Geiger-mode Avalanche Photodiodes Research Articles

Enhancing large-area Geiger-mode avalanche photodiode performance through dynamic memristor quenching: A study on improving count rate, reducing jitter, and mitigating afterpulsing

Enhancing large-area Geiger-mode avalanche photodiode performance through dynamic memristor quenching: A study on improving count rate, reducing jitter, and mitigating afterpulsing

X-ray timing detectors with HfO2 nanoparticle-loaded plastic scintillator and silicon avalanche photodiode

We successfully developed 20- and 40-wt% HfO2-nanoparticle-loaded plastic scintillators (Hf-PLSs), and subsequently, assembled X-ray timing scintillation detectors using the Hf-PLSs and silicon avalanche photodiodes (Si-APDs) as the photodetector. Two types of Si-APDs were examined for two different detectors. A proportional-mode APD (pAPD) exhibited a nanosecond response, but a relatively low internal gain of less than several hundred. By contrast, a Geiger-mode APD exhibited a high gain of 105−6, but its output pulse width was longer than several tens of nanoseconds; the pixelated device is called as SiPM. A pAPD (Hamamatsu Photonics S8664-4433, 3 mm in diameter) was used as the photodetector and was combined with 20 wt% Hf-PLS (∼3 mm cube) as the X-ray scintillation timing detector. A SiPM (Hamamatsu Photonics S13360-3025, 3 mm × 3 mm) was assembled with 40 wt% Hf-PLS (∼3 mm cube). The characteristics of the two types of scintillation timing detectors were evaluated using a 57.61-keV synchrotron X-ray beam to assess their applicability in nuclear resonant scattering experiments. Both the detectors were cooled down to −35 or −20 °C to reduce noise and obtain a higher APD gain. A good time resolution of (0.31 ± 0.04) ns (FWHM) was easily obtained with the SiPM detector using a large APD gain at −20 °C, although the time resolution of the pAPD detector finally reached (0.39 ± 0.04) ns (FWHM) at −35 °C owing to a small gain of 146. However, a maximum count rate of over 1.4 × 107 s−1 was observed owing to the fast response of the pAPD detector, whereas the SiPM detector exhibited a maximum count rate of 5.9 × 106 s−1 because of the long pulse tail of ∼30 ns.

Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.

Noise-suppression algorithm of GM-APD lidar based on Markov random field

Background light easily affects a Geiger-mode avalanche photodiode (GM-APD) laser imaging, detection, and ranging (Lidar), and its detection ability is significantly reduced in strong light environments. Improving imaging performance under a low signal-to-back ratio has become critical. A GM-APD lidar noise-suppression algorithm based on a Markov random field is proposed. The field objective function and prior model are established using similar centre and adjacent pixel features. Both enhance the feature difference between a strong background light and a weak echo signal and improve the ability of weak echo signal reconstruction under an ultra-low signal-to-background ratio (SBR). Imaging experiments of long-distance building targets under varying illumination verified the noise-suppression ability of the algorithm. When the SBR is 0.0055, target recovery can reach 19.40%, and recovery of the whole array image can reach 60.94%. Compared with the sparse Poisson intensity reconstruction algorithm, the image signal-to-noise ratio is improved by 83 dB, and the average depth error was reduced by 36.14 cm. The proposed algorithm improves the imaging performance under ultra-low SBR; it significantly promotes the development of GM-APD lidar all-time applications.

Research on 3D image reconstruction of sparse power lines by array GM-APD lidar

Power lines and other tiny obstacles in the air are deadly hazards to safe aircraft flight. However, existing detection methods have difficulty sensing power lines’ weak radiation signals submerged in noise, making power lines detection an international research hotspot. To solve this problem, this paper proposes the use of array Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (lidar) with single photon detection sensitivity to achieve remote three-dimensional (3D) imaging detection of power lines (GM-PLTR). We focus on solving the problem of noise filtering for sparse power lines under low Signal-to-noise ratio (SNR) conditions and completing 3D image reconstruction. Firstly, we propose a method based on adaptive thresholding and waveform density discrimination combined with spatial dependency to remove the pixel-wise noise, including environmental light, noise echoes from neighboring pixels, and dark count noise. Then we obtain the high SNR depth image of the targets. Secondly, we use an improved DBSCAN algorithm to separate the power lines from the background noise in the depth image to improve the reconstruction effect. Finally, we use a one-directional template matching method to fill in the missing shapes of the power lines. Experimental results show that the algorithm improves the SNR of the power lines echo signal by an average of 2.3 times and achieves sparse power lines imaging detection at 340.95 m. Moreover, the Hough transform confirms the possibility of high-precision detection of power lines, significantly reducing the difficulty of detecting small targets. Finally, it will help unmanned aerial vehicles (UAVs) complete the inspection of power lines.

