for its spec. AMI model so that model developer should not need to write any C/C++ codes or perform any .dll/.so compilation for this AMI modeling.

Modeling process:

If one needs to create AMI models from simulation or measurement results, that usually means he/she does not have original design details or even design spec. available. So the so called “top-down” approach will not work simply because there is no design at all to translate to corresponding C/C++ code. Instead, we need to create an architecture via trial-and-error to “reverse engineer” the design so that the performance will match what’s given. Here are the steps we used in this case:

  • Create Tx/Rx test bench:

While the full channel test bench demonstrate how the Tx/Rx work, it also includes information not needed in the AMI modeling. For example, package and channel are usually separated from the AMI model because they are usage or client specific. Besides, as mentioned in previous posts, one assumption of AMI model is high-Z input and output. So the first step of the modeling is to separate channel from driver/receiver and create Tx and Rx only test bench together with inputs and outputs.

For Tx, it’s a simple PRBS input with output connecting to 50 ohms loads.

And its input/output should look like this:

When we modify the test bench to connect Tx directly to Rx, i.e. bypass all the package and channel models, we get such input/output eyes:

From these eye plot, we do not see DFE like abrupt force-zero output, thus the Rx model seems to be a CTLE like boost filter. So we created Rx test bench like below and obtain its frequency response:

Note that in order to perform what-if analysis of next step, we also use VPro to capture inputs to these Tx and Rx models so that they are evenly spaced.. as required by AMI’s Init and GetWave function. These evenly spaced inputs will be used later on by SPISimAMI.exe to drive the model. For HSpice, the following option may be needed so that the .tr0 file will have evenly spaced data points even simulation is various time-step sized:

  • Define architecture:

Now that we have input data and desired output for this particular set of parameters, next step is to explore whether SPISim’s existing spec. model is sufficient to meet the performance.

Tx: for tx module, we observed the following two characteristics:

  1. Has de-emphasis, happened after the peak
  2. Swing range and offset are different between input (0 ~ 1) and outputs (-0.5 ~ 0.5). Also there are loading effect so rising and falling have RC charging/discharging like behaviors.

For the de-emphasis part. We can use Spec. AMI’s what-if analysis to see how different pre/de-emphasis settings will cause difference responses:

We change these pre and post cursors one at a time only, using -0.1 for value .

We then test drive using built-in PRBS with same UI time, 200ps:

From the summary plot above, and correlate with the Tx output, it’s apparently that this Tx has one post-cursor FFE beause only this set-up gives de-emphasis after one UI after the peak.

For the output swing and slew rate, it’s the sign of AFE (analog front-end). So we can simply add an AFE stage right after FFE, with high/low rail voltage “clamped” to the simulated peaks:

With several trial-and-errors, all within SPISim’s Spec-AMI GUI, we obtained the following Tx correlation between spice test bench and AMI results for this particular set of parameters:

Similarly, as Rx behaves like a continuous filter, we use a CTLE stage to mimic its behavior:

With several tuning, we obtain such Rx correlations:

From these correlations, we believe that the stages built-in to the SPISim’s AMI are sufficient to meet the performance needs. So next step is to find the parameters to support all these design specs.

As similar process between Tx and Rx, we will use Tx as an example to demonstrate the flow.

  • Simulation/Sweep plan:

Before generating a simulation plan, we first also make sure these design parameters are independent with each other. We performed simple 1D sweep (several points) in particular for the swing and de-emphasis level.

Notice that the slew rate and peaks are not affected by de-emphasis level much. In addition, the spacing seems linearly spaced according to the bit settings. With this, we can sweep them independently.

Now for Tx, a full factorial sweep will require 2^6 (de-emphasis level) * 2^7 (swing level) * 2 (boost flag) * 3 (corner) * 3 (voltage levels) ~= 150000 runs. Due to the linearity, we do no need to sweep full 6 and 7-bits of de-emphasis and swing levels. Instead, we increase step interval for simulation because SPISim’s model’s built-in interpolation can be applied automatically, more about this at the model release section.

For these five variables, we use MPro to generate 2403 test case (1/60 of full factorial). We then create a template from the Tx spice test bench so that parameters can be updated according to the table and its column header above:

Noticed that variables are enclosed with “%” (e.g. %XVPTX%). Their values are replaced by corresponding values in each row of the table to generate a new spice file. With all these 2403 spice file generated, each for different combinations of parameters, we kicked-off the batch mode simulation and obtained all two thousand cases within one hour.  This is done all within MPro and can also be done using user’s script.

  • Parameter tuning:

With all results being available, next step is to find corresponding AMI parameters for each of the case. SPISim’s model driver, SPISimAMI.exe, is a must have here. It will take the collected, evenly spaced inputs to drive the AMI parameters together with the SPISim’s spec. model, output results can then be correlated with the .tr0 file simulated with HSPice for that particular case. In case when the correlation is not good, AMI parameters should be updated again for another iteration of performance matching. This process is summarized in the flow below:

Apparently, such as task is too tedious and error prune for a human to perform. Since the input pattern is the same, relative timing locations of different properties (such as peak and de-emphasis levels) are also the same even between different test cases, we can process these data automatically. Regarding “adjusting” AMI parameters, a simple bi-section search will quickly converge to the desired de-emphasis tap values for the given test case.

In our process, we first parameterized the .ami file as shown below. This ami file is based on the architecture identified in the second step… only that now we need to find different values for different parameters. In particular, the swing level and FFE post tap are unknowns and need to be swept.

Because these two variables are independent, we can first identify the peak value as “Swing” from the .tr0 simulation results, then use bi-section to adjust the FFE_PARM_POS1 such that the output from SPISimAMI for this .ami file settings and SPISim’s spec .dll/.so will match the tr0 data.

With this flow and process, we use our SPIProcPro to bath process the simulation results and get all 2403 fine-tuned AMI parameters within one hour:

Here is the partial config. settings for SPIProcPro of this modeling process:

For each of the test cases, the post-processed AMI result is saved to an individual .csv file. All these csv files are also combined and then concatenated side-by-side with original input condition. The end result is a table with input condition at the left and associated AMI parameters at the right. This table will serve as pre-set data table for our AMI models to be released.

 Model release:

The combined table looks like below:

The columns boxed in blue above are those required for SPISim’s AMI spec models. These columns has header named exactly the same as those in the ami file green boxed below. The row entries for settings of each test case in the csv table is filled in to the boxed in red in ami file below.

Now this is not the format to be released to the AMI user. In stead, the design parameter we want to expose to the end user are listed in the red box in the csv table. They have the column headers matching original IP’s design parameters. Also note that this csv table only contains partial results as we didn’t perform full factorial sweep for all ~150000 cases.

The design parameters, boxed in red in csv file, need to be able to be mapped to those boxed in blue, the AMI stage’s parameters.

One unique feature of SPISim’s AMI model is it supports of table look up and up to two dimensional linear interpolation for numerical values not found. The set-up is easily done from model config. GUI. The example below is for PCIe with 11 preset tables.

In this case, we do not use preset index. Instead, we set the preset index value to “-1” to signify that model’s table look-up will be used:

In the final .ami file above, the exposed parameters (boxed in red) are exactly those defined in the original given IP. Their name and values will be used to look-up from the table to find matching row. If up to two rows have no exact match in terms of numerical value, linear interpolation will be performed by the model during initialization. A final matching row or values will be used to assign to the parameters required by spec model.

The final results of the create model is one .ami file, one .csv file (plain or encrypted), and one set of .dll/.so file. There is no need to write any C/C++ codes or even compilation for the AMI modeling purpose and the SPISim’s spec. models can be used directly. For the post-processing portion, a python/perl/matalb scripts can be used instead if SPIProPro is not sufficient.

Other thoughts:

In this case study, a look-up table based approach is used to create AMI model via reverse engineering. Alternatively, we can create a spice simulation session using our SSolver/ngspice spice engine to simulate these given Tx/Rx spice model directly. The pre-requisite for this approach is that the given hspice model is not encrypted, can be converted to the SSolver/NgSpice based syntax and has no process file involved. Regarding search mechanism, a bi-section searching algorithm is used in our modeling process here as there is only one value (EQ variable) needs to be determined. The amplitude/swing is deterministic and can be found once tr0 simulation results is post-processed. If there are more than one variable involved for optimization , such multi-tap EQs, then a gradient descent based algorithm may be needed if full surface sweep is not practical.

