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Record W2970207032 · doi:10.1088/1361-6439/ab3e8d

Large aperture surface-micromachined rotating micromirror with the majority of dimples removed

2019· article· en· W2970207032 on OpenAlexaff
Hui Zuo, Siyuan He

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDimpleSurface micromachiningOpticsAperture (computer memory)Materials scienceSurface (topology)Aerospace engineeringOptoelectronicsPhysicsFabricationEngineeringMechanical engineeringComposite materialGeometry

Abstract

fetched live from OpenAlex

Abstract This paper presents a surface-micromachined repulsive-force driven 1D rotating micromirror with a large aperture (1 mm). Two rotating beams are located on the bottom side of the mirror plate, while two repulsive-force actuators are connected to two middle sides to push the mirror plate up for out-of-plane rotation. Thus, no complex self-assembly structure is required to raise the mirror plate for rotation as most conventional surface-micromachined rotating micromirrors do. In order to increase the yield rate for the large aperture micromirror and avoid dimples used for preventing stiction during fabrication, six post-fabrication melted supporting beams are used along the circumference to increase the stiffness in fabrication, which are electrically melted and broken after fabrication to restore the initial stiffness. Only 12 dimples in the center of mirror plate are used for preventing operational stiction. The mirror’s reflectivity is increased to 25% from 20% after removing the majority of dimples. 36 prototypes are fabricated and tested and none of them suffer from stiction problem. The 6.3° optical rotation is achieved with a settling time of 26 ms.

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 score0.029
Threshold uncertainty score0.645

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 head0.003
GPT teacher head0.171
Teacher spread0.169 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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