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Record W3015108016 · doi:10.3390/act9020024

A New Non-Invasive Air-Based Actuator for Characterizing and Testing MEMS Devices

2020· article· en· W3015108016 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueActuators · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsYork University
Fundersnot available
KeywordsMicroelectromechanical systemsCantileverMicroscale chemistryDragActuatorAirflowCapacitive sensingDeflection (physics)AcousticsMechanical engineeringAerodynamic forceAerodynamicsElectronic engineeringEngineeringMaterials scienceElectrical engineeringStructural engineeringNanotechnologyAerospace engineeringOpticsPhysics

Abstract

fetched live from OpenAlex

This research explores a new ATE (Automatic Testing Equipment) method for Micro Electro Mechanical Systems (MEMS) devices. In this method, microscale aerodynamic drag force is generated on a movable part of a MEMS sensor from a micronozzle hole located a specific distance above the chip that will result in a measurable change in output. This approach has the potential to be generalized for the characterization of every MEMS device in mass production lines to test the functionality of devices rapidly and characterize important mechanical properties. The most important testing properties include the simultaneous application of controllable and non-invasive manipulative force, a single handler for multi-sensor, and non-contact characterization, which are relatively difficult to find with other contemporary approaches. Here we propose a custom-made sensing platform consisting of a microcantilever array interconnected to a data acquisition device to read the capacitive effects of each cantilever’s deflection caused by air drag force. This platform allows us to empirically prove the functionality and applicability of the proposed characterization method using airflow force stimuli. The results, stimulatingly, exhibited that air force from a hole of 5 µm radii located 25 µm above a 200 × 200 µm2 surface could be focused on a circular spot with radii of approximately 5 µm with surface sweep accuracy of <8 µm. This micro-size airflow jet can be specifically designed to apply airflow force on the MEMS movable component surface. Furthermore, it was shown that the generated air force range could be controlled from 20 nN to 60 nN, approximately, with a linear dependency on airflow ranging from 5 m/s to 20 m/s, which is from a 5 µm radius microhole air jet placed 400 µm above the chip. In this case-study chip, for a microcantilever with a length of 400 µm, the capacitance curve increased linearly from 28.2 pF to 30.5 pF with airflow variation from 5 m/s to 21 m/s from a hole. The resultant curve is representative of a standard curve for testing of the further similar die. Based on these results, this paper paves the way towards the development of a new non-contact, non-invasive, easy-to-operate, reliable, and relatively cheap air-based method for characterizing and testing MEMS sensors.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.248
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it