Encoded computational hyperspectral cameras, propelled by advances in compressed sensing theory, making both miniaturization and real-time hyperspectral imaging feasible. Spectral-encoded or spatial-encoded hyperspectral imaging strategy have limited numbers of design parameters in optical components, leading to severe ill-posedness in hyperspectral images reconstruction, which constrain overall imaging quality. However, spatial-spectral-encoded hyperspectral imaging strategy which simultaneously performs spatial and spectral encoding entailing more powerful modulation, alleviating ill-posed problems and improving the quality of hyperspectral images. In this paper, we present a co-modulation framework based on diffractive optical element (DOE) and Superposition Fabry–Perot (SFP) filter array for computational hyperspectral camera that integrates these two components with a transformer-based reconstruction network through end-to-end learning. The learned DOE and SFP filter encode the hyperspectral datacube on the sensor via phase and amplitude modulation, and the transformer-based network accurately reconstructs the images from sensor measurements. We conduct extensive simulations to analyze and validate the relatively contributions of the DOE, SFP filter, and transformer-based reconstruction algorithm to the significantly improved performance of hyperspectral image reconstruction across various ablation study models. We further investigate and identify the $mathbf {4times 4}$ SFP filter unit configuration as the most effective design for achieving a balance between spectral fidelity and spatial resolution. Our results show that the proposed system outperforms state-of-the-art methods in hyperspectral images reconstruction quality, excelling in both spatial and spectral detail recovery, and maintaining good performance against realistic noise levels.
{"title":"Computational Hyperspectral Camera Design Based on Co-Modulation of Diffractive Optical Element and Superposition Fabry-Perot Filter Array","authors":"Shiqi Feng;Xuquan Wang;Xiong Dun;Zhanshan Wang;Xinbin Cheng","doi":"10.1109/JPHOT.2025.3624799","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3624799","url":null,"abstract":"Encoded computational hyperspectral cameras, propelled by advances in compressed sensing theory, making both miniaturization and real-time hyperspectral imaging feasible. Spectral-encoded or spatial-encoded hyperspectral imaging strategy have limited numbers of design parameters in optical components, leading to severe ill-posedness in hyperspectral images reconstruction, which constrain overall imaging quality. However, spatial-spectral-encoded hyperspectral imaging strategy which simultaneously performs spatial and spectral encoding entailing more powerful modulation, alleviating ill-posed problems and improving the quality of hyperspectral images. In this paper, we present a co-modulation framework based on diffractive optical element (DOE) and Superposition Fabry–Perot (SFP) filter array for computational hyperspectral camera that integrates these two components with a transformer-based reconstruction network through end-to-end learning. The learned DOE and SFP filter encode the hyperspectral datacube on the sensor via phase and amplitude modulation, and the transformer-based network accurately reconstructs the images from sensor measurements. We conduct extensive simulations to analyze and validate the relatively contributions of the DOE, SFP filter, and transformer-based reconstruction algorithm to the significantly improved performance of hyperspectral image reconstruction across various ablation study models. We further investigate and identify the <inline-formula><tex-math>$mathbf {4times 4}$</tex-math></inline-formula> SFP filter unit configuration as the most effective design for achieving a balance between spectral fidelity and spatial resolution. Our results show that the proposed system outperforms state-of-the-art methods in hyperspectral images reconstruction quality, excelling in both spatial and spectral detail recovery, and maintaining good performance against realistic noise levels.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-9"},"PeriodicalIF":2.4,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11215898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/JPHOT.2025.3624684
Lei Sun;Xilin Chen;Xiuqiang Diao;Shiyuan Zhang;Fei Dong
In the mining industry, underground workers are mandated to wear safety helmets due to numerous potential risks. However, due to that the complex and harsh environment of underground coal mines, the object images obtained by video surveillance systems suffer from challenges such as uneven lighting, small and easily obscured targets, and significant environmental interference. Consequently, most existing deep learning-based object detection methods encounter substantial difficulties in accurately, efficiently and timely detecting safety helmets in underground coal mine. These challenges include low detection accuracy, poor model robustness, and a contradiction between the enhancement of detection performance and increased computational consumption demands. Therefore, to address these limitations, this work investigates a new detection algorithm for coal mine safety helmet wearing based on YOLOv5s, named YOLOv5s-CBCG. Firstly, an enhanced feature extraction network named FEN-CA is developed by incorporating the coordinate attention mechanism, which contributes to gain more powerful feature extraction ability for small object and suppress the interference of background noise in small target images. Secondly, it designs a new feature fusion network named FFE-BCG to enhance the multi-scale feature fusion, and further improve the small object detection accuracy while reducing computational cost. Then, the strengthened feature extraction network and feature fusion network are used as the backbone and neck of YOLOv5s-CBCG, respectively. Finally, self-built M-Helmet dataset is used to conduct extensive experiments, and the results indicate that the YOLOv5s-CBCG can reach 95.90% mAP on M-Helmet dataset with less computational expense than YOLOv5s, which outperforms other comparative methods. Specifically, the mAP is 4% and 7.9% higher compared to the latest YOLOv11n and YOLOv12n algorithm.
