A Low-Noise Micromachined Accelerometer with Reconfigurable Electrodes for Resonance Suppression
Bibliographic record
Abstract
We present a high-performance capacitive accelerometer with a sub-µg noise limit and 1.2 kHz bandwidth for particle acceleration detection applications. The low noise of the accelerometer is achieved through a combination of device design optimization and operation under vacuum to reduce the effects of air damping. Operation under vacuum, however, causes amplification of signals around the resonance region, potentially resulting in incapacitating it through saturation of interface electronics or nonlinearities and even damage. The device has thus been designed with two sets of electrodes for high and low electrostatic coupling efficiency. During normal operation, the open-loop device utilizes its high-sensitivity electrodes to provide the best resolution. When a strong signal near resonance is detected, the electrodes with low sensitivity are used for signal monitoring, while the high-sensitivity electrodes are used to apply feedback signals efficiently. A closed-loop electrostatic feedback control architecture is designed to counteract the large displacements of the proof mass near resonance frequency. Therefore, the ability to reconfigure electrodes lets the device be used in high-sensitivity or high-resiliency modes. Several experiments were conducted with DC and AC excitation at different frequencies to verify the effectiveness of the control strategy. The results showed a ten-fold reduction of displacement at resonance in the closed-loop arrangement compared to the open-loop system with a quality factor of 120.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".