AI-powered deconvolution-based super-resolution imaging for semiconductor OCD metrology and precise stage positioning
Journal
Metrology, Inspection, and Process Control XXXVIII
Series/Report No.
Proceedings of SPIE - The International Society for Optical Engineering
Journal Volume
12955
ISBN
9781510672161
Date Issued
2024-04-10
Author(s)
DOI
10.1117/12.3010986
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.
Event(s)
Metrology, Inspection, and Process Control XXXVIII 2024San Jose26 February 2024 through 29 February 2024
Subjects
AI deep learning
automatic optical inspection (AOI)
deconvolution
Semiconductor manufacturing
system transfer function
Publisher
SPIE
Type
conference paper