Bound states in the continuum (BIC) to achieve highly efficient frequency conversion using high quality-factor (high-Q) metasurfaces have been demonstrated using symmetry-broken structures with high robustness; however, the breaking-symmetry tactics are typically limited to one of the dimensions of the structures, which restricts the nonlinearity with BIC. In this work, we present a new metasurface structure in the form of an array of unit cells composed of two identical nano-bars with two mirror-symmetric corners cut into each nano-bar to break this limit. By using the high refractive index and large third-order nonlinearity of amorphous silicon (a-Si), we demonstrate ultra-high theoretical Qs up to ~ 2×10^5. Owing to the large nearfield enhancement in the meta-atoms, we observe optical Kerr effect in efficient third harmonic generation from the a-Si BIC metasurfaces via different levels of pump power, which paves the way for variational quasi-BIC for switchable nonlinear generation.
In this work, we present a new approach based on metric learning for defining new similarity measures that are well-matched for design tasks in nanophotonics. Majority of the existing approaches use mean squared error (MSE) or mean absolute error (MAE) as the similarity measure to compare the desired and optimal spectra while it is clear that point-wise distance cannot capture the important features of the responses. Here, our goal is to use deep metric learning to provide a systematic approach for defining new metrics in nanophotonics.
This talk is focused on using the intelligent aspects of machine learning (ML) for both the understanding of the subtle properties of nanophotonic devices and their inverse design to achieve a desired response. It will be shown that by reducing the dimensionality of the problem using manifold learning techniques and simplifying the resulting networks using pruning, the computation complexity of the underlying artificial intelligence (AI) algorithms will be considerably reduced. Furthermore, by optimally defining the loss function (or the metric) for AI algorithms, priceless information about the properties of photonic nanostructures can be uncovered while facilitating the better visualization of the input-output relationship in these nanostructures. In addition, the resulting manifold-learning algorithms can be optimally trained to facilitate the inverse design of such nanostructures while minimizing the structural complexity. This talk will provide the foundation for both knowledge discovery and design in photonic nanostructures using manifold learning and metric learning and their application to the highly desired metaphotonic structures as an example platform.
Subwavelength nonlinear optical sources with high efficiency have received extensive attention although
strong dynamic tunability of these sources is still elusive. Germanium antimony telluride (GST) as a well-established phase-change chalcogenide is a promising candidate for the reconfiguration of subwavelength
nanostructures. Here, we design an electromagnetically induced transparency (EIT)-based high-quality-factor (high-Q) silicon metasurface that is actively controlled with a quarter-wave asymmetric Fabry-Perot cavity incorporating GST to modulate the relative phase of incident and reflected pump waves. We demonstrate a multi-level third-harmonic generation (THG) switch with a theoretical modulation depth as high as ~ 70 dB for the fundamental C-band crossing through multiple intermediate states of GST. This study shows the high potential of GST-based dynamic nonlinear photonic switches for a wide range of applications ranging from communications to optical computing.
Traditional processes for the design of metamaterial structures are often computational heavy, time-consuming, and occasionally does not lead to the desired optical response. Deep learning can quickly optimize structures through inverse design, and create new geometries for devices. This research uses a deep learning framework for the inverse design of an optimal plasmonic structure to maximize the second-order nonlinear response from a nonlinear metamaterial. The thinfilm nonlinear metamaterial employed is a nanolaminate, and the optimal plasmonic structure is fabricated to establish the validity of the deep learning algorithm.
A new deep-learning approach based on dimensionality reduction techniques for the design and knowledge discovery in nanophotonic structures will be presented. It is shown that reducing the dimensionality of the response and design spaces in a class of nanophotonic structures can provide new insight into the physics of light-matter interaction in such nanostructures while facilitating their inverse design. These unique features are achieved while considerably reducing the computation complexity through dimensionality reduction. It is also shown that this approach can enable an evolutionary design method in which the initial design can be evolved intelligently into an alternative with favorable specification like less complexity, more robustness, less power consumption, etc. In addition to providing the details about the fundamental aspects of the latent learning approach, its application to design of reconfigurable metasurfaces will be demonstrated.
We present a new approach for design of novel loss functions and introduce an optimal similarity-metric design for machine-learning-based design and knowledge discovery in nanophotonics. Machine-learning algorithms estimate the input-output relation in a photonic nanostructure by minimizing a loss function. We show that careful selection (from the available loss functions) or design of novel loss functions that are optimized for specific tasks can considerably improve the performance of machine-learning algorithms for design and knowledge discovery in photonic nanostructures. We also discuss the limitations and inefficacies of conventional loss functions that are currently being used for machine learning algorithms.
We demonstrate a new platform for reconfigurable third-order nonlinear photonic devices formed by silicon dioxide (SiO2)-Sb2S3(Sb2Se3)-SiO2 subwavelength Fabry-Perot cavities on a gold (Au) reflector, which exhibit giant third-harmonic generation (THG) modulations with enhanced efficiency. The use of the phase-change dichalcogenides (Sb2S3 or Sb2Se3) enables a wide tuning range of the THG response. The devices work at dispersion-engineered THG resonances at the crystalline phase (c-phase) of the PCC, which numerically exhibit c-phase THG flows a few 100 times more than those at the amorphous phase (a-phase) of the PCC at near-infrared excitation wavelengths.
We present a dynamic metasurface platform by incorporating the phase-change alloy Ge2Sb2Te5 (GST) into metal-dielectric meta-atoms for active and non-volatile tuning of the optical response. We systematically design a unit cell, which selectively controls the fundamental plasmonic-photonic resonances of the metasurface via the dynamic change of the GST crystalline state. As a proof-of-concept, we experimentally demonstrate miniaturized tunable metasurfaces that globally manipulate amplitude and phase of incident light necessary for near-perfect absorption and anomalous/specular beam deflection, respectively. Our findings further substantiate reconfigurable hybrid metasurfaces as promising candidates for the development of miniaturized energy harvesting and optical signal processing devices.
We present a new approach based on manifold learning for breaking the geometrical complexity of the photonic nanostructures during solving the inverse design problem. By encoding the high-dimensional spectral responses of a class of nanostructures into the latent space, we provide intuitive information about the underlying physics of these structures. We discuss the relations between the non-Euclidean distances in the latent space and changes in the optical responses and relate the movements in the latent space to the modifications of the optical responses for a class of nanostructures. Finally, we provide a new approach to use the insight about the role of design parameters to design nanostructures with minimal design complexity for a given functionality.
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