Simulators, Models, and the Next Great Thing

Thursday, April 9, 2020
Author: Donald Telian, SiGuys - Guest Blogger 
Day one at my first engineering job and my manager is explaining the problem I need to solve.  Signals need to transmit in a new configuration at a faster data rate than before, and it wasn’t clear if it would work.  Little did I know how many times I’d get handed that same problem in the decades to come.  But this time was different.  Fresh out of college, my mind was full of circuit theory, network nodal analysis and differential equations, so I proposed a solution a bit beyond my mathematical abilities.  Somewhat amused, he smiled and said: “Are you kidding?  We use simulators for that.” 


Signal Integrity Engineers use simulators and models like carpenters use hammers and nails.  These are the tools of our trade, and with practice we discover how to use them to build things.  Our learning curve involves juggling parameters, nuances, inputs and outputs.  Simulators are also quirky and so bug fixes and software updates are the norm; deeper mathematics have been abstracted to a user interface so there’s no way to fix what has been previously compiled.


Yet when everything is working right, simulators open the door to explore and tackle electronics’ greatest challenges.  Overstated?  Maybe.  If you use a Flight Simulator correctly, you arrive at your destination without crashing your airplane.  But you’re not actually there.  When you use an SI Simulator correctly you gain insight into how to design and build your product and, unlike the airplane, you are largely “there”.  The power to hunt for solutions across manufacturing variables prior to hardware is powerful and has consistently enabled us to figure out the next great thing.


But simulation, by definition, is not the real thing.  So how much can I trust it?  How can I get good at it?  Is there a way to figure out if simulation output is correct?  …or even reasonable?  And to what degree?  Furthermore, how can I perform meaningful simulations when models are bad or not available?


Choosing a Good Simulator

Math is precise, and simulators use math.  Lots of it.  So then why do simulation outputs differ tool by tool?  Under the hood, simulators deploy differing techniques to trade-off performance, accuracy, convergence, and throughput – in addition to using unique configuration and interface paradigms.  Because simulators compete on the open market, there’s a good amount of variety – be it “secret sauce” or “snake oil” – lubricating how they function.  Looking from the outside, I typically characterize simulators as “cold” (conservative), “hot” (optimistic), or somewhere in between.  Experience and Measurement Correlation help you understand where you are in that spectrum.  And Bogatin’s Rule #9 helps you stay in the realm of reality.


So how can I recognize a “good” simulator?  Given the rapid changes in electronics I’ve learned to judge them based on two factors: (1) how the simulator functions now, and (2) how it will function in the future.  While (1) can be determined with a good evaluation and verified references, (2) requires you to examine the vision, skills, investment, support systems, and staff size of a potential vendor.  That said, as with other realms of technology, don’t underestimate the energy and breakthroughs that happen at unproven startups.  And while it seems exotic to be at a big company using “in-house” tools, history suggests that non-EDA companies eventually decide making and maintaining tools is not their core competency causing those tools to become unsupported and out-of-date.


Types of Simulation

System-level SI simulation is performed using the four methods shown in Figure 1.  There was a day when SPICE was the only solution, but in 2004 faster Convolution and Statistical computation methods arrived on the open market.  These techniques, combined with the SerDes equalization capabilities that motivated them, represent the most significant SI advancements I have witnessed – providing a way to extend the lifespan of copper interconnect on PCBs. 


Figure 1 reveals the primary difference between techniques is the number of bits you can simulate in a reasonable amount of time.  With the increasing relevance of BER prediction, the number of bits simulated becomes an important differentiator.  While some would argue the accuracy trade-offs between the methods, I view all methods as useful in providing solutions for certain tasks.  Most simulators implement all four methods, and it’s important to know how to use them all because models often dictate which type of simulation must be used.


       Figure 1:  Four Common Types of Active System SI Simulation


The second method, Peak Distortion Analysis (PDA), extended the usefulness of SPICE yet was mostly a stopgap solution until Convolution arrived.  PDA parses the interconnect to determine a worst-case bit pattern, providing the ability to study performance-limiting scenarios using a shorter SPICE simulation.  PDA lives on in most simulators yet has largely been supplanted by the newer solutions.


Regardless of the type of analysis chosen, as I discovered my first day on the job, simulators enable insight that would otherwise be inaccessible.  But simulators by themselves are like cars unable to move until you put in the gas.  Which brings us to our next topic:  Models.  Models are the gas that empowers the simulator to do something useful.


