Accelerated and Reliable Analog Circuits Yield Analysis Using SMT Solving Techniques
Bibliographic record
Abstract
Existing yield analysis methods are computationally expensive and generally encounter challenges with high-dimensional process parameters space. In this paper, we propose a new method for accelerated and reliable computation of parametric yield that combines the advantages of sparse regression and satisfiability modulo theory (SMT) solving techniques, and avoids issues in both. The key idea is to characterize the failure regions as a collection of hyperrectangles in the parameters space. Toward this goal, the method constructs sparse polynomial models based on adaptive least absolute shrinkage and selection operator to find low degree approximations of the circuit performances. A procedure inspired by statistical model checking is then introduced to assess the model accuracy. Given the constructed models, an SMT-based solving algorithm is employed to locate the failure hyperrectangles in the parameters space. The yield estimation is based on a geometric calculation of probabilistic volumes subtended by the located hyperrectangles. We demonstrate the effectiveness of our method using circuits that require expensive run-time simulation during yield evaluation. They include: an integrated ring oscillator, a 6T static RAM cell and a multistage fully-differential amplifier. Experimental results show that the proposed method is suitable for handling problems with tens of process parameters. Meanwhile, it can provide 5×-2000× speed-up over Monte Carlo methods, when a high prediction accuracy is required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".