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Multifunctional movable-type coding metasurface enabling reconfigurable diffractive neural networks.

Zhicai Yu^1,2, Xinyu Li^1,2, Ze Gu^1,2

¹State Key Laboratory of Millimeter Wave, Southeast University, Nanjing, China.

Light, Science & Applications

|February 26, 2026

Summary

This summary is machine-generated.

Researchers developed a novel movable-type coding metasurface for reconfigurable optical computing. This innovation enables versatile applications in electromagnetic computing, holography, and sensing with enhanced flexibility and scalability.

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Area of Science:

Photonics and Metamaterials
Optical Computing Architectures
Applied Electromagnetics

Background:

Optical computing offers advantages like low latency and power efficiency.
Current diffraction neural networks (DNNs) lack reconfigurability, hindering scalability.
Metasurfaces provide a platform for novel optical functionalities.

Purpose of the Study:

To propose a movable-type coding metasurface for reconfigurable optical functionalities.
To develop a movable-type reconfigurable DNN (MT-RDNN) for adaptable classification tasks.
To demonstrate versatile applications in electromagnetic computing, holography, and sensing.

Main Methods:

Designing and fabricating movable-type coding metasurfaces inspired by printing technology.
Cascading metasurface layers to create a reconfigurable DNN (MT-RDNN).
Rearranging meta-atoms for modular reconfiguration of functionalities.

Main Results:

The MT-RDNN successfully adapted from digit to letter classification by replacing meta-atoms.
A single-layer metasurface performed electromagnetic holography and vital sign sensing.
Demonstrated simple reconfiguration, high flexibility, and modular scalability.

Conclusions:

The movable-type coding metasurface enables versatile and reusable optical computing, holography, and sensing.
This technology overcomes the reconfigurability limitations of current DNNs.
The modular design offers significant potential for scalable optical systems.