• Girisha Kumar
• Peerasab Hawaldar

This paper provides a comprehensive review of the growing role of machine learning (ML) in electronics engineering, focusing on key applications such as circuit design and optimization, fault detection, signal processing, and semiconductor manufacturing. ML algorithms like support vector machines, decision trees, and neural networks are revolutionizing tasks by automating design, improving system reliability, and enabling real-time decision-making. The review highlights ML’s ability to predict circuit behavior, optimize components, enhance signal filtering, and detect anomalies in electronic systems. Furthermore, it addresses the challenges in integrating ML into electronics, particularly in low-power embedded systems, model interpretability, and transfer learning across devices. Future research opportunities are identified in the optimization of ML algorithms for resource-constrained environments and the exploration of hardware-accelerated ML and quantum computing to further enhance system performance. By examining the current advancements, challenges, and future directions, this paper underscores the transformative impact of ML on electronics engineering, offering pathways for more intelligent, efficient, and adaptive electronic systems in various industries.

Machine Learning in Electronics, Circuit Design Optimization, Fault Detection in Electronics, Signal Processing with ML, Embedded Systems and IoT

"Machine Learning in electronics engineering a review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.104-108, May-2019, Available :http://www.jetir.org/papers/JETIR1905Z14.pdf

"Machine Learning in electronics engineering a review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp104-108, May-2019, Available at : http://www.jetir.org/papers/JETIR1905Z14.pdf