{"title":"Improved YOLOv5s-CBCG Algorithm for Detecting Safety Helmets in Underground Coal Mines","authors":"Lei Sun;Xilin Chen;Xiuqiang Diao;Shiyuan Zhang;Fei Dong","doi":"10.1109/JPHOT.2025.3624684","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3624684","url":null,"abstract":"In the mining industry, underground workers are mandated to wear safety helmets due to numerous potential risks. However, due to that the complex and harsh environment of underground coal mines, the object images obtained by video surveillance systems suffer from challenges such as uneven lighting, small and easily obscured targets, and significant environmental interference. Consequently, most existing deep learning-based object detection methods encounter substantial difficulties in accurately, efficiently and timely detecting safety helmets in underground coal mine. These challenges include low detection accuracy, poor model robustness, and a contradiction between the enhancement of detection performance and increased computational consumption demands. Therefore, to address these limitations, this work investigates a new detection algorithm for coal mine safety helmet wearing based on YOLOv5s, named YOLOv5s-CBCG. Firstly, an enhanced feature extraction network named FEN-CA is developed by incorporating the coordinate attention mechanism, which contributes to gain more powerful feature extraction ability for small object and suppress the interference of background noise in small target images. Secondly, it designs a new feature fusion network named FFE-BCG to enhance the multi-scale feature fusion, and further improve the small object detection accuracy while reducing computational cost. Then, the strengthened feature extraction network and feature fusion network are used as the backbone and neck of YOLOv5s-CBCG, respectively. Finally, self-built M-Helmet dataset is used to conduct extensive experiments, and the results indicate that the YOLOv5s-CBCG can reach 95.90% mAP on M-Helmet dataset with less computational expense than YOLOv5s, which outperforms other comparative methods. Specifically, the mAP is 4% and 7.9% higher compared to the latest YOLOv11n and YOLOv12n algorithm.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-15"},"PeriodicalIF":2.4,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11215625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a cost-effective bidirectional communication system that integrates single-mode fiber (SMF), free-space optical (FSO), and a 5G new radio (NR) wireless links, employing FBG sensors. The sensors serve as wavelength selectors, which helps reduce system costs. This approach is simpler and more affordable than advanced filtering techniques, wavelength demultiplexers, and multiple distributed feedback laser diode sources. The system supports a downstream data rate of 120-Gb/s (3 × 40-Gb/s/100-GHz) using wavelength division multiplexing, while the upstream transmission supports 60-Gb/s (3 × 20-Gb/s/60-GHz). These data rates are transmitted over a 32 km SMF, a 1.6 km FSO, and a 25 m 5G NR wireless link. Experimental results show that removing the FBG sensor from the downstream receiver setup leads to significant signal degradation. Specifically, the bit error rate (BER) increases from 2.04×10−5 (λ4) to 4.68 × 10−4 (λ1) and 2.29×10−5 (λ5) to 5.75 × 10−4 (λ2). Similarly, the error vector magnitude (EVM) rises from 10.53% (λ4) to 11.57% (λ1) and 10.70% (λ5) to 11.70% (λ2). Moreover, the constellation patterns become less defined. Under foggy conditions, these issues become more severe, with BER and EVM increasing significantly. Thus, the presence of the FBG sensor in the downstream receiver setup improved system performance. This approach offers a cost-effective solution for expanding 5G NR coverage.
