Paper Citation Record · LEDGER

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study

As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2505.07576.

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

pith.paper-citation-record.v1
2505.07576 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:16:37.606988Z

measured 20 of 20 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

20 of 20 outbound references displayed

verified exact0
verified fuzzy13
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3392e620-684a-42ec-9e05-6b71d41858a4 · outbound

This paper cites Ra-unet: A new deep learning segmentation method for semiconductor wafer defect analysis on fine-grained scanning electron microscope (sem) images.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Ra-unet: A new deep learning segmentation method for semiconductor wafer defect analysis on fine-grained scanning electron microscope (sem) images

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.985519Z

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 c8c5ace2-49bd-4c4f-b766-8fb987217bd6 · outbound

This paper cites an unresolved cited work.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:16:37.967218Z

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 44f55de6-7efd-47f7-ab75-985ed32c0d21 · outbound

This paper cites Deepsem-net: Enhancing sem defect analy- sis in semiconductor manufacturing with a dual-branch cnn-transformer architecture.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Deepsem-net: Enhancing sem defect analy- sis in semiconductor manufacturing with a dual-branch cnn-transformer architecture

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.948770Z

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 06d055e5-d773-4ee7-a5c4-7f1bb4b2273d · outbound

This paper cites Towards improved semiconductor defect inspection for high-na euvl based on semi-superyolo-nas.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Towards improved semiconductor defect inspection for high-na euvl based on semi-superyolo-nas

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.930130Z

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 089c0514-e8fa-40ad-9e1e-9445eeffc108 · outbound

This paper cites Joint anomaly detection and inpainting for microscopy images via deep self-supervised learning.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Joint anomaly detection and inpainting for microscopy images via deep self-supervised learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.911483Z

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 1864ee64-be8c-41b1-afde-388dad780b25 · outbound

This paper cites Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.891874Z

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 fbfac053-df75-4e2f-887a-c71dc6777931 · outbound

This paper cites Defect detection in sem images of nanofibrous materials.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Defect detection in sem images of nanofibrous materials

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.873683Z

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 a5afb5a3-a514-4955-b813-5441de9a9119 · outbound

This paper cites Mixed supervision for surface-defect detection: from weakly to fully supervised learning.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Mixed supervision for surface-defect detection: from weakly to fully supervised learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.855666Z

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 fe02d1b3-e1e4-4ce8-8450-d14c912cefc8 · outbound

This paper cites Vt-adl: A vision transformer network for image anomaly detection and localization.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Vt-adl: A vision transformer network for image anomaly detection and localization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.836404Z

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 3518f17d-63c5-45c2-a2fe-30a9d658800a · outbound

This paper cites Spot-the-difference self- supervised pre-training for anomaly detection and segmentation.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Spot-the-difference self- supervised pre-training for anomaly detection and segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.817089Z

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 d78ed954-8f58-4105-a87e-a1208fff4776 · outbound

This paper cites Reconstruction by inpainting for visual anomaly detection.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Reconstruction by inpainting for visual anomaly detection

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.560889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77c8a2af-59fd-416b-b8fb-f58e843dc3b4 · outbound

This paper cites PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.565927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08439fb3-e75a-4f58-b459-c15c42ece222 · outbound

This paper cites Student-Teacher Feature Pyramid Matching for Anomaly Detection.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Student-Teacher Feature Pyramid Matching for Anomaly Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.571698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09649d9d-bc67-46df-83dc-67b68b313ce2 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding, 2022.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Anomaly detection via reverse distillation from one-class embedding, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.787283Z

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 5f7dc0cc-684c-45a7-aa86-5f00b554a7f0 · outbound

This paper cites PaDiM: A patch distribution mod- eling framework for anomaly detection and localization.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study PaDiM: A patch distribution mod- eling framework for anomaly detection and localization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.768904Z

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 b0c62e53-a966-453d-925a-dd6af6e197f8 · outbound

This paper cites Towards Total Recall in Industrial Anomaly Detection.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Towards Total Recall in Industrial Anomaly Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.586987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58a6203b-12be-4bb9-addf-ae0fdfa6f49d · outbound

This paper cites Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.750711Z

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 cc00e9a1-b809-4b1d-9cc8-b48b738591a3 · outbound

This paper cites Supersimplenet: Unifying unsupervised and supervised learning for fast and reliable surface defect detection.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Supersimplenet: Unifying unsupervised and supervised learning for fast and reliable surface defect detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:16:37.732095Z

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 c23346eb-21d6-4c03-9e81-c7e505d8f65f · outbound

This paper cites Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows, 2021.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows, 2021

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.602015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5701635a-5274-4b3e-bfbe-0b021dde84cb · outbound

This paper cites Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders.

Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:16:37.606988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.