Dual-Level Capacitive Micromachined Uncooled Thermal Detector
Bibliographic record
Abstract
This paper presents a novel dual-level capacitive microcantilever-based thermal detector that is implemented in the commercial surface micromachined PolyMUMPs technology. The proposed design is implemented side-by-side with four different single-level designs to enable a design-to-design performance comparison. The dual-level design exhibits a rate of capacitance change per degree Celsius that is over three times higher than that of the single-level designs and has a base capacitance that is more than twice as large. These improvements are achieved because the dual-level architecture allows a 100% electrode-to-detector area, while single-level designs are shown to suffer from an inherent trade-off between sensitivity and base capacitance. In single-level designs, either the number of the bimorph beams or the capacitance electrode can be increased for a given sensor area. The former is needed for a longer effective length of the bimorph for higher thermomechanical sensitivity (i.e., larger tilting angels per degree Celsius), while the latter is desired to relax the read-out integrated-circuits requirements. This thermomechanical response-to-initial capacitance trade-off is mitigated by the dual-level design, which dedicates one structural layer to serve as the upper electrode of the detector, while the other layer contains as many bimorph beams as desired, independently of the former's area.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".