Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection | Paper Citation Record

Observation 10b86060-6104-4b4f-be80-3a377ee3f430 · outbound

This paper cites Unsupervised pixel-wise hyperspectral anomaly detection via autoencoding adversarial networks.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unsupervised pixel-wise hyperspectral anomaly detection via autoencoding adversarial networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.352095Z

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Observation fd900d06-f941-41ee-8d59-be0fae4e39b7 · outbound

This paper cites Robust Image Wafer Inspection, in: 2020 Tenth International Conference on Image Processing Theory, Tools and Appli- cations (IPTA), IEEE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Robust Image Wafer Inspection, in: 2020 Tenth International Conference on Image Processing Theory, Tools and Appli- cations (IPTA), IEEE

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.331599Z

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This paper cites Hyperspectral anomaly detection method based on auto-encoder, in: Image and Signal Processing for Remote Sensing XXI, Spie.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Hyperspectral anomaly detection method based on auto-encoder, in: Image and Signal Processing for Remote Sensing XXI, Spie

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.307328Z

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Observation ec307abf-e5b5-44e0-923c-ca90212b9a78 · outbound

This paper cites Automatic defect classification: status and industry trends, in: Integrated Circuit Metrol- ogy, Inspection, and Process Control IX, SPIE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Automatic defect classification: status and industry trends, in: Integrated Circuit Metrol- ogy, Inspection, and Process Control IX, SPIE

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.276820Z

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Observation bfa801dd-4c7f-4678-84ea-616c7b09d490 · outbound

This paper cites Image-based surface defect detection using deep learning: A review.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Image-based surface defect detection using deep learning: A review

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.261191Z

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Observation d7f75c11-f320-4143-aecd-ec33b541359e · outbound

This paper cites Wafer Defect Inspection by Neural Analysis of Region Features.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Wafer Defect Inspection by Neural Analysis of Region Features

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.243784Z

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Observation 5594b6d3-c917-4fbc-a791-972ba823ddc1 · outbound

This paper cites Application of two Hopfield Neural Networks for Automatic four-element LED Inspection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Application of two Hopfield Neural Networks for Automatic four-element LED Inspection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.219132Z

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Observation a70fc6f8-3a44-4a20-b012-818ef3c42e58 · outbound

This paper cites Semi-supervised learning.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Semi-supervised learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.197832Z

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Observation 3a5aa4bd-6d36-4b88-89d4-6128fb3a0196 · outbound

This paper cites A Neural-Network Approach to Recognize DefectSpatialPatterninSemiconductorFabrication.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A Neural-Network Approach to Recognize DefectSpatialPatterninSemiconductorFabrication

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.111094Z

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Observation 6004d58f-6b1e-41e6-9ad0-67cd3f2fd625 · outbound

This paper cites A Neural-Network Approach for an Au- tomatic LED Inspection System.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A Neural-Network Approach for an Au- tomatic LED Inspection System

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.078555Z

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Observation 86afe67d-c275-493b-b435-fcc117b80b84 · outbound

This paper cites A light-weighted cnn model for wafer structural defect detection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A light-weighted cnn model for wafer structural defect detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.044677Z

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Observation fadce370-80d6-4c22-b0a9-513f4fbce1f0 · outbound

This paper cites Convolutional neu- ral network for wafer surface defect classification and the detection of unknown defect class.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Convolutional neu- ral network for wafer surface defect classification and the detection of unknown defect class

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:52.020918Z

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Observation 55f08613-470d-43a0-9d14-76548893fe1c · outbound

This paper cites Automated visual inspection: A survey.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Automated visual inspection: A survey

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.993208Z

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Observation 5948a129-ce0f-4b86-9a6a-c654b11ea36c · outbound

This paper cites Implementation of Reflected Light Die-to-Die Inspection and ReviewSmart to Improve 65nm DRAM Mask Fabrication, in: 25th Annual BACUS Symposium on Photomask Technology, SPIE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Implementation of Reflected Light Die-to-Die Inspection and ReviewSmart to Improve 65nm DRAM Mask Fabrication, in: 25th Annual BACUS Symposium on Photomask Technology, SPIE

