The IBIS Algorithmic Modeling Interface (IBIS-AMI) has become the de-facto methodology to model SerDes behavior for end-to-end high speed serial link simulations. Meanwhile, machine learning (ML) techniques can facilitate the computer to learn a black-box system. This paper proposes the Self-Evolution Cascade Deep Learning (SCDL) model to show a parallel approach to modeling effectively adaptive SerDes behavior. Specifically, the proposed self-guide learning methodology uses its own failure experiences to optimize its future solution search according to the prediction of the receiver equalization adaptation trend. After basic introduction of SCDL model, the paper shows examples whose results are highly correlated with the IBIS-AMI simulations, while achieving simulation time reduction of orders of magnitudes.
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