In this paper, we theoretically investigate the magnomechanically induced transparency (MIT) phenomenon and slow -fast light propagation in a microwave cavity-magnomechanical system which includes a levitated ferromag...
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In this paper, we theoretically investigate the magnomechanically induced transparency (MIT) phenomenon and slow -fast light propagation in a microwave cavity-magnomechanical system which includes a levitated ferromagnetic sphere. Magnetic dipole interaction determines the interaction between the photon, magnon, and center of mass motion of the cavity-magnomechanical system. As a result, we find that apart from coupling strength, which has an important role in MIT, the levitated ferromagnetic sphere's position provides us a parameter to manipulate the width of the transparency window. In addition, the control field's frequency has crucial influences on the MIT. Also this hybrid magnonic system allows us to demonstrate MIT in both the strong coupling and intermediate coupling regimes. More interestingly, we demonstrate tunable slow and fast light in this hybrid magnonic system. In other words, we show that the group delay can be adjusted by varying the control field's frequency, the sphere position, and the magnon-photon coupling strength. These parameters have an influence on the transformation from slow to fast light propagation and vice versa. Based on the recent experimental advancements, our results provide the possibility to engineer hybrid magnonic systems with levitated particles for the light propagation, and the quantum measurements and sensing of physical quantities.
Elliptical Gaussian beams generated by laser diodes (LDs) often exhibit asymmetrical divergence angle distribution, which limits their practical applications. In this study, we propose what we believe is a novel appro...
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Elliptical Gaussian beams generated by laser diodes (LDs) often exhibit asymmetrical divergence angle distribution, which limits their practical applications. In this study, we propose what we believe is a novel approach to shape and collimate the elliptical output beam from a LD. The design process involves the construction of two freeform reflective surfaces on a reference circle using a three-dimensional point-by-point iterative method, based on the law of conservation of energy, the vector reflection theory, and Fermat's principle. The output beam's maximum divergence angle is effectively compressed to 3.1579 mrad. The design is compact with a folded optical path and antenna size of 368.8 cm3. This paper presents a comprehensive design and optimization process, along with an in-depth analysis of the system's performance, thereby offering novel insights for emerging optical design practitioners. (c) Optica Publishing Group
Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementa...
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Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license
Active plasmonic modulators with high modulation depth, low energy consumption, ultra-fast speed, and small footprint are of interest and particular significance for nanophotonics and integrated optics. Here by constr...
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Active plasmonic modulators with high modulation depth, low energy consumption, ultra-fast speed, and small footprint are of interest and particular significance for nanophotonics and integrated optics. Here by constructing a transverse-electric (TE) plasmonic mode and maximizing the in-plane component localized on the graphene surface, we propose a high performing plasmonic modulator based on a graphene/split ring-like plasmonic waveguide (SRPW) system with a record high modulation depth (20.46 dB/mu m) and suppressed insertion loss (0.248 dB/mu m) at telecom wavelength 1310 nm, simultaneously possessing pronounced advantage in broadband ability (800-1650 nm) and superior electrical performance with energy consumption of 0.43 fJ/bit and modulation speed of 200 GHz. This innovative design provides a novel approach and idea for enhancing the interaction between light and matter in the waveguide system and will certainly inspire new schemes for the development of on-chip integrated optoelectronic devices.
Deep convolutional neural networks are known for high precision of object recognition;however, processing of high-resolution images with the use of high-resolution kernels requires a lot of calculations during trainin...
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Deep convolutional neural networks are known for high precision of object recognition;however, processing of high-resolution images with the use of high-resolution kernels requires a lot of calculations during training and inference. optical Fourier-processors and correlators provide highly parallel calculations that are robust to electromagnetic interference and potentially energy efficient. Article results demonstrate that the correlation pattern recognition problem can be efficiently solved by implementation of deep neural network for processing of downsampled output signals of coherent diffractive correlators. The results of neural network-based correlation processor architecture study, numerical training, and experimental implementation are presented and discussed in the article. It is shown that output signals of optical correlators being captured by a low-resolution sensor can be efficiently classified by a deep neural network that was trained on a numerically generated laboratory database of correlation responses. The use of auto-correlation peak-narrowing techniques such as phase modulation and contouring of input images or application of optimized distortion-invariant filters allow us to unify the form of auto-correlation peaks such that there is no need for retraining of the network if the target object is changed. Application of three trained network models with input layer sizes of 32 x 32, 16 x 16, and 8 x 8 for processing the downsampled correlation responses of different experimental implementations of 4-f and 1-f coherent diffractive correlators optoelectronic schemes, which include the schemes based on binary spatial light modulation, proved the possibility to perform recognition of objects on 256 x 256 images with precision above 92% and potential processing speed of more than 1000 frames per second. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologi
The recent emerging appearance of optical analogs of magnetic quasiparticles, i.e., optical skyrmions constructed via spin, field, and Stokes vectors, has garnered substantial interest from deep-subwavelength imaging ...
