New Analytic Model of Coupling and Substrate Capacitance in Nanometer Technologies
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Bibliographic record
Abstract
In this paper, we propose a new modeling method for computing coupling capacitance between interconnects on the same or different layers and substrate capacitance in the nanometer very large-scale integration circuits. The model has been developed based on a template, which is obtained on the basis of electric field approximation and followed by a curve-fitting technique to reach promising accuracy. To verify our proposed model, we develop scripts to generate thousands of layout samples which cover all possible geometric situations for CMOS 180-, 90-, and 65-nm technologies. The proposed model is compared with previously published works with reference to the extracted results from commercial tools. The experimental results show that the estimation errors of our method are much lower than 10% (2%-4% or less for most of the cases) but with significantly reduced computation effort. The proposed model is a general methodology that can be used for any nanometer technologies with different geometric parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it