About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
IIE-AC 2013
Conference paper
A unified outlier detection method for trace data analysis in semiconductor manufacturing
Abstract
Process trace data (PTD) is an important data type in semiconductor manufacturing and consists of a huge amount of time series data collected from many different sensors during all manufacturing processing steps. PTD contains abundant information about the tool status and thus can be used for improving tool stability and tool matching. For this aim, some industrial applications have been developed on the basis of PTD analysis, but the existence of outliers adversely affects the effectiveness of these applications. Due to the complexities of PTD and the resultant outlier patterns, this paper proposes a unified outlier detection method which takes advantages of data complexity reduction using entropy and the cumulative sum (CUSUM) method. A novel algorithm is developed to meet the practical requirements of PTD analysis by taking into account of the related domain knowledge, and its effectiveness is demonstrated by using real process trace data sets.