Thermoelastic Damping in Hollow and Slotted Microresonators
Bibliographic record
Abstract
Microresonators employed in microelectromechanical systems for sensing and communications are growing increasingly more sophisticated in terms of structural geometry and mode shapes. Accompanying this increase in sophistication is a corresponding need to develop accurate analytical models to predict the dynamic properties of such resonators. Here, we present an analytical framework to compute thermoelastic damping (TED) in the general class of microresonators characterized by structural discontinuities in the form of slots or internal channels. The temperature field within the resonators is obtained by solving the one-way coupled equation of thermoelastic heat conduction in a piecewise fashion, thereby capturing the effects of structural discontinuities interrupting heat conduction within the beam. The framework is validated by comparison with previously reported finite-element analysis and measurements of damping in slotted microresonators. The analysis leads to an expression for TED in the form of rapidly converging infinite series, and accurate closed-form expressions are obtained by retaining the leading terms. These simple formulas enable a rapid exploration of the design space over a full range of parameters, as illustrated for the case of hollow single-crystal-silicon beams containing internal channels. For constant channel volume, the peak value of TED reduces monotonically with the ratio of channel width to channel height. The analysis is used to identify designs that reduce TED to values that are less than 2 times 10-5at all frequencies.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".