A spatially correlated fractional integral-based method for denoising geiger-mode avalanche photodiode light detection and ranging depth images

Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) target echo signals are susceptible to interference from atmospheric backscatter, daylight, and other noise, and extracted target depth images contain a large amount of anomalous noise. Accordingly, this paper devises a method based on a spatially correlated fractional integral for denoising GM-APD LiDAR depth images. First, a multi-scale superpixel fusion algorithm is employed to perform null-pixel complementation on a target depth image extracted using histogram statistics. Next, a pixel neighborhood spatial correlation kernel function is used to optimize the Grünwald–Letnikov fractional integral operator to correct the large amount of anomalous noise and thus achieve GM-APD depth image denoising. Simulation and experimental results demonstrate that the denoising performance of the algorithm on GM-APD LiDAR depth images is significantly better than that of median filtering and bilateral filtering, with at least 22.8 % and 4.8 % improvements in the target reduction degree (K) and peak signal-to-noise ratio (PSNR), respectively. In addition, the K and PSNR reach 91.36 % and 18.0241 dB, respectively, with 25 statistical frames in an outdoor imaging experiment. This demonstrates that the algorithm can effectively denoise GM-APD LiDAR depth images with few statistical frames and thus improve the frame rate of GM-APD LiDAR imaging.

Geiger-Mode Operation 4H-SiC Recessed-Window Avalanche Photodiodes Fabricated by N Ion Implantation

In this work, a 4H-SiC n-i-p avalanche photodiode (APD) with n-layer formed by N ion implantation is demonstrated, in which recessed-window structure is used as termination for reducing electrical field crowding effect around the device edge. The APD with 20 μm diameter active region exhibits low dark current of ~5 pA, high gain of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> , zero-bias peak quantum efficiency of 53.8 % at 265 nm. Two-dimensional avalanche photocurrent mapping indicates that uniform gain is realized across the device central region while edge-located premature breakdown is effectively suppressed. Since there is no shading effect of top contact metal, close to 100% percent of fill factor is achieved in the multiplication region. The APD can operate in Geiger mode by using a passive quenching circuit. A room temperature single photon detection capability is obtained at 280 nm, when the dark count rate is ~ 28.8 Hz/μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

Avalanche Photodiodes and Silicon Photomultipliers of Non-Planar Designs

Conventional designs of an avalanche photodiode (APD) have been based on a planar p–n junction since the 1960s. APD developments have been driven by the necessity to provide a uniform electric field over the active junction area and to prevent edge breakdown by special measures. Most modern silicon photomultipliers (SiPM) are designed as an array of Geiger-mode APD cells based on planar p–n junctions. However, the planar design faces an inherent trade-off between photon detection efficiency and dynamic range due to loss of an active area at the cell edges. Non-planar designs of APDs and SiPMs have also been known since the development of spherical APDs (1968), metal-resistor-semiconductor APDs (1989), and micro-well APDs (2005). The recent development of tip avalanche photodiodes (2020) based on the spherical p–n junction eliminates the trade-off, outperforms the planar SiPMs in the photon detection efficiency, and opens new opportunities for SiPM improvements. Furthermore, the latest developments in APDs based on electric field-line crowding and charge-focusing topology with quasi-spherical p–n junctions (2019–2023) show promising functionality in linear and Geiger operating modes. This paper presents an overview of designs and performances of non-planar APDs and SiPMs.

A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images

High-quality image restoration is typically challenging due to low signal–to–background ratios (SBRs) and limited statistics frames. To address these challenges, this paper devised a method based on fractional-order total variation (FOTV) regularization for recovering Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (lidar) depth images. First, the spatial differential peak-picking method was used to extract the target depth image from low SBR and limited frames. FOTV regularization was introduced based on the total variation regularization recovery model, which incorporates the fractional-order differential operator, in order to realize FOTV-regularization-based depth image recovery. These frameworks were used to establish an algorithm for GM-APD depth image recovery based on FOTV. The simulation and experimental results demonstrate that the devised FOTV-recovery algorithm improved the target reduction degree, peak signal–to–noise ratio, and structural similarity index measurement by 76.6%, 3.5%, and 6.9% more than the TV, respectively, in the same SBR and statistic frame conditions. Thus, the devised approach is able to effectively recover GM-APD lidar depth images in low SBR and limited statistic frame conditions.

