Search Results (631)

Detection of trace gases with high sensitivity and weak excitation power is highly desired for long-range remote sensing. Here, we report the detection of the greenhouse gas nitrous oxide (N₂O) with the power of excitation light down to picowatts, by converting the mid-infrared laser to near-infrared photons through an intra-cavity-enhanced sum-frequency upconversion system. The intra-cavity-enhanced pumping power of 1064.0 nm reaches about 200.0 W, resulting in the conversion of the 4514.6 nm mid-infrared laser to 861.1 nm with an efficiency up to 73.4% under optimal conditions. The upconverted light is then detected by a single-photon avalanche detector, followed by a time-correlated single-photon counting module, which can measure the arrival time of each upconverted photon. By performing discrete Fourier transformations of the arrival time of the detected photons, the frequency spectrum can be determined. By using frequency modulation, this method can suppress background noise significantly. Consequently, the excitation power can be brought down to about 100 pW with the concentration of N₂O being 10 ppm. As a demonstration of application, the presented system is also used for N₂O sensing in an open-path geometry, highlighting the potential for stand-off leak detection. Our proposal offers promising applications to monitor trace gases over long distances with weak excitation powers. Full article

Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field. Full article

Large-aperture optical synthetic aperture technology, by combining multiple aperture units, breaks through the limitations of a single reflector and has become the preferred system for extending the resolution and diffraction limit of imaging systems. In particular, segmented telescopes have accumulated extensive engineering practice experience, such as the 30 m TMT and the 39 m ELT. However, the stable maintenance of wavefront coherence between multiple sub-apertures requires strict phase synchronization and group delay matching accuracy, which hinders the further development of sparse aperture telescopes and distributed interferometric telescopes (Long-Baseline Interferometers). This review systematically summarizes the research progress on synthetic aperture systems in wavefront coherence detection and stable maintenance control, focusing on two main physical architectures (Michelson and Fizeau types) and the related control algorithms. Furthermore, based on the basic logic from “measurement” to “modulation”, it prospects the development trends driven by interdisciplinary technologies such as embodied intelligent dynamic prediction, photonic integration, and real-time sensing based on deep learning. The aim is to provide a reference for wavefront-stabilization solutions in the next-generation ultra-large-aperture optical synthetic aperture systems. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs). Full article

Geiger-mode avalanche photodiode (GM-APD) array light detection and ranging (LiDAR) has significant advantages in low-light scenes due to its single-photon-level detection sensitivity. However, it is susceptible to noise, which leads to a decrease in target localization accuracy. Traditional methods rely on long-term accumulation to distinguish signal photons from noise photons, making it difficult to achieve efficient processing, especially in scenarios with sparse echo photons and low signal-to-noise ratio (SNR), where performance is limited. To quickly and accurately obtain three-dimensional (3D) information of the target under such extreme conditions, this paper proposes a method for target detection and temporal window depth estimation based on intensity information guidance. First, noise suppression is performed on the intensity image according to its statistical characteristics, and an outlier detection mechanism based on neighborhood sparsity is introduced to remove outliers, thereby completing the target detection. Next, by exploiting the spatial continuity and reflectivity similarity of the target, local fusion of photon data within the target neighborhood is performed to construct highly consistent “superpixels”. Finally, according to the distribution difference between signal photons and noise photons on the time axis, temporal window screening is applied to the superpixels to extract depth information, and empty pixels are filled using a convex segmentation method to achieve depth estimation of the target. The experimental results demonstrate that under conditions of low photon counts and strong noise, the proposed method significantly outperforms traditional and existing methods in target recovery and depth estimation by effectively integrating target intensity information. Furthermore, this method achieves faster reconstruction speed, enabling high-precision and high-efficiency 3D target reconstruction. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

With the increasing importance of securing quantum communication networks, practical and robust entity authentication is a critical requirement. Accordingly, we propose and experimentally validate a quantum entity authentication (QEA) protocol specifically designed for integration with BB84-type quantum key distribution (QKD) workflows and existing terminal architectures. We analyze the protocol’s security against intercept–resend man-in-the-middle (MitM) impersonation, showing that an unauthenticated adversary induces a characteristic 25% correlation error and that the rejection probability approaches unity as the number of detected authentication events increases. For practical realization, the protocol is deployed using weak coherent pulses (WCPs) with decoy-state estimation to bound single-photon contributions and mitigate photon-number-splitting (PNS)-enabled leakage. The system is demonstrated over a field-deployed fiber link of approximately 20 km with ~8 dB optical loss using signal/decoy intensities of ~0.5/~0.15 and sending probabilities 0.88/0.10/0.02 (signal/decoy/vacuum). Across both verification directions, stable operation is observed with quantum bit error rate (QBER) typically fluctuating between 1% and 4% while the sifted key rate remains constant over time. These results provide an experimental basis for integrating physical-layer entity authentication into deployed quantum communication networks. Full article

