Machine Learning Techniques for Modeling and Performance Analysis of Interconnects

Abstract

Interconnects are essential components of any electronic system. Their design, modeling and optimization are becoming complex and computationally expensive with the evolution of semiconductor technology as the devices of nanometer dimensions are being used. In high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of signal and power quality. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. Machine learning techniques can be used as computationally efficient alternatives in the design cycle of the interconnects. This paper presents a review of the applications of machine learning techniques for design, optimization and analysis of interconnects in high-speed electronic systems. A holistic discussion is presented, including the basics of interconnects, their impact on the system performance, popular machine learning techniques and their applications related to the interconnects. The performance evaluation, optimization and variability analysis of interconnects are discussed in detail. Future scope and overlook that are presented in the literature are also discussed.