Assessment of the Effect of Micro-Fabrication Uncertainties on the Sensitivity of Gas Sensors Using 3-D Finite Element Modeling
Bibliographic record
Abstract
The uncertainties introduced during some stages of the micro-fabrication process can introduce a variation in the thermal response and calculated sensitivity of micromachined gas sensors. In this paper a parametric study is conducted to investigate the effect of the variation in the thermal characteristics and the electrical characteristics (induced at different levels of the micro-fabrication of CMOS thin films) on the sensitivity of gas micro sensors. Three dimensional finite element modeling was used to conduct the parametric study. The multilevel substructuring modeling technique was used to reduce the computational cost of the analysis. The used substructuring technique is proven to reduce the computational cost of the parametric analysis by a factor that ranges from 57.8 to 78.7%. This expensive computational task has shown that the variation of some material properties can alter the sensitivity of gas micro sensors by a factor that can reach up to 40%. This large variation in sensor performance highlights the essential need for a close monitoring of different parameters involved in various micromachining stages of the gas micro sensors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".