Application of Artificial Intelligence/Machine Learning in VLSI Design
Abhay Pratap Singh, Vimal Kumar Mishra, Shamim Akhter · 2025
The integrated circuit (IC) industry has been following Moore’s law for the last five decades. To follow it, the size of semiconductor devices continues to reduce, creating the dependence of device characteristics on a large number of new factors as the device size decreases. This dependency exacerbates a number of difficulties the semiconductor sector faces. As a result of growing process variability and decreased chip fabrication turnaround time, the IC industry is facing challenges in reducing design complexity. The traditional approaches we use in very large-scale IC design flow are now considered laborious, resource-intensive, and time-consuming. When it comes to the designing and testing of very large-scale integrated circuits (VLSIs), artificial intelligence (AI) and machine learning (ML) algorithms offer automated approaches for managing complex and data-intensive procedures. We use these algorithms to design and test VLSI. These algorithms minimize the time and effort needed to interpret and process data, which ultimately results in an increase in the yield of ICs and a reduction in the amount of time it takes to turn production around. This chapter explores the potential for AI/ML technologies to revolutionize the industry through the adoption of high-speed, intelligent, and efficient systems in the future. It also examines the use of AI/ML in the design and manufacturing of VLSI devices.