Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques
This addresses the challenge of expensive and complex data acquisition in the semiconductor industry for electronic design automation, though it appears incremental as it builds on existing ML techniques.
The paper tackled the problem of limited training data for machine learning in semiconductor device modeling by proposing a self-augmentation strategy using variational autoencoders, achieving a 70% reduction in mean absolute error for predicting Ohmic resistance in Gallium Nitride devices.
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.