Unsupervised Semiconductor chamber matching based on shape comparison. Issue 1 (July 2017) - Marino, Julien; Rossi, Francesco; Ouladsine, Mustapha; Pinaton, Jacques
Abstract: We present a new chamber matching algorithm, which is completely data-driven and unsupervised, and designed for the semiconductor industry. The behavior of an equipment is classified as different when the shape of the time series given by one of the sensors is significantly different. Shape comparison is performed using linear regression, that authorizes both offset and change of scale. The method detects both the chamber and the sensor in which the fault is present, then helping in activating corrective maintenances. Application results are shown with two examples of real semiconductor industrial failures.