Optimizing Silicon MOSFETs: The Impact of DTCO and Machine Learning Techniques

In an era of rapid technological advancements and growing necessity for effective power management systems, the significance of silicon Metal–Oxide–Semiconductor Field-Effect Transistors (MOSFETs) in contemporary power electronics is more critical than ever. This review explores the advancements in silicon MOSFET technology through the lens of Design Technology Co-Optimization (DTCO). By integrating design and process technology strategies, DTCO optimizes power, performance, area, and cost (PPAC) metrics, addressing the limitations of traditional scaling methods. The manuscript presents an exhaustive analysis of the foundational principles of MOSFET technology, the progression of DTCO, and its implications on critical design metrics. The inclusion of machine learning techniques enhances the DTCO process, enabling vast simulations and efficient design iterations, which are crucial for navigating the complexities of advanced semiconductor device physics. Empirical evidence from TCAD simulations augmented by machine learning insights demonstrates the effectiveness of DTCO in enhancing device performance, reliability, and manufacturing yield. This review emphasizes the significance of DTCO and machine learning in addressing contemporary challenges and influencing the future trajectory of silicon MOSFET technology.