Thickness Designs for Micro-Thermoelectric Generators using Three Dimensional PDE Coefficient-COMSOL Multiphysics 4.2a Analysis
Bibliographic record
Abstract
Introduction: Predicting the optimum thickness and gap size between n-type and p-type legs of micro thermoelectric devices shown in Figure 1, are the major challenges in designing micro thermo electric generators. In this presentation we have reported the gap size and optimal thickness (see Figure 2) for optimal output power. We found that the tgap should be 0.1 microns; but, depending on fabrication capability, the gap size can be varied from 0.1 to 6 microns, by doing that, the power crossing the tgap will degrade from 0.0008 to 0.00055 Watts respectively. We expect that, to obtain 1.0 Watt for the device fabricated using SiGe, we will need to fabricate 625 pairs of micro thermoelectric generators having both n-type and p-type, same as having 1250 thermo legs on a wafer. Results:
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".