{"title":"Cost-Effective Bidirectional SMF-FSO-5G NR Wireless System Employing FBG Sensors","authors":"Stotaw Talbachew Hayle;Hai-Han Lu;Yen-Chen Chen;Wei-Zhi Jiang;Wei-Ting Huang;Jia-Hui Chou;Feng-Ti Chen;Chi-Hsiang Hsu","doi":"10.1109/JPHOT.2025.3623695","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3623695","url":null,"abstract":"This paper presents a cost-effective bidirectional communication system that integrates single-mode fiber (SMF), free-space optical (FSO), and a 5G new radio (NR) wireless links, employing FBG sensors. The sensors serve as wavelength selectors, which helps reduce system costs. This approach is simpler and more affordable than advanced filtering techniques, wavelength demultiplexers, and multiple distributed feedback laser diode sources. The system supports a downstream data rate of 120-Gb/s (3 × 40-Gb/s/100-GHz) using wavelength division multiplexing, while the upstream transmission supports 60-Gb/s (3 × 20-Gb/s/60-GHz). These data rates are transmitted over a 32 km SMF, a 1.6 km FSO, and a 25 m 5G NR wireless link. Experimental results show that removing the FBG sensor from the downstream receiver setup leads to significant signal degradation. Specifically, the bit error rate (BER) increases from 2.04×10<sup>−5</sup> (λ<sub>4</sub>) to 4.68 × 10<sup>−4</sup> (λ<sub>1</sub>) and 2.29×10<sup>−5</sup> (λ<sub>5</sub>) to 5.75 × 10<sup>−4</sup> (λ<sub>2</sub>). Similarly, the error vector magnitude (EVM) rises from 10.53% (λ<sub>4</sub>) to 11.57% (λ<sub>1</sub>) and 10.70% (λ<sub>5</sub>) to 11.70% (λ<sub>2</sub>). Moreover, the constellation patterns become less defined. Under foggy conditions, these issues become more severe, with BER and EVM increasing significantly. Thus, the presence of the FBG sensor in the downstream receiver setup improved system performance. This approach offers a cost-effective solution for expanding 5G NR coverage.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-12"},"PeriodicalIF":2.4,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11208696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/JPHOT.2025.3624147
You-Cheng Lin;You-Xin Wang;Shih-Chang Hsu;Hsing-Yi Huang;Yueh-Hsun Yang;Yung-Hsuan Li;Kuan-Wei Hu;Gong-Ru Lin
Under the quantum key distribution with differential phase-shift-keying protocol, the ultimate stability of the system is limited by the residual wavelength drift of the quantum-key carrier and the polarization/refractive-index/physical-length fluctuations induced by environmental thermal disturbance. To permanently overcome such a shortcoming, a 1-bit-delayed interferometric fiberized decoder with ultrastable visibility maintenance and long-term immunity to environmental perturbations is demonstrated by employing all polarization-maintaining-fiber to construct an asymmetric-arm Mach-Zehnder interferometer, and surrounding such a delay-line interferometric decoder with a thermal-isolated sponge and a styrofoam container. Receiving the DPS-QKD bit-stream with a high sifting key rate and low bit-error ratio is approached by maintaining the high interfered visibility and eliminating the wavelength drift with a highly adiabatic package. Such an ultrastable long-term 1-bit-delay interferometric decoder is particularly suitable for the persistent DPS-QKD operation towards future commercialization.
{"title":"Long-Term Stable Polarized Adiabatic Interfered Decoder for Differential Phase Shift Quantum Key","authors":"You-Cheng Lin;You-Xin Wang;Shih-Chang Hsu;Hsing-Yi Huang;Yueh-Hsun Yang;Yung-Hsuan Li;Kuan-Wei Hu;Gong-Ru Lin","doi":"10.1109/JPHOT.2025.3624147","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3624147","url":null,"abstract":"Under the quantum key distribution with differential phase-shift-keying protocol, the ultimate stability of the system is limited by the residual wavelength drift of the quantum-key carrier and the polarization/refractive-index/physical-length fluctuations induced by environmental thermal disturbance. To permanently overcome such a shortcoming, a 1-bit-delayed interferometric fiberized decoder with ultrastable visibility maintenance and long-term immunity to environmental perturbations is demonstrated by employing all polarization-maintaining-fiber to construct an asymmetric-arm Mach-Zehnder interferometer, and surrounding such a delay-line interferometric decoder with a thermal-isolated sponge and a styrofoam container. Receiving the DPS-QKD bit-stream with a high sifting key rate and low bit-error ratio is approached by maintaining the high interfered visibility and eliminating the wavelength drift with a highly adiabatic package. Such an ultrastable long-term 1-bit-delay interferometric decoder is particularly suitable for the persistent DPS-QKD operation towards future commercialization.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-8"},"PeriodicalIF":2.4,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11208789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/JPHOT.2025.3624017
Ying Du;Jingyuan Wang;Zhiyong Xu;Jianhua Li
Although Modulating retro-reflector (MRR) technology offers a promising low-power solution for underwater wireless optical communication (UWOC), its performance under challenging oceanic turbulence has not been comprehensively analyzed. This paper addresses this gap by developing a robust model for an MRR-UWOC system operating through a turbulent channel. This paper derives the joint probability density function (PDF) for the turbulence-induced fading of the MRR-UWOC link based on the Exponentiated Weibull (EW) distribution and the generalized oceanic turbulence optical power spectrum (OTOPS) model. Utilizing this PDF, we obtain an approximate analytical expression for the average bit error rate (BER) of an on-off keying (OOK) system using the Gauss-Hermite quadrature method. Analysis of the turbulence confirms that aperture averaging mitigates fading and reveals a supersaturation effect. This analysis also indicates that the system is more sensitive to temperature variations than to salinity fluctuations. Furthermore, a comprehensive MRR UWOC model incorporating path loss from absorption and scattering reveals that these factors play fundamentally different roles: turbulence establishes a theoretical performance floor, while path loss imposes a power penalty on this baseline. The established model offers valuable guidance for the practical design and optimization of underwater optical networks.
