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DEEP LEARNING IN PHOTONICS
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摘要
Intelligent coding metasurface is a kind of information-carrying metasurface that can manipulate electromagnetic waves and associate digital information simultaneously in a smart way. One of its widely explored applications is to develop advanced schemes of dynamic holographic imaging. By now, the controlling coding sequences of the metasurface are usually designed by performing iterative approaches, including the Gerchberg–Saxton (GS) algorithm and stochastic optimization algorithm, which set a large barrier on the deployment of the intelligent coding metasurface in many practical scenarios with strong demands on high efficiency and capability. Here, we propose an efficient non-iterative algorithm for designing intelligent coding metasurface holograms in the context of unsupervised conditional generative adversarial networks (cGANs), which is referred to as physics-driven variational auto-encoder (VAE) cGAN (VAE-cGAN). Sharply different from the conventional cGAN with a harsh requirement on a large amount of manual-marked training data, the proposed VAE-cGAN behaves in a physics-driving way and thus can fundamentally remove the difficulties in the conventional cGAN. Specifically, the physical operation mechanism between the electric-field distribution and metasurface is introduced to model the VAE decoding module of the developed VAE-cGAN. Selected simulation and experimental results have been provided to demonstrate the state-of-the-art reliability and high efficiency of our VAE-cGAN. It could be faithfully expected that smart holograms could be developed by deploying our VAE-cGAN on neural network chips, finding more valuable applications in communication, microscopy, and so on.
Photonics Research
2021年第9卷第4期 pp.0400B159
DEEP LEARNING IN PHOTONICS
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摘要
We report an approach assisted by deep learning to design spectrally sensitive multiband absorbers that work in the visible range. We propose a five-layered metal-insulator-metal grating structure composed of aluminum and silicon dioxide, and we design its structural parameters by using an artificial neural network (ANN). For a spectrally sensitive design, spectral information of resonant wavelengths is additionally provided as input as well as the reflection spectrum. The ANN facilitates highly robust design of a grating structure that has an average mean squared error (MSE) of 0.023. The optical properties of the designed structures are validated using electromagnetic simulations and experiments. Analysis of design results for gradually changing target wavelengths of input shows that the trained ANN can learn physical knowledge from data. We also propose a method to reduce the size of the ANN by exploiting observations of the trained ANN for practical applications. Our design method can also be applied to design various nanophotonic structures that are particularly sensitive to resonant wavelengths, such as spectroscopic detection and multi-color applications.
Photonics Research
2021年第9卷第4期 pp.0400B153
DEEP LEARNING IN PHOTONICS
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摘要
Over the past decades, photonics has transformed many areas in both fundamental research and practical applications. In particular, we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures. However, constructing complex photonic structures and devices is still a time-consuming process, even for experienced researchers. As a subset of artificial intelligence, artificial neural networks serve as one potential solution to bypass the complicated design process, enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy. In this review, we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices, particularly those in recent experimental works. We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.
Photonics Research
2021年第9卷第4期 pp.0400B135
DEEP LEARNING IN PHOTONICS
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摘要
As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to the intrinsic parallelism of free-space optics. At the same time, a physical nonlinearity—a crucial ingredient of an ANN—is not easy to realize in free-space optics, which restricts the potential of this platform. This problem is further exacerbated by the need to also perform the nonlinear activation in parallel for each data point to preserve the benefit of linear free-space optics. Here, we present a free-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms. We demonstrate, via both simulation and experiment, image classification of handwritten digits using only a single layer and observed 6% improvement in classification accuracy due to the optical nonlinearity compared to a linear model. Our platform preserves the massive parallelism of free-space optics even with physical nonlinearity, and thus opens the way for novel designs and wider deployment of optical ANNs.
Photonics Research
2021年第9卷第4期 pp.0400B128
DEEP LEARNING IN PHOTONICS
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摘要
We propose a modified supervised learning algorithm for optical spiking neural networks, which introduces synaptic time-delay plasticity on the basis of traditional weight training. Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity. A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method. Moreover, the proposed algorithm is also applied to two benchmark data sets for classification. In a simple network structure with only a few optical neurons, the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning. The introduction of delay adjusting improves the learning efficiency and performance of the algorithm, which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.
