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Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing

Quality inspection in semiconductor manufacturing is of great importance in the modern industries. In the recent years, intelligent data-driven condition monitoring methods have been successfully developed and applied in the industrial applications. However, despite the promising condition monitoring performance, the existing methods generally assume the training and testing data are from the same distribution. In practice, due to variations in manufacturing process, the collected data are usually subject to different distributions in different operating conditions, that significantly deteriorates the performance of the data-driven methods. To address this issue, this paper proposes a deep learning-based domain adaptation method for fault diagnosis in semiconductor manufacturing. The maximum mean discrepancy metric is optimized on the learned high-level data representation in the deep neural network. Experimental results on a real-world semiconductor manufacturing dataset suggest the proposed method offers an effective and generalized data-driven fault diagnosis approach for quality inspection.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.33 No.3 August 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 445 - 453

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

BENTUK GALAKSI
DOMAIN ADAPTATION
SEMICONDUCTOR MANUFACTURING
FAULT DIAGNOSIS
QUALITY INSPECTION

Info Detail Spesifik

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Pernyataan Tanggungjawab

Moslem Azamfar, Xiang Li, Jay Lee

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