{"title":"Performance Evaluation of Modulating Retro-Reflector Underwater Wireless Optical Communication Links in Oceanic Turbulence","authors":"Ying Du;Jingyuan Wang;Zhiyong Xu;Jianhua Li","doi":"10.1109/JPHOT.2025.3624017","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3624017","url":null,"abstract":"Although Modulating retro-reflector (MRR) technology offers a promising low-power solution for underwater wireless optical communication (UWOC), its performance under challenging oceanic turbulence has not been comprehensively analyzed. This paper addresses this gap by developing a robust model for an MRR-UWOC system operating through a turbulent channel. This paper derives the joint probability density function (PDF) for the turbulence-induced fading of the MRR-UWOC link based on the Exponentiated Weibull (EW) distribution and the generalized oceanic turbulence optical power spectrum (OTOPS) model. Utilizing this PDF, we obtain an approximate analytical expression for the average bit error rate (BER) of an on-off keying (OOK) system using the Gauss-Hermite quadrature method. Analysis of the turbulence confirms that aperture averaging mitigates fading and reveals a supersaturation effect. This analysis also indicates that the system is more sensitive to temperature variations than to salinity fluctuations. Furthermore, a comprehensive MRR UWOC model incorporating path loss from absorption and scattering reveals that these factors play fundamentally different roles: turbulence establishes a theoretical performance floor, while path loss imposes a power penalty on this baseline. The established model offers valuable guidance for the practical design and optimization of underwater optical networks.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-10"},"PeriodicalIF":2.4,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11208781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-17DOI: 10.1109/JPHOT.2025.3622901
Saadman Yasar;Murat Yuksel
Machine learning (ML) has emerged as a transformative tool in laser-based optics, driving advancements in both fundamental research and industrial applications. This review paper provides a comprehensive analysis of state-of-the-art ML-driven techniques across diverse laser technologies, including beam steering, optical tweezing, adaptive optics, mode-locking and pointing-acquisition-tracking to name among others. By leveraging deep learning, reinforcement learning, and other data-driven approaches, researchers have achieved real-time control, automated optimization, and enhanced system performance beyond the capabilities of traditional methods. The paper categorizes current ML applications in laser optics, addressing challenges such as dynamic aberration correction, precision beam shaping, and high-throughput data interpretation, with illustrative examples including intelligent surgical laser systems, defect-free optical trapping, and self-correcting adaptive optics. Emerging trends such as hybrid ML models, edge computing-based real-time feedback loops, and the development of open-source datasets and benchmarking tools are discussed, underscoring the potential of ML to propel laser-based optical systems to unprecedented levels of precision, efficiency, and adaptability.