IBIS-AMI: The black box magic

In AMI modeling domain, several “black box” magics or techniques are very useful. They exist to make the modeling process more streamlined yet flexible. In this blog post, I will share some experiences and thoughts we have during the development of our SPISimAMI flow. Modeling examples from some major companies will also be reviewed

Why the black box magic?

In contrast to other system models such as T-Line’s RLGC data, S-parameters or even traditional IBIS model, the AMI model itself is a black box. AMI models are both platform and OS dependent. They are in compiled binary format in the form of shared library… “.dll” (dynamic linked library) on windows and .”so” (shared object) on linux-like system. The other part of the ami model, the .ami file, is indeed in plain text holding model settings. However, it needs to work closely with the associated .dll/.so AMI model and can’t be changed arbitrarily.

For a model developer, even if he/she has the skills of the C/C++ coding and .dll/.so compilation, the whole process is still very tedious and error prune. For example, the same compilation and testing need to be done twice on windows due to the 32/64 bit difference. It’s even worse on linux. Different machines or OS need to be tested separately because no only there are different linux “distros”, different environments or IT set-ups may also cause discrepancies. As an example, the usage of “boost” or some other libraries like GSL are very common and widely used in C++ engineering/scientific computing development. However, they may not be included in all IT-built, production oriented environment. As a result, AMI models running perfectly fine on the developer’s system may fail to load on others. To be on the safe side, one usually needs link these “non-essential” libraries statically into the .so file, strip symbols to reduce size, and then test on different machines (virtual or not) to be assure for maximum compatibility. These details may explain why AMI modeling charge has been very high in the past and somewhat monopolized by very few companies.

In previous posts, we mentioned the similarities we felt between a circuit simulator and an AMI model. While a purposely built circuit simulator (like those for cache or memory circuit etc) may run very fast and efficient, a general purpose (e.g. MNA based) simulator usually is suffice for most of the design work. In these simulators, primitive device model such  R/L/C/V/I are built-in, even more complicated ones such as MOSFET, T-Line, IBIS are also included and configurable via external files (.snp file, .ibs file and .tab). This simulator, compiled to native format to maximize the performance, are also platform and OS dependent. However, they do not need to be design specific. Those configurable aspects are left in the netlist file…similar to the plain text .ami file. When more sensitive information needs to be provided to the simulator, such as the process file or design IP, they can be encrypted such as “encrypted hspice” does.

From this point of view, we can see that “magic” or modeling techniques can happen in both places… i.e. the .dll and .ami files

Black box magic in binary .dll/.so files:

While there are five AMI API functions defined in the spec., most important ones are AMI_Init and AMI_Getwave. From their signature shown above, we can see that the same pointer (impulse_matrix for AMI_Init and wave for AMI_GetWave) are used for both input and output. That is, the data are passed-in, the AMI functions need to calculate the response based on this black box’s behaviors, then fill the output response back in to the same pointer to return to the simulation platform on top. This can be depicted with a schematic to depict more or less like the one below:

The assumptions implied implicitly in AMI channel simulation is that the input impedance to the AMI model (black box above, such as Rx model) are infinitely high, same for the output stage of a AMI Tx circuit. So we can imagine two voltage controlled voltage sources (VCVS) with gain = 1 are used at both input and output ends, with the AMI model sitting in the middle to transfer circuit block’s response between stages.

The content inside the “black box” are not visible to outsiders, so a model developer can write spaghetti C/C++ codes to mangle everything together and produce module used only once. Or he/she can does a better job by architecting it in a modular way like the one below:

If we image this with a spice netlist, it will be something like a .subckt:

Parameters to different modules are apparent and omitted here. Construct an AMI model this way will enable module being reused more easily. With that said, the example above still has two assumptions: the first one is that only three stages (i.,e. CTLE, DFE, CDR) are used and their order are fixed. What if user want an AGC up front or LPF filter in between? Do we need to rewrite the codes and compile/test again? The second assumption is that these module are cascaded stage by stage (mostly true for SERDES design) and each block has only one input and output (not necessarily true even for SERDES), These two assumptions are not necessarily true for other design. So we believe that they do not need to be “hard coded” and compiled into the rigid AMI model.

In normal modeling process, one usually need to translate a circuit or a module’s response into describable behavior before its C/C++ codes can be written. For example, if we know this is module is a LPF (low pass filter) then we only need to get its frequency response in order to do IFFT and perform convolution in time domain. However, some blocks may not have close form formula or an easily describable behavior. Is it possible to embed a “mini circuit simulator” in a block so that its input and output will be calculated automatically? This is certainly possible. In fact, one can even implement a S-parameter block or T-Line block to serve as analog front end of the AMI model (for example, to represent a package model). In SPISim’s case, because we have developed an in house circuit simulator and associated device model, we simply need to construct a time domain PWL source representing input “impulse_matrix” or “wave” data, then run mini circuit simulation and retrieve output from simulator’s API (or even waveform on disk) and give back to the channel simulator on top.

As one can see, there are a lot of techniques or magics can be applied to this .dll/.so black boxes. Because compiling platform/OS dependent binary are not a fun, it’s our belief and practice that we should compile a comprehensive AMI model and reuse it as much as we can. The configurable part should be put in the .ami file.

Black box magic in plain text .ami file:

An .ami file has settings for both channel simulator and more importantly, the .dll/.so models being called. It contains two parts:

The “Reserved_parameters” (boxed in green) are for channel simulator to consume. The keywords used here are defined in the spec. and need to be used accordingly. All spec-compliant channel simulator needs to be able to process info. in this part (in reality, we do see individual companies defining their own keywords due to aforementioned monopoly situation).

The “Model_Specific” (boxed in red) portion, on the other hand, are only for model to consume. It’s not channel simulator’s business to judge and interfere how they should be defined and be used. As a result, we can perform many creative techniques here.

We proposed some of the techniques first late last year in previous posts. It’s to our pleasant surprise that other company, such as IBM/GlobalFoundaries also has similar thoughts. Please see their presentation at DesignCon this year linked below (Topic time is 10:15AM)

The AMI_Resolve: A Case Study for 56G PAM4   (http://ibis.org/summits/feb17/)

Simply speaking, one can embed lots of information in the .ami file to convey to the underneath .dll/.so models. Common encryption can be used to protect sensitive information. For example, the reason why SPISim can release our AMI models for free to generate and use is because our AMI .dll/.so files require a license setting and a time stamp and/or data dependent values are part of the license info. Thus, a model will expire after certain time, the parameters can be changed if locked, and the license info can’t be tampered.

The paper presented by IBM demonstrate the intercommunication between unresolved .ami file and the AMI_Resolve API function. The “netlist” or “equation” is encrypted and embedded as part of the .ami file. In the IBIS spec, a table format is also supported in the .ami model_specific syntax yet it may be a hassle to produce and use. In practice, we prefer to point to an external file directly like below:

This external file can also be encrypted up front and decrypted by the .dll/.so file on the fly. It can be a setting table, a frequency response, a S-parameter or a netlist to support the “mini-simulator” block within the AMI model. From this point of view, while the .ami file is plain text, it can still be like a black box and many aforementioned techniques to minimize the re-coding/re-compilation of the .dll/.so file can be used.

Real world examples & considerations:

While these “black box” magic are intriguing and wonderful, at the end of the day, we need to see how well they work in real world. Lets see two examples:

The first model is from Altera. Not only do they provide AMI models and documentation, they also provide a GUI tool to let user check what combination of parameter are valid. In this case, they only provide one .ami file with all possible values inside.

The second example is also from Altera but with different approach. There are many different .ami files, each supporting different usage scenarios.

In our approach, we let user to assemble a csv table first with needed columns to provide settings for different modules, each row is a pre-defined configuration so no further check of valid combination is needed.

With this approach, customer only need to change the row index, rather than different settings of different taps or modules. More over, the table is encrypted and the model publisher can provide different set of configuration to different customers, all with same set of data and .dll/.so models.

With our approach, user use our GUI front end to perform settings, then associated .ami file and pre-built .dll/.so files will be generated instantly. The .ami file contain minimized settings to make sweeping AMI parameters more easily. This minimized .ami settings will also make configuration within other EDA tool more convenient. As a result, with the help of front-end GUI, the “black box” magics resides in both the .ami file and .dll/.so models.

