Quasi-optical coupling serves as the critical interface in terahertz (THz) heterodyne receiver systems, enabling efficient transfer of incident radiation to superconducting hot-electron bolometer (HEB) mixers through a focusing element and a planar microwave antenna. With recent advances in nanofabrication, planar dielectric metalenses have emerged as promising alternatives to conventional refractive optics due to their compactness and scalability. However, unlike conventional elliptical silicon lenses that are often treated as nearly ideal optical components, the focusing efficiency of metalenses is strongly dependent on the local deflection angle across the aperture, creating an urgent need to quantitatively understand the coupling between a dielectric metalens and a planar antenna. In this work, we present a quasi-optical coupling analysis between a planar Si metalens and a logarithmic spiral antenna integrated with a THz superconducting NbN HEB mixer operating at 1.63 THz using a spherical-coordinate vectorial integration. By combining the angular radiation profile of the spiral antenna with the deflection-angle-dependent focusing efficiency of the metalens obtained from numerical simulations, the calculated coupling efficiency is directly correlated with experimentally measured double-side-band receiver noise temperatures through comparison with a conventional elliptical Si lens measured under the same receiver configuration. The analysis establishes a quantitative relationship between metalens focusing efficiency, antenna coupling, and receiver noise temperature, providing guidance for optimizing metalens design and improving the overall performance of metalens-integrated THz heterodyne receivers.

Avalanche photodiode (APD) is a kind of photodetector with important applications in optical communication, light detection and ranging (LIDAR) and other fields. APDs fabricated using the recently developed AlGaAsSb as the multiplication material exhibit excellent noise performance. In this work, we report a low-noise separate absorption, grading, charge, and multiplication (SAGCM) InGaAs/AlGaAsSb APD operating at 1550 nm. A double-mesa structure was fabricated to reduce the dark current. Numerical simulations were conducted to compare two different mesa-structured APDs. By analyzing the electric field distribution, it was found that the electric field at the edge of the multiplication region in the double-mesa APD is nearly 100 kV/cm lower than that of the single-mesa structure. Experimental results demonstrate that after device punch-through, the double-mesa APD's dark current can be reduced by up to four times compared to the single-mesa APD. Quantitative analysis of the dark current components in the AlGaAsSb APD further confirms that the low sidewall electric field in the double-mesa structure effectively suppresses the trap-assisted tunneling. Additionally, noise measurements indicate a k-value of approximately 0.014, which is significantly lower than that of traditional multiplication materials. This work provides preliminary validation for further performance improvements in low noise and low dark current AlGaAsSb APDs.

Phase change memory (PCM) technology demonstrates significant potential as a next-generation non-volatile storage solution for information applications. Ge2Sb2Te5 (GST) alloy, the most well-established material employed in commercial PCM devices, exhibits limited thermal stability. Doping, as an effective approach for enhancing thermal stability, often induces element segregation and phase separation. This study systematically investigates the impact of indium (In) doping on GST phase-change material. Experimental results demonstrate that In doping significantly enhances the thermal stability of GST film. In17GST exhibits a 130 °C increase in crystallization temperature (from 181 °C to 311 °C). Especially, the introduction of In leads to the formation of In2Te3 phase, which exhibits a remarkably similar crystal structure to GST with only a ~2% lattice mismatch. Consequently, In2Te3 phase forms a coherent structure with GST lattice, thereby promoting the stability of the phase boundary. Additionally, In2Te3 phase facilitates efficient heating with a 5.7% improvement in heating efficiency (913 K vs. 864 K at 5 ns) and contributes to improved RESET operations in PCM devices. Our study lays the foundation for the composition and structure design for high thermal stability and low power consumption in PCM devices.

To enhance the bandwidth characteristics, the size of the active region of avalanche photodiodes (APDs) has been generally reduced to less than 10 mu m, and the optical coupling efficiency and tolerance have been drastically reduced, which seriously affects the practicality of the devices. To solve the above problems, this paper presents a flip-chip bonded high-speed InGaAs/InAlAs APD integrated with a micro-lens. The fabricated APD with the integrated micro-lens exhibits a maximum responsivity of 0.7 A W-1 at unit gain and a coupling efficiency of 96%, which are much higher than the 0.5 A W-1 and 68% of the APD without the micro-lens. The maximum bandwidth is 27 GHz, an ultra-low dark current is 5.7 nA under 0.9 times breakdown voltage, and a gain-bandwidth product is 210 GHz. With the micro-lens fabricated on the device's backside, a Gaussian beam with a diameter of 10 mu m can be effectively focused on an area of 7 mu m width. The optical coupling tolerance (the radial distance from the active region center to the point where the responsivity drops to 90% of its maximum value) of the device is extended from 1.8 mu m to 7 mu m, larger than the active region radius of 3 mu m. These features will significantly enhance the practicality of the high-speed APDs.

