Multi-Objective Variation-Aware Sizing for Analog CNFET Circuits
Bibliographic record
Abstract
Carbon nanotube field-effect transistors (CNFETs) are one of the promising candidates to substitute CMOS technology for next-generation integrated circuits. Process variation currently hinders the wide adoption of CNFET technology, and many techniques have been developed to overcome the fabrication variation for digital CNFET circuits while analog CNFET circuits generally lack a proper approach to avoid performance failure. In this paper, we present a multi-objective deterministic sizing flow considering carbon nanotube diameter process variation for analog CNFET sizing design. To develop a systematic design methodology and ensure performance robustness, we use a design centering approach for circuit sizing to obtain the optimal value of design parameters against carbon nanotube process variation. We take advantage of the normal boundary intersection (NBI) method in combination with our modified generalized boundary curve (GBC) method to conduct variation-aware CNFET multi-objective optimization. With one case study of a common analog CNFET circuit tested for our optimization methodology, the experimental results demonstrate that our proposed method can reach a better estimation of the Pareto front compared to another popular multi-objective optimization method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".