Adaptive opto-electromechanical silicon-on-insulator increased bandwidth accelerometer
Bibliographic record
Abstract
Abstract This paper studies the construction of a compact one-dimension-sensing iscreased bandwidth photonic accelerometer using cascaded groups of continued sections of a 50 ng seismic mass each attached to the silicon beams of two under etched slot waveguide electrostatic phase shift elements acting as voltage-controlled adaptive-precision springs. The accelerometer sensitivity is shown to be significantly increased by applying equal electrode voltages. Simulation results indicate that the sensitivity dynamic range is about 76 dB combining both open-loop and closed-loop voltage control of the sensor. The operation bandwidth of the accelerometer may be increased up to 250 kHz due to the cascaded multi-section architecture of the sensor. This advantage gives significant relief to the limitation in bandwidth response of single section counterparts. The sensor may be designed to detect impact accelerations up to 104 ms−2 and yet can still be electrostatically driven to detect sub-gravitational accelerations. The application of negative feedback voltage control to hold the seismic mass at close distances from a standstill is shown to significantly increase the acceleration detection range. The construction uses all in-plane components based on a silicon-on-insulator template with 300 nm of silicon core thickness. The proposed electromechanical suspension system and the electric feeding arrangements are the most simple. The accelerometer performance is theoretically deterministic. The study is based on performing numerical analysis for the electromechanical suspension system. The waveguides are simulated utilizing the VPIphotonics industry standard. Applications may include the automobile and aerospace industries, underwater sonar, industrial ultrasonic detection, seismology predictions, and medical ultrasonography. Article Highlights The cascading of compact high-speed accelerometer sections allows increasing the bandwidth response of the proposed sensor by many folds compared to its single-mass single-section counterparts. The suspension structure is electrostatically controlled by two voltages enabling widely controlling the sensitivity and detection range of the accelerometer. The proposed accelerometer may fit wide applications achieving high detection speeds and super sensitivities utilizing a small footprint and power-efficient structure.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".