An ensemble neural network framework for improving the detection ability of a base control chart in non-parametric profile monitoring

Abstract

Profile monitoring is a challenging issue in statistical process control (SPC). It aims to use a functional relationship between a response variable and one or more explanatory variable(s) to summarize the quality of a process/product. Most of the existing studies consider the same form of a functional relationship for both in-control (IC) and out-of-control (OC) situations or parametric approaches. However, non-parametric profiles with different relationships in OC conditions are very common. In this paper, we propose a novel ensemble framework to monitor changes in both regression relationship and variation of the profile for Phase II applications. This approach employs a pool of artificial neural networks (ANNs) as learners to enhance the efficiency of a base control chart, which is a non-parametric exponentially weighted moving average (NEWMA) in this study. Then, a further ANN is used as a reasoning scheme (incorporator) to perform prediction by combining the outcomes of the learners. Experimental results demonstrate the effectiveness of the proposed framework, denoted by EANNN, in comparison with the base control chart, i.e., NEWMA, and other non-parametric methods. In addition, a practical example regarding a deep reactive ion-etching process from semiconductor device fabrication is provided to show the implementation of the proposed method.