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Metasurfaces and metamaterials

Can layered metasurfaces do logic with plain light waves?

Qian C, Lin X, Lin X, et al. · Light, science & applications · 2020

Open access · cc by · source: Europe PMC

A two-layer patterned dielectric sheet, trained like a neural network, sent microwaves to the correct 'on' or 'off' spot for NOT, OR and AND logic without precise control of the input beams.

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Study at a glance

Design
Other — Simulation-trained two-layer dielectric metasurface fabricated and measured with a microwave near-field scanner at 17 GHz; inputs encoded by a transmittance mask.
N
No sample; results are measured field maps and contrast ratios for each logic input combination.
Population
A fabricated two-layer dielectric (F4B) metasurface illuminated by microwave plane waves
Outcome
Location of focused output intensity (logic 1 vs 0) and contrast ratio between the two output regions

Structured fields used in claim comparison tables when every cited study has a complete layer.

Key findings

In simulation the trained layers focused most of the energy into the correct target region for every NOT, OR and AND input. In the microwave experiment, the intensity peak also landed in the correct region every time, and the contrast between the two regions exceeded 9.6 dB in all cases. The authors also sketch, in simulation, cascaded gates and a chip-scale XOR gate.

Methodology

The authors encoded logic operators and inputs as open or blocked regions of a mask and placed two layers of dielectric pillars of varying height (a Huygens metasurface) behind it. They used a diffraction model and back-propagation training to choose each pillar's phase so that light would focus onto one of two small target regions meaning 1 or 0. They built the design for 17 GHz microwaves, illuminated it with a distant horn antenna, and scanned the transmitted field with a probe.

Limitations

Only three gates were demonstrated experimentally, and only at microwave frequency; operation at optical frequencies is argued from the scalability of Maxwell's equations but not tested. The network has no nonlinear activation and the input mask was fixed rather than dynamically switchable, so the claimed programmable processor is future work. The authors deliberately trained on every case without held-out test data, so this is a designed device rather than a learned classifier that generalises.

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