Monitoring of Semiconductor Manufacturing Process Based on Functional Data Analysis

doi:10.3901/JME.2018.16.062

Abstract

Abstract: A monitoring method based on functional data analysis(FDA) is proposed to improve the quality of batch process in non-stationary condition. Each variable of the three-dimensional data is transformed into smooth functions by FDA method. After that, the three-dimensional data can be described as a two-dimensional matrix. In order to improve the accuracy of monitoring model, the second derivative of smooth functions is calculated to eliminate mean fluctuation caused by non-stationary manufacturing process. Features of the second derivative of each variable are extracted by functional principal component analysis(FPCA) to obtain FPCs. A model is built by the support vector data description method(SVDD) to monitor FPCs and the model is applied to an industrial semiconductor manufacturing process. The results show that the new monitoring method has the lowest missed rate compared with conventional methods, so the effectiveness of the proposed method is validated.

Key words: functional data analysis, functional principal component analysis, non-stationary manufacturing process, process monitoring

CLC Number:

TP277

LI Min, XIE Xuan, CHEN Ze, YANG Mengyao, YANG Debin, JIANG Jing. Monitoring of Semiconductor Manufacturing Process Based on Functional Data Analysis[J]. Journal of Mechanical Engineering, 2018, 54(16): 62-69.

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