Machine learning and explainable-AI based prediction of gate-all-around ferroelectric-FET: how ML models influence XAI

Abstract A novel integration of machine learning (ML) and eXplainable artificial intelligence (XAI) based prediction is proposed to investigate the variability of nanowire (NW) gate-all-around (GAA) ferroelectric-field effect transistors (Fe-FETs). XAI methods such as local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) enhance the explainability and robustness of ML algorithms for end - users. The NW-GAA-ferro-FETs show tremendous potential for neuromorphic computing systems and compatibility with complementary-metal-oxide-semiconductor technology. The GAA-ferro-FET model is validated using sentaurus technology computer-aided design simulations and experimental data. In this work, the first-ever ML algorithms for NW-GAA-ferro-FETs are proposed, achieving physics-based TCAD accuracy with faster learning and lower computational cost. Compared to ML-based algorithms, physics-based simulation of conventional emerging devices requires a high level of device information and a substantial amount of time to provide correct findings and well-fit models. The ML algorithm achieved a R2-score of 99.96%, a lower mean square error, and completed the average inference in just 71.82 milliseconds, compared to TCAD simulations that would take 400 h (=17 days) to process 5000 samples. The results indicate that the novel integration of ML and XAI can lead to a substantial reduction in the computational cost associated with various emerging FET devices, such as ferro-FET, feedback FET, tunnel FET, 2D material-based FET, spin-FET, bio-FET, and other next-generation FETs. End-users can receive suggestions and warnings about potential errors before initiating the investigation process, this helps speed up the development of ferro-FET and other next-generation FETs for use in aerospace, defence, and space exploration.