{"title":"A Comprehensive Review of Machine Learning Applications in Laser Optics","authors":"Saadman Yasar;Murat Yuksel","doi":"10.1109/JPHOT.2025.3622901","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3622901","url":null,"abstract":"Machine learning (ML) has emerged as a transformative tool in laser-based optics, driving advancements in both fundamental research and industrial applications. This review paper provides a comprehensive analysis of state-of-the-art ML-driven techniques across diverse laser technologies, including beam steering, optical tweezing, adaptive optics, mode-locking and pointing-acquisition-tracking to name among others. By leveraging deep learning, reinforcement learning, and other data-driven approaches, researchers have achieved real-time control, automated optimization, and enhanced system performance beyond the capabilities of traditional methods. The paper categorizes current ML applications in laser optics, addressing challenges such as dynamic aberration correction, precision beam shaping, and high-throughput data interpretation, with illustrative examples including intelligent surgical laser systems, defect-free optical trapping, and self-correcting adaptive optics. Emerging trends such as hybrid ML models, edge computing-based real-time feedback loops, and the development of open-source datasets and benchmarking tools are discussed, underscoring the potential of ML to propel laser-based optical systems to unprecedented levels of precision, efficiency, and adaptability.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-23"},"PeriodicalIF":2.4,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11206477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For dynamic optical path provisioning in optical networks, real-time path design based on fast quality-of-transmission (QoT) estimation is highly desirable. We propose an electric field feature propagation based QoT estimation technique using a new feature extraction method. The proposed feature extraction method can reduce the calculation load by a factor of 10000 compared with the conventional approach, enabling comprehensive datasets for various networks to be created in realistic time. This study confirms that the proposed QoT estimation method can accurately estimate the bit error rate (BER) for different combinations of input power, span length, and received optical power.
{"title":"Fast QoT Estimation Based on Probability Density Calculation of Electric Field Waveform","authors":"Ryo Igarashi;Ryo Koma;Kazutaka Hara;Jun-ichi Kani;Tomoaki Yoshida;Tatsuya Shimada","doi":"10.1109/JPHOT.2025.3622629","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3622629","url":null,"abstract":"For dynamic optical path provisioning in optical networks, real-time path design based on fast quality-of-transmission (QoT) estimation is highly desirable. We propose an electric field feature propagation based QoT estimation technique using a new feature extraction method. The proposed feature extraction method can reduce the calculation load by a factor of 10000 compared with the conventional approach, enabling comprehensive datasets for various networks to be created in realistic time. This study confirms that the proposed QoT estimation method can accurately estimate the bit error rate (BER) for different combinations of input power, span length, and received optical power.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-10"},"PeriodicalIF":2.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11205962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-15DOI: 10.1109/JPHOT.2025.3621698
Jun Liu;Qianke Wang;Dawei Lyu;Jian Wang
High-dimensional quantum gates based on spatial mode of photons hold promise for advancing quantum computation. We demonstrate high-fidelity spatial mode quantum gates with fidelities up to 99.6%, using diffractive neural networks (DNNs). These gates are experimentally implemented with programmable phase layers in a compact and scalable device, enabling demonstrations like the Deutsch algorithm. Our approach showcases high reliability, adaptability, and potential for integration in quantum computation.
{"title":"Photonics Breakthroughs 2024: Scalable Quantum Computing on a Single Photon Using Spatial Modes","authors":"Jun Liu;Qianke Wang;Dawei Lyu;Jian Wang","doi":"10.1109/JPHOT.2025.3621698","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3621698","url":null,"abstract":"High-dimensional quantum gates based on spatial mode of photons hold promise for advancing quantum computation. We demonstrate high-fidelity spatial mode quantum gates with fidelities up to 99.6%, using diffractive neural networks (DNNs). These gates are experimentally implemented with programmable phase layers in a compact and scalable device, enabling demonstrations like the Deutsch algorithm. Our approach showcases high reliability, adaptability, and potential for integration in quantum computation.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-5"},"PeriodicalIF":2.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1109/JPHOT.2025.3621459
Zhiyong Yang;Yihui Zhang;Zhili Zhang;Zhiwei Zhang;Shun Li;Xiaowei Wang
The key to the researches on the polarization characteristics of targets lies in the precise establishment of the polarization model—the polarized bidirectional reflectance distribution function (pBRDF). Currently, the researches on the pBRDF model for coating targets have the problem of inaccurately describing hemispherical distribution of the degree of linear polarization (DoLP), especially the insufficiency in describing the influence of the azimuth angle on DoLP. In this thesis, firstly, with the aim of minimizing the error in linear polarization degree, through simulation comparative experiments on common microfacet distribution function (NDF), geometric attenuation factor (GAF) and multiple reflection function, it is determined that the Gaussian NDF, the modified integral GAF and the Minnaert model considering roughness are more suitable for coating targets. Secondly, the combined model for coating targets was established by combining the three functions, and it is found that the combined model was insensitive to the azimuth angle. Finally, a pBRDF model incorporating a high-order polynomial of relative azimuth angle is proposed, which improved the problem of excessive error caused by the insensitivity to the azimuth angle. The results of experiments show that the relative errors when adopting the modified model have decreased by 45.8%, 66.7%, 10.5%, and 32.1% respectively. The determination coefficient has reached 0.948, 0.953, 0.917 and 0.930, and the performance indicators are superior to those of the existing models. The research results provide a reference for describing the hemispherical spatial distribution of DoLP for coating targets.
