Search Results (133)

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

Optical metasurfaces enable compact, lightweight and planar optical devices. Their performances, however, are still limited by design approximations imposed by their macroscopic dimensions. To address this problem, we propose a neural network-based multi-stage gradient optimization method to efficiently modulate nonlocal interactions between meta-atoms, which is one of the major effects neglected by current design methods. Our strategy allows for the use of these interactions as an additional design dimension to enhance the performance of metasurfaces and can be used to optimize large-scale metasurfaces with multiple parameters. As an example of application, we design a meta-hologram with a zero-order energy suppressed to 26% (theoretically) and 57% (experimentally) of its original value. Our results suggest that neural networks can be used as a powerful design tool for the next generation of high-performance metasurfaces with complex functionalities. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

Dynamic Bandwidth Allocation (DBA) techniques enable Time Division Multiplexing Passive Optical Network (TDM-PON) systems to efficiently manage upstream bandwidth by allowing the centralized Optical Line Terminal (OLT) to coordinate resource allocation among distributed Optical Network Units (ONUs). Conventional DBA techniques struggle to adapt to dynamic traffic conditions, resulting in suboptimal performance under varying load scenarios. This work suggests a Collaborative Split Learning-Based DBA (CSL-DBA) framework that utilizes the recently emerging Split Learning (SL) technique between the OLT and ONUs for the objective of optimizing predictive traffic adaptation and reducing communication overhead. Instead of requiring centralized learning at the OLT, the proposed approach decentralizes the process by enabling ONUs to perform local traffic analysis and transmit only model updates to the OLT. This cooperative strategy guarantees rapid responsiveness to fluctuating traffic conditions. We show by extensive simulations spanning several traffic scenarios, including low, fluctuating, and high traffic load conditions, that our proposed CSL-DBA achieves at least 99% traffic prediction accuracy, with minimal inference latency and scalable learning performance, and it reduces communication overhead by approximately 60% compared to traditional federated learning approaches, making it a strong candidate for next-generation 6G-grade TDM-PON systems. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

The development of artificial intelligence has resulted in significant challenges to conventional von Neumann architectures, including the separation of storage and computation, and power consumption bottlenecks. The new generation of brain-like devices is accelerating its evolution in the direction of high-density integration and integrated sensing, storage, and computing. The structural and information transmission similarity between memristors and biological synapses signifies their unique potential in sensing and memory. Therefore, memristors have become potential candidates for neural devices. In this paper, we have designed an optoelectronic memristor based on a ZnO/Cu₂O structure to achieve synaptic behavior through the modulation of electrical signals, demonstrating the recognition of a dataset by a neural network. Furthermore, the optical synaptic functions, such as short-term/long-term potentiation and learn-forget-relearn behavior, and advanced synaptic behavior of optoelectronic modulation, are successfully simulated. The mechanism of light-induced conductance enhancement is explained by the barrier change at the interface. This work explores a new pathway for constructing next-generation optoelectronic synaptic devices, which lays the foundation for future brain-like visual chips and intelligent perceptual devices. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

Ultra-high-speed integrated communication and sensing (ICS) transmission techniques are highly desired for next-generation highly reliable optical transport networks (OTNs). The inherent multiple-channel advantage of uncoupled multi-core fibers (MCFs) empowers the evolution of ICS techniques. In this paper, we demonstrate an ultra-high-speed ICS OTN system utilizing 130 Gbaud probability constellation shaping 64-ary quadrature amplitude modulation (PCS-64QAM) 1.2 Tb/s OTN transponders and polarization-based sensing technique over a field-deployed seven-core MCF cable for the first time. A real-time 267.8 Tb/s 2 × 70.76 km transmission is achieved by only utilizing C-band signals thanks to the high-performance 1.2 Tb/s OTN transponders. Moreover, the ICS system can sense environmental impacts on the MCF cable such as shaking, striking, etc., in real time. The capacity of the transmission system can also be further enhanced by using signals in the L-band. Our work demonstrates the feasibility of simultaneously achieving ultra-high-speed data transmission and the real-time sensing of environmental disturbances over a field-deployed MCF cable, which we believe is a crucial milestone for next-generation ultra-high-speed highly reliable optical transmission networks. Full article

To meet the stringent demands of next-generation flexible optoelectronic devices, a novel fabrication approach is employed that integrates the spray-coating of copper nanowires (Cu NWs) with the magnetron sputtering of SrTiO₃ thin films, thereby yielding SrTiO₃/Cu NWs/SrTiO₃ hybrid thin films. The incorporation of the SrTiO₃ layers results in improved optical performance, with the transmittance of the Cu NW network increasing from 83.5% to 84.2% and a concurrent reduction in sheet resistance from 16.9 Ω/sq to 14.5 Ω/sq. Moreover, after subjecting the hybrid thin films to 100 repeated tape-peeling tests and 2000 bending cycles with a bending radius of 5.0 mm, the resistance remains essentially unchanged, which underscores the films’ exceptional mechanical flexibility and robust adhesion. Additionally, the hybrid thin films are subjected to rigorous high-temperature, high-humidity, and oxidative conditions, where the resistance exhibits outstanding stability. These results substantiate the potential of the SrTiO₃/Cu NWs/SrTiO₃ hybrid thin films for integration into flexible and wearable electronic devices, delivering enhanced optoelectronic performance and long-term reliability under demanding conditions. Full article

