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Paper Citation Record · LEDGER

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers

As of 7 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2506.03345.

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

pith.paper-citation-record.v1
2506.03345 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:07:30.068596Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

10 of 10 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57a05396-8429-491e-977d-e31924c751b6 · outbound

This paper cites Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class,.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:29.889085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:29.889085Z digest=sha256:ab144eff4bbdd5e6b486c01c0bcd8d0bc1e1cca18e1a24fbfb5680b453e0abf7

Observation 9548427f-33be-4ef2-a8a1-fdc3dfba44c0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:29.923040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:29.923040Z digest=sha256:73a105e9221e7b91d0d0fc320a3f8679880d7ca44a5a8aa97374d702afcfd380

Observation 0bc1fecd-77eb-4e6d-a14c-2f6d1df15284 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers DINOv2: Learning Robust Visual Features without Supervision

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:29.980366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:29.980366Z digest=sha256:7b349fcbb4e93a6c24e645f86690c903acebd4cba187cb0d5d2a6820292cd8a5

Observation 6cfec57b-394b-4dd6-98be-b91c52699fe4 · outbound

This paper cites Deep learning-based detection, classification, and localization of defects in semiconductor processes.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Deep learning-based detection, classification, and localization of defects in semiconductor processes

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:07:30.448559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.042063Z digest=sha256:3eeeaedfc2f65cff316d215e9d07e0fef138f5491a8f71c726578bf37431f30e

Observation 5b79518e-fe5b-452c-9998-54a5371965c3 · outbound

This paper cites A CNN -based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing,.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers A CNN -based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing,

Reference 5

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T11:07:30.268791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.047716Z digest=sha256:8637cc04887f578da841df7dac71ef2ae2e926676c026a9502470a889acd8136

Observation 43d5075a-d16f-4417-95ca-4a7ed2253450 · outbound

This paper cites Semiconductor Wafer Surface: Automatic Defect Classification with Deep CNN,.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Semiconductor Wafer Surface: Automatic Defect Classification with Deep CNN,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T11:07:30.051581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:07:30.051581Z digest=sha256:57d6fea16ce22f3c5c961dfd9e3e1eeafd6f5225c652a200e1ef193b846c708b

Observation 252a1df6-2ed8-4c25-94df-3a8a67c186b9 · outbound

This paper cites DeepSEM -Net: Enhancing SEM defect analysis in semiconductor manufacturing with a dual -branch CNN -Transformer architecture.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers DeepSEM -Net: Enhancing SEM defect analysis in semiconductor manufacturing with a dual -branch CNN -Transformer architecture

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:07:30.434933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.056207Z digest=sha256:785d63cd861850e0dc547ef3804fcf78ec0e7030fd3a82254bd90c63c332fdcc

Observation 44972181-ab47-4c8a-b3ed-c5e056fba79e · outbound

This paper cites an unresolved cited work.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:07:30.422794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.060787Z digest=sha256:f3274db396e0e7886fb8a1466a9809cf0c3a26e35a25ec54d562c5284a8cd0a1

Observation b10c5485-36e2-4961-b2c4-685e72cdf6cf · outbound

This paper cites Visualizing Data using t-SNE.

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Visualizing Data using t-SNE

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:07:30.409695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.064389Z digest=sha256:ccfd8374c6c7d07f83e00504b471cabbdbd2408b9b9423b85c3ba1707726fd95

Observation ed9e0d66-c21c-463f-8614-ff248872ec02 · outbound

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

Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers Pseudo -label: The simple and efficient semi - supervised learning method for deep neural networks

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:07:30.396622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T11:07:30.068596Z digest=sha256:a62f5b78872974a0a2280bddf4b61371088b7ca18cb1122047c4462a18fd1862

Pith citing papers

No inbound Pith citation observations are available.