Automated process control using parameters determined with approximation and fine diffraction models
Provided is a method of controlling a fabrication cluster using a machine learning system, the machine learning system trained developed using an optical metrology model. A simulated approximation diffraction signal is generated based on an approximation diffraction model of the structure. A set of difference diffraction signal is obtained by subtracting the simulated approximation diffraction signal from each of simulated fine diffraction signals and paired with the corresponding profile parameters. A first machine learning system is trained using the pairs of difference diffraction signal and corresponding profile parameters. A library of simulated fine diffraction signals and profile parameters is generated using the trained first machine learning system and using ranges and corresponding resolutions of the profile parameters. A measured diffraction signal is input into the trained second machine learning system to determine at least one profile parameter. The at least one profile parameter is used to adjust at least one process parameter or equipment setting of the fabrication cluster.
Tokyo Electron Limited
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Weidung Shifang Manuel Wei
Yang Li Madriaga Liu