AI-powered deconvolution-based super-resolution imaging for semiconductor OCD metrology and precise stage positioning

Abstract

This study develops a parametric system transfer function (STF) model using scalar diffraction theory and Fourier optics to address the loss of precision in image-based positioning caused by the diffraction limit on marker scale. By fitting the model to observed STFs and employing deconvolution and a deep convolutional neural network, the method enhances image quality, overcoming traditional deconvolution limitations. Applied to critical dimension measurements, it improved radius accuracy for vias and pillars by 54.8% and reduced displacement measurement bias by 36.4%. The development particularly benefits automatic optical inspection (AOI) for quality control in semiconductor manufacturing.