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<p>We explore the application of artificial neural networks (ANNs) for predicting the millimetre-wave (mm-wave) and sub-millimetre-wave (sub-mm-wave) characteristics of double-drift region (DDR) Si IMPATT diodes. The proposed ANN models predict key parameters such as DC, large-signal (L-S) performance, and avalanche noise characteristics across frequencies ranging from 94 to 500&#xa0;GHz. A dataset derived from self-consistent quantum drift–diffusion (SCQDD) simulations is used to train the ANN models, which accurately capture the influence of structural, doping, and biasing variations. The ANN models showed a significant reduction in computational time, predicting device characteristics in just 4.4–15% of the time required by SCQDD simulations, while maintaining high accuracy. The mean square error (MSE) between ANN predictions and SCQDD simulations for breakdown voltage and power output was observed to be in the order of 10<sup>−3</sup> Unit<sup>2</sup>, indicating excellent predictive performance. The models were validated against experimental data, showing strong agreement in terms of power output, efficiency, and noise characteristics. This work demonstrates that machine learning can effectively replace traditional time-intensive simulations, making it a promising approach for the rapid design and optimization of high-frequency semiconductor devices.</p>

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Application of machine learning for predicting the millimetre-wave and sub-millimetre-wave characteristics of avalanche transit time sources

  • 期刊论文

  • 发表日期：

    2025年7月7日

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  • Prerona Sanyal,
  • Sneha Ray,
  • Aritra Acharyya,
  • Arndam Biswas,
  • Rudra Sankar Dhar

摘要

We explore the application of artificial neural networks (ANNs) for predicting the millimetre-wave (mm-wave) and sub-millimetre-wave (sub-mm-wave) characteristics of double-drift region (DDR) Si IMPATT diodes. The proposed ANN models predict key parameters such as DC, large-signal (L-S) performance, and avalanche noise characteristics across frequencies ranging from 94 to 500 GHz. A dataset derived from self-consistent quantum drift–diffusion (SCQDD) simulations is used to train the ANN models, which accurately capture the influence of structural, doping, and biasing variations. The ANN models showed a significant reduction in computational time, predicting device characteristics in just 4.4–15% of the time required by SCQDD simulations, while maintaining high accuracy. The mean square error (MSE) between ANN predictions and SCQDD simulations for breakdown voltage and power output was observed to be in the order of 10−3 Unit2, indicating excellent predictive performance. The models were validated against experimental data, showing strong agreement in terms of power output, efficiency, and noise characteristics. This work demonstrates that machine learning can effectively replace traditional time-intensive simulations, making it a promising approach for the rapid design and optimization of high-frequency semiconductor devices.