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The SEMATECH sponsored J-88-E project teaming Texas Instruments withNeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam9600 aluminum plasma etch reactor, used in the process of semiconductorfabrication. Fault classification was accomplished by implementing a series ofvirtual sensor models which used data from real sensors (Lam Station sensors,Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpointsand wafer state characteristics. Fault detection and classification wereperformed by comparing predicted recipe and wafer state values with expectedvalues. Models utilized include linear PLS, Polynomial PLS, and Neural NetworkPLS. Prediction of recipe setpoints based upon sensor data provides acapability for cross-checking that the machine is maintaining the desiredsetpoints. Wafer state characteristics such as Line Width Reduction andRemaining Oxide were estimated on-line using these same process sensors (Lam,OES, RFM). Wafer-to-wafer measurement of these characteristics in a productionsetting (where typically this information may be only sparsely available, if atall, after batch processing runs with numerous wafers have been completed)would provide important information to the operator that the process is or isnot producing wafers within acceptable bounds of product quality. Productionyield is increased, and correspondingly per unit cost is reduced, by providingthe operator with the opportunity to adjust the process or machine beforeetching more wafers.

Virtual Sensor Based Fault Detection and Classification on a Plasma Etch Reactor