The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.

翻译：半导体行业从机器学习技术在工艺计算机辅助设计方法中的集成中受益匪浅。然而，机器学习模型的性能在很大程度上依赖于训练数据集的质量和数量。由于半导体器件制造的复杂性和高成本，这些数据集尤其难以获取。在本文中，我们提出了一种基于变分自编码器技术的自增强策略，用于改进基于机器学习的器件建模。这些技术仅需少量实验数据点，且不依赖于工艺计算机辅助设计工具。为了证明我们方法的有效性，我们将其应用于基于深度神经网络的氮化镓器件欧姆电阻值预测任务。在预测实验结果时，平均绝对误差降低了70%。我们方法的内在灵活性使其能够轻松适应各种任务，因此与半导体行业的许多应用高度相关。