Fast JTOL

 A. Viveros-Wacher, R. Baca-Baylón, F. E. Rangel-Patiño, J. L. Silva-Cortés, E. A. Vega-Ochoa and J. E. Rayas-Sánchez, "Fast Jitter Tolerance Testing for High-Speed Serial Links in Post-Silicon Validation," in IEEE Transactions on Electromagnetic Compatibility, vol. 64, no. 2, pp. 516-523, April 2022, doi: 10.1109/TEMC.2021.3122348.

Jitter Tolerance (JTOL) tests take a long time to run in the lab as evidenced by this table from the paper:

• ME: JTOL testing is a Bit error rate (BER) test while sweeping sinusoidal jitter amplitude and frequency.
•  XAUI, PCIe, USB and SATA all have slightly different JTOL tests
• "In this article, we present a novel approach to dramatically accelerate JTOL testing based on a modified golden section direct search optimization algorithm."
• "To perform a JTOL test, each protocol specification defines a calibration procedure prior to the JTOL execution. This procedure defines the specific random jitter (JR), bounded uncorrelated jitter (JBU), duty-cycle distortion, and ISI values injected to the test pattern, which remain constant throughout the JTOL test, while JP (jitter periodic) is varied in both amplitude and frequency."
• These guys use JTOL to validate the system, in SerDes Toolbox we would want to use JTOL to characterize the CDR during design.  But regardless, both are desired.
• "In the traditional way to run JTOL testing, at each frequency point, the value of JP is initialized at a sufficiently low starting point to guarantee a PASS result from the BERT. Then, JP is increased by a certain amount, typically equivalent to the minimum value allowed by the BERT equipment for best accuracy. Then, a test is performed at the compliance BER. This is iteratively done until the BERT yields a FAIL. The result reported at each frequency point is the last JP value that yields a PASS.
• For a given frequency their algorithm adjusts SJ amplitude until the desired BER is achieved.  
• Overall an interesting idea to consider.

HilbertNet: A Probabilistic Machine Learning Framework for Frequency Response Extrapolation

 O. W. Bhatti, H. M. Torun and M. Swaminathan, "HilbertNet: A Probabilistic Machine Learning Framework for Frequency Response Extrapolation of Electromagnetic Structures," in IEEE Transactions on Electromagnetic Compatibility, vol. 64, no. 2, pp. 405-417, April 2022, doi: 10.1109/TEMC.2021.3119277.

• Wow! this looks like a useful NN for signal integrity applications.
• Introduction has a very through survey of S-parameter extrapolation techniques.  
• "Knowledge-based neural networks are used in [11] to provide extrapolated results for the design space parameters like physical length and width of copper traces. While this approach works for design space, it may not be applicable to extrapolation."
• RNN: Recurrent Neural Nets
• Predict the next sample given the previous batches of samples.
• I like this emphasis on providing uncertainty bounds:  
• "Historically, neural networks have been used to provide point estimates of their predictions. Such models lack explainability and could generalize incorrectly in the extrapolated space. Hence, it becomes necessary to provide uncertainty estimates around our predictions. This not only provides an insight to the model accuracy outside the training data but also enables the designer to choose whether to simulate more training points.
• This is a nice summary 
• "Our contributions include the following. 
• 1) Use of specialized long short term memory recurrent neural networks (LSTM-RNN) for frequency response extrapolation
• 2) HT (Hilbert Transform) to correlate real and imaginary part of the signal to enable causal complex-valued extrapolation.
• 3) Harnessing the variational inference-based Bayesian approach to assess the uncertainty of predictions in the extrapolated space.

Section II: Hilbert Transform

• They provide a nice algorithm to compute the discrete Hilbert Transform

Section IV

• Break the response up into shorter sliding sequences and train the RNN on using earlier sequences to predict later sequences.
• I like this because you are training the NN on the data to be extrapolated, not on a boat load of data that could or not be relevant to the particular problem at hand.
• This is the training phase
• Training -> Inference phase

Section IV:B Bayesian Recurrent Network Architecture

• Great explanations.  After two read throughs I think I have a good idea of what they are doing.  I'd really like to learn more about probabilistic estimation/detection theory
• Is able to extract the variance of the estimation so the uncertainty bound is tracked.

Above is their result from applying this to a PDN network.  Not only were they able to capture the resonances but they were able to provide a confidence bound to the prediction! Brilliant.  They also provide a link to some Python code.

Overall, this is very impressive.  It uses the dataset itself as all of the training data and yields a very compelling result. Their application is to predict high frequency behavior and then let the user decide if they need to simulate to these higher frequencies or not.  

My high frequency extrapolation is so I can convert to the time domain for a time response of a given time step.  Within my use case I wonder if the insights provided by this paper can be of use to me.

The Sirens of Mars: Searching for Life on Another World.

 Johnson, Sarah Stewart. The Sirens of Mars: Searching for Life on Another World. Crown, 2020.

Great popular science book about the human journey to discover what Mars is like.  It is also a personal memoir of the scientist Sarah Stewart Johnson and one that I would recommend to any young woman regardless if they have ambitions for science careers or not.