Intelligent Circuit Design: ML-Driven Optimization for Electronics

Abstract

As electronic circuits get more sophisticated, achieving their best performance creates numerous issues in electronics and computer engineering. Most manual techniques for optimization are no longer enough to overcome the challenges found in modern electronic systems. Smart circuit design can be greatly enhanced by applying machine learning-based optimisation, as this research shows. Using machine learning approaches, engineers can examine all design options systematically, see how circuit parameters affect outcomes, and eventually boost the efficiency and effectiveness of electronic circuits. The article looks closely at how machine learning strategies are applied to optimise the design of circuits. Thanks to neural networks, support vector machines, and decision trees which are forms of supervised learning creating models for electrical circuit interactions is made simpler. By using clustering and reducing features, unsupervised learning helps discover connections and similarities in the design area. Also, reinforcement learning enables circuit optimisation by repeatable learning and adjusting settings on their own. Among other applications, machine learning-based optimisation helps produce energy-efficient circuits, improves the algorithms used in signal processing, and improves how circuits are designed for enhanced feature and reliability performance. In addition, using machine learning techniques can make semiconductor production more consistent, making electronic systems dependent and resilient regardless of uncertainties. Although machine learning may improve circuit design, getting the necessary data, explaining its outcomes, and adapting to complex circuits are still problems. This requires finding new ways to research and develop, including inventing new hybrid optimisation processes and advanced hardware systems.