Search Results (77)

This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making. Full article

This paper presents a Ka-band cylindrical conformal transceiver active phased array (CCTAPA) with a full-azimuth scanning gain fluctuation of 0.8 dB and low power consumption. The array comprises 20 panels of 4 × 4 antenna elements, RF beam-control circuits, a Wilkinson power divider network, and frequency converters. The proposed three-subarray architecture enables ±9° beam scanning with minimal gain degradation. By dynamically switching subarrays and transceiver channels across azimuthal directions, the array achieves full 360° coverage with low gain fluctuation and power consumption. Fabrication and testing demonstrate a gain fluctuation of 0.8 dB, equivalent isotropically radiated power (EIRP) between 50.6 and 51.3 dBm, and a gain-to-noise-temperature ratio (G/T) ranging from −8 dB/K to −8.5 dB/K at 28.5 GHz. The RF power consumption remains below 8.73 W during full-azimuth scanning. This design is particularly suitable for airborne platforms requiring full-azimuth coverage with stringent power budgets. Full article

Encryption and decryption are essential components in signal processing and optical communication systems, providing data confidentiality, integrity, and secure high-speed transmission. We present a novel design and simulation of an all-optical encryption and decryption system operating at 120 Gb/s using carrier reservoir semiconductor optical amplifiers (CR-SOAs) embedded in Mach–Zehnder interferometers (MZIs). The architecture relies on two consecutive exclusive-OR (XOR) logic gates, implemented through phase-sensitive interference in the CR-SOA-MZI structure. The first XOR gate performs encryption by combining the input data signal with a secure optical key, while the second gate decrypts the encoded signal using the same key. The fast gain recovery and efficient carrier dynamics of CR-SOAs enable a high-speed, low-latency operation suitable for modern photonic networks. The system is modeled and simulated using Mathematica Wolfram, and the output quality factors of the encrypted and decrypted signals are found to be 28.57 and 14.48, respectively, confirming excellent signal integrity and logic performance. The influence of key operating parameters, including the impact of amplified spontaneous emission noise, on system behavior is also examined. This work highlights the potential of CR-SOA-MZI-based designs for scalable, ultrafast, and energy-efficient all-optical security applications. Full article

This study presents a reverse-optimization framework for supercontinuum (SC) generation in Ge₂₀Sb₁₅Se₆₅ suspended-core fibers (SCFs), integrating neural network modeling with the Nutcracker Optimization Algorithm to co-design optimal fiber structures and pump pulse parameters. A high-nonlinearity SCF structure (γ ≈ 6–7 W⁻¹·m⁻¹) was first designed, and a neural network model was developed to accurately predict effective modal refractive indices and mode-field areas (RMSE < 1%). The generalized nonlinear Schrödinger equation was then used to study spectral broadening influenced by structural and pulse parameters. Global optimization was performed in four-dimensional structural and seven-dimensional combined parameter spaces, significantly enhancing computational efficiency. Simulation results demonstrated that the optimized design achieved a broad and flat SC spectrum extending from 0.7 µm to 25 µm (at –20 dB intensity), with lower peak power requirements compared to previous studies achieving similar coverage. The robustness and manufacturing tolerances of the optimized fiber structure were also analyzed, verifying the reliability of the design. This intelligent reverse-design strategy provides practical guidance and theoretical foundations for mid-infrared SC fiber design. Full article

The optical spectrum resource in the C-band has been used up due to dense wavelength division multiplexing (DWDM). Because of devices’ compatibility with both the C-band and the L-band, the L-band is a good choice for further capacity expansion. Meanwhile, the mode division multiplexing (MDM) method has been applied to increase the number of channels. However, the few-mode erbium-doped fiber amplifier must be redesigned to overcome the power differences among channels. In this work, a few-mode erbium-doped fiber (FM-EDF) is optimized and manufactured. Then, an in-line gain-equalized L-band FM-EDFA is constructed. The experimental results show that the FM-EDFA works well in the wavelength range between 1575 nm and 1610 nm. The minimum differential modal gain (DMG) is 0.54 dB, and the maximum modal gain is 22.22 dB. Due to the excellent performance of the L-band FM-EDFA, a DSP-free transmission scheme in the L-band is demonstrated. The bit error rates (BERs) of each channel are below 1 × 10⁻⁵ with a DSP-free receiver. Full article

