Thermo-electrical influence on static and dynamic behaviour of cantilever type silicon waveguide
Bibliographic record
Abstract
The static and dynamic characteristics of micro-electro-mechanical-systems (MEMS) can be influenced through the application of an electrostatic field or thermal gradient. Both of these mechanisms will affect the performance of the MEMS device significantly. The thermal effects manifest themselves by varying the structural characteristics, Young's modulus of elasticity of the waveguide structure, and the material properties. These types of influences will affect the mechanical integrity through an increase in the flexibility leading to variations in the static deflections and also to the dynamic frequency eigenvalues, and changes to the device geometry can lead to faulty measurements where capacitive sensing is employed. Hence, thermal variations in the operating environment can result in unwanted thermal noise and degradation of signal integrity. Electrostatic fields or forces can be used to correct for thermal influences, for example, or as stand-alon microsystem performance tuners. The corrector characteristics can be achieved by the integration of a suspended electrode over the waveguide, for example where the induced electrostatic stiffness is aligned with the mechanical stiffness of the waveguide and are opposite in direction to the thermally induced "softening". The "stand-alone" characteristics of an applied electrostatic field can be used to selectively deflect the waveguide through an applied bias voltage and hence the static and dynamic performance can be trimmed or tuned by the application of an electrostatic field. This paper presents an experimental and theoretical investigation into coupled thermo-electrical influences on a microcantilever structure. These combined influences are typical of the operating characteristics and environments of microsystems currently in use.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".