A Unified Framework for Outlier Detection in Trace Data Analysis

Process trace data (PTD) is an important data type in semiconductor manufacturing and has a very large aggregate volume. While data mining and statistical analysis play a key role in the quality control of wafers, the existence of outliers adversely affects the applications benefiting from PTD analysis. Due to the complexities of PTD and the resultant outlier patterns, this paper proposes a unified outlier detection framework which takes advantages of data complexity reduction using entropy and abrupt change detection using cumulative sum (CUSUM) method. To meet the practical needs of PTD analysis, a two-step algorithm taking into account of the related domain knowledge is developed, and its effectiveness is validated by using real PTD sets and a production example. The experimental results show that the proposed method outperforms the Fast Greedy Algorithm (FGA) and the Grubb's test, two commonly used outlier detection techniques for univariate data.