Multi-order Differential Neural Network for TCAD Simulation of the Semiconductor Devices
Authors: 
Zifei Cai, Anaoxue Huang, Yifeng Xiong, Dejiang Mu, Xiangshui Miao, Xingsheng WangAuthors Info & Claims
Zifei Cai, Anaoxue Huang, Yifeng Xiong, Dejiang Mu, Xiangshui Miao, Xingsheng WangAuthors Info & ClaimsArticle No.: 198, Pages 1 - 6
Abstract
Technology Computer Aided Design (TCAD) is a crucial step in the design and manufacturing of semiconductor devices. It involves solving physical equations that describe the behavior of semiconductor devices to predict various device parameters. Traditional TCAD methods, such as finite volume and finite element methods, discretize relevant physical equations to achieve numerical simulations of devices, significantly burdening the computation resources. For the first time, this paper proposes a novel method for TCAD simulation based on Physics-Informed Neural Networks (PINNs). We proposed multi-order differential neural network (MDNN), an improved Radial Basis Function Neural Network (RBFNN) model. By training MDNN, it achieves the couple solution of the Poisson equation and drift-diffusion equation under steady-state conditions, without the need for a pre-existing dataset. To the best of our knowledge, this marks the first instance of an ML-TCAD simulation that does not require any pre-existing data. For an example of PN junction diode, this method effectively simulates the basic physical characteristics of the device, with a self-consistent solution error of less than 1×10−5.
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Published: 07 November 2024
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