Constrained Dynamic Time Warping and Polyline Distance for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing demands exceptional precision, as even minor process deviations can result in significant yield degradation. The increasing deployment of sensors generates extensive time-series data. However, such data are often affected by temporal misalignments, nonlinear distortions, and inter-wafer variability, complicating direct comparison and automated anomaly detection. To address these challenges, this paper proposes a robust framework that employs a Dynamic Time Warping (DTW)-based two-stage alignment strategy with Sakoe–Chiba constraint followed by a bidirectional polyline distance measure to identify subtle anomalies. This approach effectively handles scarce anomaly labels and high variability in sensor data, enabling reliable process health monitoring. Experimental results on real semiconductor production data demonstrate that the framework enhances detection accuracy, contributing to early fault identification and reduced wafer scrap in manufacturing environments.