Mating Sensitivity Analysis and Statistical Verification for Efficient Yield Estimation
Why this work is in the frame
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Bibliographic record
Abstract
Parametric yield is a significant threat to the reliability of nanoscale analog and mixed-signal circuits. A critical yet challenging problem of yield estimation is to account for multiple circuit performance. In this paper, we propose a novel nonparametric statistical verification methodology to efficiently estimate the parametric yield due to 65-nm technology for multiperformance constraints. Our proposed approach exploits the fact that circuit parameters variation has different impacts on the circuit performance. Hence, a global sensitivity analysis classifies the circuit parameters according to their influence on the desired circuit performances. Based on this classification, an efficient joint recurrence verification (JRV) algorithm, a procedure inspired from DNA analysis, is performed on the most “critical/influential” parameters. A global hypothesis testing procedure is then performed based on the computed JRV metrics. We demonstrate the effectiveness of our methodology on two benchmark circuits. The acquired results show the ability of our approach to handle multiple corners and multiple performances yield problems with up to 11× speedup compared to conventional techniques with an average error smaller than 3%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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