The nanogap Pirani—a pressure sensor with superior linearity in an atmospheric pressure range
Bibliographic record
Abstract
We have designed and fabricated a surface micromachined Pirani pressure sensor with an extremely narrow gap between its heater and heatsink (substrate) with superior output linearity in the atmospheric pressure range. The gap size of the device has been reduced to 50 nm by using a layer of PECVD amorphous silicon as a sacrificial layer and a xenon difluoride (XeF 2 ) gas phase etching technique. Such a narrow gap pushes the transition from molecular to continuum heat conduction to pressures beyond 200 kPa. The higher transition pressure increases the measurement range and sensitivity of the gauge in atmospheric pressures. The gas phase etching of the sacrificial layer eliminates stiction problems related to a wet etching process. The active area of the sensor is only a 6 × 50 µm 2 microbridge anchored to the substrate at both ends. An innovative fabrication technique was developed which resulted in a virtually flat microbridge with improved mechanical robustness. This process enabled us to have a very well-controlled gap between the microbridge and the substrate. The device was tested in a constant heater temperature mode with pressure ranges from 0.1 to 720 kPa. The heater power was only 3 mW at 101 kPa (atmospheric pressure), which increased to about 8 mW at 720 kPa. The output sensitivity and nonlinearity of the device were 0.55% per kPa at 101 kPa and ±13% of the output full scale, respectively.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".