RESUMO
In this Letter we demonstrate a fundamentally new, to the best of our knowledge, concept to enhance the magnetic modulation of the surface plasmon polaritons (SPPs) by using hybrid magneto-plasmonic structures consisting of hyperbolic plasmonic metasurfaces and magnetic dielectric substrates. Our results show that the magnetic modulation of SPPs in the proposed structures can be an order of magnitude stronger than in the hybrid metal-ferromagnet multilayer structures conventionally used in active magneto-plasmonics. We believe that this effect will allow for the further miniaturization of magneto-plasmonic devices.
Assuntos
Fenômenos Magnéticos , Miniaturização , Fenômenos FísicosRESUMO
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100-x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.