Product Quality Prediction with Convolutional Encoder-Decoder Architecture and Transfer Learning

Hao Yi Chih, Yao Chung Fan, Wen Chih Peng, Hai Yuan Kuo

研究成果: Conference contribution同行評審

摘要

Mining data collected from industrial manufacturing process plays an important role for intelligent manufacturing in Industry 4.0. In this paper, we propose a deep convolutional model for predicting wafer fabrication quality in an intelligent integrated-circuit manufacturing application. The wafer fabrication quality prediction is motivated by the need for improving product line efficiency and reducing manufacturing cost by detecting potential defective work-in-process (WIP) wafers. This work considers the following two crucial data characteristics for wafer fabrication. First, our model is designed to learn spatial correlation between quality measurements on WIP wafers and fabrication results through an encoder-decoder neural network. Second, we leverage the fact that different products share the same raw manufacturing process to enable the knowledge transferring between prediction models of different products. Performance evaluation on real data sets is conducted to validate the strengths of our model on quality prediction, model interpretability, and feasibility of transferring knowledge.

原文English
主出版物標題CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面195-204
頁數10
ISBN(電子)9781450368599
DOIs
出版狀態Published - 19 十月 2020
事件29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
持續時間: 19 十月 202023 十月 2020

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
國家Ireland
城市Virtual, Online
期間19/10/2023/10/20

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