Multi-parameter synthesis of microsystems
Bibliographic record
Abstract
Micro-electro-mechanical systems (MEMS) by definition are coupled electrical and mechanical microsystems. Additionally, microfabrication tolerances, device geometry and thermal effects, for example, will further cloud the performance characteristics. Hence, the consolidation of these individual parameters into a single output based upon "forward-step" modeling will allow for a complete performance characterization in a manner where changes to the static and dynamic outputs are monitored in a step wise fashion through the addition of the individual parameters separately. This deterministic approach aims to synthesize the "parameter-matrix" under which the microsystem is constrained, both by device design and by the eventual operating conditions. The theoretical modeling of the synthesized parameters into an output determinant would be a valuable design tool especially when targeting specific performance characteristics at the design stage of the microsystem that are tied to both the device design and operating conditions. This paper presents a method for microsystem performance modeling based on the solution of a parameter-matrix into a deterministically synthesized output response. The mathematical modeling is based upon the Rayleigh-Ritz energy method using boundary characteristic orthogonal polynomials. The synthesized output models the static and dynamic response of the step-forward addition of individual microsystem parameters, which when they have been evaluated can be used to specify design criteria under a given set of operating conditions. This analysis method will not only allow the designers of microsystems to determine the influence of intrinsic and extrinsic limitations and conditions, but also to establish viable MEMS platforms based on predetermined output performance characteristics.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".