40‐3: Waveform Analysis System for GAN‐Based Anomaly Detection of Coater Pressure in Photolithography

With the advancement of manufacturing equipment and the development of sensing technology, the measurement cycle of data is becoming very short, and the characteristics of data observed at the same time are also diversifying. A Fault Detection and Classification (FDC) system is in operation to collect and analyze measurement data in real time in the display manufacturing factory. However, the anomaly detection function provided by the FDC system is based on a threshold comparison method for data in seconds, so there is a limit to accuracy in processing measurement data in milliseconds. In particular, if the process for one panel is divided into several steps and each of them shows various characteristics, it is difficult to manage because it takes a lot of airlift to set search conditions and thresholds for anomaly detection. To overcome this, a waveform analysis system is implemented to assist the FDC system. This system extracts waveforms from milliseconds of measurement data during the processing time of one panel in the equipment, and diagnoses whether the equipment processing is normal through GAN‐based waveform pattern analysis. Generative models are used in the anomaly detection process in consideration of the manufacturing environment in which the normal data is overwhelmingly larger than the abnormal data. The DCGAN‐based model that is excellent in image processing and the TadGAN‐based model that combines Auto encoder and GAN were implemented and used. In this paper, the anomaly detection performance of models is compared and evaluated for the display photo process Coater Pressure sensor using the waveform analysis system implemented in this way.