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Characteristics prediction and optimization of InP HBT using machine learning

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Abstract

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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Funding

This work is financially supported by the National Natural Science Foundation of China (NSFC, Grant Nos.11974268 and 12111530061).

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Authors and Affiliations

  1. School of Microelectronics and Key Lab of Micro-Nano Electronics and System Integration of Xi’an City, Xi’an Jiaotong University, Xi’an, 710049, China

    Xiao Jie, Xinjian Ouyang, Yuan Zhuang, Zhilong Wang & Dawei Wang

  2. Zhejiang Key Laboratory of Large-Scale Integrated Circuit Design, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jie Wang

  3. Guangzhou Institute of Technology, Xidian University, Guangzhou, 510555, China

    Shuzhen You

  4. School of Integrated Circuits, Tsinghua University, Beijing, 100084, China

    Zhiping Yu

Authors
  1. Xiao Jie
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  2. Jie Wang
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  3. Xinjian Ouyang
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  4. Yuan Zhuang
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  5. Zhilong Wang
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  6. Shuzhen You
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  7. Dawei Wang
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  8. Zhiping Yu
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Contributions

All authors participated in the conception and design of this study. JW conducted TCAD simulation and data preparation. All authors contributed to the building of the machine learning model and analysis of the results. XJ, JW, and DW initially drafted the manuscript, and all authors provided feedback on earlier versions. The final manuscript was reviewed and approved by all authors.

Corresponding authors

Correspondence to Jie Wang or Dawei Wang.

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Jie, X., Wang, J., Ouyang, X. et al. Characteristics prediction and optimization of InP HBT using machine learning. J Comput Electron (2024). https://doi.org/10.1007/s10825-024-02139-8

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  • DOI: https://doi.org/10.1007/s10825-024-02139-8

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