Applying Cost and Regression Analyses to Optimize Semiconductor Processing Water Management

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Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/28188

Title:	Applying Cost and Regression Analyses to Optimize Semiconductor Processing Water Management
Authors:	王本正 Wang, Ben-Jeng Lee, Li-Ling Chang, Cheng-Nan Allen, C.Chao
Contributors:	東海大學企業管理學系
Keywords:	Regression analyses cost analyses semiconductor wafer manufacturing plant optimum recycle of processing water optimum water reuse rate
Date:	2004-04
Issue Date:	2016-10-03T02:29:47Z (UTC)
Abstract:	In this study, regression and cost analyses are applied to optimize the recycling of processing water for a 6-inch semiconductor wafer manufacturing plant. The analyses are also applied to obtain the optimum reuse rate if the plant is expanded for producing 8-inch or 12-inch wafers. A mass balance diagram is first developed based on three systems: processing system, wastewater system and recovery system. The information contained in the diagram is then modified including the total costs of various water treatment processes to analyze the costs for the above three systems. These costs are then used as the bases for optimizing the water recycle operation. Various parameters, which influence the operation cost of each system e.g. the recovery rate of processing water, water usage, operation and maintenance costs and quantities of pollutants, have also been investigated. The resulting optimum recovery rates are 77% for the 6-in wafer plant, 80% for the 8-in wafer plant and 86% for the 12-in wafer plant. Thus, when the wafer size upgraded, the water recycling rate should also be raised for achieving the most cost-effective water reuse. The analyses also evaluate the water recycling practice for various assumed unit water costs. Using the 6-in wafer plant as an example, the optimum water recycling rate should be raised to 74%, 76%, 78%, 81% and 84% for water costing $0.20, $0.36, $0.51, $0.71 and $1.00 per tons, respectively. Applying the cost analysis methods proposed in this study, the optimum water recycling rate can be rapidly determined in response to variations of wafer manufacturing operations. Additionally, results of the regression analyses have demonstrated that the wafer size, costs of the three aforementioned systems, and water cost influence significantly the recycling rate of processing water.
Relation:	JOURNAL OF ENVIRONMENTAL INFORMATICS, 2, 174-185
Appears in Collections:	[企業管理學系所] 期刊論文

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