Conference proceeding
WaferCap: Open Classification of Wafer Map Patterns using Deep Capsule Network
2024 IEEE 42nd VLSI Test Symposium (VTS), pp 1-7
22 Apr 2024
Abstract
In integrated circuit design, analysis of wafer map patterns is critical to enhance yield and detect manufacturing issues. With the emergence of novel wafer map patterns, there is increasing need for robust artificial intelligence models that can both accurately classify seen patterns and while also detecting ones not seen during training, a capability known as open world classification. We develop a novel solution to this problem: WaferCap, a Deep Capsule Network designed for wafer map pattern classification and equipped with a rejection mechanism. When evaluated using the WM-811k dataset, WaferCap significantly surpasses existing methods, achieving 99% accuracy for fully seen patterns while demonstrating robust performance in open-world settings by effectively detecting unseen wafer map patterns.
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Details
- Title
- WaferCap: Open Classification of Wafer Map Patterns using Deep Capsule Network
- Creators
- Abhishek Mishra - Drexel UniversityMohammad Ershad Shaik - The University of Texas at AustinAnush Lingamoorthy - Drexel UniversitySuman Kumar - Drexel UniversityAnup Das - Drexel UniversityNagarajan Kandasamy - Drexel UniversityNur A. Touba - The University of Texas at Austin
- Publication Details
- 2024 IEEE 42nd VLSI Test Symposium (VTS), pp 1-7
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Web of Science ID
- WOS:001239933000028
- Scopus ID
- 2-s2.0-85195237117
- Other Identifier
- 991021881383704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic