论文标题

多层超材的神经网络设计用于时间差异化

Neural network design of multilayer metamaterial for temporal differentiation

论文作者

Knightley, Tony, Yakovlev, Alex, Pacheco-Peña, Victor

论文摘要

鉴于它们能够显着提高计算处理速度的能力,控制数学操作计算的波 - 物质相互作用(MTMS)已成为模拟计算的重要范式。在这里,由于在时间信号上执行数学操作的重要性,我们建议,设计和研究多层MTM,能够计算入射调制的时间信号的导数,这是信号处理的重要计算过程的一个示例。为此,我们利用基于神经网络(NN)算法来设计多层结构(二氧化物(ITO)的交替层和二氧化钛(TIO2)),这些结构可以计算构成电信波长的电磁信号信号的构造的第一个时间衍生物(1550)。使用多个入射的时间信号(包括调制的高斯和调制任意功能)提出了不同的设计,证明了预测结果(NN结果)与理论(理想)值之间的良好一致性。据展示,对于所有设计,拟议的基于NN的算法如何在短短几秒钟后完成对多层MTM层厚度的设计空间的搜索,并且在将预测的结果与理想时间衍生的理想时间范围进行比较时,在10^-4的顺序(或以下)均为低平方误差(或以下)。

Controlling wave-matter interactions with metamaterials (MTMs) for the calculation of mathematical operations has become an important paradigm for analogue computing given their ability to dramatically increase computational processing speeds. Here, motivated by the importance of performing mathematical operations on temporal signals, we propose, design and study multilayer MTMs with the ability to calculate the derivative of incident modulated temporal signals, as an example of a significant computing process for signal processing. To do this, we make use of a neural network (NN) based algorithm to design the multilayer structures (alternating layers of indium tin oxide (ITO) and titanium dioxide (TiO2)) that can calculate the first temporal derivative of the envelope of an impinging electromagnetic signal at telecom wavelengths (modulated wavelength of 1550 nm). Different designs are presented using multiple incident temporal signals including a modulated Gaussian as well as modulated arbitrary functions, demonstrating an excellent agreement between the predicted results (NN results) and the theoretical (ideal) values. It is shown how, for all the designs, the proposed NN-based algorithm can complete its search of design space for the layer thicknesses of the multilayer MTM after just a few seconds, with a low mean square error in the order of (or below) 10^-4 when comparing the predicted results with the theoretical spectrum of the ideal temporal derivative.

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