Packaging-Induced Range Tunability of Tactile Sensors for Physiological Signal Monitoring Applications
Bibliographic record
Abstract
Packaging-induced performance changes of microelectromechanical systems (MEMS) usually are detrimental in the device development. This paper presents a methodology of tuning the force range of a designated MEMS tactile sensor for broader application potentials by packaging this millinewton-level force sensor with different polymers. The force range of the tactile sensor has been enhanced from 30 mN to 400 mN and 100 N, respectively, which are more than tenfolds or 1000 folds of its original capability, by ruggedizing with the polydimethylsiloxane and polyurethane. Numerical analysis and experimental characterization have verified the tuned force range by these two packaging approaches. Three potential applications for the physiological signal monitoring have been revealed by preliminary emulation test results, including emulated lip pressure measurement, heart rate monitoring, and the finger strength evaluation. This methodology sheds light on reconciling the sensitivity changes induced by the packaging phase from defects to merits for broader application potentials in the MEMS development, accelerating the fabrication flow and lowering the cost with the reusability of concurrent devices.
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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 | 0.001 |
| 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) | 0.001 | 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".