Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140220
Title: Cyclic scheduling analysis of single-arm cluster tools with wafer residency time constraint and chamber cleaning operations
Authors: Yang, FaJun
Wu, NaiQi
Su, Rong
Qiao, Yan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Yang, F., Wu, N., Su, R., & Qiao, Y. (2018). Cyclic scheduling analysis of single-arm cluster tools with wafer residency time constraint and chamber cleaning operations. Proceedings of 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 241-246. doi:10.1109/coase.2018.8560461
Abstract: In semiconductor manufacturing, a cleaning operation that takes significant time is required for eliminating the chemical residual in a chamber after a wafer being processed and removed from it. Such a cleaning operation makes a traditional backward strategy inefficient. In the existing work, it is shown that the productivity can be improved if some of chambers at a step are kept empty. With this idea, an extended backward strategy is proposed by deciding the optimal number of empty chambers. Based on such a strategy, this work studies the challenging problem for scheduling a single-arm cluster tool with both chamber cleaning operations and wafer residency time constraint for the first time. By building timed Petri net model for the system, two linear programs are proposed to determine the minimal cycle time and test the existence of a feasible schedule. At last, an industrial example is used to demonstrate the obtained results.
URI: https://hdl.handle.net/10356/140220
ISBN: 978-1-5386-3593-3
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/COASE.2018.8560461
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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