In this paper, photonic reservoir computing chip architectures for noise equalization in optical fiber communication channels are proposed. These architectures leverage optical computing instead of electrical computing to reduce computational pressure at the receiver and decrease processing latency. We examine the impact of factors such as the number of reservoir nodes, waveguide delay line length, and the number of input/output ports on equalization performance. We discuss the equalization ability of these architectures under various types of noise. After parameter optimization, the 36-node reservoir layout achieves a three-orders-of-magnitude reduction in bit error rate for 20 km OOK signals after equalization. Additionally, the chip architecture facilitates easy expansion of the all-optical readout layer, offering the possibility for further increasing the equalization speed. Full article

As the basic physical infrastructure of various networks, optical networks are crucial to the advancement of information technology. Meanwhile, as new technologies emerge, the security of optical networks is facing serious threats. To improve the security of optical networks, optoelectronic firewalls primarily leverage all-optical pattern recognition to perform direct detection and analysis of data transmitted through the optical network at the optical layer. However, the current all-optical pattern recognition system still faces some problems when deployed in optical networks, including phase-lockingand relatively low recognition efficiency and scalability. In this paper, we propose a tunable all-optical pattern recognition system based on a nonlinear optical loop mirror (NOLM) for bit-flip BPSK targets. The operational principles and simulation setup of the proposed system are comprehensively described. Numerical simulations demonstrate that the system can accurately recognize and determine the position of 4-bit and 8-bit bit-flip BPSK targets in 16-bit input data with tunable frequencies of 192.8 THz and 193.4 THz at a data rate of 100 Gbps. Finally, the impact of input noise is evaluated by extinction ratio (ER), contrast ratio (CR), Q factor, bit error rate (BER), amplitude modulation (AM), and signal-to-noise ratio (SNR) under both frequencies. Full article

Optical neural networks are hardware neural networks implemented based on physical optics, and they have demonstrated advantages of high speed, low energy consumption, and resistance to electromagnetic interference in the field of image processing. However, most previous optical neural networks were designed for coherent light inputs, which required the introduction of an electro-optical conversion module before the optical computing device. This significantly hindered the inherent speed and energy efficiency advantages of optical computing. In this paper, we propose a diffraction algorithm for incoherent light based on mutual intensity propagation, and on this basis, we established a model of an incoherent optical neural network. This model is completely passive and directly performs inference calculations on natural light, with the detector directly outputting the results, achieving target classification in an all-optical environment. The proposed model was tested on the MNIST, Fashion-MNIST, and ISDD datasets, achieving classification accuracies of 82.32%, 72.48%, and 93.05%, respectively, with experimental verification showing an accuracy error of less than 5%. This neural network can achieve passive and delay-free inference in a natural light environment, completing target classification and showing good application prospects in the field of remote sensing. Full article

Three-dimensional neuronal organoids, spheroids, and tissue mimics are increasingly used to model cognitive processes in vitro. These 3D constructs are also used to model the effects of neurological and psychiatric disorders and to perform computational tasks. The brain’s complex network of neurons is activated via feedforward sensory pathways. Therefore, an interface to 3D constructs that models sensory pathway-like inputs is desirable. In this work, an optical interface for 3D neuronal constructs was developed. Dendrites and axons extended by cortical neurons within the 3D constructs were guided into microchannel-confined bundles. These neurite bundles were then optogenetically stimulated, and evoked responses were evaluated by calcium imaging. Optical stimulation was designed to deliver distinct input patterns to the network in the 3D construct, mimicking sensory pathway inputs to cortical areas in the intact brain. Responses of the network to the stimulation possessed features of neuronal population code, including separability by input pattern and mixed selectivity of individual neurons. This work represents the first demonstration of a pathway-like activation of networks in 3D constructs. Another innovation of this work is the development of an all-optical interface to 3D neuronal constructs, which does not require the use of expensive microelectrode arrays. This interface may enable the use of 3D neuronal constructs for investigations into cortical information processing. It may also enable studies into the effects of neurodegenerative or psychiatric disorders on cortical computation. Full article

