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A stainless-steel-based capacitive pressure sensor chip and its microwelding integration

2015· article· en· W1599651418 on OpenAlexaff
Xing Chen, Daniel Brox, Babak Assadsangabi, Mohamed Sultan Mohamed Ali, Kenichi Takahata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceCapacitive sensingWeldingOptoelectronicsChipPressure sensorComposite materialDiaphragm (acoustics)EpoxyElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

A stainless-steel (SS) chip of capacitive pressure sensor and its new integration method are developed. The sensors are microfabricated through thermal bonding of the chip dies made of medical-grade SS to the Au-polyimide diaphragm film. The capacitive cavity design with dead-end holes is verified to increase the pressure sensitivity. Laser microwelding is applied to permanently bond the sensor chips onto SS substrates. The microwelding is revealed to provide twice the mechanical strength and ∼6× electrical conductance in its bond compared with a conductive epoxy case. The microwelded sensor exhibits an average sensitivity of 120 ppm/mmHg close to its pre-welding level. The welding integration with a SS antenna stent is demonstrated. The results suggest that laser microwelding is a promising packaging technique for SS-based biomedical and implant microdevices that require long-term bond reliability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.028
GPT teacher head0.234
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations2
Published2015
Admission routes1
Has abstractyes

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