Weakly-Coupled Resonators in Capacitive Readout Circuits
Bibliographic record
Abstract
Low energy consumption and wide operating temperature range of capacitors made them common in sensor designs, e.g., MEMS accelerometers, and hence increased the popularity of capacitive readout circuits. Their main challenges, in either discrete or integrated implementations, are sensitivity, noise, energy consumption, and parasitic components at the analog front end. Compared to conventional “frequency-shift monitoring” which is one of the most accurate and common methods for capacitance measurements, weakly-coupled resonators (well-known in mechanical systems) can offer up to three orders of magnitude increase in sensitivity. Therefore, this concept has been recently applied to the design of micromechanical sensors, e.g., for sensitive mass sensing. This paper applies, for the first time in the electrical domain, the concept of monitoring the eigenstates variations in weakly-coupled resonators as a generic readout circuit technique for measuring very small capacitance changes. The outstanding sensitivity of this method is verified analytically and demonstrated using both extensive circuit simulations and experimental measurements.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".