As integrated circuit designs become more complex, in compliance with Moore's Law, assuring the production quality of these complex integrated circuits becomes increasingly difficult. Consequently, semiconductor manufacturers must focus on achieving tighter real-time process control in order to obtain justifiable production yields as well as sustain profitability in an increasingly competitive marketplace. Traditionally, equipment and process faults are being discovered by "in-line" measurements done between process steps. However, due to an increased pressure to produce of a highly diverse product mixture in shorter cycle times, equipment and process faults must be detected in real-time. However, because real-time process control requires the analysis of real-time equipment sensor data, traditional statistical process control (SPC) techniques [1] cannot be readily applied to the sensor data due to their non-stationary, auto-correlated and cross-correlated characteristics. The Berkeley Computer-Aided Manufacturing (BCAM) Real-Time SPC system utilizes econometric time-series models [2] in order to filter real-time readings of any existing autocorrelations. In addition, multivariate statistics, in particular, the Hotelling's T squared statistic [3], are then used in order to combine the various cross-correlated signals into a single statistical score. This T squared statistic is monitored with a single-sided control chart for real-time SPC [4].




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