PRODUCTION EVALUATION OF REAL-TIME STATISTICAL PROCESS-CONTROL ON A SUB-O.5 MU-M INDUCTIVELY-COUPLED METAL ETCH PROCESS

The semiconductor manufacturing industry is demanding improved algorit hms for the detection and isolation of equipment faults. The real-time statistical process control (RTSPC) algorithm analyzes real-time sens or and actuator data for the detection of such faults. The software wa s evaluated using SECS-II (Serial Equipment Communication Standard) da ta from several commercial plasma etchers. These data were used to bui ld statistical models which were then applied to data recorded during subsequent wafer processing. RTSPC was able to detect several faults r elated to failure of rf matching networks. For one of the rf match fai lures, the RTSPC alarm preceded the component failure by several weeks . This alarm was correlated to a shift in the critical dimension of wa fers processed in this etcher. The statistical data filters included w ithin RTSPC are discussed, as well as the characterization of the real -time data. Emphasis is placed on whether uni-variate, multivariate, o r time-varying statistics are required to detect the faults seen on th e etchers, and additional algorithms which would enhance the fault det ection capability. (C) 1997 American Vacuum Society.