Crosstalk has become an urgent issue for single-photon avalanche diode arrays. In previous work, trenches were introduced between pixels to block the crosstalk optical path in planar InGaAs/InP single-photon avalanche diode arrays, since the optical crosstalk was considered as the main crosstalk mechanism. However, the crosstalk suppression effect of this solution is not satisfactory. Here, we demonstrate a carrier extraction structure to efficiently reduce crosstalk by electrically guiding photogenerated crosstalk holes in the non-pixel region to the surface, since we find that the optical-electrical crosstalk is the dominant crosstalk mechanism. Experimental measurements show that a narrow carrier extraction structure makes a 91.52% (96.22%) crosstalk reduction between the nearest neighbor pixels in arrays with 100 (50) μm pixel pitch, and it does not cause any etching damage. These results reveal the primary source of crosstalk in InGaAs/InP single-photon avalanche diode arrays and provide a practical route to fabricate low-crosstalk, high-pixel-density arrays for use in high-resolution three-dimensional imaging and quantum technologies.

Single photon LIDAR exhibits exceptional detection sensitivity and finds extensive applications in long-distance measurement and three-dimensional imaging domains. However, it is susceptible to background noise interference, leading to a trade-off between scanning imaging efficiency and angular resolution, thereby limiting its overall performance. In this paper, we propose a novel ‘compression-resampling’ high-resolution imaging method for single-photon scanning LIDAR based on the spatial correlation of the target scene. This approach significantly enhances the system's angular resolution without compromising imaging efficiency. By employing this method, we successfully conducted 3D imaging over distances exceeding 26 km even under challenging conditions with high levels of background noise during daylight hours. Within a target field of view measuring 1° × 1°, we reconstructed a high-resolution 3D point cloud image comprising 1000 × 1410 pixels. Notably, this achievement yielded an impressive angular resolution of 17.4 urad while maintaining an acceptable imaging time of approximately 20 minutes. The results unequivocally demonstrate the effectiveness of our proposed method in improving the quality of three-dimensional imaging and enhancing angular resolution while simultaneously reducing imaging time requirements, thus expanding potential application scenarios for single-photon LIDAR technology.

The excess noise factor(F) is an important parameter for Avalanche Photodiodes (APDs), indicating the extent to which the noise of the diodes exceeds what it would be without multiplication. This paper presents a method of measuring the excess noise factor for high-speed low-noise APDs using the high-sensitivity spectrum analyzer. The F factor test for the high-speed low-noise APDs was performed by comparing two conditions with and without illumination to remove the interference of the system, including the thermal noise and analyzers. The setup conditions of the light source are analyzed in detail. The F factor was obtained as 3.03 when the multiplication factor M is 10, which corresponds to an effective ionization coefficient ratio k of 0.14 for the high-speed InAlAs APD. For comparison, the k value of a commercial Si APD is tested to be ∼0.06.

Photon counting lidar has been widely used in long-distance target detection and long range active three-dimensional imaging fields due to its extremely high detecting sensitivity. The single-photon level sensitivity extends the detection range of the photon counting lidar, meanwhile makes it suffer from the noise interference. Especially, the strong sunlight background during the daytime limits the detection ability and applications of the photon counting lidar. In this paper, a new type of ultra-narrowband spectral filtering system is designed for the lidar, and based on which, a daytime working photon counting lidar system with 20 μJ pulse energy and 25 mm receiving aperture was established. A 24 km long-distance ranging experiment and a 22 km fast 3D imaging experiment were completed in daylight environment, which demonstrated outstanding daytime working performance of such a “small” lidar.

