A DATA-DRIVEN APPROACH FOR AUTOMATED INTEGRATED CIRCUIT SEGMENTATION OF SCAN ELECTRON MICROSCOPY IMAGES

Zifan Yu, Bruno Machado Trindade, Michael Green, Zhikang Zhang, Pullela Sneha, Erfan Bank Tavakoli, Christopher Pawlowicz, Fengbo Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages2851-2855
Number of pages5
ISBN (Electronic)9781665496209
DOIs
StatePublished - 2022
Externally publishedYes
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: Oct 16 2022Oct 19 2022

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period10/16/2210/19/22

Keywords

  • deep learning
  • image segmentation
  • integrated circuit segmentation
  • scan electron microscopy images

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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