Recently, machine learning (ML) techniques have gained widespread diffusion, since they
have been successfully applied in several research fields. This paper investigates the …

This article introduces a probabilistic machine learning framework for the uncertainty
quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR) …

A database is presented that allows the investigation of machine learning (ML) tools and
techniques in the signal integrity (SI), power integrity (PI), and electromagnetic compatibility …

Abstract Design optimization of many electromagnetic and multiphysics problems have
multiscale issues that require a fast, efficient, and accurate surrogate‐based model to be …

The design level of channel physical characteristics has a crucial influence on the
transmission quality of high-speed serial links. However, channel design requires a complex …

This letter presents the adaptation algorithm for setting the parameters of a multistage
continuous-time linear equalizer (CTLE) in serial link applications. A differential evolution …

This paper describes the work-flow of developing black-box transceiver models for channel
cascading using feedforward neural network (FNN). Compared to transistor level models …

This paper presents a feed-forward neural network (FNN) framework devised for cascade-
able transceiver behavior modeling. Voltage waveforms after each cascading block are …

This paper proposes a Serializer/Deserializer (SerDes) equalization parameter optimization
technique based on circular convolutional neural network (CNN). This method facilitates …

We propose a machine-learning-based optimization approach for the Tx equalization. Our
target is practical high-speed channels used in systems requiring industrial communication …