Accelerating Failure Analysis in Emerging Transistor Architecture: The TCAD-Augmented Machine Learning Approach

#simulation #nanostructure

Reliability and failure analysis are crucial in transistor manufacturing to ensure quality assurance. The vulnerability of semiconductor devices due to inherent self-heating increases with the continuous shrinking of device dimension, leading to device aging problems and ultimately reducing the transistor's lifespan. The transistor failure due to self-heating effects has been particularly dominant in emerging sub-3nm FETs, where devices are closely packed together on a chip or have a multi-channel configuration. In this talk, we introduce a methodology that combines TCAD (Technology Computer-Aided Design) simulations with machine learning to streamline failure analysis in emerging transistor architectures. Traditional methods like experimental characterization often struggle to keep pace with the intricacies of novel designs, resulting in prolonged development timelines and increased costs. Our approach addresses this challenge by leveraging TCAD simulations to generate extensive datasets covering various operating conditions and failure scenarios. This fusion of TCAD and machine learning enables rapid and accurate failure analysis, facilitating quicker design iterations and enhancing the reliability of emerging transistor technologies. Through brief insights and key findings, we illustrate how our TCAD-augmented machine-learning technique offers a transformative solution for accelerating failure analysis in transistor architecture.