Switch ON/OFF learning of one-dimensional convolutional neural network and one-dimensional generative adversarial network for fault detection

초록

Fault detection and classification (FDC) has been adopted to minimize equipment errors in semiconductor manufacturing. The FDC monitors production facilities in real-time to detect abnormalities and ensure compliance with specifications. If the FDC detects that it is out of specification, an interlock is triggered. This interlock forces a safe action. Recent advancements in FDC require a more thorough understanding of interlocked data. This analysis process is called the problem of true/false alarm detection. However, true/false alarm detection is challenging not only because it requires more sophisticated work but also a smaller percentage of true alarms. In this study, we propose a novel approach for fault detection in semiconductor manufacturing using Switch ON/OFF learning that combines one-dimensional convolutional neural network (1D CNN) and one-dimensional generative adversarial network (1D GAN). The noise-resistant 1D CNN is responsible for feature extraction and distinguishing between true/false alarms, whereas the 1D GAN generates additional true alarm samples to address the imbalance in the dataset. In Switch OFF, both the models are trained in parallel, and in Switch ON, the true alarms generated by the trained 1D GAN are used to update the 1D CNN. The results of experiments conducted on two real-world semiconductor datasets demonstrated the superior performance of the proposed model over other sampling techniques. Our study provides a foundation for advancing neural networks in monitoring process facilities, specifically for detecting equipment errors in semiconductor manufacturing.

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