<i>Q</i>-factor enhancement for self-actuated self-sensing piezoelectric MEMS resonators applying a lock-in driven feedback loop
Bibliographic record
Abstract
This paper presents a robust Q -control approach based on an all-electrical feedback loop enhancing the quality factor of a resonant microstructure by using the self-sensing capability of a piezoelectric thin film actuator made of aluminium nitride. A lock-in amplifier is used to extract the feedback signal which is proportional to the piezoelectric current. The measured real part is used to replace the originally low-quality and noisy feedback signal to modulate the driving voltage of the piezoelectric thin-film actuator. Since the lock-in amplifier reduces the noise in the feedback signal substantially, the proposed enhancement loop avoids the disadvantage of a constant signal-to-noise ratio, which an analogue feedback circuit usually suffers from. The quality factor was increased from the intrinsic value of 1766 to a maximum of 34 840 in air. These promising results facilitate precise measurements for self-actuated and self-sensing MEMS cantilevers even when operated in static viscous media.
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 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) | 0.000 | 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 | 0.000 |
| 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".