A Comprehensive Review of Machine Learning Applications in VLSI Testing: Unveiling the Future of Semiconductor Manufacturing

The semiconductor industry’s relentless pursuit of increased device integration and performance has propelled Very Large-Scale Integration (VLSI) technology into an era of unparalleled complexity. Ensuring the reliability and quality of VLSI devices demands innovative solutions, and machine learning has emerged as a transformative force in this domain. This comprehensive review paper delves into the intersection of Very Large-Scale Integration (VLSI) testing and Machine Learning (ML), exploring the evolving landscape of testing methodologies. We analyze the challenges associated with traditional VLSI testing techniques and showcase how ML algorithms are increasingly being employed to enhance fault detection, diagnosis, and overall test quality. The article provides an overview of key ML applications in VLSI testing, including anomaly detection, and adaptive testing strategies. Furthermore, focusing the contemporary challenges and limitations in this dynamic field, the review paper concludes by illuminating the integration of ML techniques in addressing the escalating complexities of modern semiconductor devices and the potential future directions and emerging trends. Through a critical examination of recent research and advancements, this review article aims to offer insights into the promising role of ML in revolutionizing VLSI testing methodologies, ultimately contributing to more efficient and reliable semiconductor manufacturing processes.