Multi-Functional Optical Meta-Systems Enabled by Deep-Learning-Aided Inverse Design

由深度学习辅助逆向设计实现的多功能光学元系统

• 批准号: 1916839
• 负责人: Yongmin Liu
• 金额: $ 52.95万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916839&HistoricalAwards=false
• 关键词: Multi; Functional; Optical; Meta; Systems

Nontechnical Description: Artificial intelligence especially deep learning has enabled many breakthroughs in both academia and industry. This project aims to create a generative and versatile design approach based on novel deep learning techniques to realize integrated, multi-functional photonic systems, and provide proof-of-principle demonstrations in experiments. Compared with traditional approaches using extensive numerical simulations or inverse design algorithms, deep learning can uncover the highly complicated relationship between a photonic structure and its properties from the dataset, and hence substantially accelerate the design of novel photonic devices that simultaneously encode distinct functionalities in response to the designated wavelength, polarization, angle of incidence and other parameters. Such multi-functional photonic systems have important applications in many areas, including optical imaging, holographic display, biomedical sensing, and consumer photonics with high efficiency and fidelity, to benefit the public and the nation. The integrated education plan will considerably enhance outreach activities and educate students in grades 7-12, empowered by the successful experience and partnership previously established by the PIs. Graduate and undergraduate students participating in the project will learn the latest developments in the multidisciplinary fields of photonics, deep learning and advanced manufacturing, and gain real-world knowledge by engaging industrial collaborators in tandem with Northeastern University's renowned cooperative education program.Technical Description: Metasurfaces, which are two-dimensional metamaterials consisting of a planar array of subwavelength designer structures, have created a new paradigm to tailor optical properties in a prescribed manner, promising superior integrability, flexibility, performance and reliability to advance photonics technologies. However, so far almost all metasurface designs rely on time-consuming numerical simulations or stochastic searching approaches that are limited in a small parameter space. To fully exploit the versatility of metasurfaces, it is highly desired to establish a general, functionality-driven methodology to efficiently design metasurfaces that encompass distinctly different optical properties and performances within a single system. The objective of the project is to create and demonstrate a high-efficiency, two-level design approach enabled by deep learning, in order to realize integrated, multi-functional meta-systems. Proper deep learning methods, such as Conditional Variational Auto-Encoder and Deep Bidirectional-Convolutional Network, will be investigated, innovatively reformulated and tailored to apply at the single-element level and the large-scale system level in combination with topology optimization and genetic algorithm. Such a generative design approach can directly and automatically identify the optimal structures and configurations out of the full parameter space. The designed multi-functional optical meta-systems will be fabricated and characterized to experimentally confirm their performances. The success of the project will produce transformative photonic architectures to manipulate light on demand.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.

非技术性描述：人工智能，尤其是深度学习，在学术界和工业界都实现了许多突破。该项目旨在创建一种基于新型深度学习技术的生成和通用设计方法，以实现集成的多功能光子系统，并在实验中提供原理验证演示。与使用大量数值模拟或逆向设计算法的传统方法相比，深度学习可以从数据集中揭示光子结构与其属性之间的高度复杂关系，从而大大加快新型光子器件的设计，这些器件可以同时编码不同的功能，以响应指定的波长，偏振，入射角和其他参数。这种多功能光子系统在光学成像、全息显示、生物医学传感和消费光子学等许多领域具有重要的应用，具有高效率和保真度，以造福公众和国家。综合教育计划将大大加强外联活动，并教育7-12年级的学生，通过成功的经验和先前由PI建立的伙伴关系增强能力。参与该项目的研究生和本科生将学习光子学、深度学习和先进制造等多学科领域的最新发展，并通过与东北大学著名的合作教育项目合作，与工业合作者一起获得现实世界的知识。技术描述：超表面是由亚波长设计结构的平面阵列组成的二维超材料，已经创造了一种新的范例，以规定的方式定制光学特性，有望实现上级可集成性、灵活性、性能和可靠性，以推进光子技术。然而，到目前为止，几乎所有的元表面设计都依赖于耗时的数值模拟或随机搜索方法，这些方法局限于小的参数空间。为了充分利用超颖表面的多功能性，高度期望建立通用的功能驱动的方法来有效地设计在单个系统内包含明显不同的光学性质和性能的超颖表面。该项目的目标是创建和展示一种通过深度学习实现的高效两级设计方法，以实现集成的多功能元系统。适当的深度学习方法，如条件变分自动编码器和深度双向卷积网络，将被研究，创新地重新制定和定制，以应用于单元件级和大规模系统级，并结合拓扑优化和遗传算法。这种生成式设计方法可以直接和自动地从整个参数空间中识别出最优的结构和构型。所设计的多功能光学元系统将被制造和表征，以实验确认其性能。该项目的成功将产生变革性的光子架构，以按需操纵光。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Harnessing Evanescent Waves by Bianisotropic Metasurfaces

• DOI: 10.1002/lpor.201900244
• 发表时间: 2020-09-13
• 期刊: LASER & PHOTONICS REVIEWS
• 影响因子: 11
• 作者: Li, Lin;Yao, Kan;Liu, Yongmin
• 通讯作者: Liu, Yongmin

Breaking the limitation of polarization multiplexing in optical metasurfaces with engineered noise

• DOI: 10.1126/science.ade5140
• 发表时间: 2023-01-20
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Xiong, Bo;Liu, Yu;Wang, Mu
• 通讯作者: Wang, Mu

Software-defined nanophotonic devices and systems empowered by machine learning

• DOI: 10.1016/j.pquantelec.2023.100469
• 发表时间: 2023-04
• 期刊: Progress in Quantum Electronics
• 影响因子: 11.7
• 作者: Yihao Xu;Bo Xiong;Wei Ma;Yongmin Liu

Structured Light Generation Using Angle‐Multiplexed Metasurfaces

• DOI: 10.1002/adom.202300299
• 发表时间: 2023-06
• 期刊: Advanced Optical Materials
• 影响因子: 9
• 作者: Lin Deng;Renchao Jin;Yihao Xu;Yongmin Liu

Decoupled Phase Modulation for Circularly Polarized Light via Chiral Metasurfaces

• DOI: 10.1021/acsphotonics.2c01397
• 发表时间: 2022-12
• 期刊: ACS Photonics
• 影响因子: 7
• 作者: Ren-chao Jin;Lin Deng;LiLi Tang;Yue Cao;Yongmin Liu;Z. Dong