Development of MEMS-based piezoresistive 3D stress/strain sensor using strain technology and smart temperature compensation
Bibliographic record
Abstract
Abstract This paper presents the microfabrication and testing of a membrane-free eight-element single-polarity (n-type) sensing rosette integrated with strained silicon technology over (111) silicon plane to measure the full 3D stress/strain tensor with full temperature compensation. Such n-type piezoresistive (PR) sensor has low sensitivity to the out-of-plane components compared to the in-plane components. To improve the sensitivity of such sensors to the out-of-plane components, a strained silicon technique was integrated into the sensing rosette during the microfabrication process using a highly compressive film produced by plasma enhanced chemical vapor deposition silicon nitride. For experimental verification, a prototype device featuring the proposed sensing rosette was microfabricated using semiconductors fabrication processes. The experimental analysis applied both, in-plane and out-of-plane stresses at different temperatures over a range from −20 °С to 60 °С. In this work, a smart sensing calibration algorithm, utilizing machine learning, is employed to reduce the temperature impact on both sensitivity and resistance of PR coefficients during stress measurement. The developed sensor is capable of accurately extracting the applied stress/strain components with temperature compensation.
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".