This article proposes a novel framework for probabilistic estimation of surface soil moisture (SSM) based on polarimetric decomposition and copula quantile regression, mainly focusing on solving the low correlation between synthetic aperture radar (SAR) backscattering coefficients and SSM in corn-covered areas. Cloude–Pottier decomposition and adaptive nonnegative eigenvalue decomposition can extract more polarization parameters, explaining the implicit information in polarization data from different theoretical levels. Polarization parameters and the backscattering coefficients for different polarizations constitute predictor variable parameters for estimating the SSM. The dimensionality of the predictor variable parameters is reduced by supervised principal component analysis to derive the first principal component. SPCA ensures a high correlation between the first principal component and the SSM. Finally, the Archimedes copula function simply and effectively constructs the nonlinear relationship between SSM and the first principal component to complete the quantile regression estimation of SSM. Results show that the root-mean-square error range of SSM estimation is 0.039–0.078 cm$^{3}$/cm$^{3}$ and the correlation coefficient (R) is 0.401–0.761. In addition, copula quantile regression constructs an uncertainty range for the SSM estimate, which can be used to judge the reliability of the estimate.

Digital elevation model (DEM) generated by bistatic synthetic aperture radar interferometry (InSAR) usually has systematic plane and elevation errors. However, the DEM adjustment method based on the function model can only correct systematic elevation error, affecting the plane accuracy of DEM mosaic products. Given this issue, this article proposes a 3-D InSAR-DEM block adjustment method based on general models with a separate adjustment strategy, that is, first correct vertical error using the adjustment method based on the function model and then correct horizontal error using the method based on rational function model after compensating for elevation errors. In order to extract the tie points (TPs) and ground control points (GCPs) used in adjustment, we propose a normalized correlation coefficient matching method based on complex slope maps (NCC-CSM). We conduct experiments using 29 TanDEM-X coregistered single look slant range complex data. The accuracies of adjustment results are verified using ICESat-2 ATL08 data and selected GCPs, respectively. The adjustment experiments show that the elevation accuracy of InSAR-DEM is improved from 1.70 to 1.35 m, the absolute and relative plane accuracies are improved by 5.58 and 11.53 m, respectively, and the horizontal and vertical geometric discontinuity in the overlapping area disappear. The matching experiments of TPs and GCPs show that the proposed NCC-CSM method is superior to the traditional NCC method based on DEM (NCC-DEM), especially in flat areas.

Photon counting LiDAR can capture the 3D information of long-distance targets and has the advantages of high sensitivity and high resolution. However, the noise counts restrict improvements in the photon counting imaging quality. Therefore, how to make full use of the limited signal counts under noise interference to achieve efficient 3D imaging is one of the main problems in current research. To address this problem, in this paper, we proposes a 3D imaging method for undulating terrain depth estimation that combines constant false alarm probability detection with the Bayesian model. First, the new 3D cube data are constructed by adaptive threshold segmentation of the reconstructed histogram. Secondly, the signal photons are extracted in the Bayesian model, and depth estimation is realized from coarse to fine by the sliding-window method. The robustness of the method under intense noise is proven by sufficient undulating terrain simulations and outdoor imaging experiments. These results show that the proposed method is superior to typical existing methods.

The block adjustment method can correct systematic errors in the bistatic Synthetic Aperture Radar Interferometry (InSAR) satellite system and effectively improve the accuracy of the InSAR-generated Digital Elevation Model (DEM). Presently, non-parametric methods, which use the polynomial to model the systematic errors of InSAR-generated DEMs, are most frequently used in spaceborne InSAR-DEM adjustment. However, non-parametric methods are not directly related to the physical parameters in the InSAR imaging process. Given the issue, this paper conducts adjustments in the parameter domain and proposes a three-dimensional block adjustment method for spaceborne bistatic InSAR systems based on the Range-Doppler-Phase (RDP) model. First, we theoretically analyze the sensitivities of spatial baseline, azimuth time, and slant range to the RDP geolocation model and confirm the analysis method with a simulated geolocation result. Second, we use total differential and differential geometry theories to derive adjustment equations of available control data based on sensitivity analysis. Third, we put forward an iterative solution strategy to solve the corrections of parallel baseline, azimuth time, and slant range to improve the plane and elevation accuracies of InSAR-generated DEMs. We used 29 scenes of TanDEM-X Co-registered Single look Slant range Complex (CoSSC) data to conduct simulated and real data experiments. The simulated results show that the proposed method can improve the accuracies of baseline, range, and timing to 0.05 mm, 0.1 m, and 0.006 ms, respectively. In the real data experiment, the proposed method improves the plane and elevation accuracies to 4.14 m and 1.34 m, respectively, and effectively suppresses the fracture phenomenon in the DEM mosaic area.

