High-Dimensional Many-Objective Bayesian Optimization for LDE-Aware Analog IC Sizing
Bibliographic record
Abstract
With the advancement of complementary metal–oxide–semiconductor (CMOS) technologies, layout-dependent effects (LDEs) become increasingly influential to MOSFET characteristics and in turn analog integrated circuit performance. Early awareness of LDEs before the layout stage gets critical in order to help subsequent layout synthesis meet performance requirements and thus reduce design iteration. In this article, we propose a high-dimensional many-objective Bayesian optimization (HMBO)-based LDE-aware sizing methodology to address such challenges. It can effectively tackle the huge configuration space that is incurred by the increased number of optimization variables for considering the LDEs in addition to the conventional sizing variables. Moreover, our proposed method is able to aim for simultaneously satisfying multiple circuit specifications to identify an optimum design point within the enlarged configuration space. In addition, we propose a performance-driven pattern learning scheme called Gibbs-upper confidence bound (UCB) for better managing the dimension splitting. Our method is compared with several prevalent evolutionary algorithms as well as state-of-the-art Bayesian optimization works designed for analog circuit sizing problems. The experimental results demonstrate the high efficacy of our proposed sizing methodology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".