A Unified Framework for Layout Pattern Analysis With Deep Causal Estimation
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AbstractThe decrease of feature size and the growing complexity of the fabrication process lead to more failures in manufacturing semiconductor devices. Therefore, identifying the root cause layout patterns of failures becomes increasingly crucial for yield improvement. In this article, a novel layout-aware diagnosis-based layout pattern analysis framework is proposed to identify the root cause efficiently. At the first stage of the framework, an encoder network trained using contrastive learning is used to extract representations of layout snippets that are invariant to trivial transformations, including shift, rotation, and mirroring, which are then clustered to form layout patterns. At the second stage, we model the causal relationship between any potential root cause layout patterns and the systematic defects by a structural causal model, which is then used to estimate the average causal effect (ACE) of candidate layout patterns on the systematic defect to identify the true root cause. Experimental results on real industrial cases demonstrate that our framework outperforms a commercial tool with higher accuracies and around ×8.4 speedup on average.
All Author(s) ListRan Chen, Shoubo Hu, Zhitang Chen, Shengyu Zhu, Bei Yu, Pengyun Li, Cheng Chen, Yu Huang, Jianye
Hao
Journal nameIEEE Transaction on Computer-Aided Design of Integrated Circuits and Systems
Year2023
Month4
Volume Number42
Issue Number4
PublisherIEEE
Pages1199 - 1211
ISSN0278-0070
eISSN1937-4151
LanguagesEnglish-United States
KeywordsLayout, Systematics, Feature extraction, Pattern analysis, Neurons, Neural networks, Task analysis, Causality, deep learning, design for manufacturability, failure analysis, fault diagnosis

Last updated on 2024-03-01 at 16:59