InGaAs Geiger‐mode three‐dimensional image sensor for flash LIDAR

AbstractThree‐dimensional (3D) image sensors are requested by planetary explorers to detect obstacles around landing sites during the landing process and to measure the relative distance and attitude of an orbiting spacecraft during rendezvous docking procedures. Three‐dimensional image sensors are also requested in many other fields, including as sensors for autonomous vehicles. In flash LIDAR, a sensor acquires 3D images using the diffuse emission of a pulsed laser, making it suitable for applications like obstacle detection and terrain measurement. We fabricated a prototype 3D image sensor with a temporal resolution of 500 ps and dimensions of 128 × 128 pixels using an InGaAs Geiger mode avalanche photodiode, which is capable of counting photons in the eye‐safe band, as the light receiving sensor. This report gives an overview of the prototype sensor and its functions, a detailed evaluation of its time measurement performance, and details of imaging experiments under sunlight.

Research on infrared image guided GM-APD range image recovery algorithm under limited detections

Geiger-mode avalanche photodiode (GM-APD) focal plane array lidar has a high photon-sensitive detection ability and can acquire three-dimensional(3D) distance information. However, high noise intensity and limited detections can easily lead to the loss of target pixels in the range image, and excellent signal extraction and noise suppression technology become crucial. To solve this problem, we propose a guided image recovery (GIR) method which first uses the infrared image information to recover the GM-APD range image. Unlike image fusion technology, which sacrifices the true range value or the objectively existing 3D range image to get the complete image, it cannot only recover the complete image but also guarantee the 3D image is true. The main innovation of the proposed algorithm is to search the missing pixels of the range image based on the boundary information of infrared images and extracts the range values from their own original measurement data according to the adjacent pixels’ information. According to Monte Carlo simulation and experimental data analysis, the proposed method has excellent target recovery, noise suppression, and fidelity performance under limited detections and low SBRs conditions, which provides an important reference for GM-APD lidar real-time detection. It also provides a new research direction for GM-APD lidar range image reconstruction.

Prediction of the Number of Cumulative Pulses Based on the Photon Statistical Entropy Evaluation in Photon-Counting LiDAR.

Photon-counting LiDAR encounters interference from background noise in remote target detection, and the statistical detection of the accumulation of multiple pulses is necessary to eliminate the uncertainty of responses from the Geiger-mode avalanche photodiode (Gm-APD). The cumulative number of statistical detections is difficult to select due to the lack of effective evaluation of the influence of the background noise. In this work, a statistical detection signal evaluation method based on photon statistical entropy (PSE) is proposed by developing the detection process of the Gm-APD as an information transmission model. A prediction model for estimating the number of cumulative pulses required for high-accuracy ranging with the background noise is then established. The simulation analysis shows that the proposed PSE is more sensitive to the noise compared with the signal-to-noise ratio evaluation, and a minimum PSE exists to ensure all the range detections with background noise are close to the true range with a low and stable range error. The experiments demonstrate that the prediction model provides a reliable estimation of the number of required cumulative pulses in various noise conditions. With the estimated number of cumulative pulses, when the signal photons are less than 0.1 per pulse, the range accuracy of 4.1 cm and 5.3 cm are obtained under the background noise of 7.6 MHz and 5.1 MHz, respectively.

Suppressing the influence of GM-APD coherent lidar saturation by signal modulation

Geiger-mode Avalanche Photo Diode (GM-APD) coherent lidar improves coherent detection sensitivity to single photon level, which is more suitable for remote target detection. However, due to the limited pixels, the photon count will be affected by the system saturation, resulting in low utilization of local oscillator (LO), reducing the system's signal-to-noise ratio (SNR). A GM-APD coherent lidar system based on signal modulation is proposed, the method uses an electro-optic modulator to modulate the slope of signal light, then GM-APD coherent detection. Firstly, the detection and optimal parameter model based on signal modulation are derived. Secondly, the effectiveness of this system is analyzed through simulation. Finally, GM-APD coherent detection experiment based on signal modulation is carried out to verify the effectiveness of the system. When the echo position is 2 μs, the pulse width is 200 ns, the number of signal photons is about 8000, LO photons is about 820, signal modulation improves the system SNR by more than two times. It is concluded that the greater influence of saturation on GM-APD coherent lidar, the better signal modulation effect. The proposed scheme improves the utilization rate of LO and SNR from equipment level, to further enhance system detection ability.

3D reconstruction of the dynamic scene with high-speed targets for GM-APD LiDAR

With the advantages of high distance resolution, long detection distance, small size, and low power consumption, Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) has excellent potential for applications such as three-dimensional earth mapping and autonomous driving. The reconstruction for GM-APD LiDAR is based on the statistics of multiple-laser-pulse data, leading to a long imaging time. There are problems such as blurring when the targets are high-speed, which limits its application scope. A reconstruction algorithm of the dynamic scene for GM-APD LiDAR is proposed in this paper to address this problem. Firstly, the motion features (such as velocity and position) of the targets in the scene are extracted by applying the Hough transform and used as a basis to isolate the targets’ echo from the background noise, significantly reducing the noise interference. With these features, the data are corrected for the spatial location to attenuate or eliminate the effects caused by the targets’ motion. Finally, the reconstruction is completed utilizing parameter estimation. Also, we discuss the super-resolution reconstruction capability of this algorithm with sufficient data. Reconstruction results of the scene with targets moving at 100–300 m/s are demonstrated at last. Compared to the conventional algorithms, the peak signal-to-noise ratio (PSNR) is improved by 3–4 dB, and the root-mean-square error (RMSE) of distance is improved by 6–10 times. In addition to a super-resolution reconstruction of the dynamic scene, this method also enables the detection, motion feature extraction, and position prediction of high-speed moving targets in the scene, significantly expanding the application scope of GM-APD LiDAR and having good practical application value.

