Variation-Aware Analog Circuit Sizing in Carbon Nanotube
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Bibliographic record
Abstract
Although deemed as one of the promising candidates to substitute CMOS transistors in the sub-10 nm regime, fabrication of Carbon Nanotube Field-Effect Transistors (CNFET) is still experiencing significant process variations. In this paper, we consider carbon nanotube diameter process variation in CNFET analog circuit sizing design. We systematically study a robust sizing methodology for designing analog CNFET circuits. We propose a multi-objective deterministic sizing flow to approach the best performance of analog CNFET circuits even under device parameter process variation. We use a design centering approach to obtain the optimal value of design parameters to ensure a robust circuit. Moreover, we have developed a generic multi-objective deterministic sizing optimization methodology using SPICE simulation for circuit performance verification by combining generalized boundary curves and normal boundary intersection schemes. The experimental results demonstrate that our proposed method can better approach the Pareto front than another common stochastic multi-objective optimizer.
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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 | 0.001 | 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 | 0.001 | 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