High-speed link analysis with system identification approach

Choi, Yongjin; Cheng, Christopher

doi:10.1109/isemc.2017.8077965

Cited by 7 publications

(1 citation statement)

References 2 publications

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“…For example, Bowen Li et al use ML to accelerate the physical verification for high-speed link IC design [8]. In 2017, his team use ML and system identification approach to mimic the behavior model of receiver respectively and the result indicates the ML method has a good performance [9], [10]. Furthermore, they build a powerful ML architecture which can self-evaluate according to its own failure experiences to predict adaptive codes of receiver equalizers [11].…”

Section: Introductionmentioning

confidence: 99%

A COM Based High Speed Serial Link Optimization Using Machine Learning

Yan

2022

IEICE Trans. Electron.

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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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