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Record W4362575802 · doi:10.22215/etd/2023-15406

Silicon-Based Polarization-Insensitive Optical Antenna Design and Experimental Characterization of Optical Phased Arrays

2023· dissertation· en· W4362575802 on OpenAlexaff
Xiaochen Xin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCarleton University
Fundersnot available
KeywordsBeamwidthPhased-array opticsPhased arrayOpticsPolarization (electrochemistry)Electronic engineeringBeam steeringEngineeringAntenna (radio)Antenna arrayChipOptoelectronicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Optical Phased Array (OPA) that operates within the optical wavelength range (400nm -1550nm) has emerged as one of the most popular technologies in recent years with the advancement of integrated photonics industry.The integration of optoelectronics components on-chip allows the OPA to steer the beam to achieve ranging, detection, and free-space communication without having any moving parts.The thesis includes two parts.Firstly, a polarization-independent optical surface grating antenna designed for OPA is presented.The designed antenna emits both quasitransverse electric (TE) and quasi-transverse magnetic (TM) modes towards the same angle with similar beamwidth.With the increasing demanding for mode-division multiplexing systems, the incorporation of such antenna in an OPA system allows an additional channel of data transmission while preserving the steerability of the array.In the second part of the thesis, the optical testing setup for a fabricated on-chip OPA system is designed and presented.With the testing setup designed and assembled, a comparison between the observed and simulated far-field images is also presented.

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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