Photonics Research
2021年第9卷第4期 pp.0400B119
DEEP LEARNING IN PHOTONICS
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摘要
A new approach to optical fiber sensing is proposed and demonstrated that allows for specific measurement even in the presence of strong noise from undesired environmental perturbations. A deep neural network model is trained to statistically learn the relation of the complex optical interference output from a multimode optical fiber (MMF) with respect to a measurand of interest while discriminating the noise. This technique negates the need to carefully shield against, or compensate for, undesired perturbations, as is often the case for traditional optical fiber sensors. This is achieved entirely in software without any fiber postprocessing fabrication steps or specific packaging required, such as fiber Bragg gratings or specialized coatings. The technique is highly generalizable, whereby the model can be trained to identify any measurand of interest within any noisy environment provided the measurand affects the optical path length of the MMF’s guided modes. We demonstrate the approach using a sapphire crystal optical fiber for temperature sensing under strong noise induced by mechanical vibrations, showing the power of the technique not only to extract sensing information buried in strong noise but to also enable sensing using traditionally challenging exotic materials.
Photonics Research
2021年第9卷第4期 pp.0400B109
DEEP LEARNING IN PHOTONICS
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摘要
The connection between Maxwell’s equations and artificial neural networks has revolutionized the capability and efficiency of nanophotonic design. Such a machine learning tool can help designers avoid iterative, time-consuming electromagnetic simulations and even allows long-desired inverse design. However, when we move from conventional design methods to machine-learning-based tools, there is a steep learning curve that is not as user-friendly as commercial simulation software. Here, we introduce a real-time, web-based design tool that uses a trained deep neural network (DNN) for accurate far-field radiation prediction, which shows great potential and convenience for antenna and metasurface designs. We believe our approach provides a user-friendly, readily accessible deep learning design tool, with significantly reduced difficulty and greatly enhanced efficiency. The web-based tool paves the way to present complicated machine learning results in an intuitive way. It also can be extended to other nanophotonic designs based on DNNs and replace conventional full-wave simulations with a much simpler interface.
Photonics Research
2021年第9卷第4期 pp.0400B104
摘要
We discover single and homocentric optical spheres of the three-dimensional inhomogeneous nonlinear Schr?dinger equation (NLSE) with spherical symmetry, which is a novel model of light bullets that can present a three-dimensional rogue wave. The isosurface of this light bullet oscillates along the radius direction and does not travel with the evolution of time. The localized nature of rogue wave light bullets both in space and in time, which is in complete contrast to the traveling character of the usual light bullets, is due to the localization of the rogue wave in the one-dimensional NLSE. We present also an investigation of the stability of the optical sphere solutions. The lower modes of perturbation are found to display transverse instabilities that break the spherical symmetry of the system. For the higher modes, the optical sphere solutions can be classified as stable solutions.
Photonics Research
2021年第9卷第4期 pp.04000643
Hongwei Li  Bo Zhao  Jipeng Ni  Wei Gao  
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摘要
Brillouin spectroscopy is an important topic and powerful tool in modern optics, as the acquisitions of acoustic velocities and elastic moduli are one of the keys to investigate and analyze the contents of material science and condensed matter physics. Although stimulated Brillouin spectroscopy based on the pump-probe technique has striking advantages that include higher spectral resolution and signal-to-noise ratio, it is challenging to accomplish high-speed acquisition in the presence of pump background noise. In this paper, we propose a method for signal–noise separation through spiral phase precoding of the Brillouin spectrum signal. We achieve on-demand tailoring spatial distribution of the signal, and hence the signal can be separated from the background noise. Furthermore, this approach has little energy loss due to phase-only modulation, and retains the advantages of high efficiency and high gain in Brillouin interaction. The proof-of-principle demonstration provides a practical way to reshape the spatial structure of Brillouin spectra, and shows the potential in quasi-noise-free nonlinear interactions.
Photonics Research
2021年第9卷第4期 pp.04000637
Lasers and Laser Optics
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摘要
Broadband mid-infrared (IR) supercontinuum laser sources are essential for spectroscopy in the molecular fingerprint region. Here, we report generation of octave-spanning and coherent mid-IR supercontinua in As2S3-silica nanospike hybrid waveguides pumped by a custom-built 2.8 μm femtosecond fiber laser. The waveguides are formed by pressure-assisted melt-filling of molten As2S3 into silica capillaries, allowing the dispersion and nonlinearity to be precisely tailored. Continuous coherent spectra spanning from 1.1 μm to 4.8 μm (30 dB level) are observed when the waveguide is designed so that 2.8 μm lies in the anomalous dispersion regime. Moreover, linearly tapered millimeter-scale As2S3-silica waveguides are fabricated and investigated for the first time, to the best of our knowledge, showing much broader supercontinua than uniform waveguides, with improved spectral coherence. The waveguides are demonstrated to be long-term stable and water-resistant due to the shielding of the As2S3 by the fused silica sheath. They offer an alternative route to generating broadband mid-IR supercontinua, with applications in frequency metrology and molecular spectroscopy, especially in humid and aqueous environments.
Photonics Research
2021年第9卷第4期 pp.04000630
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