Dynamic Time Warping Constraints for Semiconductor Processing

We present a new method for preprocessing semiconductor fabrication sensor signals that improves fault detection model performance. Machine learning has allowed for advances in fault detection and classification, but there are still difficulties in applying these techniques to the monitoring of fabrication processes when some variation in processing time is acceptable. The new method uses domain knowledge - specifically, process recipe steps - to create constraints that better align signals along the time dimension, which addresses this problem of nonlinear signal alignment.