LithoExp: Explainable Two-stage CNN-based Lithographic Hotspot Detection with Layout Defect Localization
Bibliographic record
Abstract
Convolutional neural networks (CNNs) successfully detect lithographic hotspots by learning from hand-designed features of layout patterns or entire layouts, as images, in an end-to-end fashion. However, compared to lithography simulation, CNN-based solutions demonstrate inferior hotspot detection accuracy and a high false-alarm rate. Moreover, the interpretability of the hotspot prediction process has yet to be considered due to the “black-box” nature of CNNs. In this work, inspired by conventional lithography simulation where defect regions are simulated as direct evidence for hotspot identification, we propose an explainable two-stage CNN-based hotspot detector that considers both the accuracy and interpretability of hotspot detection. Our architecture learns to locate the defect areas in the first stage as extracted hotspot features. In the second stage, we combine the strength of feature engineering and end-to-end learning, incorporating the original layout input, the learned defect location map from the first stage, and a fixed auxiliary region of interest (ROI) map for final hotspot detection. Experimental results for our technique exhibit the highest hotspot accuracy (98.1%) and the lowest false-alarm rate (4.0%) thus far compared to all prior CNN solutions. We also demonstrate the best overall qualitative and quantitative interpretability results with the highest increase in confidence (IC) and the lowest average drop (AD) in scores when CNN interpretation methods such as Grad-CAM-based approaches are applied. We further demonstrate use cases of our technique for successfully justifying and pinpointing hotspot mispredictions by examining the prediction evidence from our learned defect locations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".