Characterization of a Multilayered SAW Pressure Sensor with Low Temperature Variability
Bibliographic record
Abstract
A method to reduce temperature effects in SAW pressure sensors is presented. A layer of SiO2 is deposited on the surface of 128YX LiNbO3 in order to achieve a high coupling coefficient (κ 2 ) as well as a low temperature coefficient of delay (TCD). The FEM tool COMSOL Multiphysics is used to estimate the phase velocity and frequency of the input RF signal using an eigenfrequency analysis. Then by using a time domain analysis, a surface acoustic wave (SAW) is generated by IDTs and its wave propagation characteristics are obtained. During this process, the boundary reflection is removed by adding a perfectly matched layer (PML). The reflected signal on the IDT can be detected; as such the phase angle is calculated. By applying different pressures to the sensor, a relationship between pressure and phase angle is determined. Using frequency domain analysis, the coupling coefficient is computed with high accuracy. The TCD is calculated at different SiO2 thicknesses and the SiO2 thickness corresponding to a zero TCD and high coupling coefficient is obtained. A prototype is tested to validate these values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".