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.968932Z

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Observation 76be8dcd-68d5-4beb-bd29-9161b1f3edd6 · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:51.940370Z

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This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 16

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Observation cf1664b6-34c0-4dc8-8e15-5e6567af9e7a · outbound

This paper cites An image-based pipeline for defect local- ization in photonic InP multi-project wafers.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection An image-based pipeline for defect local- ization in photonic InP multi-project wafers

Reference 17

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Observation da6a741b-6f75-4106-a563-f352a8f9423c · outbound

This paper cites Recent advances in the automatic in- spection of integrated circuits for pattern defects.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Recent advances in the automatic in- spection of integrated circuits for pattern defects

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.848970Z

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Observation 721a00b0-230b-425b-8a8c-fe294e76f246 · outbound

This paper cites Age and gender estimation of unfiltered faces.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Age and gender estimation of unfiltered faces

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.821369Z

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Observation 9590f917-f663-43bb-88b7-7cbe2a7d6a68 · outbound

This paper cites Unsuper- vised fabric defect detection based on a deep convolutional generative adversarial network.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unsuper- vised fabric defect detection based on a deep convolutional generative adversarial network

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.799902Z

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Observation 69c87f62-357c-4941-8846-f4a9bf694158 · outbound

This paper cites Runet: A robust unet architecture for image super-resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition Workshops, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Runet: A robust unet architecture for image super-resolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition Workshops, pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.776040Z

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Observation 4fe3c9f8-6706-4940-80f2-ca499d6b3f74 · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:51.737444Z

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Observation 650d7483-4652-4676-9a25-058eb68b921a · outbound

This paper cites Automated visual inspection in the semi- conductor industry: A survey.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Automated visual inspection in the semi- conductor industry: A survey

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.719863Z

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Observation 9c804c05-a21e-4380-9294-913f85c8c89b · outbound

This paper cites A cnn-based transfer learning method for defect classification in semiconductor man- ufacturing, in: 2018 international symposium on semiconductor manu- facturing (ISSM), IEEE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A cnn-based transfer learning method for defect classification in semiconductor man- ufacturing, in: 2018 international symposium on semiconductor manu- facturing (ISSM), IEEE

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.700208Z

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Observation ffaa269b-61f9-4a59-827f-6ac9aa8b6a96 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, pmlr.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, pmlr

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.681704Z

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Observation adce0d19-ef7c-411d-9ec0-4e4cdeff3eda · outbound

This paper cites Image-to-image transla- tion with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Image-to-image transla- tion with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.658726Z

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Observation 7436d67f-ee14-4730-a7e9-49e346d07c00 · outbound

This paper cites Color image generation from lidar reflection data by using selected connection unet.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Color image generation from lidar reflection data by using selected connection unet

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.636990Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7aeb332-10a4-4e7a-8fd8-696d1f9a0c36 · outbound

This paper cites Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.606717Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dccecc9b-1319-4c3a-9c90-97e4103b8134 · outbound

This paper cites System-on- chip photonic integrated circuits.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection System-on- chip photonic integrated circuits

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.571992Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2a67794-7fdd-4903-acde-3bc132a21cc8 · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:51.524980Z

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Observation a9a0228b-0c29-4b3c-84c5-8a34b49e9165 · outbound

This paper cites Epitaxial growth and layer-transfer techniques for heterogeneous integration of materials for electronic and photonic devices.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Epitaxial growth and layer-transfer techniques for heterogeneous integration of materials for electronic and photonic devices

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.495771Z

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Observation 40a0208a-cfba-4269-b6b8-5106fcaf1684 · outbound

This paper cites Wavelet-baseddefectdetectioninsolarwafer images with inhomogeneous texture.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Wavelet-baseddefectdetectioninsolarwafer images with inhomogeneous texture

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.474753Z

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Observation 55498cc7-2f46-463a-a86e-dd33fb7cdf79 · outbound

This paper cites Concealed object segmenta- tion in terahertz imaging via adversarial learning.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Concealed object segmenta- tion in terahertz imaging via adversarial learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.434668Z

