P‐167: Autoencoder with Variables Added Model for Manufacturing Process Anomaly Detection Using Multivariate Time‐series Data that Minimizes Data Information Loss

Abstract

In the display industry, the technology of the FAB process is continuously being advanced. As process quality management technology improves, data can be stored, analyzed, and monitored in real time using various types of sensors. The manufacturing process is so complex that it is difficult to detect anomalies simply by analyzing data from on e or two sensors. For t his reason, multivariate data analysis is essential, and time‐series data analysis is particularly effective for detecting process state changes. In this study, we propose a multimodal AVA that can detect process anomalies using multivariate time‐series sensor data. In order to minimize t he information loss of the original data, multivariate time‐series data are processed into tabular and image forms and applied to the proposed model. We demonstrate that the proposed model performs better in anomaly detection compared to a general anomaly detection model in highly imbalanced datasets. The proposed method is expected to reduce cost and time due to defects by detecting abnormal situations in the process in real time and responding quickly when abnormal situations occur.