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Record W2903288016 · doi:10.1088/1361-6439/aaf46d

Reconfigurable MEMS latching-type capacitors for high power applications

2018· article· en· W2903288016 on OpenAlexaff
Ahmed K S Abdel Aziz, Maher Bakri-Kassem, Raafat R. Mansour

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCapacitorMicroelectromechanical systemsMaterials scienceElectrical engineeringPower (physics)NanotechnologyElectronic engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

Abstract This paper reports the design and characterization of two reconfigurable MEMS discrete capacitor banks for high power and low frequency applications. The first design is a 3-bit capacitor bank built using three parallel-connected series-switched comb-drive capacitors with appropriately scaled values. The second design is a compact discretized comb-drive varactor that can be operated as 2-bit or 3-bit capacitor bank depending on the number of latched states. Both devices are demonstrated using the METALMUMP process where 20 µ m-thick plated nickel is the only structural and metal layer. The MEMS switches use thermal actuation with chevron actuator design. This allows for large displacements at relatively low actuation voltages. Using mechanical latching for both designs, the switch reconfiguration energy is minimized. The relatively very high mechanical stiffness inherent to both thermal actuator designs provides an excellent immunity to self-actuation at high RF power levels. The proposed MEMS switches are capable of operating under low-pressure conditions (e.g. in a package), without degradation of their thermo-mechanical performance.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.551

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 teacher head, 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

Citations4
Published2018
Admission routes1
Has abstractyes

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