AI-guided optical-model-based superresolution for semiconductor CD metrology

An AI-assisted computational method is developed to achieve superresolution imaging by overcoming the limitations of optical imaging caused by the diffraction limit. The method builds and fits a parametric optical imaging model by resolving an inverse problem. High-resolution imaging often requires a high-magnification lens, but this reduces the field of view. The transfer function of the imaging system is parameterized using diffraction theory and simulations of real imaging disturbances. "Known sample" and corresponding "Measured image" pairs are used to train the model and fit the real transfer function of the system. A deconvolution algorithm is applied to resolve the reverse problem and maintain high resolution under an enlarged field of view. The spatial resolution can be improved by 2.33 times compared to the diffraction limit. This method is useful for semiconductor critical dimension metrology in automated optical inspection.