Check before use AMI:

It’s worth mentioning that the latest golden checker published by IBIS are now both OS and platform dependent. This is because they support embedded .dll/.so file checking on the most basic level now as well. Regardless whether you are a model developer or a model user, three steps need to be performed to validate the given or modified AMI models:

  1. Use IBIS’s golden checker (IbisChk6) to check the .ibs file. If this ibis file contains algorithmic model section, then the associated .ami and .dll/.so file will also be checked based on the platform. This will identify compatibility issue up front.
  2. Use a test driver to drive the .dll/.so and .ami file. This step is like connecting an ibis model to a test load and run simulation. A model may pass the golden checker, yet if the test load waveform is not meaningful, then there is no need to put into the full channel simulation. This step makes sure not only the model can be loaded (has all required API function defined), but it can also function properly. In HSpice and Ansys, this test driver is called “amicheck.exe” and require respective license to run. We SPISim also provide free checking/driver tool call SPISimAMI.exe for this purpose.
  3. The last step is to put into channel simulator such as HSpice, Cadence’s SystemSI, Mentor’s HyperLynx or sort. They will provide simulation measurement such as eye, BER and bath-tub plot for further validation. These features will also be in our SPICPro later this year (2017)

IBIS-AMI: An economical yet efficient modeling flow

Preface:

It has been often believed that IBIS-AMI modeling imposes a comparably high cost and technical barriers to get started. An AMI modeling engineer certainly has the due diligence to be familiar and understand the basic of link analysis or AMI flow. However, the requirements of implementing the models in C/C++ codes, compiling them into .dll/.so libraries, and being able to run on third party (often expensive) EDA tools with consistent results are often too much to ask for… or at least will increase the design cycle. To meet these challenges, EDA companies such as KeySight or Matlab provide top-down based flow for AMI generation directly from architecture codes. In exchange with the “click-button” convenience, SERDES needs to be designed in its environment first and tool’s up-front expense also needs to be considered. Further more, the long term cost of the generated AMI models (in terms of model support, maintenance and extensibility) are often ignored. It’s also true that even with these top-down flows, compilation on different platforms (win32, win64, linux) etc are still inevitable.

We published several free tools recently and presented a paper at the recent IBIS summit regarding AMI modeling. Together with other open source/free link analysis tool to be mentioned at the end of this post, we think it’s now a good time to consider an alternative AMI modeling flow. This proposed methodology in this post will be economic (no front end tools cost) and efficient (give SERDES/AMI developer most control).

[Note that in this post, we use link tool/simulator alternatively to represent the application loading IBIS-AMI models and calling their functions.]

Concepts:

Engineers familiar with Spice-like simulator know that we usually only need one platform specific simulator (binary). Schematics netlist is in plain text format and can be used cross different platforms (assuming good practice such as relative modeling path, file line endings are followed). A general purpose spice simulator is not design specific so there is no need to have different simulators for different circuit or IC design. This compatibility is achieved by building on simple rules, i,e KCL and KVL and linear algebra. This way a simulator can decoupled from the implementation details of design specifics.

In the “AMI-modeling” world, are there such simple rules to be found so that we don’t need to compile a binary just because the design is different? Understood that there are always trade-offs: e.g. simulation speed of design specific binary model vs slower yet convenience during modeling cycle, then at least we should find a way so that modeling engineers can focus more on the basic of the algorithmic blocks and hopefully, still find a way in a later stage to generate a speedy model with minimum extra tasks (C/C++,dll etc)

AMI model, at its current scopes, is mainly for SERDES application. They are mostly point-to-point system or can be processed stage by stage. We may also think it this way by looking at the defined AMI-API function prototypes:

AMI_Init and AMI_GetWave are two main processing routines defined in the API spec. Various arguments are passed-in and the AMI models are responsible to perform designed algorithmic within the model then return these values in place. By “in place”, we mean the impulse_matrix’s and wave’s content will be modified in the same memory address before returning back to the calling application… which is mostly simulation platform or circuit simulator.

Based on these observations, we can liberate the couplings between 1) link tool and models, and 2) models and underline algorithmic processing. Via these decoupling, an economic yet efficient flow can be made possible.

Decoupling within the model:

First, we want to observe how data are being passed from the link tool to the model. In the schematic above, the input to the Tx stage is either channel’s response (LTI mode) or bit pattern (NLTV). If the Tx is a simple pass through, then Rx will receive similar information without being affected by Tx:

Take AMI_Init as an example, we can achieve this decoupling using SPISim’s free SPISimProxy model. SPISimProxy will write the argument received from the link tool to a plain text file. After the AMI model’s processing, it will again write out the processed data to another text file before finally giving back to the link tool. This way data being exchanged between link tool and the model are now exposed… even if they both are compiled binaries. The two blue boxes above represent such generated text data. The main function of the AMI_Int function, purple block in the middle and existed in the model’s .dll/.so file as a form of the C function, is to transform input to output response.

With this, we can now replace the AMI_Init function with our own… we can write this function in matlab, python, perl, java etc language instead of more demanding C/C++ form. It only needs to interface with two text files just exposed with the following operation sequence:

  1. SPISimProxy will expose the calling arguments into a text file, the top blue box
  2. User’s script will read the text file and perform necessary processing
  3. User’s script will write out the data in similar text format
  4. SPISimProxy will read the generated text data, form argument and pass back to simulator.

Because each of these steps can be customized by Proxy’s .ami settings, a configuration file or even environment variables, AMI developers are now liberated to use what ever languages they preferred without dealing any C/C++ if they like. There is also no need for .dll/.so compilation as SPISimProxy has been pre-compiled to support most of the platforms.

The example above uses AMI_Init as an example, other API calls, such as AMI_GetWave or AMI_Resolve can be done in similar fashion. Clean-up calls such as AMI_Close or AMI_Resolve_Close are also supported in SPISimProxy model so if needed, AMI modeler can also clean-up all these file traces at the end.

Part of the arguments passed from the simulator is the model pointer. This model pointer (void*) is supposed to be persistent during the AMI process. The script author may use file based persistence mechanism across AMI_Init/AMI_GetWave calls to store constructed data structure or settings etc. By avoiding using C/C++ specific pointers or data structures, the process becomes neutral and can support many different languages.

Decoupling with the EDA tool:

The aforementioned process requires an application to drive the proxy model, and thus the modeling scripts. This can be achieved with our free SPISimAMI.exe. It’s again pre-compiled to support most platforms. Its built-in pulse response enable user to model LTI based AMI process directly. For bit-by-bit based input data, user may use our SPILite or other free simulator such as our SSolver or NGSpice to generate bit sequence, then feed this input in .csv format to the application. SPISimAMI will then take this user’s input, form the proper arguments and send to underlying AMI models… it can be modeling script current under development, or an existing IBIS-AMI models for testing or validation purpose. SPISImAMI can also be used to drive an existing AMI models via SPISimProxy so that user may get insights about this process. In the demo video we posted on SPISimProxy page, a matlab script has been used to demonstrate the AMI_Init process.

Test drive with open-source link tool:

Once the given response work properly with the prototype implemented in modeling scripts, next step is to run them in a link tool for BER like analysis. For this purpose, pyBert may be used as it also load IBIS-AMI models, including our SPISimProxy.

Up to this point, there is no front-end cost involved in the AMI modeling process and a developer only needs to use his/her own favorite language to deal with plain-text input/output. In addition, the debugging and testing of the model prototype can be done with direct command call instead of multi-step GUI operation/invocation. This not only avoid license needed with 3rd party tool, but also ensures an efficient work flow with easily repeated consistent results.

Optimization and model release:

There are several possibilities to release the models from on this process:

  • The model publisher may encrypt the script if needed, then distribute as they are. This will produce most accurate results as they have been validated by the author during the modeling process. The disadvantages include that: 1) it’s less efficient as the data exchanged between the SPISimProxy and the modeling scripts are file based, 2) the model recipient may need to install other run time interpreters such as perl, python etc in order to run the encrypt/compiled script, and 3) the client also needs to download the SPISimProxy from our site as unlicensed redistribution is strickly prohibited.
  • We can work with the model publisher to provide specific API to the prototype model such that the IP and accuracy are still maintained, yet the performance will be improved dramatically. We can also remove the unlicensed terms and add the proxy class to have your company’s name so that you can distribute SPISimProxy together with your model.
  • We can also create the corresponding AMI model with pure C/C++ codes to that there is only one model to be released with best performance and convenience for the clients.

