Fabrication and Characterization of the Micro-Heater and Temperature Sensor for PolyMUMPs-Based MEMS Gas Sensor
Bibliographic record
Abstract
This work describes the fabrication and characterization of a Micro-Electro-Mechanical System (MEMS) sensor for gas sensing applications. The sensor is based on standard PolyMUMPs (Polysilicon Multi-Users MEMS Process) technology to control the temperature over the sensing layer. Due to its compact size and low power consumption, micro-structures enable a well-designed gas-sensing-layer interaction, resulting in higher sensitivity compared to the ordinary materials. The aim of conducting the characterization is to compare the measured and calculated resistance values of the micro-heater and the temperature sensor. The temperature coefficient of resistance (TCR) of the temperature sensor has been estimated by raising and dropping the temperature throughout a 25-110 °C range. The sensitivity of these sensors is dependent on the TCR value. The temperature sensor resistance was observed to rise alongside the rising environmental temperatures or increasing voltages given to the micro-heater, with a correlation value of 0.99. When compared to the TCR reported in the literature for the gold material 0.0034 °C-1, the average TCR was determined to be 0.00325 °C-1 and 0.0035 °C-1, respectively, indicating inaccuracies of 4.6% and 2.9%, respectively. The variation between observed and reported values is assumed to be caused by the fabrication tolerances of the design dimensions or material 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) | 0.000 | 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 | 0.001 | 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".