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Record W2976347207 · doi:10.3390/mi10100667

External Electromagnet FPCB Micromirror for Large Angle Laser Scanning

2019· article· en· W2976347207 on OpenAlexafffund
Karlmarx G. K. Periyasamy, Vixen Joshua Tan, Siyuan He, Nikolaos Kourtzanidis

Bibliographic record

VenueMicromachines · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlatness (cosmology)OpticsMaterials scienceRotation (mathematics)CurvatureDeflection (physics)PhysicsComputer science

Abstract

fetched live from OpenAlex

An external electromagnet plus moving PM (permanent magnet) FPCB (flexible printed circuit board) micromirror is proposed in this paper that can overcome two limitations associated with the previous FPCB micromirror with a configuration of an external PM plus moving coil, i.e., (1) it reduces the overall width beyond the mirror plate, and (2) increases the maximum rotation angle. The micromirror has two external electromagnets underneath an FPCB structure (two torsion beams and a middle seat) with two moving PM discs attached to the back and a metal-coated mirror plate bonded to the front of the FPCB middle seat. Modeling and simulation were introduced, and the prototype was fabricated and tested to verify the design. The achieved performance was better than that of the previous design: a maximum resonant rotation angle of 62° (optical) at a driving voltage of ±3 V with a frequency of 191 Hz, the required extra width beyond the mirror plate was 6 mm, and an aperture of 8 mm × 5.5 mm with a roughness of <10 nm and a flatness of >10 m (ROC, radius of curvature). The previous FPCB micromirror's performance was: strain limited maximum rotation angle was 40° (optical), the extra width beyond the mirror plate was 14.7 mm, and had an aperture of 4 mm × 4 mm with a similar roughness and flatness.

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 score0.090
Threshold uncertainty score0.738

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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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

Citations26
Published2019
Admission routes2
Has abstractyes

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