Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise
This addresses the problem of limited prediction capability in TCAD simulations for semiconductor design, offering an incremental improvement by reducing domain expertise requirements.
The paper tackles the challenge of predicting out-of-training-range TCAD solutions for Si nanowires using a physics-informed neural network (PINN), achieving a 2.5 times larger prediction range and inversion region prediction from subthreshold training data.
Machine learning (ML) is promising in assisting technology computer-aided design (TCAD) simulations to alleviate difficulty in convergence and prolonged simulation time. While ML is widely used in TCAD, they either require access to the internal solver, require extensive domain expertise, are only trained by terminal quantities such as currents and voltages, and/or lack out-of-training-range prediction capability. In this paper, using Si nanowire as an example, we demonstrate that it is possible to use a physics-informed neural network (PINN) to predict out-of-training-range TCAD solutions without accessing the internal solver and with minimal domain expertise. The machine not only can predict a 2.5 times larger range than the training but also can predict the inversion region by only being trained with subthreshold region data. The physics-informed module is also trained with data without the need for human-coded equations making this easier to be extended to more sophisticated systems.