Artificial Intelligence in VLSI and Integrated Circuits: Opportunities and Challenges

Artificial Intelligence (AI) has emerged as a transformative force in VLSI and integrated circuit (IC) design, offering new approaches to overcome the increasing complexity of modern electronic systems. This review highlights the integration of AI techniques such as machine learning, deep learning, reinforcement learning, evolutionary algorithms, and generative models into various stages of the design process, including placement, routing, performance prediction, fault detection, verification, and hardware security. The opportunities provided by AI include automation of design flows, multi-objective optimization, improved energy efficiency, and enhanced reliability, all of which contribute to reducing design time and cost. At the same time, significant challenges remain, such as limited availability of high-quality datasets, high computational requirements, lack of interpretability, and security vulnerabilities. The paper further discusses future research directions, emphasizing the importance of data-efficient learning, explainable AI, secure design frameworks, and hardware-AI co-design. Overall, AI is rapidly evolving from a supportive tool to a central enabler in VLSI and IC design, paving the way toward intelligent, adaptive, and sustainable semiconductor technologies.

Keywords:

Artificial Intelligence , VLSI Design , Integrated Circuits , Machine Learning