Identification of Predictive Anomolous Signals in the Noise of Epitaxy Process Control : Topics: APC, DM, EO, NS/NC

In a world of chip shortages and constrained supply, unplanned manufacturing downtime is extremely costly. Avoiding unplanned equipment downtime through predictive maintenance results in increased yield, increased throughput, and reduced costs. An advanced statistical method for the evaluation of time series sensor data is herein proposed for the purpose of predicting equipment failures in Chemical Vapor Deposition (CVD) systems. Through the simultaneous evaluation of all available sensor data, the algorithm develops an expected model of the total process' behavior. Deviation from expected behavior may be interpreted as pointing to elevated risk of equipment failure, or of a process upset that may result in product that falls outside of manufacturers’ specifications, leading to costly containment measures, customer returns, and other costs.Examples from recent Industrial Experiments in Wafer Epitaxy processes are shared to demonstrate the efficacy of the method.