Planar semiconductor InGaAs/InP single photon avalanche diodes with high responsivity and low dark count rate are preferred single photon detectors in near-infrared communication. However, even with well-designed structures and well-controlled operational conditions, the performance of InGaAs/InP SPADs is limited by the inherent characteristics of avalanche process and the growth quality of InGaAs/InP materials. It is difficult to ensure high detection efficiency while the dark count rate is controlled within a certain range at present. In this paper, we fabricated a device with a thick InGaAs absorption region and an anti-reflection layer. The quantum efficiency of this device reaches 83.2%. We characterized the single-photon performance of the device by a quenching circuit consisting of parallel-balanced InGaAs/InP single photon detectors and single-period sinusoidal pulse gating. The spike pulse caused by the capacitance effect of the device is eliminated by using the characteristics of parallel balanced common mode signal elimination, and the detection of small avalanche pulse amplitude signal is realized. The maximum detection efficiency is 55.4% with a dark count rate of 43.8 kHz and a noise equivalent power of 6.96 × 10 −17 W/Hz 1/2 at 247 K. Compared with other reported detectors, this SPAD exhibits higher SPDE and lower noise-equivalent power at a higher cooling temperature.

With the continued development of high-power laser technology, research on laser protection has become extremely important. In this study, an analysis of Z-scan and femtosecond transient absorption spectra data for lead (II) tetrakis (4-cumylphenoxy) phthalocyanine (4-PbPc) solutions prepared using different concentrations and solvents established that charge transfer plays an important role in the optical limiting dynamics of this molecule. Based on the correlation of decay-associated difference spectra (DADS) for different decay channels, it was confirmed that greater charge transfer efficiency was associated with more highly conjugated solvent molecules. In a dilute solution, the polarity of the solvent molecules will influence the optical limiting performance of the solution to an extent, whereas in a highly concentrated solution, the degree of conjugation of the solvent molecules is the main influence. Therefore, the more highly conjugated the solvent and the higher the solution concentration, the higher the excited state absorption cross-section and effective two-photon absorption coefficient, and the longer the triplet lifetime. These results should provide theoretical guidance and experimental clues for the design of materials with better optical limiting performance and promote the further development of laser protection.

The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability of adjustment parameters and the GCP distribution pattern theoretically using matrix perturbation theory. Second, we put forward the Constraint Slices (CSs) concept and first introduce CSs into the adjustment optimization model as constraint conditions rather than actual values as GCPs. Finally, we propose a novel DEM block adjustment method for spaceborne InSAR using CSs based on an optimization model with nonlinear constraints. The simulated experiment shows the instability of the conventional method and validates the proposed method under different parallel baseline errors. Four groups of real experiments were carried out according to the size of the uncontrolled area using twelve Co-registered Single-look Slant–range Complex (CoSSC) datasets for Henan Province, China. The adjustment results verified by the ICESat-2 ATL08 data demonstrate that the performance of the proposed method is better than the conventional method in the uncontrolled area; the corresponding improvements in adjustment accuracies compared with the conventional method are 0.13 m, 1.02 m, 2.12 m, and 8.18 m, respectively. At the same time, the proposed method can enhance the height consistency in overlapping areas, which is vital for seamless DEM production.

The measurement of surface soil moisture (SSM) assists in making agricultural decisions, such as precision irrigation and flooding or drought predictions. The critical challenge for SSM estimation in vegetation-covered areas is the coupling between vegetation and surface scattering. This study proposed an SSM estimation method based on polarimetric decomposition and quantile regression forests (QRF) to overcome this problem. Model-based polarimetric decomposition separates volume scattering, double-bounce scattering, and surface scattering, while eigenvalue-based polarimetric decomposition provides additional parameters to describe the scattering mechanism. The combined use of these parameters explains the polarimetric SAR scattering information from multiple perspectives, such as vegetation, surface roughness, and SSM. As different crops differ in morphology and structure, it is essential to investigate the potential of varying polarimetric parameters to estimate SSM in areas covered by different crops. QRF, a regression method applicable to high-dimensional predictor variables, is used to estimate SSM from these parameters. In addition to the SSM estimates, QRF can also provide the predicted uncertainty intervals and quantify the importance of the different parameters in the SSM estimates. The performance of QRF in SSM estimation was tested using data from the soil moisture active passive validation experiment 2012 (SMAPVEX12) and compared with copula quantile regression (CQR). The SSM estimated by the proposed method was consistent with the in situ SSM, with the root-mean-square-error ranging from 0.037 cm(3)/cm(3) to 0.079 cm(3)/cm(3) and correlation coefficients ranging from 0.745 to 0.905. Meanwhile, the method proposed in this study can provide both the uncertainty of SSM estimation and the importance of different polarimetric parameters.

