Semiconductor Fault Diagnosis Using Deep Learning-Based Domain Adaption
Keywords:
Deep learning, domain adaption, fault diagnosis, semiconductor manufacturingAbstract
In contemporary industries, quality control in semiconductor manufacturing is crucial. Recent years have seen the effective development and use of intelligent data-driven condition-monitoring techniques in industrial applications. The current approaches generally assume that the testing and training data are drawn from the same distribution, despite the condition monitoring performance being quite promising. The acquired data are typically prone to varied distributions in different operating situations in practice due to the difference in the process of manufacturing, which considerably degrades the performance of the data-driven approaches. This research presents a domain adaption approach for fault diagnosis in semiconductor production that is deep learning-based in order to address this problem. The deep neural network’s learned high-level data representation is optimized for the maximum mean discrepancy measure. The implemented method appears to offer an efficient and generalized fault diagnosis methodology for quality inspection, according to experimental results using a dataset from a real-world semiconductor manufacturing facility. When compared with existing methods such as VAE-IDF, FDC, SCSDAE, and KABSL, the implemented Deep learning-based domain adaption (DL-DA) achieves 99.56% accuracy in fault diagnosis detection in semiconductors.
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