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Robust Fulcrum-Type Wafer-Level Packaged MEMS Switches Utilizing Al-Ru/AlCu Contacts Fabricated in a Commercial MEMS Foundry

2023· article· en· W4385337663 on OpenAlexaff
Tejinder Singh, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicroelectromechanical systemsWaferMaterials scienceInsertion lossWafer-level packagingOptoelectronicsStictionReturn lossElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper reports robust wafer level packaged radio-frequency (RF) microelectromechanical systems (MEMS) switches fabricated in a commercial high volume MEMS foundry. A novel wafer level packaging method is developed to improve the mechanical robustness of the movable shuttle. Two SPST switches are packaged in a single die utilizing a shared seesaw-type membrane that pivots on a fulcrum. Single metal contact (S1) and triple metal contact (S2) switch tips provides low-loss and high isolation respectively. The proposed seesaw-type switch offers a unique solution to improve MEMS reliability with the use of non-metallic thick membrane that minimizes stiction, cantilever sagging/curling, contact degradation and micro-welding problems commonly found in MEMS switches. The packaging method does not degrade the RF performance and operates at 30 V. Packaged S1 MEMS switch shows less than 0.6 dB of insertion loss and better than 20 dB of isolation while S2 switch demonstrates lower than 1 dB of insertion loss and higher than 45 dB of isolation from dc to 20 GHz.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.111
GPT teacher head0.278
Teacher spread0.167 · 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
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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