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