Does Super-Resolution Preserve Defect Evidence? A Low-False-Call Benchmark for Semiconductor Inspection
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Abstract (arXiv)
Super-resolution can make inspection images appear sharper without preserving the evidence needed to detect a defect. We study this failure mode with a benchmark that separates reconstruction from detection and evaluates both at a predeclared low false-positive rate. Ten end-to-end repetitions combine independently generated line/space and contact-hole images with model training, calibration, clean controls, weak defects, and a held-out defect morphology. Every reconstruction is scored by the same local residual detector, while direct and jointly trained detectors form a separate comparison track. Reconstruction fidelity and inspection utility diverge: the two learned reconstruction models attain the highest structural similarity yet detect fewer defect pixels than bicubic interpolation in every paired repetition. A direct DeepLabV3 detector reaches 0.1984±0.0385 recall at 0.000174±0.000084 false-positive rate and satisfies the held-out feasibility criterion in all ten repetitions. An illustrative joint model, DPU-WaferSR, passes independent clean calibration but exceeds the held-out limit in all ten repetitions, demonstrating that calibration success does not guarantee transfer. Weak-defect recall remains near zero for every feasible method. Applying the unchanged policies to 4,591 public Carinthia-S masks further reveals large method-dependent shifts on real SEM texture. These results support a simple conclusion: super-resolution for inspection should be judged by preserved task evidence and operating-point transfer, not reconstruction quality alone.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 19 Jul 2026.
Builds on
25 of 45 references.
References · 25
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017On Calibration of Modern Neural Networkscited by 4,860
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
- 2021Image Super-Resolution via Iterative Refinementcited by 1,506
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
- 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
13 more
- 2020Pre-Trained Image Processing Transformercited by 891
- 2018Residual Dense Network for Image Super-Resolutioncited by 796
- 2022Simple Baselines for Image Restorationcited by 701
- 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
- 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
- 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
- 2019Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Modelcited by 400
- 2018Task-Driven Super Resolution: Object Detection in Low-resolution Imagescited by 52
- 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 28
- 2024Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Losscited by 17
- 2023Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Reviewcited by 2
- 2026Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integrationcited by 1
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