Design and analysis of a dual-actuator force-balanced MEMS pressure sensor
Bibliographic record
Abstract
Abstract Capacitive pressure sensors are valued for their simplicity, low power consumption, high sensitivity, and minimal temperature cross-sensitivity, making them particularly suitable for medical applications. Despite their advantages, these sensors face challenges such as pull-in instability at one-third of the capacitive gap and nonlinearity between pressure and capacitance changes. The electrostatic force balance principle has emerged as an effective solution, stabilizing the sensor within its linear operational range and enhancing performance. Widely applied in gyroscopes, microvalves, and accelerometers, this approach improves sensitivity, bandwidth, and stability. This research explores the application of electrostatic force balance in capacitive pressure sensors, where a feedback electrostatic force counteracts diaphragm displacement, rendering the output independent of mechanical properties. To address high voltage requirements for balancing pressures, we introduce a novel dual actuator system combining electrostatic and electromagnetic forces, enabling improved performance, reduced power requirements, and serving a wide range of applications. The findings are demonstrated through comprehensive modeling and simulation, offering a promising advancement for next-generation pressure sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".