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- 01HSRBK2STXNBH7G9E36HNFET8 classification C1.
- 01HSRBK2STXNBH7G9E36HNFET8 date "2023".
- 01HSRBK2STXNBH7G9E36HNFET8 language "eng".
- 01HSRBK2STXNBH7G9E36HNFET8 type conference.
- 01HSRBK2STXNBH7G9E36HNFET8 hasPart 01HSRBMGKTJR8C72ABZC8V4WYE.pdf.
- 01HSRBK2STXNBH7G9E36HNFET8 subject "Technology and Engineering".
- 01HSRBK2STXNBH7G9E36HNFET8 doi "10.1117/12.2675570".
- 01HSRBK2STXNBH7G9E36HNFET8 isbn "9781510668607".
- 01HSRBK2STXNBH7G9E36HNFET8 isbn "9781510668614".
- 01HSRBK2STXNBH7G9E36HNFET8 issn "0277-786X".
- 01HSRBK2STXNBH7G9E36HNFET8 presentedAt urn:uuid:9bc777eb-89e9-44ec-815f-74ec8e3babb5.
- 01HSRBK2STXNBH7G9E36HNFET8 volume "12802".
- 01HSRBK2STXNBH7G9E36HNFET8 abstract "Continual shrinking of pattern dimensions in the semiconductor domain is making it increasingly difficult to inspect defects due to factors such as the presence of stochastic noise and the dynamic behavior of defect patterns and types. Conventional rule-based methods and non-parametric supervised machine learning algorithms like k-nearest neighbors (kNN) mostly fail at the requirements of semiconductor defect inspection at these advanced nodes. Deep Learning (DL)-based methods have gained popularity in the semiconductor defect inspection domain because they have been proven robust towards these challenging scenarios. In this research work, we have presented an automated DL-based approach for efficient localization and classification of defects in SEM images. We have proposed SEMI-CenterNet (SEMI-CN), a customized CN architecture trained on Scanning Electron Microscope (SEM) images of semiconductor wafer defects. The use of the proposed CN approach allows improved computational efficiency compared to previously studied DL models. SEMI-CN gets trained to output the center, class, size, and offset of a defect instance. This is different from the approach of most object detection models that use anchors for bounding box prediction. Previous methods predict redundant bounding boxes, most of which are discarded in postprocessing. CN mitigates this by only predicting boxes for likely defect center points. We train SEMI-CN on two datasets and benchmark two ResNet backbones for the framework. Initially, ResNet models pretrained on the COCO dataset undergo training using two datasets separately. Primarily, SEMI-CN shows significant improvement in inference time against previous research works. Finally, transfer learning (using weights of custom SEM dataset) is applied from ADI dataset to AEI dataset and vice-versa, which reduces the required training time for both backbones to reach the best mAP against conventional training method (using COCO dataset pretrained weights).".
- 01HSRBK2STXNBH7G9E36HNFET8 author 21526244-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HSRBK2STXNBH7G9E36HNFET8 author b9714688-f155-11ea-9619-e05b44c34bc3.
- 01HSRBK2STXNBH7G9E36HNFET8 author urn:uuid:35f22cfc-abb2-4181-ac6a-55ae5bda0071.
- 01HSRBK2STXNBH7G9E36HNFET8 author urn:uuid:46699456-a868-4c9b-b2fb-f198ab721c67.
- 01HSRBK2STXNBH7G9E36HNFET8 author urn:uuid:4705444d-ffd3-4996-ba6e-3e8a9348d206.
- 01HSRBK2STXNBH7G9E36HNFET8 author urn:uuid:5bcbddef-b172-4352-8eb2-bf98628a5696.
- 01HSRBK2STXNBH7G9E36HNFET8 dateCreated "2024-03-24T13:49:51Z".
- 01HSRBK2STXNBH7G9E36HNFET8 dateModified "2024-07-09T15:30:41Z".
- 01HSRBK2STXNBH7G9E36HNFET8 editor urn:uuid:5b3e358f-d54c-41b2-8764-89ff4fb3656c.
- 01HSRBK2STXNBH7G9E36HNFET8 editor urn:uuid:9618836a-a36c-4919-a2da-f5a920827349.
- 01HSRBK2STXNBH7G9E36HNFET8 name "SEMI-CenterNet : a machine learning facilitated approach for semiconductor defect inspection".
- 01HSRBK2STXNBH7G9E36HNFET8 pagination urn:uuid:0c1ea982-cea8-4e22-abc0-be39e8a5dddd.
- 01HSRBK2STXNBH7G9E36HNFET8 publisher urn:uuid:6a08d21a-9b72-4930-960f-04036e795be8.
- 01HSRBK2STXNBH7G9E36HNFET8 sameAs LU-01HSRBK2STXNBH7G9E36HNFET8.
- 01HSRBK2STXNBH7G9E36HNFET8 sourceOrganization urn:uuid:0975f2f0-9769-4bd0-81c3-7d11c92438de.
- 01HSRBK2STXNBH7G9E36HNFET8 sourceOrganization urn:uuid:643c76cd-d968-45f2-91df-10ee9ecd6c97.
- 01HSRBK2STXNBH7G9E36HNFET8 sourceOrganization urn:uuid:aaf1dda3-093a-4ff4-aa40-c3299396b59f.
- 01HSRBK2STXNBH7G9E36HNFET8 type C1.