Enhancing the Capabilities of Quantum Transport Simulations Utilizing Machine Learning Strategies

The ongoing pursuit of exploring novel materials for potential future device applications continues to strengthen the vital role of Technology Computer-Aided Design (TCAD) simulations within the device community. However, its computationally intensive and time-consuming nature necessitates novel methodologies to overcome the limitations. Machine learning (ML) is a potential remedy in our contemporary data-driven society. In this work, we present a unique approach based on Neural Networks (NNs) to generate an efficient potential profile guess. The predicted ML potential enhances the convergence of the coupled Schrodinger and Poisson equation and speeds up the simulation process, ~ 2.5x, by reducing the number of self-consistent iterations at each bias point. Moreover, our method demonstrates versatility by providing reasonable prediction capacity and accuracy for grid reduction, doping, and channel material variations.