Paper Citation Record · LEDGER

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing

As of 24 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.15260.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.15260 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:41:39.530391Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact3
verified fuzzy21
unresolved12
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fb4b651-f6be-4081-991c-ae0f0fbb4379 · outbound

This paper cites A deep convolutional autoencoder-based approach for anomaly detection with industrial, non-images, 2-dimensional data: A semiconductor manufacturing case study.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing A deep convolutional autoencoder-based approach for anomaly detection with industrial, non-images, 2-dimensional data: A semiconductor manufacturing case study

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.700573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cfda3224-5261-4c23-b2e9-b1bc35810f3a · outbound

This paper cites Formula: A deep learning approach for rare alarms predictions in industrial equipment.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Formula: A deep learning approach for rare alarms predictions in industrial equipment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.679173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fb810a3b-479d-4672-a4b7-07b386e32078 · outbound

This paper cites Adamatch: a unified approach to semi-supervised learning and domain adaptation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Adamatch: a unified approach to semi-supervised learning and domain adaptation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.655904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e3471a24-775f-4bbb-be52-bf9a92296770 · outbound

This paper cites Heterogeneous domain adaptation and equipment matching: Dann-based alignment with cyclic supervision (dbacs).

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Heterogeneous domain adaptation and equipment matching: Dann-based alignment with cyclic supervision (dbacs)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.632505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9d54e63d-caf0-423c-be78-b95047807785 · outbound

This paper cites Domain-adversarial training of neural networks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Domain-adversarial training of neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.609402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7300e570-2c92-4757-add9-b9dfdb86c0f7 · outbound

This paper cites Wafer map failure pattern recognition and similarity ranking for large-scale data sets.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Wafer map failure pattern recognition and similarity ranking for large-scale data sets

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.586487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ffb5a9d6-22ce-4d16-b82a-7bd2206a0fe7 · outbound

This paper cites Predictive maintenance in the industry: A comparative study on deep learning-based remaining useful life estimation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Predictive maintenance in the industry: A comparative study on deep learning-based remaining useful life estimation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.563829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 49520955-2242-4372-a0c3-6862c1fc81ef · outbound

This paper cites A cnn-based transfer learning method for defect classification in semiconductor manufacturing.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing A cnn-based transfer learning method for defect classification in semiconductor manufacturing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.538623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26a4a2aa-2f52-489b-b660-ab75f71ab67d · outbound

This paper cites A novel visual fault detection and classification system for semiconductor manufacturing using stacked hybrid convolutional neural networks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing A novel visual fault detection and classification system for semiconductor manufacturing using stacked hybrid convolutional neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.517665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0b5a2cad-5af7-45e5-871e-0fc83fe0a5c1 · outbound

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

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Convolutional neural network for wafer surface defect classification and the detection of unknown defect class

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.498621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e8ee7ef-7d65-4070-9276-c73e894864ff · outbound

This paper cites Deep learning for classification of the chemical composition of particle defects on semiconductor wafers.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Deep learning for classification of the chemical composition of particle defects on semiconductor wafers

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.478499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 16c4c581-faea-46e6-95a6-5570e0224877 · outbound

This paper cites Defect classification on limited labeled samples with multiscale feature fusion and semi-supervised learning.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Defect classification on limited labeled samples with multiscale feature fusion and semi-supervised learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.457454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 470962f7-89b1-41a7-8840-dbae5f99e62f · outbound

This paper cites Chen, Hsing-Chung Liang, and Chun-Lung Hsu.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Chen, Hsing-Chung Liang, and Chun-Lung Hsu

Reference 13

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T19:41:40.132853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ad7df6a-dfd4-4ecc-bced-4bf2754322d8 · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.434209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9a518824-181a-4224-a339-adf1abe425e3 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.408392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 69cff330-c036-4e29-a8ad-698e150be3d9 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.380125Z

Source-reported events for the cited work

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Observation c74dbb72-a8f6-469c-929c-300fd1112d7c · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.385649Z

