Switch mode capacitive pressure sensors
Bibliographic record
Abstract
Switch mode capacitive pressure sensors are proposed as a new class of microfabricated devices that transform pressure into a mechanically switching capacitance to form an analog-to-digital signal with zero power, high sensitivity, and a high signal-to-noise ratio. A pressure-sensitive gold membrane suspended over a capacitive cavity makes ohmic contact with patterned gold leads on the substrate, closing circuits to fixed on-chip capacitors outside the cavity and leading to significant step responses. This function is achieved by allocating the switch leads on the part of the counter electrode area, while the remaining area is used for touch mode analog capacitive sensing. The sensor microchip is prototyped through a novel design approach to surface micromachining that integrates micro-Tesla valves for vacuum sealing the sensor cavity, showing an unprecedented response to applied pressure. For a gauge pressure range of 0-120 mmHg, the sensor exhibits an increase of 13.21 pF with resultant switch events, each of which ranges from 2.53-3.96 pF every 12-38 mmHg, in addition to the touch mode linear capacitive increase between switches. The equivalent sensitivity is 80-240 fF/mmHg, which is 11-600× more than commercial and reported touch mode sensors operating in similar pressure ranges. The sensor is further demonstrated for wireless pressure tracking by creating a resonant tank with the sensor, showing a 32.5-101.6 kHz/mmHg sensitivity with frequency jumps led by the switch events. The developed sensor, with its promising performance, offers new application opportunities in a variety of device areas, including health care, robotics, industrial control, and environmental monitoring.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".