Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventionally metasurface device design relies on trial-anderror methods to obtain target electromagnetic (EM) responses, which demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for metaatoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.Here we propose an implicit way to construct and train the networks to predict the amplitude and phase responses of meta-structures. For a typical meta-structure, like the one shown in Fig. 1A,
An electrically tunable negative permeability metamaterial consisting of a periodic array of split ring resonators infiltrated with nematic liquid crystals is demonstrated. It shows that the transmitted resonance dip of the metamaterial can be continuously and reversibly adjusted by an applied electric field, and the maximum shift is about 210MHz with respect to the resonance frequency around 11.08GHz. Numerical simulation shows that the permeability is negative near the resonance frequency, and the frequency range with negative permeability can be dynamically adjusted and widened by about 200MHz by the electric field. It provides a convenient means to design adaptive metamaterials.
The effects of a controlled Al2O3 buffer layer on the behavior of highly efficient vacuum evaporated aqua regia-treated indium tin oxide (ITO)/triphenyl diamine (TPD)/8-tris-hydroxyquino-line aluminum Alq3/Al2O3/Al light-emitting diodes are described. It is found that, with a buffer layer of suitable thickness, both current injection and electroluminescence output are significantly enhanced. The enhancement is believed to be due to increased charge carrier density near the TPD/Alq3 interface that results from enhanced electron tunneling, and removal of exciton-quenching gap states that are intrinsic to the Alq3/Al interface.
Metasurfaces have enabled precise electromagnetic (EM) wave manipulation with strong potential to obtain unprecedented functionalities and multifunctional behavior in flat optical devices. These advantages in precision and functionality come at the cost of tremendous difficulty in finding individual meta‐atom structures based on specific requirements (commonly formulated in terms of EM responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, a generative adversarial network that can tackle this problem and generate meta‐atom/metasurface designs to meet multifunctional design goals is presented. Unlike conventional trial‐and‐error or iterative optimization design methods, this new methodology produces on‐demand free‐form structures involving only a single design iteration. More importantly, the network structure and the robust training process are independent of the complexity of design objectives, making this approach ideal for multifunctional device design. Additionally, the ability of the network to generate distinct classes of structures with similar EM responses but different physical features can provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. The network's ability to produce a variety of multifunctional metasurface designs is demonstrated by presenting a bifocal metalens, a polarization‐multiplexed beam deflector, a polarization‐multiplexed metalens, and a polarization‐independent metalens.
Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom’s wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface.
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