稳健性(进化)
晶体管
环形振荡器
人工神经网络
逻辑门
电子工程
工程类
计算机科学
物理
电气工程
材料科学
电压
化学
人工智能
生物化学
基因
作者
Chien-Ting Tung,Chenming Hu
标识
DOI:10.1109/ted.2023.3244901
摘要
We present a neural network (NN)-based transistor modeling framework, which includes drain, source, and gate currents and charges and their variabilities. The training data are generated by a Berkeley short-channel IGFET model (BSIM) with ranges of channel lengths, widths, and oxide thicknesses. The NNs are trained to learn the geometry dependence. The drain, source, and gate currents are modeled with one NN and the charges by another NN. The NNs are trained to produce accurate variability prediction and derivatives of currents and charges. Quality and robustness tests, such as Gummel symmetry, harmonic balance, and ring oscillator, are performed and show excellent results.
科研通智能强力驱动
Strongly Powered by AbleSci AI