Optimization of geometric characteristics to improve sensing performance of MEMS piezoresistive strain sensors
Bibliographic record
Abstract
In this paper, the design of MEMS piezoresistive strain sensor is described. ANSYS®, finite element analysis (FEA) software, was used as a tool to model the performance of the silicon-based sensor. The incorporation of stress concentration regions (SCRs), to localize stresses, was explored in detail. This methodology employs the structural design of the sensor silicon carrier. Therefore, the induced strain in the sensing chip yielded stress concentration in the vicinity of the SCRs. Hence, this concept was proved to enhance the sensor sensitivity. Another advantage of the SCRs is to reduce the sensor transverse gauge factor, which offered a great opportunity to develop a MEMS sensor with minimal cross sensitivity. Two basic SCR designs were studied. The depth of the SCRs was also investigated. Moreover, FEA simulation is utilized to investigate the effect of the sensing element depth on the sensor sensitivity. Simulation results showed that the sensor sensitivity is independent of the piezoresistors' depth. The microfabrication process flow was introduced to prototype the different sensor designs. The experiments covered operating temperature range from −50 °C to +50 °C. Finally, packaging scheme and bonding adhesive selection were discussed. The experimental results showed good agreement with the FEA simulation results. The findings of this study confirmed the feasibility of introducing SCRs in the sensor silicon carrier to improve the sensor sensitivity while using relatively high doping levels (5 × 10 19 atoms cm −3 ). The fabricated sensors have a gauge factor about three to four times higher compared to conventional thin-foil strain gauges.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".