Impact of support material deformation in MEMS bulk micromachined diaphragm pressure sensors
Bibliographic record
Abstract
Abstract In this work, experimental data and finite element analysis reveals deflection in diaphragm supporting material leading to non-linear pressure-reflection response. These results are contrary to the standard assumptions presented in literature, where modelling of deflection response in diaphragm pressure sensors is primarily carried out assuming a rigid supporting structure. An extrinsic fiber-optic Fabry–Perot pressure sensor, based on micro-electromechanical system, is developed and used to investigate optical deflection response. While the sensor is not novel, a series of experiments to validate support deflection phenomena are designed and carried out using a silicon membrane at gauge pressures from 0 to 1000 PSI in ambient temperature. The device is packaged with an industry standard stainless-steel housing typically used in plastic injection moulding. Sensor performance is compared to analytical and finite element modelling. Results suggest that the device is experiencing greater deflection than analytically predicted at pressures above 200 PSI, where a rigid support is assumed in existing literature. Based on these results, a modified analytical model is proposed to correct for this behaviour. The modified model is created through addition of a nonlinear component to an existing model, which is then fitted to the experimental data using least-square methods. Prediction of the experimental deflection results is improved from 81% error using a fixed-support analytical model to 4% error using the presented adjusted model. It is demonstrated that nonlinear effects are present and optically measurable in cases where deflection is lower than 1% of the membrane thickness. This work will aid in the implementation of high-resolution pressure sensors operating in harsh environment conditions.
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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".