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Eye Diagram & Jitter Estimation in SerDes Designs Using Surrogate Models Generated with Polynomial Chaos Theory

Majid Ahadi Dolatsara (Student, Georgia Institute of Technology)

Jose Hejase (Senior Engineer, IBM)

Dale Becker (Computer Hardware R&D Executive, IBM)

Madhavan Swaminathan (Professor, Georgia Institute of Technology)

Location: Ballroom A

Date: Thursday, January 31

Time: 8:00am - 8:45am

Track: 09. Measurement, Simulation & Optimization of Jitter, Noise & Timing to Minimize Errors, 15. Machine Learning for Microelectronics, Signaling & System Design

Session Type: Technical Session

Vault Recording: TBD

Audience Level: All

In modern SerDes channels, calculation of data dependent jitter and ISI is challenging since the traditional transient eye analysis has slow convergence and statistical models have limited use. This paper suggests a methodology to train an end-to-end surrogate model using an expansion of orthogonal polynomials which is based on the generalized Polynomial Chaos theory. This model can be used for estimation of the receiver voltage, statistical analysis and jitter and eye calculations. A significant speedup compared to the transient eye is observed since the length of simulation to train the model is much smaller and the overhead is negligible.


Difficulties in popular eye analysis methods in SerDes Channels are discussed and an approach based on the polynomial chaos theory is proposed to speedup estimation of the data dependent jitter and the respective eye-diagram. Efficiency and accuracy of this approach is validated by a SerDes channel simulator provided by IBM.

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