A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. Due to the magnetostrictive effect, an optical phase shift induced by magnetic field variation is generated between two orthogonal light waves transmitted in the upper and lower arms of the MZI. The polarization-dependent property of a Mach–Zehnder modulator (MZM) is utilized to transform the magnetostrictive phase shift into the phase difference between the sidebands and optical carrier, which is mapped to the oscillating frequency upon the completion of an OEO loop. High-sensitivity magnetic field sensing is achieved by observing the frequency shift of the radio frequency (RF) signal. Temperature-induced cross-sensitivity is mitigated through precise length matching of the MZI arms. In the experiment, the high magnetic field sensitivity of 6.824 MHz/mT with a range of 25 mT to 25.3 mT is achieved and the sensing accuracy measured by an electrical spectrum analyzer (ESA) at “maxhold” mode is 0.002 mT. The proposed sensing structure has excellent magnetic field detection performance and provides a solution for temperature-insensitive magnetic field detection, which would have broad application prospects. Full article

In this paper, an adaptive frequency-domain block equalizer (FDBE) implementing the adaptive moment estimation (Adam) algorithm is proposed for mode-division multiplexing (MDM) optical fiber communication systems. By packing all frequency components into frequency-dependent blocks of a specified size $B$, we define an adaptive equalization matrix to simultaneously compensate for multiple frequency components at each block, which is computed iteratively using the Adam, recursive least squares (RLS) and least mean squares (LMS) algorithms. Simulations show that the proposed FDBE using the Adam algorithm outperforms those using the LMS and RLS algorithms in terms of adaptation speed and symbol error rate (SER) performance. The FDBE using the Adam algorithm with $B=1$ has the fastest adaption time, requiring about $n_{tr}=100$ and $n_{tr}=900$ less training blocks than the RLS algorithm at the SER of $3.8\times {10}^{-3}$ for the accumulated mode-dependent loss (MDL) of $\xi =1 \mathrm{d}$B and $\xi =5 \mathrm{d}$B, respectively. The Adam algorithm with $B=16$ and $B=8$ has $0.4 \mathrm{d}$B and $0.3 \mathrm{d}$B SNR better than the RLS algorithm with $B=4$ for MDL and $\xi =1 \mathrm{d}$B and $\xi =55 \mathrm{d}$B, respectively. Full article

Diffractive optical elements (DOEs) are specialized optical components that manipulate light through diffraction for various applications, including holography, spectroscopy, augmented reality (AR) and virtual reality (VR), and light detection and ranging (LiDAR). The performance of DOEs is highly determined by fabricated materials and fabrication methods, in addition to the numerical simulation design. This paper presents a microfabrication technique optimized for DOEs, enabling precise control of critical parameters, such as refractive index (RI) and thickness. Using photolithography, we fabricated high-precision photoresist patterns on silicon and sapphire substrates, with 3 × 3 and 3 × 5 DOE beam splitter as examples. The results show a strong match between simulation and experimental data, with discrepancies of just 0.53% and 0.57% for DOE on silicon and sapphire substrates, respectively. This approach offers potential for advancing high-performance DOE devices in semiconductor manufacturing, supporting next-generation optical systems. Full article

A block-based mode decomposition (BMD) algorithm is proposed in this paper, which reduces computational complexity and enhances noise resistance. The BMD uses randomly selected sample blocks of the beam images to restore mode coefficients instead of all pixels in the beam images. It allows for blocks of any shape, such as triangles. In noise-free simulations, compared to the spatially degenerated mode decomposition (SPMD) algorithm, the BMD algorithm requires only 1% of the multiplication operations, thereby significantly increasing the computational efficiency while maintaining a high mode decomposition accuracy. In simulations with noise, increasing the signal-to-noise ratio (SNR) reduces decomposition errors across all configurations. The amplitude error of BMD can outperform SPMD by 15 dB. The experimental results show that BMD has a better performance than SPMD. Full article

Diffractive deep neural networks (D²NNs) typically adopt a densely cascaded arrangement of diffractive masks, leading to multiple reflections of diffracted light between adjacent masks, thereby affecting the network’s inference capability. It is challenging to fully simulate this multiple-reflection phenomenon. To eliminate this phenomenon, we designed tilted-mode all-optical diffractive deep neural networks (T-D²NNs) and proposed a theoretical model for diffraction propagation in the tilted mode. Simulation results indicate that T-D²NNs address the performance degradation caused by interlayer reflections in D²NNs constructed with high-index diffractive masks. In classification tasks, T-D²NNs achieve better classification results compared to D²NNs that consider interlayer reflections. Full article