A monolithic digital Silicon Photomultiplier (SiPM) featuring 1024 microcells with a 30-micrometer pitch and a 50% fill factor has been designed in a 110-nanometer CMOS image sensor technology. The device under consideration integrates both SPAD sensors and front-end electronics in the same substrate. It can count up to 1024 photons in less than 22 ns, while assigning timestamps to the first and last detected photons with a time resolution of less than 100 ps. A parallel counter structure combined with a fast adder tree provides photon counting in digital form with low latency, whereas a carefully balanced fast NAND tree ensures a fixed-pattern time uncertainty not exceeding 26 ps. The architecture incorporates in-pixel memory for individual cell disabling and configurable thresholding on the timing signal for noise mitigation. In order to optimize the fill factor, a part of the electronics is placed outside the array, while the most sensitive elements of the timing and counting circuits are laid out close to the sensor, in the SPAD array. A serial readout is employed to provide a single output connection per SiPM, thereby simplifying system integration. Full article

Artificial intelligence computing systems increasingly demand high-bandwidth, high-extinction-ratio, chip-to-chip optical transceivers. Silicon microring modulators (MRMs) are attractive for such transmitters due to their compact footprint and wavelength-division multiplexing capability. However, for a specified extinction ratio, the optical bandwidth for high-Q MRMs and the driver’s RC time constant prevent conventional single-segment MRM drivers from supporting 100 GBaud class PAM4 transmission. This work presents a broadband driver exploiting the feedforward technique for dual-segment MRMs. It extends electro-optical bandwidth while maintaining a high Q-factor and extinction ratio. The input signal is split into low- and high-frequency components that drive the long and short segments of the MRM, respectively. The long segment uses a broadband low-pass driver, whereas the short segment employs a driver with a programmable bandpass response near the Nyquist frequency. The design space is obtained from an equivalent electro-optical model under constant group-delay constraints. Simulations at 1310 nm show that the 3 dB electro-optical bandwidth improves from ~50 to >70 GHz and that a 200 Gb/s PAM4 optical eye diagram exhibits an open eye; the energy efficiency is 1.44 pJ/bit, and the extinction ratio improves from 2 dB to 4.1 dB. The proposed technique provides a tunable electro-optical co-design approach for high-bandwidth-density, high-extinction-ratio silicon photonic transmitters. Full article

Percentage depth–dose (PDD) distributions are fundamental to characterizing radiation beams in radiotherapy. This review provides an overview of both methods and sensor technologies for measuring PDD in photon, electron, proton, and carbon-ion beams. We summarize conventional dosimetry techniques, including water-phantom scanning with ionization chambers (cylindrical and parallel-plate) and radiochromic film, and discuss their strengths (established accuracy, calibration traceability) and limitations (volume averaging, delayed readout). We then examine emerging sensor technologies designed to improve spatial resolution, speed, and radiation hardness: multi-layer ionization chambers and Faraday cups for one-shot PDD acquisition; scintillator-based detectors (liquid, plastic, and fiber-optic) enabling real-time and high-resolution depth–dose measurements; advanced semiconductor detectors including silicon carbide diodes; as well as novel approaches such as ionoacoustic range sensing for proton beams. For each modality and detector type, we emphasize clinical relevance, measurement accuracy, spatial resolution, radiation durability, and suitability for high dose-per-pulse environments (e.g., FLASH radiotherapy). Current challenges, such as detector response in regions of steep dose gradient, saturation or recombination at ultra-high dose rates, and energy-dependent sensitivity in mixed radiation fields, are analyzed in detail. We also highlight the limitations of each technique and discuss ongoing improvements and prospects for clinical implementation. In summary, no single detector technology fully satisfies all requirements for fast, high-accuracy, high-resolution, radiation-hard PDD measurement, but the integration of emerging sensor innovations into clinical dosimetry promises to enhance the precision and efficiency of radiotherapy quality assurance. Full article