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Observation 77501255-7fc0-460b-8ed6-6bca73b83dd5 · outbound

This paper cites Foundry 31 capabilities for photonic integrated circuits, in: Optical Fiber Telecom- munications VII.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Foundry 31 capabilities for photonic integrated circuits, in: Optical Fiber Telecom- munications VII

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.391763Z

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Observation f2789b7f-9f16-43da-982c-ef25ace1d03a · outbound

This paper cites Focal loss for dense object detection, in: Proceedings of the IEEE international conference on computer vision, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Focal loss for dense object detection, in: Proceedings of the IEEE international conference on computer vision, pp

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Observation 59ba4406-eef7-4a61-b606-ce3c97c0c2a7 · outbound

This paper cites Defect detec- tion of ic wafer based on spectral subtraction.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Defect detec- tion of ic wafer based on spectral subtraction

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.360228Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f9c60a4-e1e6-46c3-ade3-27a215e0a58b · outbound

This paper cites Deep learning for generic object detection: A survey.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Deep learning for generic object detection: A survey

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.337654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70ac95cd-2b1b-4b56-b293-35aae1507400 · outbound

This paper cites Independent component analysis-based de- fect detection in patterned liquid crystal display surfaces.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Independent component analysis-based de- fect detection in patterned liquid crystal display surfaces

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.316596Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f2721f77-69d4-45f2-8844-8a8c29153aae · outbound

This paper cites A review of automatic fabric defect detection techniques.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A review of automatic fabric defect detection techniques

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.293476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f267e46-1081-400d-89db-713a7c49130a · outbound

This paper cites Unsupervised surface defect detection using deep autoencoders and data augmentation, in: 2018 International Conference on Cyberworlds (CW), IEEE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unsupervised surface defect detection using deep autoencoders and data augmentation, in: 2018 International Conference on Cyberworlds (CW), IEEE

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.275832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3f639864-d884-437b-ada6-0cd3168e0543 · outbound

This paper cites Patterned fabric defect detection and classification (fddc) techniques: a review.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Patterned fabric defect detection and classification (fddc) techniques: a review

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.236105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d754613-1d50-4691-9029-24905b8214cd · outbound

This paper cites Video generation from single semantic label map, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Video generation from single semantic label map, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.215532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 00f8a7f6-74d2-469c-849d-5344dc17096b · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:23:50.306850Z

Source-reported events for the cited work

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Observation bcd77d29-ac8a-4f91-9a9f-877ae9a9bfec · outbound

This paper cites Unsupervised anomaly detection with generative adversar- ial networks to guide marker discovery, in: International conference on information processing in medical imaging, Springer.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unsupervised anomaly detection with generative adversar- ial networks to guide marker discovery, in: International conference on information processing in medical imaging, Springer

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.195734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 69a445e5-77ce-44c0-8f20-06e11c490341 · outbound

This paper cites Training region-based object detectors with online hard example mining, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Training region-based object detectors with online hard example mining, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.177064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4dc3d67f-9bb8-4a05-bab9-b707c92fad5e · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:51.154461Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6382299-e97e-414e-9605-81ca51d9fa0c · outbound

This paper cites Past, present, and future of inp-based photonic integration.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Past, present, and future of inp-based photonic integration

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.090373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 056a4d06-066c-4477-b21c-b9cb91008247 · outbound

This paper cites InP-based foundry PICs for optical interconnects.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection InP-based foundry PICs for optical interconnects

Reference 48

Resolution: verified exact; doi, observed 2026-08-07T04:23:50.519468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a20fc2ad-43f9-4e66-b92c-e8bc960a4e4e · outbound

This paper cites A Neural-Network Approach for Semiconductor Wafer Post-sawing Inspection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A Neural-Network Approach for Semiconductor Wafer Post-sawing Inspection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.065819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1cabecf1-cdde-4da5-b43f-0295bc15fcb4 · outbound

This paper cites Segmentation- based deep-learning approach for surface-defect detection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Segmentation- based deep-learning approach for surface-defect detection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.038195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd3d7ebe-5a43-4963-9f53-fbbabc88f1fd · outbound

