Magnetic Resonances in Nonlinear Dielectric Nanostructures: New Light-Matter Interactions and Machine Learning Enhanced Design

非线性介电纳米结构中的磁共振：新的光-物质相互作用和机器学习增强设计

• 批准号: 2240562
• 负责人: Natalia Litchinitser
• 金额: $ 45万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240562&HistoricalAwards=false
• 关键词: Magnetic; Resonances; Nonlinear; Dielectric; Nanostructures

Interactions between light and matter play an important role in many fields of science, giving rise to important applications in sensing, spectroscopy, solar cells, computing, quantum information processing, communications, light-emitting diodes, and lasers. While light is an electromagnetic wave consisting of both electric and magnetic field components, most natural optical materials mainly interact with the electric component of light and leave the magnetic component of light largely unexploited. However, optical metamaterials—engineered nanostructures— fundamentally change the light-matter interaction by making light "ambidextrous" in the optical range, with its magnetic and electric components playing equally important roles In particular, judiciously designed dielectric particles provide strong light-induced magnetic properties as a reaction to the magnetic component of external electromagnetic waves. The proposed project aims at discovering new light-matter interactions originating from the effect of magnetic field enhancement in low-loss nonmagnetic dielectrics as well as hybrid metamaterials made of strongly nonlinear glasses and magnetic materials designed using physics-based machine learning approaches. The proposed research will contribute to the fundamental science of nonlinear light-matter interactions and will likely enable new approaches for light generation and modulation, magnetometry, and sensing.While nonlinear optical interactions, enabled by the electric field enhancement, have been studied by many research groups, magnetic field enhancement-induced light-matter interactions have not been explored in detail. The proposed project aims at discovering new light-matter interactions originating from the effect of magnetic field enhancement, contributing through a magnetic portion of the Lorentz force that contains intrinsic surface and bulk components in engineered optical materials designed using physics-based machine learning based optimization of the resonant and magneto-optical nonlinear interactions in subwavelength single-layer or cascaded metasurfaces consisting of hybrid meta-atoms made of strongly nonlinear chalcogenide glasses and magnetic materials. The proposed research will focus on the following thrusts: Thrust 1: Investigate theoretically and numerically the contribution of the intrinsic nonlinear optical processes in nonlinear metasurfaces due to the magnetic portion of the Lorentz force; Thrust 2: Using the synergy between the physical model and machine learning approach, enhance nonlinear light-matter interactions due to the strongly localized magnetic fields via Mie, quasi-bound states in the continuum, and guided-mode mechanisms; enhance extrinsic, magneto-optical interactions enabled by hybrid magneto-photonic materials based metasurfaces. Thrust 3: Investigate theoretically and experimentally the possibility of enhancement of the nonlinear frequency conversion in stacked metasurfaces and broadband tapered multilayer structures. Exceptionally high magnetic field localization inside the meta-atoms will be achieved using a unique, physics-based machine learning approach developed to solve the inverse design problem of resonant meta-atoms under the guidance of multipole expansion theory to optimize the shape of the meta-atoms in order to maximize a particular multipolar resonance and the overlap of the modes at both fundamental frequency and a harmonic wavelength.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

光与物质之间的相互作用在许多科学领域中起着重要作用，在传感、光谱学、太阳能电池、计算、量子信息处理、通信、发光二极管和激光器中产生了重要应用。虽然光是由电场和磁场分量组成的电磁波，但大多数天然光学材料主要与光的电分量相互作用，而使光的磁分量在很大程度上未被利用。然而，光学超材料-工程纳米结构-通过使光在光学范围内成为“环境光”，从根本上改变了光与物质的相互作用，其磁和电成分扮演着同样重要的角色。特别是，明智设计的介电颗粒提供了强烈的光致磁特性，作为对外部电磁波的磁成分的反应。拟议的项目旨在发现新的光-物质相互作用，这些相互作用源于低损耗超导体中的磁场增强效应，以及由强非线性玻璃和磁性材料制成的混合超材料，这些材料是使用基于物理的机器学习方法设计的。这项研究将为非线性光-物质相互作用的基础科学做出贡献，并可能为光的产生和调制、磁场测量和传感提供新的方法。虽然许多研究小组已经研究了电场增强引起的非线性光学相互作用，但磁场增强引起的光-物质相互作用尚未详细探索。拟议的项目旨在发现新的轻物质相互作用源于磁场增强的影响，通过洛伦兹力的磁性部分做出贡献，所述洛伦兹力包含工程光学材料中的固有表面和体成分，所述工程光学材料是使用基于物理的机器学习对亚波长单层或级联超颖表面中的共振和磁光非线性相互作用进行基于优化而设计的，所述超颖表面由混合Meta颖材料组成。由强非线性硫属玻璃和磁性材料制成的原子。 拟议的研究将集中在以下推力：推力1：从理论上和数值上研究由于洛伦兹力的磁性部分导致的非线性超颖表面中的固有非线性光学过程的贡献;推力2：利用物理模型和机器学习方法之间的协同作用，增强由于Mie强局部磁场引起的非线性光-物质相互作用，准束缚态的连续，和导模机制;增强非本征，磁光相互作用，使混合磁光子材料的超颖表面。推力3：从理论和实验上研究了在叠层超颖表面和宽带渐变多层结构中增强非线性频率转换的可能性。超原子内部异常高的磁场定位将使用独特的，基于物理学的机器学习方法，在多极展开理论的指导下，解决共振元原子的逆设计问题，以优化Meta原子的形状，原子，以最大限度地提高一个特定的多极共振和重叠的模式在基频和谐波波长。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。