In this paper, we establish a modified equivalent circuit model suitable for all bias conditions for high-speed InGaAs/InAlAs avalanche photodiodes (APDs). The model includes three mechanisms: the carrier transit time, the avalanche buildup time, and the RC time constant. The physical meaning of each component in it is elaborated in detail. Component parameters in it of the demonstrated APD are extracted by fitting the RF reflection parameters S22 and transmission parameters S21 measured at different bias voltages. To verify the accuracy and regularity of these parameters, some relevant finite element simulations and theoretical analyses are carried out. Values and regularities of these parameters are consistent with the results of finite element simulations and theoretical analyses. With the help of this model and these accurate parameters, impedance distribution and bandwidth limiting factors of the demonstrated APD are analyzed thoroughly. According to the above analysis results, the performance optimization scheme for the demonstrated APD is obtained.

Resistive switching random access memory (RRAM) is considered as one of the potential candidates for next-generation memory. However, obtaining an RRAM device with comprehensively excellent performance, such as high retention and endurance, low variations, as well as CMOS compatibility, etc., is still an open question. In this work, we introduce an insert TaO x layer into HfO x -based RRAM to optimize the device performance. Attributing to robust filament formed in the TaO x layer by a forming operation, the local-field and thermal enhanced effect and interface modulation has been implemented simultaneously. Consequently, the RRAM device features large windows (> 10 3 ), fast switching speed (~ 10 ns), steady retention (> 72 h), high endurance (> 10 8 cycles), and excellent uniformity of both cycle-to-cycle and device-to-device. These results indicate that inserting the TaO x layer can significantly improve HfO x -based device performance, providing a constructive approach for the practical application of RRAM.

This study proposes a new set of processing procedures based on the strain model and the Kalman filter (SM-Kalman) to obtain high-precision three-dimensional surface deformation time series from interferometric synthetic aperture radar (InSAR) and global navigation satellite system (GNSS) data. Implementing the Kalman filter requires the establishment of state and observation equations. In the time domain, the state equation is generated by fitting the pre-existing deformation time series based on a deformation model containing linear and seasonal terms. In the space domain, the observation equation is established with the assistance of the strain model to realize the spatial combination of InSAR and GNSS observation data at each moment. Benefiting from the application of the Kalman filter, InSAR and GNSS data at different moments can be synchronized. The time and measurement update steps are performed dynamically to generate a 3-D deformation time series with high precision and a high resolution in the temporal and spatial domains. Sentinel-1 SAR and GNSS datasets in the Los Angeles area are used to verify the effectiveness of the proposed method. The datasets include twenty-seven ascending track SAR images, thirty-four descending track SAR images and the daily time series of forty-eight GNSS stations from January 2016 to November 2018. The experimental result demonstrates that the proposed SM-Kalman method can produce high-precision deformation results at the millimeter level and provide two types of 3-D deformation time series with the same temporal resolution as InSAR or GNSS observations according to the needs of users. The new method achieves a high degree of temporal and spatial fusion of GNSS and InSAR data.

A self-organizing map (SOM) is a powerful unsupervised learning neural network for analyzing high-dimensional data in various applications. However, hardware implementation of SOM is challenging because of the complexity in calculating the similarities and determining neighborhoods. We experimentally demonstrated a memristor-based SOM based on Ta/TaOx/Pt 1T1R chips for the first time, which has advantages in computing speed, throughput, and energy efficiency compared with the CMOS digital counterpart, by utilizing the topological structure of the array and physical laws for computing without complicated circuits. We employed additional rows in the crossbar arrays and identified the best matching units by directly calculating the similarities between the input vectors and the weight matrix in the hardware. Using the memristor-based SOM, we demonstrated data clustering, image processing and solved the traveling salesman problem with much-improved energy efficiency and computing throughput. The physical implementation of SOM in memristor crossbar arrays extends the capability of memristor-based neuromorphic computing systems in machine learning and artificial intelligence.