Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

In this paper, we demonstrate a computationally ecient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redene the conventional many-to-one design problem in EM nanostruc-tures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, 1 we applied such an ecacious method to design a new class of on-demand recongurable optical metasurfaces based on phase-change materials (PCM). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages oers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding , and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques

Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices

In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing significant error. Such an approach implicitly provides useful information about the role of different features (or design parameters such as geometry) of the nanostructure in its response functionality. To demonstrate the practical capabilities of this DL-based technique, we apply it to a reconfigurable optical metadevice enabling dual-band and triple-band optical absorption in the telecommunication window. Combination of the proposed approach with existing commercialized full-wave simulation tools offers a powerful toolkit to extract basic mechanisms of wave-matter interaction in complex EM devices and facilitate the design and optimization of nanostructures for a large range of applications including imaging, spectroscopy, and signal processing. It is worth to mention that the demonstrated approach is general and can be used in a large range of problems as long as enough training data can be provided

A New Paradigm for Knowledge Discovery and Design in Nanophotonics Based on Artificial Intelligence

The design of photonic devices in the nanoscale regime outperforming the bulky optical components has been a long-lasting challenge in state-of-the-art applications. Accordingly, devising a comprehensive model to understand and explain the physics and dynamics of light-matter interaction in these nanostructures is a substantial step toward realizing novel photonic devices. This thesis presents a new paradigm based on leveraging the intelligent aspect of artificial intelligence (AI) to design nanostructure and understand the underlying physics of light-matter interactions. Considering a large number of design parameters and the complex and non-unique nature of the input-output relations in nanophotonic structures, conventional approaches cannot be used for their design and analysis. The dimensionality reduction (DR) techniques in this research considerably reduce the computing requirements. This thesis also focuses on developing a reliable inverse design approach by overcoming the non-uniqueness challenge. This thesis presents a double-step DR technique to reduce the complexity of the inverse design problem while preserving the necessary information for finding the optimum nanostructure for the desired functionality. I established an approach based on defining physics-driven metrics to explore the low-dimensional manifold of design-response space and provide a sweet region in the reduced design space for the desired functionality. In the later part of the thesis, we have shown that we achieved the optimum nanostructure for a particular desired response by employing manifold learning while minimizing the geometrical complexity. Also, in this thesis, we have developed a manifold learning-based technique for accelerating the design of nanostructures focusing on selecting the optimum material and geometric parameters.Ph.D

Global design optimization in photonics: from high performance to fabrication robustness

Modern photonic devices are characterized by a large number of parameters and the need for an “holistic” optimization of their behavior taking into account multiple figures of merit, noteworthy tolerance to fabrication uncertainty. We present here a set of tools based on dimensionality reduction capable of handling such multi-parameter, multi-objectives design problems

Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on demand. Metasurfaces are versatile, novel platforms for manipulating the scattering, color, phase, or intensity of light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters, such as various materials’ properties and coupling effects, as well as the geometrical parameters. Ideally, this would require multidimensional space optimization through direct numerical simulations. Recently, an alternative, popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted method. We utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude, and spectral position. We exploit such high-Q resonances for enhanced light–matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation; at the same time, they also contribute to 100-fold enhancement of the amplitude of optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses

Algorithmic design of photonic structures with deep learning

The advent and development of photonics in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of photonic structures and devices, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell’s equations until a locally optimized solution can be attained. Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. The objective of this PhD thesis is to explore deep learning models and optimization approaches for the design of future photonic devices, with various applications such as imaging, hologram, sensing, and display. In specific, the theme of thesis is to utilize various deep generative models to find simple representations for highly complex photonic structures, such that optional optimization algorithms can be efficiently applied to identify the photonic structures with optimal performance. The developed design framework has potential applications in the optimization of future highly compact optical systems such as photonic computing, LIDAR, telecommunications, and virtual/augmented reality display.Ph.D