The modeling flow suggested above is not proprietary and can also be implemented within the corporation or the modeling team. We believe that with the liberation of the modeling engineers from these unrelated AMI modeling process, they will be able to focus more on the core business logic, i.e. the algorithmic part and deliver the best quality model for the industry’s progress. Those non directly related tasks can be left for other EDA professionals (* cough *) if needed.

 

S-Param: Analysis report

Preface:

A S-parameter model can be obtained from lab measurement using VNA, full-wave field solved from a 3D structure, analytically computed via AC simulation or analysis process such as cascading of various stages. There are many situations when more than one s-parameters will be generated for similar setup. When dealing with multiple s-parameters, one should be able to quickly batch process and generate a summarized report for desired performance or measurements. Equality important is to be able to investigate and debug on a case by case basis. Both of these functions are also must haves for an EDA tool supporting S-parameter models.

Example report from the PLTS tool

In this post, we would like to discuss points of considerations when planning for these types of analysis and reporting capabilities. We would also demonstrate how they are accomplished in our SPIPro tool. Most of the mathematics for these processing can be found on website such as RFCafe or matlab RF toolbox.

 

Analysis:

There are many analyses often used so far as s-parameter is concerned.  They can be classified into several categories:

  • Conversion: such as converting a single ended S-parameter to a differential one or one with mixed mode (mixed single and differential), converting between S and Y, Z, ABCD formats, etc
  • Extraction: such as trimming unwanted frequency data points, extracting subset of S-parameter, or calculating physical properties such as impedance, effective inductance etc
  • Generation:  such as cascading different stages together, either assuming generalized 2N port in a point-to-point topology, or arbitrary ports branching out like those multi-drop situations in DDR, combining S-parameter data in other fashions such as merging, synchronizing frequency samples etc.
  • Processing: such as smoothing the data using moving average, extrapolating toward DC and high frequency based on physical properties, renormalizing reference impedance to different values.

10 of 30 SPIPro’s S-Param analysis capabilities.

Based on our experience, Convert, Cascade, Renormalization and Quality check are most frequently used. For mixed mode conversion, several port ordering scheme should be supported: incremental, interleaved or even-odd mode.

The outcome of the processing above are mostly another s-parameter. Thus one should be able to inspect selected individual Sij element either visually or textually further. For the same set of data, it can be plot in different format to reveal different properties or behaviors. For example, X-Y format by default, polar plot for causality inspection and smith chart in either Y or Z format etc. Since only several Sij elements are plot instead of the full S-parameter, some analysis can be done almost in real time to provide feedback: time domain reflection (TDR), time domain transmission (TDT), worst case eye analysis based on pulse distortion analysis or even passivity plot etc.

Texture representation can be useful when one needs to copy part of the calculated data for further analysis, such as taking effective inductance for power integrity simulation. Color coded matrix at the first and last frequencies are also useful for quickly checking connectivity or high inductance ports.

 

Report figure:

The analysis process mentioned above are good for investigation or experiment purpose. When there are more than several s-parameters need to go through similars sequence of operations, being able to batch process and report becomes important. In this case, the input conditions such as number of ports, their mode (single ended or differential etc) port ordering and reference impedance etc are specified up front (via GUI or a template). This settings will be applied to all the given s-parameter such that after they are read in by the tool, this sequence of actions will be performed automatically. After calculation,  report in the form of various figures, statistics and csv can be generated:

  • Frequency domain figure: one or more selected Sij traces from the processed data can be plot in the X-Y chart in linear or log scale. Waveform calculation using real or complex number with various operator including log and power should also be supported. Multi stage limit line can be added to identify boundaries which signal traces should not cross.

One or more such figures can be defined for different data and measurements.

  • Time domain figure: For time domain, Sij needs to be converted using iFFT first. To achieve certain time domain resolution, extrapolation to higher frequency and data smoothing using moving average may be needed. There are several time domain metrics often used: TDT, TDR, crosstalk, inter-pair and intra-pair skews etc. Input pulse’s rise time may affect the resulting slew rate so it needs to be part of the settings. Again, one or more such time domain figures can be pre-configured so that their settings will be applied to all s-parameter data.
  • Statistics figure: The timing or skew measurement can be summarized using a histogram with distribution curve. Their data in csv format can provide more detailed info. for individual cases.

Allowing further customization such as various font size, frequency unit, grid line etc will make the generated reports more appealing aesthetically. Finally, these settings should be allowed to be imported or exported for later use. Resulting report can be in html or pdf format with indexing at the top of the page. With this kind of capability, analyzing a set of s-parameters become easy and efficient.

 

Report data:

When there are even more s-parameters to be processed, such as hundreds or even thousands of cases from analysis methodology such as design-of-experiment or multi-dimensional sweep, even visually check with reporting becomes time consuming. In this situation, batch processing to extract performance value, summarized in a multi-column csv fashion to be used for statistic tool such as our MPro or JMP is more practical.

User first specify all the files to be processed, then specify one or more time domain or frequency domain performance targets. The processed results can be visualized as a summary plot. Interested cases or outliers can then be selected to generate more detailed report or investigate individually.

Unless specified, all the screen captures used in this post are from our SPIPro product. All the mentioned functions and capabilities are also supported in this product.

S-Param: Quality checklist

Preface:

In a typical system, there are usually three type of models involving in simulation or analysis:

  1. IBIS/IBIS-AMI: At the both ends of the channel are driver and receiver models, usually in the forms of spice, IBIS and/or AMI.
  2. RLGC: Interconnects with homogeneous structure such as transmission line and/or layer stackup are usually 2D/2.5D field solved and represented with frequency dependent RLGC matrices
  3. S-Parameter: Complicated passive structures such as package, connector or via must be solved with full-wave simulation to have accurate representations, which are almost always S-parameter.

In the recent years, due to the popularity of link analysis for low BER requirement (i.e. various SERDES interfaces), S-parameter has become more and more important. A s-parameter is now often used to represent the full channel (package + t-line + via + connector etc). In addition, there are lab measurements from VNA which are also in the form of S-parameter. Even when behavior models (broadband spice circuit) of these components are used for time domain simulation, they are also converted originally from S-param via algorithms like rational fitting.

Each of these S-parameter may have their own noise sources, bandwidth limitation or numerical inaccuracies. As a result, one has to check the quality of the S-parameter models to avoid numerical errors or instability (e.g. simulation convergence) of the analysis at the later stages.

There have been several documents floating on the web discussing about checking list for a given S-parameter, albeit lack of systematic approach or industrial support across different vendors. For this purpose, IEEE P370 committee was formed in 2015 to discuss and define a spec. as a reference. Task group 3 of this committee focuses in particularly on the S-parameter quality check. As a participating company in this joint effort since the very beginning, also as the spec. draft is coming into shape at this moment, we think it’s a good time now to share the collective thoughts and also demonstrate how these ideas are implemented in tool like our flag ship product, SPIPro.

 

S-Parameter quality check:

A S-parameter checking list can usually be classified into two categories: generic check and application specific check. This post focuses on the generic check, which is defined as checking based on their physical properties. For the latter one, application specific condition such as signaling speed, typologies, modulation method or even coding are further imposed. Generally speaking, there are several generic checks need to be done:

  • Passivity: to make sure data is passive and has no amplification
  • Causality: to make sure properties of cause and consequence are maintained
  • Reciprocity: to make sure data is symmetric
  • Even/Odd/Asymptotic behavior: to make sure even and odd behaviors are maintained at both DC and high frequencies
  • Connectivity: to make sure connectivity of structure extracted is correct and has no broken link(s)

In addition, reports from all of these check should be presented clearly with an overview and further details available. The overview summary should also use a single number for each check as well as quality metric coded with color for different level:

  • Good: Green
  • Acceptable: Blue
  • Inconclusive: Yellow
  • Bad: Red
  • NA: for information only, such as connectivity

Numerical criteria for these levels depends on the types of check. Lastly, visual checks should also be used in some cases to provide frequency specific information such as where the issue happened.

