Exploring Engineers' Perspectives on the Adoption of Artificial Intelligence in Logic Circuit Design and Optimization

Abstract

Abstract The increasing complexity of digital circuits has encouraged the use of Artificial Intelligence (AI) in Electronic Design Automation (EDA) to assist with logic synthesis, circuit optimization, and design analysis. Machine learning approaches, including Graph Neural Networks (GNNs) and Reinforcement Learning (RL), provide new possibilities for supporting engineers in exploring circuit designs and optimization strategies. However, the technical capabilities of AI do not alone determine its adoption in engineering practice. Engineers may also consider reliability, functional verification, explainability, data security, training, workflow compatibility, and human involvement. This paper explores engineers’ perspectives on the adoption of AI-assisted tools in logic circuit design and optimization. It focuses on engineers’ awareness, perceived benefits, concerns, and factors that may encourage or hinder adoption. By connecting developments in AI-assisted EDA with studies on human-AI collaboration and professional AI adoption, this study aims to provide a human-centered understanding of AI adoption in logic circuit design and optimization. Keywords—Artificial Intelligence, Electronic Design Automation, Logic Circuit Design, Circuit Optimization, AI Adoption, Human-AI Collaboration, Engineers’ Perspectives.