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Open Access

Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System

Department of Control Science and Engineering, College of Electronics and Information Engineering, Shanghai Institute of Intelligent Science and Technology, and also with Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China
Department of Mechanical Engineering, School of Engineering, Cardiff University, The Parade, CF24 3AA, UK
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Abstract

Numerous performance indicators exist for semiconductor manufacturing systems. Several studies have been conducted regarding the performance optimization of semiconductor manufacturing systems. However, because of the complex manufacturing processes, potential complementary or inhibitory correlations may exist among performance indicators, which are difficult to demonstrate specifically. To analyze the correlation between the performance indicators, this study proposes a performance evaluation system based on the mathematical significance of each performance indicator to design statistical schemes. Several samples can be obtained by conducting simulation experiments through the performance evaluation system. The Pearson correlation coefficient method and canonical correlation analysis are used on the received samples to analyze linear correlations between the performance indicators. Through the investigation, we found that linear and other complex correlations exist between the performance indicators. This finding can contribute to our future studies regarding performance optimization for the scheduling problems of semiconductor manufacturing.

Keywords

correlation analysissystem modelingsemiconductor manufacturing systemevaluation system

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Complex System Modeling and Simulation
Volume 1 Issue 3,
September 2021
Pages 218-231
DOI: 10.23919/CSMS.2021.0015
Cite this article:
Yu Q, Li L, Zhao H, et al. Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System. Complex System Modeling and Simulation, 2021, 1(3): 218-231. https://doi.org/10.23919/CSMS.2021.0015

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Received: 29 April 2021
Revised: 18 June 2021
Accepted: 25 June 2021
Published: 29 October 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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