Capacity pricing mechanism for wafer fabrication

Chung, Shu-Hsing; Lee, Amy H. I.; Huang, Chun-Ying; Chuang, Chia-Chien

doi:10.1016/j.cie.2008.02.006

Cited by 2 publications

(1 citation statement)

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“…In wafer fabrication, hundreds of machines operate together under various constraints and procedures to construct multiple layers of chemical patterns on a silicon wafer (Kumar 1994;Mason et al 2002). The initial layers after release are basic operations for all types of wafers, and several layers, including poly and metal operations, can be distinctly identified based on product specification (Chung et al 2008). Every layer should be processed in a similar manner.…”

Section: Introductionmentioning

confidence: 99%

Developing a parametric carbon footprinting tool for the semiconductor industry

Huang

Yin

et al. 2015

Int. J. Environ. Sci. Technol.

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The advent of global awareness of sustainable development resulted in consumer demand for low-carbon electronic products. Thus, semiconductor industry has to develop additional core competencies in that direction to remain competitive. The present study aims to establish a parametric-based tool that can identify key parameters of the complicated manufacturing processes in the semiconductor industry to simplify the calculation of the carbon footprint of products (CFP). Six regression models for CFP were developed by applying process and statistical analyses on 7114 wafer products. Results indicate that these regression models with the three key parameters (mask layer, metal layer, and technology node) can effectively predict the CFP of wafer with six different function types because the adjusted R 2 of all regression models was [0.5. Moreover, the mask layer could be the most important parameter for predicting CFP of wafer because of its higher standardized coefficients (b) in each regression model. This methodology reduces the time, cost, and information requirements of the product in traditional life cycle assessment. The proposed method introduces criteria for low-carbon decision making in the semiconductor sector.

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Section: Introductionmentioning

confidence: 99%