Title: Design of decision support system for yield management in semiconductor industry: application to artificial intelligence
Authors: Sun Yong Lee; Min Jae Park
Addresses: Business School Lausanne (BSL), Route de la Maladière 21, 1022 Chavannes, Switzerland; Seoul School of Integrated Sciences and Technologies, 46 Ewhayeodae 2-Gil, Seodaemun-Gu, Seoul, South Korea ' Department of e-Business, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon, South Korea
Abstract: The situation in the semiconductor production lines change from time to time, requiring extemporal decisions. Under such circumstances, the decision-making method of production management has been developed based on the decision support system approach, which utilises an information system based on partial data generated by real time line and managers' experience and intuition. We propose a system designed through deep learning method, to construct an optimal decision system for yield improvement. The aim of this study is to propose a system design that can support decision-making for yield improvement by using manufacturing deep neural network method in the semiconductor industry. The semiconductor manufacturing process data used is the production data consisting of 1,000 lots and 100 processes. The model developed in this system proposes decision variables with optimal yields within the constraint condition and supports the decision-making in the semiconductor production process by using the corresponding decision variables.
Keywords: yield improvement; semiconductor; deep neural network; decision support system; DSS.
International Journal of Business Information Systems, 2022 Vol.40 No.1, pp.60 - 84
Received: 09 Aug 2019
Accepted: 28 Sep 2019
Published online: 16 May 2022 *