Deep Neural Network-Driven Reliability Assessment Methodology for Total Ionizing Dose Effects in FDSOI MOSFETs

This work investigates the effects of total ionizing dose (TID) on 22 nm FDSOI MOSFET devices through combined technology computer-aided design (TCAD) simulations and actual TID irradiation experiments, establishing a comprehensive dataset of radiation-induced degradation characteristics. By utilizing both simulated and experimental radiation data as training foundations, this research developed and optimized a deep neural network (DNN) capable of conducting efficient and accurate reliability analysis of TID effects under various radiation conditions, with demonstrated effectiveness in predicting device performance degradation across different TID ranges. The methodology integrates physical simulations with deep learning approaches, where TCAD simulations and irradiation experiments capture fundamental device physics under radiation exposure, while the DNN enables rapid performance prediction. The resulting framework achieves superior prediction accuracy while significantly reducing computational costs compared to conventional simulation methods, providing a powerful solution for evaluating electronic device reliability in complex radiation environments.