Stereolithography as a meso-structure for input force reduction to a capacitive force MEMS sensor
Bibliographic record
Abstract
This paper focuses on the design and development of a novel MEMS based force sensor for use in a smart electrical switch which can be used to sense forces applied during the disconnection/connection of the switch. Sensed forces will permit the power to the switch to be turned on/off electronically to prevent arcing at 42 Volts, which would otherwise damage the switch electrical contacts. This paper focuses on the design of a packaging cover for the switch incorporated with a meso-structure, for input force reduction, using a Stereolithography fabrication process. This packaging cover will be installed on a standard ceramic pin grid array (PGA) package to which a MEMS force sensor will be wire-bonded. The complete sensor is proposed for use in smart electrical connectors within automobiles. The purpose of the packaging cover is to transform the macroscopic input force imparted by a technician during disconnection or connection of the switch into a grasping action on the sensor. The macroscopic input force is estimated to be 60N at maximum. To prevent potential damage on the MEMS sensor, the cover converts the applied force to a smaller force in the milli-Newton scale. Since the sensor is to be operated under the harsh environment of the automobile, transverse comb-drive capacitors are selected as the force sensing technique. The capacitive MEMS sensor will be fabricated using PolyMUMPs surface micromachining. To ensure linearity, the displacement of the comb drive is limited to 1 &mgr;m for a net capacitance change of 0.013 pF. Principles of strain energy and Castigliano's Theorem are used to model the proposed cover design. It is found that for a 60 N input force, the design is capable of converting that force to a lateral displacement of 30.86 &mgr;m, which is equivalent to a 0.01 N force onto the sensor. Design analysis, and results from Finite Element Method (FEM) simulation of the cover design will be presented in this paper.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".