@inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} }