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- 01HSRAVBTX7QRAM1X0FMVW7B26 classification C1.
- 01HSRAVBTX7QRAM1X0FMVW7B26 date "2023".
- 01HSRAVBTX7QRAM1X0FMVW7B26 language "eng".
- 01HSRAVBTX7QRAM1X0FMVW7B26 type conference.
- 01HSRAVBTX7QRAM1X0FMVW7B26 hasPart 01HSRB4P5JWW7NT8VMPN6A3TQ0.pdf.
- 01HSRAVBTX7QRAM1X0FMVW7B26 hasPart 01HSTGJN2NJ54BYAK2D7ADDC1P.pdf.
- 01HSRAVBTX7QRAM1X0FMVW7B26 subject "Technology and Engineering".
- 01HSRAVBTX7QRAM1X0FMVW7B26 doi "10.1109/elmar59410.2023.10253920".
- 01HSRAVBTX7QRAM1X0FMVW7B26 isbn "9798350325119".
- 01HSRAVBTX7QRAM1X0FMVW7B26 isbn "9798350325126".
- 01HSRAVBTX7QRAM1X0FMVW7B26 issn "2835-3781".
- 01HSRAVBTX7QRAM1X0FMVW7B26 presentedAt urn:uuid:7faebe45-ef79-48b0-b84e-0a1903eeb149.
- 01HSRAVBTX7QRAM1X0FMVW7B26 abstract "With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework “SEMI-DiffusionInst” is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%,respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed.".
- 01HSRAVBTX7QRAM1X0FMVW7B26 author 21526244-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HSRAVBTX7QRAM1X0FMVW7B26 author b9714688-f155-11ea-9619-e05b44c34bc3.
- 01HSRAVBTX7QRAM1X0FMVW7B26 author urn:uuid:8553a749-24a3-4d2d-8b23-2f7d9a8b604d.
- 01HSRAVBTX7QRAM1X0FMVW7B26 author urn:uuid:f951f165-ab05-4c48-ba8c-9d4c39288e20.
- 01HSRAVBTX7QRAM1X0FMVW7B26 dateCreated "2024-03-24T13:36:54Z".
- 01HSRAVBTX7QRAM1X0FMVW7B26 dateModified "2024-07-09T15:30:41Z".
- 01HSRAVBTX7QRAM1X0FMVW7B26 editor urn:uuid:48514831-5905-4d43-a435-a0fa891f1200.
- 01HSRAVBTX7QRAM1X0FMVW7B26 editor urn:uuid:9914a2cf-4141-4e58-a7d9-a76989e0965c.
- 01HSRAVBTX7QRAM1X0FMVW7B26 editor urn:uuid:d9954fe3-26ec-420a-b7e8-e9fd414416aa.
- 01HSRAVBTX7QRAM1X0FMVW7B26 name "SEMI-DiffusionInst : a diffusion model based approach for semiconductor defect classification and segmentation".
- 01HSRAVBTX7QRAM1X0FMVW7B26 pagination urn:uuid:9551d2ad-a8d4-4c61-b61d-f13e1b1ef7fb.
- 01HSRAVBTX7QRAM1X0FMVW7B26 publisher urn:uuid:166f9b71-2e89-4cbc-ad02-049d8c26ff56.
- 01HSRAVBTX7QRAM1X0FMVW7B26 sameAs LU-01HSRAVBTX7QRAM1X0FMVW7B26.
- 01HSRAVBTX7QRAM1X0FMVW7B26 sourceOrganization urn:uuid:282db083-a202-466b-a4fa-d6633ee4f89a.
- 01HSRAVBTX7QRAM1X0FMVW7B26 sourceOrganization urn:uuid:2c605467-f2d3-445c-98dd-d5357de10e94.
- 01HSRAVBTX7QRAM1X0FMVW7B26 sourceOrganization urn:uuid:7c8214ef-4559-41fb-ad4a-22c98686033b.
- 01HSRAVBTX7QRAM1X0FMVW7B26 type C1.