Hybrid Smart Temperature Compensation System for Piezoresistive 3D Stress Sensors
Bibliographic record
Abstract
This paper proposes a new hybrid temperature compensation system for doped silicon-based piezoresistive 3D stress sensors. The developed compensation system integrates a temperature sensor, placed in close proximity to the stress sensing rosettes, with the artificial neural networks (ANNs). In this work, the n-type circular piezoresistor featured over (111) silicon plane was employed to capture the local temperature variations, within the sensing chip. The extracted temperature changes, along with the resistance changes, are fed, as inputs, to the ANNs to compensate the temperature effect on the acquired signals for more accurate stress measurement. The proposed compensation system was experimentally evaluated while extracting stress applied up to 60 MPa at different temperatures within a range from 0 °C to 50 °C. The developed system was successfully able reduce the maximum full scale error, obtained from using only a temperature sensor for compensation, by ~55%. The new system has merit since it has the capability to compensate for both resistance and sensitivity, for 3D stress sensor, with no need for additional circuitry. Moreover the employed temperature sensor shares the same thermal environment with the stress sensing rosette.
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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 | 0.000 |
| 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 | 0.001 |
| 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".