A spiking photonic reservoir computing system based on photonic spiking neuron is proposed in this paper. This system utilizes the high nonlinearity and excitation characteristics of selected photonic spiking neuron to perform nonlinear classification task. It has been proved that the proposed spiking reservoir can well perform nonlinear classification task. Furthermore, our research also study the effect of different input dimensions and output processing methods on the system. The system can still have good performance under a low input dimension. The results show that the proposed system has strong learning ability and can be used to implement more machine learning tasks.
Plasmonic devices are considered as a potential platform to realize highly integrated photonic circuits. Optical logic gates are fundamental computing and light-controlling elements in optical circuits and optical computing, and many plasmonic logic gates have been studied. However, these devices can only realize single or several specific logical operations. In this work, we propose a multiport plasmonic system to realize all-logical processing based on coding metamaterials (CMs) and inverse design. We utilize nondominated sorting genetic algorithm-II to optimize the distributions of CMs. After optimization, the simulation results exhibit that all types of logic gates (AND, OR, NOT, NAND, NOR, XOR and XNOR) can be obtained with the operating wavelength of 1.31μm and a small footprint (0.8×1.1 μm2). Moreover, the extinction ratio between logical "1" and "0" states exceeds 20 dB for OR, NOT, XNOR and XOR logic gates.
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