Adaptive sampling for efficient failure probability analysis of SRAM cells
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an adaptive sampling method is proposed for the statistical SRAM cell analysis. The method is composed of two components. One part is the adaptive sampler that manipulates an alternative sampling distribution iteratively to minimize the estimated yield error. The drifts of the sampling distribution are re-configured in each iteration toward further minimization of the estimation variance by using the data obtained from the previous circuit simulations and applying a high-order Householder's method. Secondly, an analytical framework is developed and integrated with the adaptive sampler to further boost the efficiency of the method. This is achieved by the optimal initialization of the alternative multi-variate Gaussian distribution via setting its drift vector and covariance matrix. The required number of simulation iterations to obtain the yield with a certain accuracy is several orders of magnitude lower than that of the crude-Monte Carlo method with the same confidence interval.
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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 | 0.000 | 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