An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example. (January 2017)

• Record Type: Journal Article
• Authors: Chen, Toly
• Is Part Of: Computers & industrial engineering. Volume 103(2017)
• Page Start: 98
• Page End: 104
• Publication Date: 2017-01
• Subjects: Yield -- Learning -- Semiconductor -- Artificial neural network; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.11.021
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The future yield of a product in a wafer fabrication factory is estimated. An innovative approach is proposed for modeling the yield learning process with artificial neural networks. The effects of various sources of yield learning were separated. Abstract: Estimating the future yield of a product is a crucial task for semiconductor manufacturers. However, existing methods cannot differentiate the effects of various sources of yield improvement. To address this concern, this study proposes an innovative approach for modeling the yield learning process of a semiconductor product with artificial neural networks, which enable separating the effects of various sources of yield learning. Two real cases were used to demonstrate the effectiveness of the proposed methodology.