A Dual-Resonator Temperature Sensing Approach With Time Base Error Suppression
Bibliographic record
Abstract
In this study we present a novel dual-resonator temperature sensor which can be embedded in other MEMS sensors for improved thermal compensation and on-the-run calibration. For accurate temperature measurements, the proposed method mitigates time base errors in frequency counting, eliminates the need for a highly accurate reference clock and can cancel out the effects of aging of the time base without using a calibration process. The sensor structure is composed of a strain amplifying beam and two Double Ended Tuning Fork (DETF) resonators with different temperature sensitivities.The DETFs are kept at resonance-simultaneously with a dual PLL circuit. Experiments reveal that at the expense of decreasing sensitivity, one can suppress the measurement errors which can be as high as 0.164 °C for the long resonator and 0.240 °C for the short resonator when a time base of 50 ppm accuracy is used. Moreover, while the frequency stability characteristics of the single sensing elements deteriorate drastically as the accuracy of the time base decreases, the frequency stability of the proposed frequency ratio remains unaffected and it is superior compared to both of the resonators.
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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 | 0.000 | 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 | 0.001 |
| 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".