The radially polarized vortex beams with excellent focusing characteristics, provide a useful tool for the self-assembly of chiral nanostructures of nanoparticles. Here we presented a simple method for producing cylindrical vector vortex beams. This technique involves converting linearly polarized scalar vortex beams into cylindrically polarized vector vortex beams without the need for strict dual-optical path alignment. The phase hologram loaded onto the spatial light modulators is newly designed to generate linearly polarized vortex beams with different topological charges, concentrating the energy mainly on the first-order diffractions and significantly increasing the energy utilization. Then, the generated beams are introduced to vector polarization using a q-plate to produce radial or azimuthal vector vortex beams. We prepared an AuNPs growth solution comprising CTAB (50 mM, 21.08 mL), HAuCl4 (20 mM, 140 μL), and AA (100 mM, 0.653 mL). SEM images and circular dichroism spectra of the AuNPs exhibit strong chiral nanostructures (L/D-PX AuNPs). We believe that the cylindrical vector vortex beams generated by this method can provide a useful tool for the self-assembly of chiral nanostructures, super-resolution microscopy, optical trapping, and more.
Reservoir computing (RC) is a computational framework for information processing based on neural network. It can be implemented with different physical platforms, principally, electronic architectures and photonic architectures. Photonic RC shows potential path to ultra-fast and efficient processing beyond the traditional Turing-von Neumann computer architecture. Typical photonics RC consider specifically a semiconductor laser (SL) with delayed feedback as reservoir substrate. Basically, the SL is a kind of type B laser, needing enough long delay feedback for the high dimensional chaos generation and for the RC mapping. But on the other hand, long delay feedback leads to the setup big size, being nonconductive of integration implement and stable operation performance in real world. To solve the problem of a huge size, we propose a new photonics RC scheme that using chaotic SL hybrid with Si3N4 micro-resonator, which works as the storage layer and feedback loop. The Si3N4 micro-resonator could help SL producing high-dimensional chaos and reaching high-complexity RC. Meanwhile, the size of Si3N4 micro-resonator is highly compressed at the level of ten micrometers, thereby realizing a size compression of over ten times than that of typical photonics RC setup. In our experiment, we make the free spectrum range (FSR) of micro-resonator is 35GHz, reaching the nonlinear frequency of SL. Then, with careful operation, two-mode mixing chaos can be realized, being very conductive for the photonics RC applications. These results are conducive for the development of on-chip photonic RC.
Convolutional neural network (CNN) has attracted widespread attention in image feature extraction and speech recognition owing to greatly reducing the complexity of model parameters and the number of weights, but it cannot be separated from the support of hardware accelerator. The limitations of electronic devices in terms of power, speed, and size make it difficult for current electron accelerators to meet the computational power requirements of future large-scale convolution operations. Here, we proposed a photonic vector architecture. This structure combines time, space and wavelength, and the non-volatile phase change material and the integrated microcomb form an optical matrix multiplier to realize memory calculation, thus reducing the energy consumption of reading weight data. The tooth spacing of the integrated microcomb is more than 100 GHz, and the microcomb coverage is from 1510 nm to 1610 nm. Finally, we replace the weight values in the CNN with the optimal weight values that the optics can achieve. The final recognition accuracy reached 97.04%, which is comparable to the efficiency of the first electronic equipment. Our results could be helpful for the development of non-volatile and ultra-fast optical neural network (ONN) with feathers of low energy consumption and high integration.
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