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Record W3003431293 · doi:10.1088/1361-6439/ab71e6

Design and characterization of compact digital RF MEMS capacitors and phase shifters in CMOS 0.35 <i>µ</i> m technology

2020· article· en· W3003431293 on OpenAlexaff
Ahmed K S Abdel Aziz, Maher Bakri-Kassem, Raafat R. Mansour

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCMOSMicroelectromechanical systemsCapacitorCharacterization (materials science)Electrical engineeringElectronic engineeringEngineeringMaterials scienceOptoelectronicsVoltageNanotechnology

Abstract

fetched live from OpenAlex

Abstract This work reports on the design, fabrication and characterization of compact digital RF MEMS capacitors and phase shifters that are built in the back end of line (BEOL) of CMOS 0.35 µ m technology. The devices are micromachined using a maskless post-processing sequence with cryogenic cooling on to control the out-of-plane warping of the metal-oxide structural layers. The implemented vertical electrostatic actuation in the developed post process allows the realization of highly compact designs of both devices, and an operating voltage of the devices is stay below 70 V. The 4-bit capacitors provide a measured tuning ratio close to 10:1 which corresponds to 0.15 pF to 1.2 pF over the frequency range 3–10 GHz. The overall footprint of the digital capacitor is 0.6 mm × 0.9 mm. The measured quality factor after de-embedding the losses due to the RF probing pads is up to 120. The measurement of the 4-bit phase shifters reveals low insertion loss &lt;3 dB at 20 GHz along with low variation of the insertion loss &lt;1 dB up to the same frequency. The measured phase shift is 150 at 20 GHz. The overall footprint of the 4-bit phase shifter is around 1 mm × 1 mm.

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 score0.077
Threshold uncertainty score0.582

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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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