Self-Evolution Cascade Deep Learning Model for High-Speed Receiver Adaptation

The IBIS algorithmic modeling interface (IBIS-AMI) has become the standard methodology to model Serializer/Deserializer (SerDes) behavior for end-to-end high-speed serial link simulations. Meanwhile, machine learning (ML) techniques can mimic a black-box system behavior. This article proposes the self-evolution cascade deep learning (SCDL) model to show a parallel approach to effectively modeling 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. The proposed SCDL model can provide the high-correlation adaptation results, while the adaptation simulation time is much faster than conventional IBIS-AMI models.