A power optimization method for CMOS Op-Amps using sub-space based geometric programming
Bibliographic record
Abstract
A new sub-space max-monomial modeling scheme for CMOS transistors in sub-micron technologies is proposed to improve the modeling accuracy. Major electrical parameters of CMOS transistors in each sub-space from the design space are modeled with max-monomials. This approach is demonstrated to have a better accuracy for sub-micron technologies than single-space models. Sub-space modeling based geometric programming power optimization has been successfully applied to three different op-amps in 0.18 ¿m technology. HSPICE simulation results show that sub-space modeling based GP optimization can allow efficient and accurate analog design. Computational effort can be managed to an acceptable level when searching sub-spaces for transistors by using practical constraints. An efficient scheme in dealing with non-convex constraint inherent in Kirchhoff's voltage law is suggested in this paper. By using this scheme, the non-convex constraint, such as posynomial equality, can be relaxed to a convex constraint without affecting the result.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".