An Examination of Machine Learning Techniques for Automating and Optimizing VLSI Design

This research examines the use of machine learning (ML) techniques to automate and optimise Very Large Scale Integration (VLSI) design procedures. Very large scale integrated circuits (VLSI) require a number of laborious and complex processes to be designed, such as placement, routing, and logic synthesis. Historically, manual involvement and specialised knowledge have been needed to complete these jobs. The goal of this research is to use machine learning approaches to increase chip performance, improve design efficiency, and streamline the workflows involved in VLSI design. Numerous machine learning (ML) techniques, including evolutionary algorithms, reinforcement learning, and supervised learning, are being investigated to determine their efficacy in automating design tasks, optimising design parameters, and cutting down on design cycle durations. This research shows that incorporating machine learning techniques into VLSI design processes is not only possible but also advantageous through extensive tests and case studies. The results of this study are highly relevant to the semiconductor industry in this era of sophisticated microelectronics. They could open the door to more scalable and effective VLSI design processes