Applications of Machine Learning in Electronic Circuit Design

Abstract

Machine learning (ML) has emerged as a transformative tool in electronic circuit design, revolutionizing traditional methodologies by offering new avenues for automation, optimization, and innovation. This article explores the diverse applications of ML across various facets of electronic circuit design, from automated synthesis and performance prediction to fault detection, power optimization in IoT devices, and beyond. By leveraging vast datasets and advanced algorithms, ML enables engineers to achieve unprecedented levels of efficiency, reliability, and functionality in electronic systems. The integration of ML with traditional design practices not only enhances productivity but also fosters technological advancements that are shaping the future of electronics engineering across industries.