MOSFET
人工神经网络
缩放比例
泊松方程
半导体器件
计算机科学
电子工程
拓扑(电路)
统计物理学
物理
晶体管
人工智能
数学
电气工程
工程类
量子力学
材料科学
电压
纳米技术
几何学
图层(电子)
作者
Shijie Huang,Lingfei Wang
出处
期刊:Micromachines
[MDPI AG]
日期:2023-02-04
卷期号:14 (2): 386-386
被引量:8
摘要
The continued scaling-down of nanoscale semiconductor devices has made it very challenging to obtain analytic surface potential solutions from complex equations in physics, which is the fundamental purpose of the MOSFET compact model. In this work, we proposed a general framework to automatically derive analytical solutions for surface potential in MOSFET, by leveraging the universal approximation power of deep neural networks. Our framework incorporated a physical-relation-neural-network (PRNN) to learn side-by-side from a general-purpose numerical simulator in handling complex equations of mathematical physics, and then instilled the “knowledge’’ from the simulation data into the neural network, so as to generate an accurate closed-form mapping between device parameters and surface potential. Inherently, the surface potential was able to reflect the numerical solution of a two-dimensional (2D) Poisson equation, surpassing the limits of traditional 1D Poisson equation solutions, thus better illustrating the physical characteristics of scaling devices. We obtained promising results in inferring the analytic surface potential of MOSFET, and in applying the derived potential function to the building of 130 nm MOSFET compact models and circuit simulation. Such an efficient framework with accurate prediction of device performances demonstrates its potential in device optimization and circuit design.
科研通智能强力驱动
Strongly Powered by AbleSci AI