{"title":"A Modified pBRDF Model Considering the Influence of Relative Azimuth Angle for Coating Targets","authors":"Zhiyong Yang;Yihui Zhang;Zhili Zhang;Zhiwei Zhang;Shun Li;Xiaowei Wang","doi":"10.1109/JPHOT.2025.3621459","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3621459","url":null,"abstract":"The key to the researches on the polarization characteristics of targets lies in the precise establishment of the polarization model—the polarized bidirectional reflectance distribution function (pBRDF). Currently, the researches on the pBRDF model for coating targets have the problem of inaccurately describing hemispherical distribution of the degree of linear polarization (DoLP), especially the insufficiency in describing the influence of the azimuth angle on DoLP. In this thesis, firstly, with the aim of minimizing the error in linear polarization degree, through simulation comparative experiments on common microfacet distribution function (NDF), geometric attenuation factor (GAF) and multiple reflection function, it is determined that the Gaussian NDF, the modified integral GAF and the Minnaert model considering roughness are more suitable for coating targets. Secondly, the combined model for coating targets was established by combining the three functions, and it is found that the combined model was insensitive to the azimuth angle. Finally, a pBRDF model incorporating a high-order polynomial of relative azimuth angle is proposed, which improved the problem of excessive error caused by the insensitivity to the azimuth angle. The results of experiments show that the relative errors when adopting the modified model have decreased by 45.8%, 66.7%, 10.5%, and 32.1% respectively. The determination coefficient has reached 0.948, 0.953, 0.917 and 0.930, and the performance indicators are superior to those of the existing models. The research results provide a reference for describing the hemispherical spatial distribution of DoLP for coating targets.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-14"},"PeriodicalIF":2.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11202864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electro-optic (EO) modulators with ultra-high speed and low driving voltage are essential for high-performance optical computing, optical communication, and broadband microwave photonic links. In this work, we have proposed an electrode optimization method for periodic structures, based on the RGLC transmission line model and exemplified by sawtooth traveling-wave electrodes (STWEs). We have established analytical expressions for characteristic parameters including the resistance, inductance, capacitance, as well as the slot depth and width of STWEs for the first time, achieving synchronous optimization of microwave loss minimization and velocity matching. Validated through simulations on thin-film lithium niobate modulators, the approach achieves an ultra-high EO bandwidth of up to 290 GHz and a low voltage-length product of 2.5 V·cm. The optimized STWEs feature a minimum linewidth of 5 μm and a metal thickness as thin as 500 nm, which are compatible with deep ultraviolet lithography and suitable for large-scale, low-cost fabrication. This work would pave the way for developing scalable ultra-high-speed EO modulator arrays.
{"title":"Optimizing Sawtooth Electrodes for Ultra-High-Speed Electro-Optic Modulators","authors":"Shurui Huang;Deyang Kong;Huijie Sun;Yongzhuo Li;Xue Feng;Wei Zhang;Yidong Huang","doi":"10.1109/JPHOT.2025.3620360","DOIUrl":"https://doi.org/10.1109/JPHOT.2025.3620360","url":null,"abstract":"Electro-optic (EO) modulators with ultra-high speed and low driving voltage are essential for high-performance optical computing, optical communication, and broadband microwave photonic links. In this work, we have proposed an electrode optimization method for periodic structures, based on the RGLC transmission line model and exemplified by sawtooth traveling-wave electrodes (STWEs). We have established analytical expressions for characteristic parameters including the resistance, inductance, capacitance, as well as the slot depth and width of STWEs for the first time, achieving synchronous optimization of microwave loss minimization and velocity matching. Validated through simulations on thin-film lithium niobate modulators, the approach achieves an ultra-high EO bandwidth of up to 290 GHz and a low voltage-length product of 2.5 V·cm. The optimized STWEs feature a minimum linewidth of 5 μm and a metal thickness as thin as 500 nm, which are compatible with deep ultraviolet lithography and suitable for large-scale, low-cost fabrication. This work would pave the way for developing scalable ultra-high-speed EO modulator arrays.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"17 6","pages":"1-12"},"PeriodicalIF":2.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11200483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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