Boundary characterization of microstructures through thermo-mechanical testing
Bibliographic record
Abstract
A variety of silicon foundry processes available for microsystem implementation are available at the present time. The manufacturing methods and the associated process tolerances employed at a particular foundry will determine the performance of the finished devices. Moreover, micro-electro-mechanical systems (MEMS) often require processes that are difficult to control. Device-to-device variations can occur even in batch microfabricated systems. One particular limitation of MEMS foundry processes, in general, is associated with non-classical boundary support conditions due to over/under etching of silicon. These non-classical support conditions will affect the static and dynamic performance of the microsystem. This condition has important implications in atomic force microscopy applications where the targeted natural frequencies are given a wide tolerance due in large part to microfabrication limitations. This paper presents the boundary characterization of single crystal silicon microcantilevers through thermo-mechanical testing. A non-contact optical sensing approach is used for the experimentation. The Rayleigh–Ritz energy method incorporating boundary characteristic orthogonal polynomials is used for the prediction analysis.
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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 | 0.001 | 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) | 0.002 | 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".