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Record W4290465734 · doi:10.3390/act11080224

Resonant Adaptive MEMS Mirror

2022· article· en· W4290465734 on OpenAlexaff
Amr Kamel, Samed Kocer, Lyazzat Mukhangaliyeva, Resul Saritas, Ahmet Gülsaran, Alaa Elhady, Mohammad Abd Alkhalik Basha, Parsin Haji Reza, Mustafa Yavuz, Eihab Abdel‐Rahman

Bibliographic record

VenueActuators · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicroelectromechanical systemsDeformable mirrorZernike polynomialsMonochromatic colorSilicon on insulatorOpticsComputer scienceModal analysisMaterials scienceAcousticsElectronic engineeringAdaptive opticsPhysicsOptoelectronicsEngineeringSiliconVibrationWavefront

Abstract

fetched live from OpenAlex

A novel MEMS continuous deformable mirror (DM) is presented. The mirror can be integrated into optical systems to compensate for monochromatic and chromatic aberrations. It is comprised of a 1.6 mm circular plate supported by eight evenly spaced flexural springs. Unlike traditional bias actuated DMs, it uses resonant electrostatic actuation (REA) to realize low- and high-order Zernike modes with a single drive signal. Instead of the hundreds or thousands of electrodes deployed by traditional DMs, the proposed DM employs only 49 electrodes and eliminates the need for spatial control algorithms and associated hardware, thereby providing a compact low-cost alternative. It also exploits dynamic amplification to reduce power requirements and increase the stroke by driving the DM at resonance. The DM was fabricated using a commercial silicon-on-insulator (SOI) MEMS process. Experimental modal analysis was carried out using laser Doppler vibrometry (LDV) to identify mode shapes of the DM and their natural frequencies. We are able to observe all of the lowest eight Zernike modes.

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 score0.002
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.208
Teacher spread0.196 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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