Full mathematical details and explanations of the sections below will be in IEEE P370 spec.. It will be made available free to public upon completion. We will not duplicate the details here but only giving high level description and demonstration usage below.

 

Passivity:

Power injected into a S-parameter, which includes those from incident and reflected waves, should be preserved without amplification. When this is true, then S-parameter is passive and does not contain any energy source. Mathematical requirement is:

Visually, we can plot the given traces (Sij) and see whether they cross threshold of 1.0:

And/or compute a summarized metric, Passive Quality Metric (PQM):

reported as a single value:

One way to fix a non-passive system can be use the largest (violated) value to normalize the rest such that no value is larger than 1.0, thus passive.

 

Causality:

Causality check is the most important, yet more complicated one for a S-parameter. A necessary but non sufficient condition of being causal is that the even and odd function behavior of the real and imaginary data at DC should be satisfied. Adding being passive across full frequency range will make the condition sufficient. So a causality check essentially includes many of the other checks needed for a s-param.

Due to the complexity of the math, such comprehensive check is not easy. As a result, a visual check (heuristic) may be used. This check is based on the observation that a polar plot of a causal system rotates mostly clockwise and is often very smooth. The small inner circle is usually where resonant happens which may degrade the causality of the overall data:

The progress of phase between two adjacent vectors (Sf0, i, j to Sf1, i, j) can be calculated and normalized with their dot product and magnitude. The results should be a value falling between -1 ~ 1. Negative value represents decreasing group delay and thus a counter-clockwise phase change between these two vectors. This is where resonance has occurred. One can plot such measure against each frequency and got its visual representation:

Often time, through type traces of a given s-parameter (S12 or S21) are used for iFFT for corresponding pulse or impulse responses. One can calculate this for each frequency of each through through type signal and have a summarized metric: Causality Quality Metric (CQM)

And report can contain more details about violations of different frequencies:

The fix of a non-causal system usually involves reconstructing a causal system with parameters fit the given data. Vector fitting or rational approximation are such approaches, which will be discussed in future post in more details.

 

Reciprocity:

As a s-parameter is passive, it should not be directional regarding input and output ports. Thus a S-parameter’s content should be symmetric across the diagonal part. Note that this may not be the case for materials which has non-diagonalizable property such as ferrites, yet it should hold true for most of the materials used in system elements and materials.

For a large multi-port system, color coded cell values can be used as a visual check for symmetry (reciprocity):

This needs to be done at least at the first (low) and last (high) frequency content. Regarding an overall evaluation, a Reciprocity Quality Metric (RQM) can be used:

A reciprocity violation’s fix is usually the easiest… one may average values from both sides of diagonal and replace in place.

 

Even/Odd/Asymptotic behavior:

This check is based on the fact that a transfer function should be equal to its conjugate in the negative frequency domain:

As a result, the real part of the data should be an even function while the imaginary part should be an odd one. That means at the dc level, the real data should be flat across freq = 0 axis while the image should cross origin point. Also at the highest frequency, the imaginary data should approach zero. One can think of all the inductive and reactive elements such as capacitors and inductors are either open or short at the lowest (DC) and highest (infinity) frequency point, thus make the transfer function real only.

This even/odd/asymptotic behavior is very useful when the extrapolation toward DC and highest frequency points are needed, such as the case for TDR/TDT from iFFT. DC point in frequency domain will affect the corresponding DC value at the time domain, while the highest frequency available will decide the resolution in the converted data in the time domain.

 

Connectivity:

This is to make sure the structure the s-parameter extracted from does not contain any broken channel. A simple conversion of S-parameter to Y or Z parameter and check their value should reveal the connectivity. Usually doing so at the first data point (low frequency) is enough. Color coded matrix value again will be very useful especially for large s-parameter matrix:

The example shown above is for a full DDR byte lane which involves many DQ and a pair of DQS signals. As DQS is differential, it may also be useful to separate them from the rest of the data with proper scaling for better distinction.

 

Report and summary:

To apply these checks to more than one  s-parameter, a batch mode process is often preferred and will save lots of mouse clickings:

The produced summary should contain with color coded cell and various quality metric number, plus further details for each check of different data. This will certainly gives user better idea about the quality of their s-parameter files.

All the screen captures used in this post are from our SPIPro product, which also implemented all the checks according to the IEEE P370 spec. draft.

 

IBIS-AMI: Analysis using a proxy class

Preface:

A hot topic in recent years about IBIS-AMI is co-optimization through back channel. During this process, either Tx, Rx or both participate to select optimized set of parameters in order to have best link performance. The IBIS spec. as of today does not support this type of communication or data exchange between models, thus new protocols have been proposed. In addition, there are also occasional needs to support 1. simulator or AMI models from other vendors, 2. implemented in different AMI-API spec version, or 3. written in different languages such as matalb. Most of these cases, only their binary formats rather than source codes are available. To overcome these obstacles and bridge the gap, a proxy class based approach is proposed.

This post is written in preparation for the presentation of the same topic at the IBIS Summit during DesignCon 2017 on Feb. 3rd. Audio recording of the presentation and slides have been made available at the bottom of the post.

Roles in an AMI flow:

Readers of the latest IBIS spec. will find that Chapter 10, Algorithmic Modeling, is all about defining two roles in an AMI flow and how they communicate through API. These two roles are 1: EDA tool (usually circuit simulator) and 2: one or more AMI models being simulated. AMI API is defined such that EDA tool knows where the .ami file is and which functions are available in the model .dll and can be loaded. In the current spec., no interaction between different models is possible.

Take the .ami file above as an example, the EDA tool is responsible for parsing the ami file and do type and range check first. It then needs to figure out that an “AMI_GetWave” function implemented in the model since the parameter “GetWave_Exists” is set to true. In addition, the waveform process should happen in the AMI_GetWave” call rather than AMI_Init because “Use_Init_Output” is set to false. Beyond this, the only thing EDA tool needs to do regarding “Model_Specific” keyword section is to parse these data and form key-value pairs before passing into model. The simulator itself doe not need to know what they are and how they will be used.

When a break point is set at the function call inside the model’s .dll/.so, one will find that the parameters now form pairs already and can now proceed to consume these settings. Note that this breakpoint will only be hit if the APIs are implemented correctly (thus can be recognized by the simulator and loaded according to the spec.)

With the introduction of the back-channel interface (BCI), new AMI APIs are being defined to support communications between models themselves:

Interested reader can find BIRD147.,1_Draft on IBIS website fore more details. Simply speaking, models can pass data back and forth through this protocol via files. Since this is a reserved parameter, this BCI parameters should also be supported by the EDA tool.

Reader keeping track of this topic probably knows that for the past several years, several EDA companies have been arguing about how these back channel communications should be implemented. Some of the main considerations include:

  • In case when legacy models can’t be re-compiled, how are they going to participate in the optimization process
  • Whether or not a simulator should perform solution exploration actively or should leave this to the models themselves
  • Should the communication protocol be only between models involved or it should also be part of the defined spec. as well

On top of these, we would also argue that:

  • As a simulator/EDA tool developer, how can I test the AMI models at hand for co-optimization?
  • As a model developer, my EDA tool may not be updated or up to date. How can I test the co-optimization if I want to add such support my model?
  • As a model/EDA tool user, can I inspect what’s going on between simulator and models? I only get post-processed results from EDA tool and they are not correct!

For these purposes, we think that a proxy based class can be used in AMI analysis flow.

A proxy class:

The proxy pattern’s UML is shown above. The dot line at the top represents “has a” relationship while the solid vertical arrow line represents “is a” relationship. This UML diagram can be read as: A client has one or more subjects which implement interface function called “DoAction”. When the proxy class’s DoAction is called, it delegate to RealSubject’s DoAaction” function.

Put this in the IBIS-AMI’s context, the statement above can be read as: An EDA tool can load one or more .dll(s)/.so(s) which implement AMI-API’s interface such as AMI_GetWave etc. When the EDA tool calls a proxy’s AMI-APIs, this proxy object delegates calls to RealSubject (i.e. real model)’s AMI-APIs for data processing.

So this Proxy class is a “wrapper” around real model. It’s loaded by the EDA tool yet itself also load the real model’s .dll(s)/.so(s). It’s a “man in the middle” and can be used to intercept, modify input/output data or even perform customized analysis flow beyond what a simulator can do.

