Reliability forecasting and Accelerated Lifetime Testing in advanced CMOS technologies

This study harnesses a machine learning approach to precisely forecast the reliability of 22 nm Bulk Complementary Metal Oxide Transistor (CMOS) and 22 nm Metal Gate High-k (MGK) technologies, particularly at room temperature. Extending its capabilities, it also delves into the realm of varying temperature conditions and the rigors of Accelerated Lifetime Testing (ALT), employing advanced statistical models. This initiative empowers engineers and designers with the knowledge needed to make well-informed decisions concerning the performance and longevity of these advanced semiconductor technologies. Through thorough data analysis and rigorous training, our machine learning framework delivers highly accurate and efficient reliability predictions. Consequently, it enhances the overall quality and durability of 22 nm Bulk CMOS and 22 nm MGK devices. This research stands as a substantial contribution to the semiconductor industry, offering a reliable and efficient solution for reliability prediction, thus ensuring the optimal utilization of these cutting-edge technologies in practical applications.