Source-reported events for the cited work

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Observation cde7d494-b06c-45f3-a591-98df1c90823e · outbound

This paper cites ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.393759Z

Source-reported events for the cited work

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Observation c5fcdd40-4161-4a02-960f-6ae0d264d602 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Improved Regularization of Convolutional Neural Networks with Cutout

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.402872Z

Source-reported events for the cited work

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Observation 0f6b6187-0460-4385-9f84-dad6b594114f · outbound

This paper cites AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:41:39.974698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8eea7626-de96-4144-b16d-c92bc9174649 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Image-to-image translation with conditional adversarial networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.416928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42e1f792-94bf-4050-a6ee-e696d2dfbdf0 · outbound

This paper cites Conditional Generative Adversarial Nets.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Conditional Generative Adversarial Nets

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.422537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19c9d8ae-c39d-4c89-9e66-912c45d0449a · outbound

This paper cites Generative adversarial nets.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Generative adversarial nets

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.345234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fe61f451-4e6f-4cfb-8009-7d2ce0591e5f · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.435789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b14ff3-a01a-41aa-8eb1-1089971c98df · outbound

This paper cites Simultaneous deep transfer across domains and tasks.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Simultaneous deep transfer across domains and tasks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.308912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6878871e-5bfb-4163-9896-5f4595fe4a9d · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Deep coral: Correlation alignment for deep domain adaptation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.449242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3a1c51e-075c-472a-8960-64b9eff3e0fe · outbound

This paper cites Maximum classifier discrepancy for unsupervised domain adaptation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Maximum classifier discrepancy for unsupervised domain adaptation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.271172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3af31dcf-717f-4329-ac73-eb221a3db5fe · outbound

This paper cites Ad-aligning: Emulating human-like generalization for cognitive domain adaptation in deep learning.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Ad-aligning: Emulating human-like generalization for cognitive domain adaptation in deep learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.463158Z

Source-reported events for the cited work

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Observation 909a9b32-535f-40e6-96f7-0ff4d81efd11 · outbound

This paper cites AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:41:39.832379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a9d7b28e-371f-48a3-a5f4-6ce3da42e158 · outbound

This paper cites Dbam: Making virtual metrology/soft sensing with time series data scalable through deep learning.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Dbam: Making virtual metrology/soft sensing with time series data scalable through deep learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.248928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 092725f3-8a8b-48c4-ab5d-efb23239dbf2 · outbound

This paper cites Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.227963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4defd224-c10e-418d-b3b8-faaf1bf2f9c2 · outbound

This paper cites Li and H.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Li and H

Reference 32

Resolution: verified exact; doi, observed 2026-08-15T19:41:39.579864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f6752a39-620c-4d77-8335-79ce4b1759dc · outbound

This paper cites Inter-domain mixup for semi-supervised domain adaptation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Inter-domain mixup for semi-supervised domain adaptation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.496882Z

Source-reported events for the cited work

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Observation 37fb796b-f514-400c-b00b-b059ac668870 · outbound

This paper cites Semi-supervised generalized source-free domain adaptation (ssg-sfda).

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Semi-supervised generalized source-free domain adaptation (ssg-sfda)

Reference 34

Resolution: malformed identifier; no resolver link, observed 2026-08-15T19:41:39.505077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7a5c96c-4f2d-466d-9948-25bad31595f5 · outbound

This paper cites Improving shape deformation in unsupervised image-to-image translation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing Improving shape deformation in unsupervised image-to-image translation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.202344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8a65caa9-a635-443e-a735-cab61a88c3a6 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing U-net: Convolutional networks for biomedical image segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.516379Z

Source-reported events for the cited work

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Observation cfcaabfb-a358-4d9d-81d4-b1c37a34683a · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing AutoAugment: Learning Augmentation Policies from Data

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:41:39.523747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3689136c-3a2e-4e85-b8f0-6a0851017994 · outbound

This paper cites A comprehensive review of deep learning architectures for computer vision applications.

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing A comprehensive review of deep learning architectures for computer vision applications

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:41:40.156173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.