Abnormal Chamber Detection in the Etching Process Using Time-Series Data Augmentation and Soft Labeling

Abstract

In the semiconductor manufacturing etching process, a considerable amount of good-quality data can be obtained by the measurement process. As the etching process has an impact on subsequent processes and to check and control the condition of the wafer, the measurement process should be performed as much as possible. However, with the rapid development of technology in manufacturing, the process recipe has undergone frequent changes. Accordingly, the amount of data available in the field is limited, and the effective utilization of measured data is needed to improve the model's performance in abnormal chamber detection. In this article, we propose to augment time-series data that are collected from equipment sensors in the etching process to improve the model's performance. To accomplish this task, we utilize dynamic time-warping barycenter averaging (DBA) and soft labeling. DBA is applied to calculate the average of the time series, and the new label is given in the form of a soft label. To verify the efficiency of the proposed method, we compared several regression models with the proposed method and with several benchmark methods. In addition, we compared the proposed method with soft labeling and hard labeling. The experimental results show that the model with the proposed method is superior to the model with its other benchmark methods.