Department of Physics, Faculty of Sciences, Kavala Campus, International Hellenic University, St. Loukas, 654 04 Kavala, Greece.
School of Science & Technology, Informatics Studies, Hellenic Open University, 263 35 Patra, Greece.
Sensors (Basel). 2022 Jan 18;22(3):720. doi: 10.3390/s22030720.
In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.
在过去的几年中，已经提出了基于纳米光子排列的神经形态电路的实现，其中包含完整的光学电路、激光、光电探测器、光子晶体、光纤、平面波导和其他纳米结构材料的无源光学元件，这些元件利用量子视角消除了同时处理大数据组的时间，从而极大地提高了当代智能计算系统的潜力。本文旨在记录和研究有关纳米光子排列神经网络的神经形态电路的开发和实现方法的研究。具体来说，对纳米光子神经形态处理器的开发方法、其在神经结构上的创新性、训练方法及其优化进行了调查研究，同时还研究了光学激活函数和成本函数等特殊问题。这项研究工作的主要贡献在于，这是文献中首次在一篇论文中介绍了最著名的纳米光子网络架构、训练方法、优化和激活函数。本研究还包括对纳米光子网络的优缺点进行了广泛的详细元审查分析。
