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This paper introduces TRACE-GPT, which stands for Time-seRiesAnomaly-detection with Convolutional Embedding and Generative Pre-trainedTransformers. TRACE-GPT is designed to pre-train univariate time-series sensordata and detect faults on unlabeled datasets in semiconductor manufacturing. Insemiconductor industry, classifying abnormal time-series sensor data fromnormal data is important because it is directly related to wafer defect.However, small, unlabeled, and even mixed training data without enoughanomalies make classification tasks difficult. In this research, we capturefeatures of time-series data with temporal convolutional embedding andGenerative Pre-trained Transformer (GPT) to classify abnormal sequences fromnormal sequences using cross entropy loss. We prove that our model shows betterperformance than previous unsupervised models with both an open dataset, theUniversity of California Riverside (UCR) time-series classification archive,and the process log of our Chemical Vapor Deposition (CVD) equipment. Our modelhas the highest F1 score at Equal Error Rate (EER) across all datasets and isonly 0.026 below the supervised state-of-the-art baseline on the open dataset.

Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing