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Record W4408116127 · doi:10.3390/photonics12030229

MMI Couplers and the Talbot Effect, Symmetries and Golden Ratio

2025· article· en· W4408116127 on OpenAlexaff
Gazi Mahamud Hasan, Mehedi Hasan, Karin Hinzer, Trevor J. Hall

Bibliographic record

VenuePhotonics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Mathematical Theories and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTalbot effectOpticsPhysicsHomogeneous spaceGolden ratioOptoelectronicsGeometryDiffractionMathematics

Abstract

fetched live from OpenAlex

The Talbot effect concerns the periodic self-imaging along an optical axis of a free-space optical field that is periodic in an initial transverse plane. It may be modeled by a shift-invariant linear system, fully characterized by the convolution of its impulse response. Self-imaging at integer and fractional Talbot distances of point sources on a regular grid in free space may then be represented by a transmission matrix that is circulant, symmetric, and persymmetric. The free-space Talbot effect may be mapped to the Talbot effect in a multimode waveguide by imposing the anti-symmetry of the mirror-like sidewalls created by the tight confinement of light within a high-index contrast multimode waveguide. The position of the anti-symmetry axis controls the distribution of discrete lattice points in a unit cell. For different distributions, interesting features such as conditional flexibility in the placement of access ports without altering amplitude and phase relationships, omitting ports without power penalty, closed form uneven splitting ratios, and offset access ports can be derived from the MMI coupler. As a specific example, a simple 2×2 MMI coupler is shown to provide a power-splitting ratio related to the golden ratio φ. The structure is amenable to planar photonic integration on any high-index contrast platform. The predictions of the theory are confirmed by simulation and verified by experimental measurements on a golden ratio MMI coupler fabricated using an SOI process.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.184

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 head0.245
Teacher spread0.242 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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