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The recent emerging appearance of optical analogs of magnetic quasiparticles, i.e., optical skyrmions constructed via spin, field, and Stokes vectors, has garnered substantial interest from deep-subwavelength imaging and quantum entanglement. Here, we investigate systematically the topological state transitions of skyrmionic beams constructed by the Stokes vectors in the focusing configuration. We theoretically demonstrated that in the weak focusing, the skyrmion topological number is protected. Whereas, in the tight focusing, a unique topological transformation with skyrmion number variation is exhibited for the optical skyrmion, antiskyrmion, and 2nd-order skyrmion structures. The significant difference between the topological state transitions of these two cases originates from the transformation from the paraxial optical system to the nonparaxial optical system, and the approximate two-dimensional polarization structure to the three-dimensional polarization structure. The results provide new insights into the topological state transitions in topological structures, which promote applications in information processing, data storage, and free-space optical communications.
Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1,000-10,000. Th...
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Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1,000-10,000. The hyperdimensional distributed representation enables energy-efficient, low-latency, and noise-robust computations with low-precision and basic arithmetic operations. In this study, we propose optical hyperdimensional distributed representations based on laser speckles for adaptive, efficient, and low-latency optical sensor processing. In the proposed approach, sensory information is optically mapped into a hyperdimensional space with >250,000 dimensions, enabling HDC-based cognitive processing. We use this approach for the processing of a soft-touch interface and a tactile sensor and demonstrate to achieve high accuracy of touch or tactile recognition while significantly reducing training data amount and computational burdens, compared with previous machine-learning-based sensing approaches. Furthermore, we show that this approach enables adaptive recalibration to keep high accuracy even under different conditions. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Mode-division multiplexing (MDM) in chip-scale photonics is paramount to sustain data capacity growth and reduce power consumption. However, its scalability hinges on developing efficient and dynamic modal switches. E...
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Mode-division multiplexing (MDM) in chip-scale photonics is paramount to sustain data capacity growth and reduce power consumption. However, its scalability hinges on developing efficient and dynamic modal switches. Existing active modal switches suffer from substantial static power consumption, large footprints, and narrow bandwidth. Here, we present, for the first time, to the best of our knowledge, a novel multiport, broadband, non-volatile, and programmable modal switch designed for on-chip MDM systems. Our design leverages the unique properties of integrating nanoscale phase-change materials (PCM) within a silicon photonic architecture. This enables independent manipulation of spatial modes, allowing for dynamic, non-volatile, and selective routing to six distinct output ports. Crucially, our switch outperforms current dynamic modal switches by offering non-volatile, energy-efficient multiport functionality and excels in performance metrics. Our switch exhibits exceptional broadband operating bandwidth exceeding 70 nm, with low loss (< 1 dB), and a high extinction ratio (> 10 dB). Our framework provides a step forward in chip-scale MDM, paving the way for future green and scalable data centers and high-performance computers. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Two-photon autofluorescence (TPAF) imaging is able to offer precise cellular metabolic information with high spatiotemporal resolution, making it a promising biopsy tool. The technique is greatly hampered by the compl...
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Two-photon autofluorescence (TPAF) imaging is able to offer precise cellular metabolic information with high spatiotemporal resolution, making it a promising biopsy tool. The technique is greatly hampered by the complexity of either the optical system or dataprocessing. Here, the excitation wavelength was optimized to simultaneously excite both flavin adenine dinucleotide and nicotinamide adenine dinucleotide and eliminate the unexpected TPAF. The optical redox ratio (ORR) images were robustly achieved without additional calibration under the optimized single-wavelength excitation. The in vitro, , ex vivo, , and in vivo biopsy by the TPAF method were systematically studied and compared using hepato-cellular carcinoma and metastasis as examples. It was demonstrated that the proposed TPAF method simplified the optical system, improved the robustness of ORR, and enabled early-stage cancer diagnosis, showing distinguished advantages as compared with previous methods. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
Glass is an ideal material for optical applications, even though only a few micromachining technologies for material ablation are available. These microstructuring methods are limited regarding precision and freedom o...
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Glass is an ideal material for optical applications, even though only a few micromachining technologies for material ablation are available. These microstructuring methods are limited regarding precision and freedom of design. A micromachining process for glass is laser induced deep etching (LIDE). Without generating micro-cracks, introducing stress, or other damages, it can precisely machine many types of glass. This work uses LIDE to subtractive manufacture structures in glass carrier substrates. Due to its transmission characteristics and refractive index, the glass substrate serves as optical cladding for polymer waveguides. In this paper, the described fabrication process can be divided into two sub-steps. The doctor blade technique and subsequent additive process step is used in manufacturing cavities with U-shaped cross-sections in glass in order to fill the trenches with liquid optical polymers, which are globally UV-cured. Based on the higher refractive index of the polymer, it enables optical waveguiding in the visible to near-infrared wavelength range. This novel, to the best of our knoowledge, manufacturing method is called LDB (LIDE -doctor -blade);it can be the missing link between long-distance transmissions and on -chip solutions on the packaging level. For validation, optical waveguides are examined regarding their geometrical dimensions, surface roughness, and waveguiding ability, such as intensity distribution and length-dependent attenuation.
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