mlsanityData to 7 Oct 2026

LDU-Bench: Multimodal LLM Evaluation for Lithography Defect Understanding under Layout-Varying Circuit Backgrounds

arXiv 2608.03078 ↗First posted 4 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

Multimodal large language models have demonstrated strong defect recognition capability in industrial anomaly detection. However, in lithography review, merely determining whether an image contains a defect is insufficient for engineering inspection; models must also understand defect morphology, spatial location, and the potential causes supported by visible evidence. To this end, this paper proposes LDU-Bench, a multi-task multimodal benchmark for lithography defect understanding. Constructed from real lithography and integrated-circuit review images, LDU-Bench decomposes the review workflow into four independent tasks: defect triage, morphology recognition, coarse localization, and image-conditioned cause analysis. It systematically evaluates models using task-level metrics, diagnostic readouts, and the Lithography Closure Score (LCS). Experimental results show that although existing MLLMs can perform defect triage relatively reliably, this ability does not stably transfer to downstream review stages. Morphology alignment, effective localization, and evidence-to-cause mapping remain the major bottlenecks. Further diagnostics indicate that this capability break is not a fluctuation of a single metric, but reflects insufficient structured understanding across semantic levels. Overall, LDU-Bench provides a quantifiable and diagnostic unified platform for evaluating the usability, failure points, and capability boundaries of industrial MLLMs in lithography review chains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 4 Aug 2026.

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References · 10

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Kosmos-2: Grounding Multimodal Large Language Models to the Worldcited by 1,075
  3. 2023Shikra: Unleashing Multimodal LLM's Referential Dialogue Magiccited by 891
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  5. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  6. 2023Ferret: Refer and Ground Anything Anywhere at Any Granularitycited by 498
  7. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  8. 2022Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approachcited by 8
  9. 2023SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspectioncited by 3
  10. 2024Scanning Electron Microscopy-based Automatic Defect Inspection for Semiconductor Manufacturing: A Systematic Reviewcited by 1

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  1. 2026Failure-Aware Refinement of Vision-Language Model for Lithography Defect Detection
  2. 2024FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
  3. 2026GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
  4. 2025Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
  5. 2026Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space
  6. 2026Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
  7. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
  8. 2024Few-shot Defect Image Generation based on Consistency Modeling