Background/Objectives: The PHOTON trial established the efficacy of aflibercept 8 mg using fixed-interval dosing in treatment-naïve patients; however, real-world evidence regarding pro re nata (PRN) regimens in switch cases remains limited. This pilot study evaluated the short-term efficacy and safety of switching to aflibercept 8 mg with PRN dosing in eyes with DME. Methods: This retrospective study included 20 eyes from 12 patients with DME who switched to aflibercept 8 mg and were followed for 6 months. Patients received initial induction doses (1–3 injections based on predetermined anatomical and functional criteria) followed by PRN dosing based on clinical findings. Primary outcomes were changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Treatment intervals and injection frequency were also analyzed. Results: Mean logMAR BCVA was maintained from baseline (0.242 ± 0.252) throughout the follow-up period: 0.164 ± 0.218 at 1 month, 0.138 ± 0.241 at 2 months, 0.145 ± 0.204 at 3 months, 0.143 ± 0.181 at 4 months, 0.149 ± 0.166 at 5 months, and 0.180 ± 0.224 at 6 months. No statistically significant changes in BCVA from baseline were observed at any time point. Mean CRT decreased from baseline (369.6 ± 138.3 μm) at all follow-up time points: 251.5 ± 82.1 μm at 1 month, 269.1 ± 104.5 μm at 2 months, 255.8 ± 67.8 μm at 3 months, 275.2 ± 76.6 μm at 4 months, 301.4 ± 81.2 μm at 5 months, and 302.7 ± 86.8 μm at 6 months. Statistically significant reductions in CRT were observed at 1 through 4 months (1 month: p = 0.000010; 2 months: p = 0.000243; 3 months: p = 0.000035; 4 months: p = 0.000597), whereas the reductions at 5 months (p = 0.0317) and 6 months (p = 0.0424) were not statistically significant. The mean number of injections over 6 months was 1.45 ± 1.05 (median 1; range 1–4), with 70% of eyes achieving treatment intervals ≥ 4 months. Five eyes (25%) required only the switching dose with no additional treatment during follow-up. No intraocular inflammation or retinal vasculitis was observed. Conclusions: Switching to aflibercept 8 mg with PRN dosing provided sustained anatomical improvement and maintained visual acuity in DME, with one quarter of the cases maintaining these outcomes with only a single additional injection. These real-world findings from a pilot study suggest that the PRN approach appears feasible and effective in real-world practice, offering a practical treatment option that may help reduce treatment burden while maintaining disease control. Full article

This paper presents a process-controlled study of illumination engineering in single-photon avalanche diodes (SPADs) fabricated in a 110 nm standard CMOS image sensor (CIS) technology. Front-illuminated (FI) and back-illuminated (BI) SPADs were implemented with identical front-end-of-line (FEOL) structures, including the junction and guard-ring configurations, enabling the isolation of the effects of illumination direction and back-end-of-line (BEOL) configuration without modifying the junction structure. Through TCAD simulations and comprehensive experimental characterizations, including current–voltage, light-emission, dark count rate (DCR), photon detection probability (PDP), and timing-jitter measurements, we systematically analyze the performance trade-offs introduced by the BI configuration. The BI SPAD exhibits enhanced near-infrared PDP and a broader spectral response due to its deeper absorption region and the incorporation of a metal reflector, while maintaining identical avalanche characteristics, as evidenced by an unchanged 72 ps full-width-at-half-maximum (FWHM) timing jitter. However, the backside illumination increases the diffusion tail, indicating a trade-off between near-infrared sensitivity and diffusion-related timing performance. These results provide design guidelines for optimizing SPAD performance through illumination-direction and BEOL engineering while preserving the FEOL design and demonstrate a useful approach for SPAD integration in standard CMOS technology. Full article

Advancing satellite and CubeSat quantum key distribution (QKD) requires receiver-level engineering trade studies, because secure-key feasibility in space is limited by single-photon detectors (SPDs) operating under SWaP, thermal, and radiation constraints. However, the question arises: does the literature provide sufficiently consistent evidence to guide detector selection for space QKD? This systematic evidence map examines how recent research connects SNSPDs, Si SPAD/APD, InGaAs SPAD/APD, and NFAD variants to CubeSat QKD and space-based quantum communication links. To do so, a concept-token methodology identifies mission contexts and detector families through targeted keywords and key phrases, followed by structured extraction of detection efficiency η, dark count rate (DCR), timing jitter, receiver timing window Δt, operating mode, temperature/cooling, and radiation evidence. The results show an upward trend in publications, with many appearing in the last two years. SNSPDs and APD/SPAD families are most regularly discussed, yet key parameters—especially η, jitter, and explicit Δt—are reported unevenly, limiting cross-study comparability. CubeSat-tagged studies emphasize APD/SPAD feasibility and radiation-driven DCR evolution, while SNSPDs remain performance-leading but cryogenics-limited. Standardized reporting of η, DCR, jitter, Δt, temperature, and radiation conditions emerges as a practical avenue for accelerating deployable space-QKD receivers. Full article

Terahertz (THz) communication has become a key enabling technology for the future sixth generation (6G) due to its rich spectrum resources, supporting emerging applications such as holographic communication and ultra-wideband transmission. This article provides a comprehensive review of recent advances in photonic THz communications, covering device, system, and antenna technologies. First, the electronic bottlenecks in conventional THz systems, including limited bandwidth and severe phase noise generated by frequency doubling, are discussed, emphasizing the advantages of photonic methods in ultra-wideband signal generation and seamless integration with fiber-optic networks. Then, the effects of the carrier transit time, absorber layer thickness, and saturation effects on the bandwidth efficiency performance in single-row carrier photodiodes are reviewed, as well as multi-parameter co-optimization strategies for an enhanced performance. In addition, the latest progress in high spectral efficiency (SE) multi-dimensional multiplexing, lightweight high-gain lens antennas and multi-antenna MIMO transmission mechanisms in multi-antenna THz systems are also summarized. Finally, the prospects and challenges of photonic THz communications in long-distance links and space applications are discussed. Full article