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Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morphologies near class boundaries, for which forcing a single prediction may be less informative than providing plausible diagnostic alternatives. This paper proposes a morphology-aware ambiguity learning framework that suppo

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#25 most recent of 270 cs.CV papers we have recorded · ↑ newer: Chronosphere: Space-Time Tessellation of Local Climate Experts · ↓ older: The Weight Is Over - Interactive Diffusion on Consumer GPUs
Cite this page: Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support: the #25 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/morphology-aware-ambiguity-learning-for-wafer-defect-decision-support.html
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