The Imperfect World of Models

With any Project, the first thing I work on is models.  When the model type is new and/or hard to get, I might even work on models before the Project starts.  Good models are imperative so, as unglamorous as it is, step one is to procure and validate them.  As one engineer put it, “Models are like underwear.  No one sees them and you don’t want to think about them, but they are essential and go on first.”  Sorry for that imagery, but it’s true.


A few vendors don’t have models for their products while others keep them locked up in Fort Knox  (translation: they are difficult to acquire in a reasonable amount of time).  My favorite vendors let you download models from their websites using click-through license agreements.  Figure out which vendors are protective and slow and act accordingly.  And if you’re a model maker, realize that a great model your customers cannot obtain in a reasonable amount of time is the same as no model at all.


I recently finished a Project one month before a troublesome vendor’s model finally arrived.  If that seems impossible, these are important paragraphs for you.  Succeeding at Signal Integrity Engineering will require you to produce meaningful and actionable data even when you can’t get a certain model.  The secret: develop a proxy model that is conservative (pessimistic) yet reasonable (practical) and bounds the behaviors you will see when the model finally arrives.  To make this mental jump you must move beyond thinking that the device defines the model because, in practice, the model defines the device.  If you think about it, all devices start that way; behaviors are desired and expected, yet not built yet.  Once again, Engineering Judgment Required.


For active models (Tx/Rx), when you can’t get a model for one end of a signal path the most common solution is to substitute the model from the other end.  However, now that all simulators have IBIS and AMI template models, it’s reasonable to build your own model using a component’s datasheet for the missing model.  Another good solution is “spec” models.  These models capture the boundary behaviors defined by a Specification and are often used when the only thing you know about a device is that it is compliant.  Figure 2 illustrates SATA spec model behaviors, highlighting voltage (left) and edge rate (right) ranges for fast, typical, and slow (red, green, and blue, respectively) corners.  If spec models are not provided with your simulator, they can be built by inserting the Specification’s characteristics into template or IBIS models.


          Figure 2:  SATA Spec Model Characteristics, Voltage (left) and Time (right)


Working without the models you need brings out the creative side of SI.  If you have a large passive assembly (e.g., multiple cables, connectors, and PCBs) that is built and not performing well, it may be the simplest – if not the most accurate – way to model it is to measure the end-to-end path using a Vector Network Analyzer (VNA).  Or send it to someone who can do that.  This will produce S-parameters which may in themselves (perhaps converted to TDR) reveal where the problem is.  The measurement produces a model you can place into your system simulation with confidence the nuances of the passives are modeled correctly.  The only downside is that it’s difficult to tolerance that type of model, however I have adjusted characteristics by changing S-parameter reference impedances.


And what about that connector model that isn’t available?  Aside from the ideas of measuring it or switching to a vendor that has a model, how about constructing a boundary model?  A connector is just interconnect, and interconnect is a series of impedances, propagation delays, and perhaps couplings.  After seeing enough connector models trends emerge that can be mimicked with a few transmission lines, ideally parameterized to cover the boundaries (Figures 1 and 2).  Again, prove the system works in a conservative scenario and you’ve substantially removed risk that it will work in the real one.  Don’t forget, “all models are wrong, but some are useful”.  Learn to make useful models when vendor models cannot be obtained.


While you might be inclined to trust a vendor model, be forewarned they can lead you astray too.  Never use anyone’s model until you qualify it – particularly S-parameters.  Some vendors reliably deliver quality models, while others are learning how.  Expect to participate in the process of ensuring your models are good, and budget time for it. 


The best SI Engineers learn how to manage the imperfect world of models and produce meaningful results; results that may not be “perfect” yet are accurate enough to inform and guide the design process.  Here’s the challenge:  know when your simulation results are “good enough”.  Good enough to be actionable, provide design insight, and remove risk.  As one SI group manager warns: “I’ve worked with a few SI engineers who were considered technical experts yet ineffective simply because they got bogged down studying details.  These “scientists” studied and analyzed everything, worked to the nth degree of simulation accuracy, yet were unable to come up with the answer in a timely fashion (if at all).”


In Conclusion

Simulators delegate complex mathematics to computers, allowing us to focus on design tasks.  History has proven this partnership’s ability to advance our understanding and definition of “high-speed”.  The practice of Signal Integrity requires proficiency with simulators, along with an understanding of their strengths and weaknesses.  Simulators require good models to be useful, and often model development and qualification must be performed by the SI Engineer.  For those who brave the learning curve the rewards are substantial.  Climb this mountain and solve the next great thing.


Donald Telian, SiGuys - Guest Blogger 4/9/2020

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