Machine Learning Methods for FEOL/MEOL Defects Measurement through SRAM Bitmap
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper introduces the use of machine learning models in the characterization of bitmap fail patterns occurring on SRAM to identify FEOL/MEOL layers defectivity distribution. The results of bitmap patterns with test conditions are used for fault analysis post-processing and manufacturing yield improvement methodologies. Several machine learning models were built for prediction of the FEOL/MEOL layer defects based on hundreds of bitmap physical failure analysis results. A model utilizing a multilayer perceptron (MLP) architecture with backpropagation of error were optimized and it can be easily applied to volume products with millions of bitmap test results with >80% accuracy. It is the first time we are able to investigate the FEOL/MEOL defects density quantitatively through an automatic diagnosis tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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