Paper
10 December 2024 Large-scale chip layout pattern clustering method based on graph matching
Ziwen Wang, Jialong He, Wenzhan Zhou, Kan Zhou, Xintong Zhao, Shujing Lyu, Jiwei Shen, Yue Lu
Author Affiliations +
Proceedings Volume 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024); 134230Y (2024) https://doi.org/10.1117/12.3052980
Event: 8th International Workshop on Advanced Patterning Solutions (IWAPS 2024), 2024, Jiaxing, Zhejiang, China
Abstract
In the integrated circuits field, the rapid and accurate detection of defects and anomalies is a critical factor in improving lithography process yields. Research on large-scale chip layout pattern feature extraction and clustering algorithms plays a crucial role in enhancing chip manufacturing yield and improving manufacturing processes. This paper proposes a graph matching-based clustering method, leveraging the high redundancy and relatively simple circuit structure of chip layout patterns. Our method innovatively employs a graph-based representation to capture keypoint information in layout patterns, applies dual-similarity constraints to ensure both node and edge similarities, and utilizes agglomerative hierarchical clustering to merge structurally similar patterns, reducing the reliance on typical values. These enhancements allow for better handling of complex geometries, thus improving the efficiency and stability of pattern clustering. Compared to traditional clustering methods based on image statistical characteristics, our approach considers the geometric constraints within the chip layout, achieving effective clustering on large-scale chip layout patterns.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ziwen Wang, Jialong He, Wenzhan Zhou, Kan Zhou, Xintong Zhao, Shujing Lyu, Jiwei Shen, and Yue Lu "Large-scale chip layout pattern clustering method based on graph matching", Proc. SPIE 13423, Eighth International Workshop on Advanced Patterning Solutions (IWAPS 2024), 134230Y (10 December 2024); https://doi.org/10.1117/12.3052980
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KEYWORDS
Matrices

Design

Tolerancing

Deep learning

Distance measurement

Lithography

Feature extraction

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