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Record W3185764222 · doi:10.3390/mi12080891

Analysis of Optical Diffraction Profiles Created by Phase-Modulating MEMS Micromirror Arrays

2021· article· en· W3185764222 on OpenAlexafffund
Tarek Mohammad, Siyuan He, Ridha Ben Mrad

Bibliographic record

VenueMicromachines · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsDiffractionMicroelectromechanical systemsOpticsIntensity modulationMaterials scienceBeam (structure)Modulation (music)Phase modulationPhase (matter)Optical powerOptical switchOptoelectronicsPhysicsAcousticsLaserPhase noise

Abstract

fetched live from OpenAlex

This paper presents modeling and analysis of light diffraction and light-intensity modulation performed by an optical phased array (OPA) system based on metal-coated silicon micromirrors. The models can be used in the design process of a microelectromechanical system (MEMS)-based OPA device to predict its optical performance in terms of its field of view, response, angular resolution, and long-range transmission. Numerical results are derived using an extended model for the 1st-order diffracted light intensity modulation due to phase shift. The estimations of the optical characteristics are utilized in the designs of an OPA system capable of active phase modulation and an OPA system capable of array pitch tuning. Both designs are realized using the Multi-User MEMS Processes (PolyMUMPs) in which polysilicon is used as structural material for the MEMS-actuated mirrors. The experiments are performed to evaluate the optical performance of the prototypes. The tests show that the individually actuated micromirrors, which act as phase shifters, can transmit the most optical power along the 1st-order diffracted beam by actively changing their out-of-plane positions. In addition, the 1st-order diffracted beam with high optical intensity can be steered for distance measurement.

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 score0.202
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.260
Teacher spread0.250 · 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
Published2021
Admission routes2
Has abstractyes

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