IEICE Transactions on Electronics
Online ISSN : 1745-1353
Print ISSN : 0916-8524
Special Section on Recent Advances in Simulation Techniques and Their Applications for Electronics
A COM Based High Speed Serial Link Optimization Using Machine Learning
Yan WANGQingsheng HU
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2022 Volume E105.C Issue 11 Pages 684-691


This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.

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© 2022 The Institute of Electronics, Information and Communication Engineers
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