In the example above, the proxy’s AMI_Init function loads the model’s dll, identifies the AMI_Init function and then delegates call.

With proxy class, there are several experiments/test can be done:

Consistency/Stress test:

In this example, a model’s functions are called many times. The purpose is to test:

  • Consistency: i.e. when given the same data, will the model return the same result? This should be the case for LTI model.
  • Stress: will there be resource leak such as memory usage keep  increasing?

As mentioned in the IBIS spec, an EDA tool may break up lengthy bit stream into several chunks and call AMI_GetWave many times, thus a well behaved model should pass these two tests.

 

Co-optimization (internal):

The second experiment is to bundle Tx and Rx’s response together within one function call and try to do something with it (such as iterate to find combinations for best performance, i.e. optimization)

First, from the IBIS spec., or diagram shown above, Tx’s response will be computed first. Most of the Tx’s processing is LTI and its function is mainly to convolve signals with 1 or 4. After this step, EDA tools convolves again with signal 6 before passing on to 7.

Now convolutions is a commutative process, meaning the order can be exchanged:

So if we use a proxy class in Tx’s place and implement a delta response (e.g. simply return the data passed in), then EDA tool will convolve step 4 and 6 as before while we can combine the actual Tx model’s function with Rx together after that and get the same result:

However, these two models are now put together with the Rx proxy’s AMI_GetWave function call. So we have great control by manipulating the models’ pointers directly. This way, setting such as CTLE’s curve index and/or sweep the number of taps and their weights on the Tx side can be performed. This example also demonstrates how a proxy class can intercept call from EDA tool and perform customized flow.

Co-optimization (external):

In the two examples above, data execution is within the same process where the proxy and/or model’s .dll(s)/.so(s) are loaded. However, it’s also possible to implement these in separated process, or on different machine over the network. The diagram below shows an example:

In this case, we want to use EDA tool’s post-processing capability to obtain performance matrices such as eye-width or BER. However, we would also like to adjust the model’s parameters continuously in order to obtain best channel performance. The EDA tool can be called repeatedly, each time the AMI_Init or AMI_GetWave is called by the simulator, a proxy class intercepts and delegates the call to a remote process via communication such as file based IO, socket or shared memory. The remote process is persistent and can be considered as a server. It will return the data back to proxy class upon completing processing, wait until EDA tool’s post-process and updates results, then read and adjust the settings for next call from another simulator run. On the “server” side, a simple AMI test driver loading similar proxy class can serve as the stand-alone, persistent process.  This way all the .ami processing can be done up front by the model driver and no simulator license is needed.

The shaded block in the middle execute socket communication to get response from a remote process.

Other possible uses of the proxy class:

In addition to the examples discussed above, an AMI proxy class may also serve:

  • As a wrapper class to support model implemented in different language, such as matlab, octave, perl etc in either AMI-API compatible library form or executable
  • As a server to centrally manage the SERDES models, provide extra IP protection as it can now sit on some server always updated without being distributed.
  • As a wrapper to support legacy models by implementing support of new spec. or parameters at the proxy level without re-compiling the old models.

We have used such proxy class in our AMI analysis and find it very useful. We also expect it will be even more widely used as AMI models become prevalent and IBIS spec. being updated to support new protocol/parameters.

Materials:

Click [HERE] for the audio recording of the presentation.

Click [HERE] or visit ibis.org for the pdf of the presentation.

 

IBIS-AMI: Modeling architecture study

Preface:

In previous post, we discussed several possible roles involving IBIS-AMI and their points of considerations. In this post, we would like to focus on the AMI model developer’s role and explore several modeling flows along with their pros and cons. The materials and example discussed here are from publicly available sources and articles, which are also listed at the end of this post.

IBIS-AMI models:

Regardless how sophisticated a SERDES design is, at the end of the day, the work of generating a corresponding AMI model is to create the following the 3 ~ 5 (depending on IBIS spec. version) AMI API functions in C language and compile them as .dll(s)/.so(s) across different platforms and OSes. Noted that the actual implementations (how the SERDES or EQ operates) are not bounded by C/C++ language. For example, one may use matlab, octave, perl or other language to implement the core function, only that they have to be wrapped with C AMI-API functions. Regardless, the main objectives are still the same: translate the design to corresponding codes. On top of that, one also needs to consider how to debug, maintain and extend the generated models for model users and future design revision.

IBIS-AMI API functions

A very popular, and relatively easy approach to achieve this goal is to use tool/program to generate codes directly from design collateral.

Top-down AMI modeling flow:

A typical design flow is usually top-down based: floor plan is made and functional blocks are defined. At first, only abstract level behaviors, budgets and/or spec. are given. Each designer or team then dives into the detailed implementation of these functional blocks and finally assemble them together for full design simulation or verification. Using this approach, a schematic is usually used for design entry and connect different block first, each block may have several hierarchies. Architecture codes/schematics are translated to C/C++ by machine.

Example SERDES schematic from SystemVue of KeySight

User also usually can right click on each block to specify parameters for customization or exporting. When it’s done, an add-on module will translate this design to corresponding C/C++ format most of the time. So far as AMI API is concerned, a special AMI kit may be also needed such that generated codes will be AMI-API compatible. EDA tools using this flow include the followings AFAIK:

  • Keysight: SystemVue + AMI kit + ADS
  • Mathworks: Simulink + AMI kit + Matlab

With this approach, user focuses on SERDES design rather than the coding or API details. Since these architecture blocks/codes are pre-generated and verified, the generated codes are considered well tested and should produce good correlations.

 

Machine generated codes:

Now let’s look at machine generated codes from design above:

(Shown above) If I am doing code review, I will first comment that the data are not encapsulated. All of them are exposed as public without any getter/setter functions. This basically violate the first rule of of OO language, which this codes is written with (C++).

(Shown above) Regarding execution efficiency… I would say they will not run very fast. For example, circular buffer allocated with size 1 here is meaningless. I do understand that the original intent is to consume input one at a time (or as soon as they become available). However, if one looks at the AMI_Init and AMI_GetWave functions, he or she should see that the whole waveform array or matrix as are passed as part of the argument, so why “consume” them one at a time? It’s far more efficient to compute all the data in one shot!

When looking their implementation more closely: one will find at first that this is a very tidy machine generated code. As shown above, different stages are cascaded sequentially just like the original schematic. However, seeing so much codes (not to mention all with buffer size 1, again) will arguably make one feel not wanting to step through the debugger to support,  maintain or even expend the codes.

While it’s certainly possible to rewrite part of the codes for fine tuning or customization, one should not forget that next time when the designer click “generated c/c++” button again, all the changes will be overwritten. So the update made at the bottom (i.e. generated codes) will not back propagate or bag annotate to original design. One almost always have to change from the top.

My observations for this top-down approach are:

  • Suitable for designer who has original collateral, don’t know or want to code.
  • These are mostly machine generated codes:
    • Should run correctly as it has been tested
    • May not be efficient, (most of the case, certainly in this case above)
    • Not easy to maintain…. bad readability and can’t enforce coding guideline
    • One direction only, code changes can’t back propagate.

Bottom-up AMI modeling flow:

If a software developer is going to tackle the AMI modeling challenge, his/her approach probably will be different from that used by the SERDES/IC designer. If it’s me, a bottom-up approach will probably be used:

  • 1st, one will identify several common blocks to be used, such as
    • FFE: Feed-forward equalizer, LTI, Time or frequency domain
    • LPF: Low pass filter, LTI, freqeuncy domain
      • CTLE, Bassel, filter based IIR/FIR etc
    • DFE: Decision feedback equalizer, NLTV/digial, time domain only
    • CDR: Clock data recovery, NLTV/digital, time domain only
    • Coder: various coding page
      • 64b66b, 8b10b etc
    • PRBS: Pseudo random bit stream, PRBS7, 10, 15 etc
    • AFE: analog front end to convert pulse to shape with Rt/Ft/Swing etc
  • 2nd, one may define common interface, data member etc between these blocks and use OO principles to construct base and derived class etc

  • 3rd, being able to assemble different stages elegantly and form an AMI model of this stage:

  • As a bonus, one can then easily add extra mechanism such as security, encryption and/or different selector of different CTLE responses as an example.

