Yu-Ting ChengWei-Yun LeeMing-Jie LiuWei-Hsin CheinLIANG-CHIA CHEN2024-07-032024-07-032024-04-1097815106721610277786Xhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85193027972&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/719592This 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.falseAI deep learningautomatic optical inspection (AOI)deconvolutionSemiconductor manufacturingsystem transfer functionconference paper10.1117/12.30109862-s2.0-85193027972