Robust Inspection of Micro-LED Chip Defects Using Unsupervised Anomaly Detection
Why this work is in the frame
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Bibliographic record
Abstract
Inspection of defects in light emitting diode (LED) chips have been studied to reduce manufacturing cost. Recent studies proposed visual defect inspection methods based on supervised learning of deep neural networks. However, they require datasets with the ground-truth label of each chip, which accompanies prohibitively laborious tasks. In addition, they require class balanced datasets, which is difficult to obtain in an actual industrial process. In order to tackle these limitations, this paper proposes an unsupervised learning based inspection method using anomaly detection that requires no labeled data. On the micro-LED dataset, we demonstrate that our method outperforms previous anomaly detection methods. We achieve 95.82% AUROC result, which is 20.87% higher than convolutional autoencoder and 0.67% higher than Deep SVDD.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it