 

Since this is hand cranked, the developer should feel very comfortable supporting, debugging and extending the functions. Accompanies with good documentation, it can be used for very long time. In order to make sure the model’s performance matches those from the original design:

  • The classes should not be hard coded with number. Instead, settings etc should be parameterized and can be tune either programmingly or from the .ami file
  • A “sweep”, least-squared-error based fit or other optimization methodology can be used to find the set of parameters which match the original design’s performance best
    • In other words, the good correlation is not “given”. It needs effort.

My observation of this bottom-up approach are:

  • Suitable for engineer person who is good at OO and comfortable coding:
  • Human written codes:
    • Need effort to make sure codes can run correctly
    • When doing it right, will run very efficiently
    • When doing it right, will be easy to maintain and extend
    • Code usability is very high, save effort/resources in the long run.

 

Spec/Datasheet based AMI modeling:

From the discussion above, it seems both top-down and bottom-up approaches each has its own merits. Is it possible to have best of both worlds?

It’s not easy… since each engineering’s disciplines are different, and that’s also why doing IBIS-AMI modeling is technical challenging. It demands cross-domain knowledge and experience at least. We haven’t even discussed algorithms such as FFT, iFFT, DSP, BER etc above yet…

Having that said, I believe that this still possible if we can eliminate short comings of either side. For example, One can look at ADS’s AMI library parts and find that they are actually well organized and architect like the bottom-up thinking above. So it must be the process which translates schematic to codes which compromised results. This is most likely due to its general purpose usage: “design anything and tool can translate to c/c++”.

Since it’s not easy to change big EDA company’s product and application scope, if one can find suitable DSP/filter design library to use in the first place in the bottom-up flow, then a well behaved, efficient, maintainable and extensible modeling is not that out of reach. Fortunately, there are plenty of such open source libraries available. Further more, commonly used functional blocks can be assembled together based on settings or parameter. They can even be pre-built/pre-compiled so the compilation can be further avoided. The end result is template/spec/datasheet based, AMI modeling approach.

SPISimAMI: Spec/Datasheet based AMI modeling

Many recent publications, alone with the adoption of this approach from smaller EDA company like us, has supported this as a better modeling flow so far as IBIS-AMI is concerned.

Reference:

2015 DesignCon paper: [HERE]
2016 IBIS Summit paper: [HERE]
2016 DesignCon paper: [HERE]

IBIS-AMI: Scenerios

IBIS-AMI: roles and scenerios

SERDES based links, such as USB, PCIe and SATA, are now ubiquitous. Their SI analysis poses special challenges as low bit error rate required by their spec. means millions of bits needs to be simulated. In stead of using transistor level design details or proprietorial behavior blocks (e.g. Verilog-A or matalb model), an industrial standard, exchangeable modeling format are usually required. This is because Tx and Rx IPs may not be from the same vendors, nor are the simulator or analysis tools used by IC and system integrator. IBIS-AMI is currently the industrial standard for this purpose. Just like a traditional IBIS, only more cross-domain knowledge demanding and technical challenging. It requires certain flows to be able to test, generate, validate and release IBIS-AMI models. In addition, the considerations related to IBIS-AMI also depend on the these different roles and scenarios. We believe when developing or adopting AMI workflow, three usage situations, including 1. an end user, 2. a model developer and 3. a model publisher should all be considered thoroughly.

 

As an AMI model user:

This is the most common scenario. As a model user, one will often want to validate and test the received model first before running full simulation. Take an IBIS model as an example, the validation tasks usually include first: check with golden parser, then run time domain simulation driving IBIS model to simple test load and check response. More diligent users will also want to extract the performance matrices such as impedance and Rtt etc to make sure the it matches what’s claimed by the model name.

The example below show pretty good matches in terms of impedance of different corners (34 and 40 ohms).

When it comes to an AMI model, things are not that easy. First of all, AMI binary model files (.dll(s)/.so(s)) are not only OS (e.g. windows vs linux vs OSX) but also platform (e.g. 32 bit and 64 bit) dependent. This means that unless a model’s file name contain correct info (e.g. TX_Win32.dll), one usually can’t easily check by just looking into the content of the binary file. Secondly, depending on the version of the spec. the implemented APIs in the model are also different. Again, these implementations are in compiled format and one can’t easily check. Lastly, even the latest golden parser will not exercise these API calls, let alone checking their performance. And to excite an AMI model usually involve third party (often expensive) EDA tools usage and required license.

EDA tools like HSpice does provide a simple utility called AMICheck for this purpose. Similarly, companies like SiSoft and Cadence also provide some development kit which enable AMI model checking. However, in the HSpice’s case, a license is required to run this utility. For the later two cases, the development kit needs to be compiled into respective OS/paltform first and the input and output to the models are not flexible. All these poses obstacles which prevents a model user to be able to quickly check the received model.

We believe a simpler yet  more elegant solution should exist. That’s why we developed the SPISimAMI and release it as free tool. It’s also been compiled into different platform and can be freely download to use directly. With this utility, a user can drive given AMI dll file with accompanied .ami settings. Tool will first load the dll to make sure it matches the OS version of the executing SPISimAMI utility. It will then load the models and check mandatory API functions such as Ami_Init and Ami_Close. User can then use embedded rising/falling step and pulse response to drive these Ami_Init and Ami_GetWave functions if available (too will check whether the GetWave_exist is set to true). Alternatively, an user can pass the input response to the model in either .csv, .tr0 or .raw format. The output waveform from the model will then be saved as non-proprietorial .csv and .raw formats to be inspected by tool like excel or our free SPILite and SPIPro. The screenshot below show its usage:

An example of quick AMI check using this features is to feed input bit sequence and check the equalization level output from the AMI model:

Regardless which approach or tool one takes, we believe that as an AMI model user, these checking process and capabilities should be considered due diligence and must be done before performing full link analysis with these AMI models.

As an AMI model developer:

AMI model development usually requires cross domain expertise. For example, a model developer needs to know how AMI and associated IBIS models are used. Then computer science skills such as programming in C/C++, implement APIs and use compilers like Visual Studio and g++/gcc to produce .dll/.so are also required. Finally the domain knowledge such as DSP, equalization and link analysis come to play. In terms of implementation, a model developer not only needs to comprehend these technical details but also needs to architecture codes in a way that the model is modular and can be easily extended. This is often important as design of different generations within the same company may be different only slightly, thus a proper modeling architecture will allow new model to be created by deriving from the common base class (think about object oriented design). From the discussions above, one can easily see that this modeling effort may either require dedicated engineering resource or expensive contracting service to get the job done.

CS skills are required in AMI modeling

To address thess hurdles, EDA companies has provided several solutions (albeit expensive). For example, there are two work flow enabling automatic AMI model generation as of today:

  • Keysight: SystemVue + AMI kit + ADS
  • Mathworks: Simulink + AMI kit + Matlab

In my opinions, both flows are very capable. I think their advantages and strength come from the fact that many building blocks (mainly for RF purpose originally) are already available in their library, thus translate these propitiatory structure into C/C++ language and make them compatible with AMI API are obvious solutions. However, it’s also my belief that an AMI model developer should not fully rely on these flows. At the end of the day, just like any other software product, the developed model should be maintainable, extensible and efficient. An owner of these tools can take a look at the machine generated template and find that these flow’s results are usually not the cases:

SystemVue’s codes, using buffer of size 1?

After looking these codes, as a developer, you should ask yourself how you are going to debug these machine generated codes when your user report bugs or issues. And will you be able to extend their functionalities with ease?

Fortunately for SERDES applications, where AMI are used most often, the function blocks are usually has common behaviors. For example, feed forward equalizer, low pass filter and pulse shape shaver are often required as part of the modeling functions. So for long term considerations, a company or a developer should really bite the bullet and build the AMI infrastructure from ground up like it should. Take all factors such as modeling architecture and code performance and maintainability into account. After this “exercise”, one will often find that a company’s SERDES designs do not change dramatically between generations and may developed codes/blocks can actually be reused just like their silicon IP. On top of these, one can also add functions not available from these tools such as encryption and other parameter locking mechanism.

A note worthy taken is that the aforementioned SPISimAMI utility can serve as a driver of the development stage. This way one does not require 3rd party (expensive?) tool or license usage to be able to drive and test the non executable .dll/.so under development.

