Study, design, and characterization of anchor loss reduction and tuning the frequency of MEMS resonators for timing applications
Bibliographic record
Abstract
Electrically powered devices and machines with electronic components require precise timing to function properly. The need for accuracy has become increasingly critical with the growth of high-speed telecommunications and data processing. To meet the demand for more efficient and faster electronic circuits and compact devices, the development of new generations of micro-electromechanical systems (MEMS) is necessary. \n \nThe purpose of this study is to contribute to the advancement of knowledge on anchor loss and tunability of MEMS resonant devices. \n \nTo begin, a comprehensive review of the literature was conducted to provide an overview of MEMS devices, their applications, and fabrication processes. Next, this thesis presents the modeling, design, and fabrication of clamped-clamped beam MEMS resonant devices with a focus on reducing anchor loss. The work examines the impact of different shapes of energy reflector holes on the anchoring structure’s anchor loss and the beam resonator’s quality factor. \n \nThis thesis also explores the design and fabrication of a tunable MEMS resonator. The device comprises a resonator anchored on both sides through suspended beams, situated between two DC actuators with springs. The activation of each actuator increases the frequency of the resonator through the added stiffness of the anchoring structure and resonator. The simplicity of the structure provides high tunability of the resonant structure, which is a notable advantage of this work.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".