Space and wavelength multiplexing of holographic fields in longitudinal and transverse planes using waveguide metasurfaces
Bibliographic record
Abstract
Waveguide-integrated metasurfaces, subwavelength scatterers patterned atop planar waveguides, offer a compact platform for three-dimensional (3D) holography, near-eye AR/VR, and on-chip photonics. In this work, we design and experimentally demonstrate a waveguide-metasurface capable of multiplexing holographic images of different colors at different spatial planes-either at the same elevation (transverse plane, i.e., plane perpendicular to the propagation direction) or along the direction of propagation (longitudinal plane, i.e., plane parallel to the propagation direction). Our approach enables multi-wavelength, multi-plane holography within a single metasurface by combining a Band-Limited Angular Spectrum (BAS) formalism with an Optical Rotation Angle (ORA) algorithm. Experimentally, we first demonstrate multi-wavelength single-transverse-plane holography, projecting the letters "U," "O," and "T" with correlation coefficients of 70-80% and PSNR < 28 dB, followed by multi-wavelength, multi-transverse plane holography with correlation values of 65-82% and PSNR < 25 dB. We further achieve wavelength- and plane-multiplexed holography, displaying digits "1-4" across two transverse planes with 70-82% fidelity and PSNR up to 31 dB. Finally, we realize longitudinal multi-wavelength holography, demonstrating a 50/50 beam-splitter (λ = 532 nm) and a reciprocal beam-combiner (λ = 635 nm) with 72-74.6% correlation. This robust, fabrication-friendly scheme establishes a versatile route toward compact, multi-plane, multi-color holography for integrated photonics and immersive AR/VR systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".