Abstract: A statistical simulation of a semiconductor fabrication process is performed in parallel with the actual process. Input parameters derived from a probability density function are applied to the simulator which, in turn, simulates an actual fabrication process which is modeled as a probability density function. Each simulation step is repeated with a random seed value using a Monte Carlo technique, a trial-and-error method using repeated calculations to determine a best solution to a problem. The simulator generates an output in the form of a probability distribution. The statistical simulation uses single-step feedback in which a simulation run uses input parameters that are supplied or derived from actual in-line measured data. Output data generated by the simulator, both intermediate output structure data and WET data, are matched to actual in-line measured data in circumstances for which measured data is available.

Abstract: A statistical simulation of a semiconductor fabrication process is performed in parallel with the actual process. Input parameters derived from a probability density function are applied to the simulator which, in turn, simulates an actual fabrication process which is modeled as a probability density function. Each simulation step is repeated with a random seed value using a Monte Carlo technique, a trial-and-error method using repeated calculations to determine a best solution to a problem. The simulator generates an output in the form of a probability distribution. The statistical simulation uses single-step feedback in which a simulation run uses input parameters that are supplied or derived from actual in-line measured data. Output data generated by the simulator, both intermediate output structure data and WET data, are matched to actual in-line measured data in circumstances for which measured data is available.