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Record W2104099389 · doi:10.1109/ccece.2008.4564712

Design of a PZT-based MEMS Rotman lens

2008· article· en· W2104099389 on OpenAlexaffvenue
Ahmad Sinjari, Sazzadur Chowdhury

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsLens (geology)Microelectromechanical systemsLuneburg lensBeam steeringMicrostripRadarOpticsMaterials scienceEngineeringComputer scienceOptoelectronicsBeam (structure)PhysicsElectrical engineeringAntenna (radio)Aerospace engineering

Abstract

fetched live from OpenAlex

The design of a MEMS Rotman lens that uses a high dielectric constant PZT (Lead Zirconium Titanate) thick film to fill the lens cavity has been presented. The 56 mum thick Rotman lens has a footprint area of 5.5x7.1 mm2, incorporates 3 beam ports, 5 array ports, and operates at 77 GHz. The small size of the Rotman lens beamformer enables to fabricate both the transmit and receive beamformers on the opposite sides of the same wafer to realize a compact MEMS based radar when integrated with necessary microelectronic circuits and microstrip antennas. Analytical simulation results show that the developed beamformer can resolve an angular resolution of up to four degrees. A fabrication procedure for the MEMS Rotman lens has been developed and verified by simulating in IntelliSuitetrade. This microscale implementation enables the Rotman lens to be used as an integral part of a radar unit for automotive collision avoidance applications.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.027
GPT teacher head0.186
Teacher spread0.159 · 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

Citations5
Published2008
Admission routes2
Has abstractyes

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