TY - GEN
T1 - A DATA-DRIVEN APPROACH FOR AUTOMATED INTEGRATED CIRCUIT SEGMENTATION OF SCAN ELECTRON MICROSCOPY IMAGES
AU - Yu, Zifan
AU - Trindade, Bruno Machado
AU - Green, Michael
AU - Zhang, Zhikang
AU - Sneha, Pullela
AU - Tavakoli, Erfan Bank
AU - Pawlowicz, Christopher
AU - Ren, Fengbo
N1 - Funding Information:
This work is supported by a research contract from TechInsights Inc. Corresponding author email: [email protected].
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - deep learning
KW - image segmentation
KW - integrated circuit segmentation
KW - scan electron microscopy images
UR - http://www.scopus.com/inward/record.url?scp=85146692865&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146692865&partnerID=8YFLogxK
U2 - 10.1109/ICIP46576.2022.9897544
DO - 10.1109/ICIP46576.2022.9897544
M3 - Conference contribution
AN - SCOPUS:85146692865
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2851
EP - 2855
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -