Plasmonic nanostructure design and characterization via Deep Learning

Abstract

Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light–matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell’s equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications.

Design of task-specific optical systems using broadband diffractive neural networks

“…The photonics community has also benefited from deep learning methods in various applications such as microscopic imaging [6][7][8][9][10] and holography [11][12][13] , among many others [14][15][16][17] . Aside from optical imaging, deep learning and related optimization tools have recently been utilized for solving inverse problems in optics related to e.g., nanophotonic designs and nanoplasmonics [18][19][20][21][22] . These successful demonstrations and many others have also inspired a resurgence on the design of optical neural networks and other optical computing techniques motivated by their advantages in terms of parallelization, scalability, power efficiency and computation speed [23][24][25][26][27][28][29] .…”

Compounding Meta‐Atoms into Metamolecules with Hybrid Artificial Intelligence Techniques

“…[15][16][17][18][19][20] To identify the optimized parameters of a device, the algorithm computes the gradient, or sensitivity, through the corresponding adjoint problem and updates the parameters along the deepestgradient direction. [27][28][29][30][31] In conjunction with traditional optimization techniques, it has been proved that deep learning can substantially mitigate problems such as the convergence to local minima and the curse of dimensionality in other optimization schema. [21][22][23] The philosophy of the algorithms is to treat photonic structures as a population of individuals, and carry out bio-inspired operations such as selection, reproduction, and mutation to the population in order to identify the optimized individual through evolution.…”

“…Applying deep learning approaches to the inverse design of photonics have effectively alleviated the problems of traditional design methods such as the slow convergence of optimization and the expensive cost of failure design. [27][28][29][30][31] In conjunction with traditional optimization techniques, it has been proved that deep learning can substantially mitigate problems such as the convergence to local minima and the curse of dimensionality in other optimization schema. [26,[32][33][34] Despite the development of techniques for the optimization of photonic structures, the inverse design of metasurfaces with metamolecules, of which the degrees of freedom are astronomical, is still not resolved.…”

A Bidirectional Deep Neural Network for Accurate Silicon Color Design

“…In the field of nanophotonics, computational inverse design can reshape the landscape and techniques available to complex and emerging applications . Recent advancements in deep neural networks (DNNs) have demonstrated efficient forward‐modeling that can predict resonance spectrum accurately, and perform the inverse design of photonic device structures . The general steps usually involve a one‐time investment of sufficient EM simulation data, which are composed of variable device parameters and corresponding optical resonance at different wavelengths, followed by constructing DNNs.…”

“…[9] Recent advancements in deep neural networks (DNNs) have demonstrated efficient forward-modeling that can predict resonance spectrum accurately, and perform the inverse design of photonic device structures. [10][11][12][13][14][15][16][17][18] The general steps usually involve a one-time investment of sufficient EM simulation data, which are composed of variable device parameters and corresponding optical resonance at different wavelengths, followed by constructing DNNs. In a forward modeling network, Silicon nanostructure color has achieved unprecedented high printing resolution and larger color gamut than sRGB.…”