Characteristics prediction and optimization of InP HBT using machine learning

被引：0
作者：
Xiao Jie
Jie Wang
Xinjian Ouyang
Yuan Zhuang
Zhilong Wang
Shuzhen You
Dawei Wang
Zhiping Yu
机构：
[1] Xi’an Jiaotong University,School of Microelectronics and Key Lab of Micro
[2] Hangzhou Dianzi University,Nano Electronics and System Integration of Xi’an City
[3] Xidian University,Zhejiang Key Laboratory of Large
[4] Tsinghua University,Scale Integrated Circuit Design
来源：
Journal of Computational Electronics | 2024年 / 23卷
关键词：
Machine learning; Parameter optimization; Technology computer-aided design (TCAD); First order partial derivative;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study introduces a novel application of machine learning using indium phosphide heterojunction bipolar transistors as an example. The objective is to predict the device performance and optimize the device structure by utilizing an artificial neural network (ANN) to calculate the device direct current (DC) and frequency characteristics. To this end, we develop a physics-inspired ANN that emphasizes the significance of the first-order partial derivative of the current over voltage. The ANN is trained on a data set generated by technology computer-aided design simulations, covering a range of voltage setups, device geometries, and doping concentrations. The resulting model accurately predicts the DC and frequency characteristics of the device, and obtain key performance indicators such as the DC current amplification factor, cut-off frequency, and maximum oscillation frequency. This approach can significantly speed up the device parameter optimization and provide a potential numerical tool for design technology co-optimization.
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页码：305 / 313
页数：8
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