Machine Learning Techniques for Modeling and Performance Analysis of Interconnects

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. In the high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of quality of signal as well as power. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. The machine learning techniques can be used as computationally efficient alternatives for the design process of 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.