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article

In the past two decades, there has been a tremendous increase in demand for services requiring a high bandwidth, a low latency, and high data rates, such as broadband internet services, video streaming, cloud computing, IoT devices, and mobile data services (5G and beyond). Optical wireless communication (OWC) technology, which is also envisioned for next-generation satellite networks using laser links, offers a promising solution to meet these demands. Establishing a line-of-sight (LOS) link and initiating communication in laser links is a challenging task. This process is managed by the acquisition, pointing, and tracking (APT) system, which must deal with the narrow beam divergence and the presence of satellite platform vibrations. These factors increase acquisition time and decrease acquisition probability. This study presents a framework for evaluating the acquisition time of four different scanning methods: spiral, raster, square spiral, and hexagonal, using a probabilistic approach. A satellite platform vibration model is used, and an algorithm for estimating its power spectral density is applied. Maximum likelihood estimation is employed to estimate key parameters from satellite vibrations to optimize scan parameters, such as the overlap factor and beam divergence. The simulation results show that selecting the scan path, overlap factor, and beam divergence based on an accurate estimation of satellite vibrations can prevent multiple scans of the uncertainty region, improve target satellite detection, and increase acquisition probability, given that the satellite vibration amplitudes are within the constraints imposed by the scan parameters. This study contributes to improving the acquisition process, which can, in turn, enhance the pointing and tracking phases of the APT system in laser links. Full article

Two-dimensional (2D) Xenes, including graphene where X represents C, Si, Ge, and Te, represent a groundbreaking class of materials renowned for their extraordinary electrical transport properties, robust photoresponse, and Quantum Spin Hall effects. With the growing interest in 2D materials, research on germanene-based systems remains relatively underexplored despite their potential for tailored optoelectronic functionalities. Herein, we demonstrate a facile and rapid chemical synthesis of tellurium-doped germanene hydride (Te-GeH) nanostructures (NSs), achieving precise atomic-scale control. The 2D Te-GeH NSs exhibit a broadband optical absorption spanning ultraviolet (UV) to visible light (VIS), which is a critical feature for multifunctional photodetection. Leveraging this property, we engineer photoelectrochemical (PEC) photodetectors via a simple drop-casting technique. The devices deliver excellent performance, including a high responsivity of 708.5 µA/W, ultrafast response speeds (92 ms rise, 526 ms decay), and a wide operational bandwidth. Remarkably, the detectors operate efficiently at zero-bias voltage, outperforming most existing 2D-material-based PEC systems, and function as self-powered broadband photodetectors. This work not only advances the understanding of germanene derivatives but also unlocks their potential for next-generation optoelectronics, such as energy-efficient sensors and adaptive optical networks. Full article

Free-space optical communications have emerged as a powerful solution for inter-satellite links, playing a crucial role in next-generation satellite networks. This paper introduces a comprehensive model that enables the dynamic evaluation of optical power requirements for realistic low Earth orbit satellite constellations throughout the orbital period. Our approach incorporates the constellation architecture, link budget analysis, and optical transceiver design to accurately estimate the power required for sustaining connectivity for both intra- and inter-orbit links. We apply the model considering Walker delta-type constellations of varying densities. We show that in dense constellations, even at high data rates, the required transmission power can be low enough to mitigate the need for optical amplification. Dynamically estimating the power requirements is vital when evaluating energy savings in adaptive scenarios where terminals adaptively change the emitted power depending on the link status. Our model is implemented in Python and is openly available under an open-source license. It can be easily adapted to various alternative constellation configurations. Full article

Atmospheric aerosols play an important role in the ecological environment, climate change, and human health. Aerosol optical depth (AOD) is the main measurement of aerosols. The next-generation geostationary satellite Himawari-8, loaded with the Advanced Himawari Imager (AHI), provides observation-based estimates of the hourly AOD. However, a highly accurate evaluation of AOD using AHI is still limited. In this paper, we establish a Stacked Denoising AutoEncoder (SDAE) model to retrieve highly accurate AOD using AHI. We explore the SDAE to retrieve AOD by taking the ground-observed AOD as the output and taking the AHI image, the month, hour, latitude, and longitude as the input data. This approach was tested in the Hubei province of China. Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. The results show that the proposed method has the highest accuracy. We also compare the proposed method with ground-observed AOD measurements at the Wuhan University site, showing good consistency between the satellite-retrieved AOD and the ground-observed value. The study of the spatiotemporal change pattern of the hourly AOD in the Hubei province shows that the algorithm has good stability in the face of changes in the angle and intensity of sunlight. Full article

In recent years, many studies have been conducted on OFDM-based optical wireless communications to develop a 6G communication infrastructure to improve data transmission and reduce the BER. Real-time optimal power management can enhance the data transmission speed and received power in an optical wireless channel under various conditions. This paper discusses implementing a real-time optimal power allocation system using a neural network for OFDM-based optical wireless communications. The system is designed to manage transmitter power, enhancing data transmission rates in optical wireless channels. In system design, data concerning power allocation for various types of OFDM-based optical wireless channels are calculated analytically, including the BER, SNR, fog effects, and fading types in the channel model. Next, a DNN neural model is trained using data generated from the analytical method. The trained model is finally integrated into wireless optical communication transmitter hardware. The experimental results indicate that the embedded power allocation system processes power allocation quickly. The proposed system achieves an average accuracy of 98% in power allocation, surpassing the analytical method. When used in wireless optical communication transmitters, this embedded system enhances speed and accuracy in power management, optimizing the data transmission rate up to 16 Gbps for a 500 m channel. Full article