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 0.18µm 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".