Production evaluation of real-time statistical process control on a sub-0.5 μm inductively coupled metal etch process

Abstract

The semiconductor manufacturing industry is demanding improved algorithms for the detection and isolation of equipment faults. The real-time statistical process control (RTSPC) algorithm analyzes real-time sensor and actuator data for the detection of such faults. The software was evaluated using SECS-II (Serial Equipment Communication Standard) data from several commercial plasma etchers. These data were used to build statistical models which were then applied to data recorded during subsequent wafer processing. RTSPC was able to detect several faults related to failure of rf matching networks. For one of the rf match failures, the RTSPC alarm preceded the component failure by several weeks. This alarm was correlated to a shift in the critical dimension of wafers processed in this etcher. The statistical data filters included within RTSPC are discussed, as well as the characterization of the real-time data. Emphasis is placed on whether uni-variate, multivariate, or time-varying statistics are required to detect the faults seen on the etchers, and additional algorithms which would enhance the fault detection capability.