Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components
With the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/155 |
| _version_ | 1797626327015096320 |
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| author | Guoliang Tan Zexiao Liang Yuan Chi Qian Li Bin Peng Yuan Liu Jianzhong Li |
| author_facet | Guoliang Tan Zexiao Liang Yuan Chi Qian Li Bin Peng Yuan Liu Jianzhong Li |
| author_sort | Guoliang Tan |
| collection | DOAJ |
| description | With the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample data of ICs. Paradoxically, it is more challenging to invent an efficient algorithm for high-precision noiseless data. Thus, we recently proposed a fusion clustering framework based on low-quality chip images named High-Frequency Low-Rank Subspace Clustering (HFLRSC), which can provide the data foundation for the verification task by effectively clustering those noisy and low-resolution partial images of multiple target ICs into the correct categories. The first step of the framework is to extract high-frequency texture components. Subsequently, the extracted texture components will be integrated into subspace learning so that the algorithm can not only learn the low-rank space but also retain high-frequency information with texture characteristics. In comparison with the benchmark and state-of-the-art method, the presented approach can more effectively process simulation low-quality IC images and achieve better performance. |
| first_indexed | 2024-03-11T10:08:50Z |
| format | Article |
| id | doaj.art-5e2d1296b4264d80a3743b28c1edbb9e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:08:50Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5e2d1296b4264d80a3743b28c1edbb9e2023-11-16T14:51:26ZengMDPI AGApplied Sciences2076-34172022-12-0113115510.3390/app13010155Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture ComponentsGuoliang Tan0Zexiao Liang1Yuan Chi2Qian Li3Bin Peng4Yuan Liu5Jianzhong Li6School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, ChinaThe Fifth Electronics Research Institute of the Ministry of Industry and Information Technology of China, Guangzhou 510630, ChinaThe Fifth Electronics Research Institute of the Ministry of Industry and Information Technology of China, Guangzhou 510630, ChinaNeuroeconomics Laboratory, Guangzhou Huashang College, Guangzhou 511399, ChinaSchool of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, ChinaWith the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample data of ICs. Paradoxically, it is more challenging to invent an efficient algorithm for high-precision noiseless data. Thus, we recently proposed a fusion clustering framework based on low-quality chip images named High-Frequency Low-Rank Subspace Clustering (HFLRSC), which can provide the data foundation for the verification task by effectively clustering those noisy and low-resolution partial images of multiple target ICs into the correct categories. The first step of the framework is to extract high-frequency texture components. Subsequently, the extracted texture components will be integrated into subspace learning so that the algorithm can not only learn the low-rank space but also retain high-frequency information with texture characteristics. In comparison with the benchmark and state-of-the-art method, the presented approach can more effectively process simulation low-quality IC images and achieve better performance.https://www.mdpi.com/2076-3417/13/1/155low-quality dataintegrated circuitshigh-frequency texture componentsubspace clustering |
| spellingShingle | Guoliang Tan Zexiao Liang Yuan Chi Qian Li Bin Peng Yuan Liu Jianzhong Li Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components Applied Sciences low-quality data integrated circuits high-frequency texture component subspace clustering |
| title | Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components |
| title_full | Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components |
| title_fullStr | Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components |
| title_full_unstemmed | Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components |
| title_short | Low-Quality Integrated Circuits Image Verification Based on Low-Rank Subspace Clustering with High-Frequency Texture Components |
| title_sort | low quality integrated circuits image verification based on low rank subspace clustering with high frequency texture components |
| topic | low-quality data integrated circuits high-frequency texture component subspace clustering |
| url | https://www.mdpi.com/2076-3417/13/1/155 |
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