Patent ID: 6564116
Filing Date: 2003-05-13
Classification: G05B

Abstract:
A method for determining efficient parameters in chemical-mechanical polishing (CMP) processes comprising:a. using a Taguchi experiment to lead the chemical-mechanical polishing (CMP) procedure and determine essential parameters to calculate a maximum material removal rate (MRR) and the within wafer non-uniformity (WIWNU), wherein four essential coefficients are picked out as changeable parameters and adjusted during the experiments and a polishing time is set to be a fifth parameter to complete a model, experiments are conducted according to a L25(5Ã—5) orthogonal array of the Taguchi experiment, the L25(5Ã—5) orthogonal array is built based on five parameters and five levels on each parameter, the material removal rate (MRR) and the within wafer non-uniformity (WIWNU) for each experiment are calculated; b. analyzing signal-to-noise (S/N) ratios of the material removal rate (MRR) and the within wafer non-uniformity (WIWNU), wherein the S/N ratios of the material removal rate (MRR) and the within wafer non-uniformity (WIWNU) in each trial are calculated and used to make response tables and response figures, and then analysis of variance (ANOVA) is employed in estimating contributing degrees of each parameter to decide a parameter set of the material removal rate (MRR) and the within wafer non-uniformity (WIWNU); c. establishing the chemical-mechanical polishing (CMP) process with the parameter set of the material removal rate (MRR) and the within wafer nonuniformity (WIWNU), wherein a neural network simulates the chemical-mechanical polishing (CMP) procedure and optimizes the parameter automatically, whereby the data of step (a) are used as training samples of the neural network, wherein the standard deviation of t2 (wafer thickness after polishing) and the material removal rate (MRR) are outputs that define a performance index including the material removal rate (MRR) and the standard deviation of t2, said performance index being optimum in its largest mode; and d. optimizing the parameter set of the chemical-mechanical polishing (CMP) procedure, wherein the neural network based chemical-mechanical polishing (CMP) model in step (c) is adopted in this step and a fine-tuning process is conducted to optimize each parameter of the parameter set, and then an optimum parameter set is obtained.