Acceleration of Semiconductor Device Simulation With Approximate Solutions Predicted by Trained Neural Networks

• Published in: IEEE transactions on electron devices Vol. 68; no. 11; pp. 5483 - 5489
• Main Authors: Han, Seung-Cheol, Choi, Jonghyun, Hong, Sung-Min
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computational modeling; Convolutional neural network (CNN); deep learning; drift diffusion; Electric potential; Electrostatics; Field effect transistors; initial guess; Mathematical model; Metal oxide semiconductors; metal–oxide–semiconductor field-effect transistor (MOSFET); MOSFETs; neural network; Neural networks; Numerical models; Poisson equations; Semiconductor devices; Simulation; TCAD simulation
• ISSN: 0018-9383; 1557-9646
• DOI: 10.1109/TED.2021.3075192

In order to accelerate the semiconductor device simulation, we propose to use a neural network to learn an approximate solution for desired bias conditions. With an initial solution (predicted by a trained neural network) sufficiently close to the final one, the computational cost to calculate several unnecessary solutions is significantly reduced. Specifically, a convolutional neural network for the metal-oxide-semiconductor field-effect transistor (MOSFET) is trained in a supervised manner to compute the initial solution. In particular, we propose to consider a device template for various devices and a compact expression of the solution based on the electrostatic potential. We empirically show that the proposed method accelerates the simulation significantly.