As an AMI model publisher:

A model publisher should prepare to support AMI models he/she has released. The most common scenario requiring support is that the model does not behave properly or can’t achieve desired link performance. The complications here is that the user’s link analysis tool may often be different from the one a publisher is using. Without a common ground, it’s often difficult to root cause the issue without becoming blame game between models and the analysis tools.

We believe a free tool like SPISimAMI can again resolve this issue. By providing a free available tool supporting data capture both input to and output from the model in non-proprietorial format, data exchange becomes easy. An end user simply needs to probe the input signals to the model,  save it as customized input and send it back to publisher. A model publisher can then use same AMI driver tool to feed in this input and observe model response. All these are done with most basic/simplest manner directly with the models instead of other analysis functions. Together with more advanced programming techniques such as proxy based modeling pattern, data will become even more transparent and not bonded by a particular link simulator. This way, both model publisher and end user can focus more on the AMI model’s proper usage and performance rather than the discrepancies between different EDA vendors’ tools.

Differential modeling flow: Development

Flow considerations:

Two major considerations when developing a modeling flow are consistency and flexibility. This is particular true when it comes to differential buffer modeling. As discussed in our previous post, a half/true differential buffer goes through most of the same steps as single ended buffer yet certain process must be elaborated (e.g. modeling of the 2d surface sweep) and order needs to be preserved (i.e. extract C_Diff, I_Diff before performing VT simulation). In this post, we will briefly discuss how these considerations are incorporated into design concepts and realized in our SPIBPro modeling flow.

Design setup:

Conceptually, user does not need to know whether a buffer is true/half/pseudo differential before modeling. The IBIS cookbook V4 also states this and also use encrypted hspice as an example… as it’s black box and can’t be deciphered, a model developer needs to assume coupling exists between P and N (thus true differential). The computed differential current from both 2D sweeps of IV and C_Diff will reveal whether such (true differential) assumption is valid. The decision can then be made based on the value of current.. if it’s in uA or nA range, then coupling is insignificant and can be modeled as pseudo differential.

In reality, we may argue that one should start with pseudo-differential approach and switch to half/full differential only if the final validation suggest so. This is because modeling engineer usually can get insights about buffer to be modeled by talking to the circuit designer (usually in the same company, also no needs to reveal or know much of the design details during such inquiry). In addition, the DC sweep of extra dimension and modelings of the half/true differential flow are often overkill for many of the designs.

In terms of modeling setup, only input and output (for N pin) are needed in addition to common single-ended buffer. A model type selection of “differential” is sufficient to indicate the differential modeling flow rather than linear single-ended modeling.

DiffDrvCfg

Modeling flow overview:

The modeling flow is summarized in the picture below: the key change here is that flow is not linear anymore. The simulation and post-processing stages need to be gone through twice. When simulating for the first time, only IV/C_Die sweep are needed. The data is then post-process for the first time to extract the common-mode and differential-mode current. The latter will also be modeled as a separated component in this stage. This differential component is then inserted between P and N pins in the transient netlist and simulated in the second iteration of the simulation step. However, only VT data is needed this time. When that’s done, all IV, VT and C-Die data are now available and the flow can continue linearly just like that of single-ended buffer.DiffMdlFlow

Modeling of the IV data:

While PU/PD is steady state DC sweep in theory, their simulation is performed in “pseudo DC” most of the time due to likely existence of clock signal, thus not possible for true DC sweep. In “pseudo-DC” simulation, voltage are swept very slowly. In addition, sweeping along X coordinate of different Y values are mutually independent. So they can be simulated in parallel (multi threaded or can be distributed). Simulation under certain biasing condition may have convergence issue, so a flow must be tolerant about missing sweep data on some grid points due to non-convergence.

In our SPIBPro example below, sweep of different Y bias points (e.g. voltage at output N) are separated in different .sp files and they can be simulated in parallel.IVSweep

The task of post-processing step in the first iteration is to extract the simulation data just swept, calculate common-mode current, shift the surface data vertically, summarized as a table output and also perform initial modeling. Such a table is important for user to validate the the result and also perform some what-if modeling using tool like Excel if neded.

DiffMdlCsv

  • Surface modeling:

    Depending on the symmetric differential mode data of shifted surface table (data on the surface alone the blue line), one may decide whether it’s pure resistive, linear or non-linear. The symmetric differential mode is orthogonal to the zeroed common mode curve (red straight line) and it represents the most likely differential operation (i.e. symmetric output of P and N)  For example, the surface plot below suggest a resistor will suffice for the content of the series element:

DiffMdlSerR

With the csv table, one can quickly confirm whether the resistance is same across different voltage or not. If positive, then a linear resistor can be used. Otherwise, the non-linear resistance need to be modeled either with PWL spice element or as a series current (another IV table) in the series element. To be able to visualize the data with certain degrees of manipulation, a flow needs to have such 3D plotting capabilities. Otherwise, tool like matlab and familiarity of its syntax becomes necessary.

DiffMdlSurf

If the sweeping data shows surface like above, then either a surface modeling is needed or a “series MOSFET” needs to be created  as a series element. For surface modeling, one can use “response surface modeling” like method to calculate fitting coefficients in minimizing the mean squared error sense. Other general modeling approach like neural network is certainly also possible.

DiffMdlRSM

One should also check the residue of the prediction formula and maybe visualized as a scattered plot. SPIMPro’s general modeling and plotting functions are demonstrated below:

DiffMdlRSM2

DiffMdlRSM3

A good fit should have very small residue, shown as grey line across 0.0 in the picture above (other red dots are nominal values from sweeping grids). With valid results, one will need to translate this “prediction formula” into spice netlist using E/F/G/H control elements.

In matlab, similar process can be done using “lsfit” function.

  • Series element:DiffMdlSerThe same table content is sufficient to construct a series element. The steps needed here is to translate data into a IBIS compatible format. Such process is trivial when the model is simply R/L/C. For series current and series MOSFET (can have up to 100 tables of different bias condition), the attention needed to perform such work manually is not economical and a tool/flow should be used instead.

Modeling of the C-Diff:

CDiff

Similar process (tabulated and modeling) can be applied to calculated C_Diff. However, it’s much more limited in terms of series element’s syntax and E/F/G/H equation such that describing such surface (both frequency and voltage dependent) for C_Diff is not practical using either spice or series element. As a result, a trade-off needs to be made when picking values (or to average) from the surface and a single value is used when constructing model for C_Diff.

Verilog-A modeling:

In our submission to the IBIS summit later this year, we proposed another flow which is Verilog/VHDL based. One of the advantages of this implementation is that the raw, table-like data can be used directly using built-in $table_model function. Its usage also enables polarity differentiation and elaborated description of C_Diff, which is very crucial to the transient data accuracy. The details about this will be published here once our paper is accepted.

Combined model:

In previous post, we mentioned that in addition to the “series element” for differential model, pure [External Model] of the differential model type may also be used. Using the proposed Verilog-A/VHDL for series element only model, these two methods can then work together, as shown below:

CombSer

A series model is still declared in the “[Series PIn Mapping]” section to be connected between buffer’s N and P outputs. Its definition, can optionally include an external model such as the one implemented with behavioral language. This “[external model]”  works on top of existing series model definitions and can provide extra info. such as frequency/voltage dependent C_Diff if the simulator supports. Optionally, the “top” series model can be simply a  shell (e.g. with high impedance) and all the info. regarding the differential current is encapsulated inside the added model. When comparing to external model attached under output/IO buffer, this external model can be significantly less complicated and also provide more tuning capabilities.

Differential flow validation:

To validate a differential modeling flow,  one may construct a differential buffer by creating an artificial coupling (e.g. using some R/C elements) and then connect it between two known IBIS buffers. During the validation, the calculated common-mode data from DC sweep should reconstruct exactly the same PU/PD/PC/GC tables as those in the known IBIS buffer. Differential current will reveal the resitive element of the coupling portion. Calculated C_Diff should reveal the coupling capacitive element. Both of these steady state differential current and C_Diff can be validated using generated csv table or raw data. User should then find that with both captured accurately, the transient VT data calculated will also correlate to those of original IBIS buffer very well.

Paper and audio recording:

We presented this study at the 2016 Asian IBIS Summits. Readers may download the presentation from IBIS website or [HERE]. Audio recording at Tokyo is also available [HERE]