This paper cites Background modelling based on generative unet, in: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Background modelling based on generative unet, in: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.001846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cb2758b9-58df-46e8-90f8-462c1db29b05 · outbound

This paper cites An eigenvalue-based similarity measure and its application in defect detection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection An eigenvalue-based similarity measure and its application in defect detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.972870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9fad97d1-4362-43fd-9c11-b8fcfde4817a · outbound

This paper cites De- tection defect in printed circuit boards using unsupervised feature ex- traction upon transfer learning, in: 2019 International Conference on Cyberworlds (CW), IEEE.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection De- tection defect in printed circuit boards using unsupervised feature ex- traction upon transfer learning, in: 2019 International Conference on Cyberworlds (CW), IEEE

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.943642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cfb396d7-720a-4afb-838d-9d90fb9044da · outbound

This paper cites Detection and classification of defect patterns on semiconductor wafers.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Detection and classification of defect patterns on semiconductor wafers

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.909102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3bfd3311-2861-4d08-83a8-46a80fcba392 · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:50.882711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 193c3ccc-aa80-480f-83e6-66d91bc88622 · outbound

This paper cites A wafer surface defect detection method built on generic object detection network.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A wafer surface defect detection method built on generic object detection network

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.823316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cfdb41d0-4b8d-48a3-81dc-7d2cea4a29d0 · outbound

This paper cites PhotorealisticSimulationofPhotonic-Integrated Circuit Designs using Digital Image Processing Techniques and Neural Networks.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection PhotorealisticSimulationofPhotonic-Integrated Circuit Designs using Digital Image Processing Techniques and Neural Networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.789731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2d5391bc-ddb3-44e5-b103-36ab039e7f66 · outbound

This paper cites A review of recent advances in surface defect detection us- ing texture analysis techniques.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A review of recent advances in surface defect detection us- ing texture analysis techniques

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.771995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 86d4452d-26c7-4b7d-815a-1635c5ce6798 · outbound

This paper cites Evaluating Explanation Without Ground Truth in Interpretable Machine Learning.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T04:23:50.429163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92e3de86-fcfe-4f0f-a06c-e8f0dd0bff1c · outbound

This paper cites Weakly supervised object localization and detection: A survey.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Weakly supervised object localization and detection: A survey

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.747754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 268b2998-a166-4c11-a915-9c676ae4c496 · outbound

This paper cites The development of an automatic post-sawing inspection system using computer vision tech- niques.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection The development of an automatic post-sawing inspection system using computer vision tech- niques

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.725822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ace7e5e2-7a34-44cb-9831-77d98a1ca8ad · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric, in: 34 Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection The unreasonable effectiveness of deep features as a perceptual metric, in: 34 Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.691660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f1d8b3e3-cf19-49ce-9ccc-0d20ca5e2edd · outbound

This paper cites Object Detection in 20 Years: A Survey.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Object Detection in 20 Years: A Survey

Reference 63

Resolution: malformed identifier; no resolver link, observed 2026-08-07T04:23:50.455683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04eabccb-903c-44d4-ac20-5ee64e3e9024 · outbound

This paper cites Machine vision and applications 9, 201–214.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Machine vision and applications 9, 201–214

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.920050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc31387b-5fc1-4656-aae7-4f3b6545beed · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 2006

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:52.150388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e064fbe6-8613-44d1-9263-6334afe35d02 · outbound

This paper cites Semi- conductor Science and Technology 29, 083001.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Semi- conductor Science and Technology 29, 083001

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:51.109788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e452b59d-c29a-4786-840f-629aff0a04f5 · outbound

This paper cites an unresolved cited work.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection Unresolved cited work

Reference 2016

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:23:51.875209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 28572def-7019-4548-a1a7-bdb20e948b66 · outbound

This paper cites 8798–8807.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection 8798–8807

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:23:50.864838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f5504814-8252-4efc-9ba1-cf59b4f626d0 · outbound

This paper cites A Survey of Self-Supervised and Few-Shot Object Detection.

Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection A Survey of Self-Supervised and Few-Shot Object Detection

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:23:50.659456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers