人工智能助力纳米光子学:机器学习辅助设计法诺共振超表面

超表面是由亚波长尺寸纳米颗粒组成的阵列。由于超表面可以实现对于光场的高效调控,它在现代纳米科技领域正扮演着越来越重要的角色。超表面有望在多个光子学领域实现重大的应用,例如超透镜、可调谐成像、全息技术等。通过超表面结构产生法诺共振效应可以实现对入射光能量的高效聚集。这一效应可进一步应用于多个领域,如增强非线性光转换、光传感技术、光声振动以及窄带滤波。

传统的纳米结构设计通常采用直接优化的方法来达到所需的光学效应,比如超表面的法诺共振效应。而当多个相互关联的参数需要同时优化时,传统的直接扫描参数方法通常需要大量的尝试,会花费大量的时间,甚至有时会无法给出一个满意的结果。

近年来,由于计算机领域的蓬勃发展,人工智能神经网络已经成为了一个强大并且具有革命性的方法,可以用来解决之前费时费力的难题。相比于传统的正向设计方法,利用深度学习算法来反向设计纳米结构体现出了其显著的灵活性。并且,这种神经网络技术在设计具有复杂结构、参数种类繁多的纳米结构时更能体现出其快速灵活的特点,并且更容易设计出理想的结构,这种优越性是传统的方法所无法比拟的。

最近,来自新南威尔士大学、澳大利亚国立大学和昆士兰科技大学的研究团队在发表于Advanced Photonics 2020年第2期的文章中详细阐述了一种利用深度学习来反向设计高品质法诺共振超表面的新方法()。该方法设计了一个最小单元由两个相同纳米棒组成的周期性超表面,能够允许在给定的波长下通过法诺共振效应存储和控制光的能量。

在文章中,作者展现了深度学习方法的强大之处。该方法可以用来设计满足特殊要求的超表面结构。如图1所示,可以按照需求控制法诺线性的线宽、振幅和光谱位置,同时进一步实现了两种不同的光与物质相互作用的应用:1)非线性增强效应:与在非法诺共振处的转换效率相比,该超表面在共振波长处能够实现从近红外到可见光波段超过400倍的非线性转换效率增强;2)在法诺共振波长处可以有效实现光诱导纳米颗粒的机械振动,相对于非法诺共振波长位置,该机械振动能实现超过100倍的增强。因其对电磁波能量的有效操控,法诺共振纳米结构有望在探索光子-光子以及光子-声子相互作用中发挥巨大的优势。

这种双功能的法诺共振介质超表面有望进一步在精密质量传感器、微操控、光机械学和基于光子-声子转换的感测生化材料等方面发挥广泛的应用。同时,如此独特的包含人工智能和纳米光子学的组合在实现其他用于开发超透镜的多功能超表面、全息防伪和图像处理等多方面均有巨大的潜力。

图1(a)用于反向设计的串联神经网络的示意图;(b)非线性的产生和光机械振动。

Artificial intelligence propels nanophotonics: Machine-learning assisted Fano resonant metasurface

Arrays of subwavelength nanoparticles, so-called metasurfaces, have been a subject of intense research recently, due to their extraordinary optical properties, which can find diverse applications, such as superlenses, tunable images, and holograms。 Over the past decade, one of the widely studied response of metasurfaces is Fano resonances, which generally occur as a result of close interaction of a discrete (localized) state with a continuum of propagation modes。 It has been shown that Fano resonances can increase the storage time of photons, within subwavelength resonators。 Such a characteristic can facilitate various applications including enhanced mixing light colours, optical sensing, optoacoustic vibrations and narrowband filtering。

Traditionally, direct optimization is the dominant approach for designing metasurfaces to exhibit desired properties, e。g。 an intense Fano resonance。 In this approach, a large number of parameters, such as material properties, height, length and width of each nanoparticle, and separation of nanoparticles, are kept constant, where only one or two parameters get optimized via brute-force simulations。 This approach is very time-consuming and sometimes is not even capable of offering a solution。

Recently, propelled by its success in computer vision and natural language processing, deep learning, using an artificial neural network, has emerged as a revolutionary and powerful methodology in the field of nanophotonics. Applying the deep learning algorithms to the nanophotonic inverse design can introduce remarkable design flexibility that can go far beyond the conventional methods. This technique enables a fast prediction of complex optical properties of nanostructures with intricate architectures, which is impossible to achieve based on conventional optimization methods.

A team of researchers from the University of New South Wales, The Australian National University and The Queensland University of Technology has recently published a paper in Advanced Photonics, Vol。 2, Issue 2, 2020, which demonstrates a novel application of deep learning approach to inversely design high-quality Fano resonant metasurfaces ()。

Such a metasurface consists of two identical silicon nanobars that are periodically arranged in the plane. It allows to store and control the optical energy within the metasurfaces at a given wavelength. The authors have shown that this approach can predict a metasurface design with desired characteristics, such as the linewidth, amplitude and spectral position (as illustrated in Fig. 1).

The resonance characteristics were chosen to fulfil the requirements of two distinct applications, simultaneously: i) converting the light colour from near-infrared to visible (nonlinear generation), with 400+ times more robust response, as compared with the off-resonant responses from the same metasurface; ii) enhancing the light-induced mechanical vibrations (phonons) of nanoparticles by 100+ folds, as compared with off-resonant frequencies of the same metasurface.

Such a bi-functional metasurface can find a wide range of applications in precision mass sensors, micro-manipulation, optomechanics, and sensing biochemical materials based on photon-phonon conversions. Meanwhile, such a unique combination of artificial intelligence with nanophotonics holds tremendous potential for realizing other multi-functional metasurfaces for being exploited in meta-lenses, security holograms, image manipulation and many others.

Fig。 1。 (a) Schematic of a tandem neural-network used for the inverse design of a Fano resonant metasurface for enhancing (b) nonlinear generation and optomechanical vibrations。