Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors.

Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets.

Real-time terahertz wave channeling via multifunctional metagratings: a sparse array of all-graphene scatterers.

Acquiring full control over a large number of diffraction orders can be strongly attractive in the case of realizing multifunctional devices such as multichannel reflectors. Recently, the concept of metagrating has been introduced, which enables obtaining the desired diffraction pattern through a sparse periodic array of engineered scatterers. In this Letter, for the first time, to the best of our knowledge, a tunable all-graphene multichannel meta-reflector is proposed for operating at terahertz (THz) frequencies.