Multichannel FPGA-based Data Acquisition System for Fan-beam Dual Energy X-ray Absorptiometry

This study aimed to develop a multichannel field programmable gate array (FPGA) based data acquisition (DAQ) system that could process 8 analog signals from a 1 × 8 LYSO array coupled with a Geiger-mode avalanche photodiode (GAPD) for fan-beam dual-energy X-ray absorptiometry (DEXA) system. Each analog input signal was digitized using an 8-channel analog-to-digital converter (ADC) with a 125 MHz sampling rate and an input range of -1.0 to 1.0 V. The 14-bit digital signals were then fed into a Xilinx Virtex-6 FPGA-based evaluation board that implemented digital signal processing logic. The collected low- and high-energy events of each pixel for each input channel were stored in an on-chip block ram-based first-in-first-out (FIFO) module, and then further transmitted to a personal computer (PC) via the USB port. The intrinsic characteristics, spectral responses, and image acquisition were performed. The estimated coefficient of determination (R²) was 0.999 in voltage linearity and no considerable alterations in voltage resolution were observed. The energy resolutions of the peak 59.5 keV (Am-241) were ~28.2 %. Dual-energy peaks were isolated in the X-ray energy spectra. The synchronization between the movement part of the DEXA system and the timing control block inside the FPGA chip was well controlled in various conditions. The DEXA phantom images for each pixel were clearly resolved. This study demonstrated that FPGA with only on-chip memory resources can be used to build a multichannel DAQ system for fan-beam DEXA systems.

Scanning-based photon-limited imaging through scattering media

Imaging through scattering media has attracted considerable research interest in recent years. Most existing studies are dedicated to reconstructing objects from the detected speckle pattern in an array-based system. However, the inevitable aliasing effect among pixels degrades the imaging quality significantly. Therefore, most existing studies usually could only work well under a weak scattering medium and a short working distance. To address this issue, we innovatively propose a robust scanning-based photon-limited imaging system for imaging through scattering media. To boost the imaging capability, we specifically adopt a point-to-point scanning strategy to eliminate the aliasing effect, and utilize a Geiger-mode avalanche photo diode to detect extremely weak signals after passing through the scattering media. As the images captured by the proposed imaging system always suffer from Poisson noise at unknown levels, we further construct a blind deep-learning based denoising framework with “one-to-all” strategy. By introducing a noise estimation sub-network with dense connection and batch normalization operations into the whole denoising framework, one trained model with synthetic datasets based on the realistic noise model could be generalized well to all real captured photon-limited images. Extensive experiments in real situations demonstrate that significant improvements can be achieved in the proposed method. For example, clear objects can be observed through several thick scattering media under an approximate working distance of 7 m, even with a low-power (average power of approximately 20 mW) light source.

Chaos single photon LIDAR and the ranging performance analysis based on Monte Carlo simulation.

With the advent of serial production lidars, single photon lidar faces an increasingly severe threat of crosstalk. In this paper, we first propose the concept of Chaos Single Photon (CSP) lidar and establish the theoretical model. In CSP lidar system, chaos laser replaces pulsed laser, and the physical random sequence generated by a Geiger mode avalanche photodiode (GM-APD) responding to chaos laser substitutes the traditional pseudo-random sequence. The mean density of '1' code of the CSP lidar system can exceed 10 million counts per second (Mcps) with a dead time immunity. The theoretical models of detection probability and false alarm rate are derived and demonstrated based on Poisson distribution. The bit error rate (BER) is introduced into the CSP lidar system for evaluating the range walk error intuitively. Additionally, the simulation results indicate that the CSP lidar system has a robust anti-crosstalk capability. Compared with the traditional pseudo-random single photon (PRSP) lidar system, the CSP lidar system not only overcomes range ambiguity but also has a signal to noise rate (SNR) of 60 times, reaching 10000 when the mean echo photoelectron number is 10 per nanosecond. Benefited from large-scale arrays and extremely high sensitivity properties of GM-APDs, we are looking forward to the applications of the CSP lidar in weak signal detection, remote mapping, autonomous driving, etc.

Measurement of focal light spot at single-photon level with silicon photomultipliers

Focal spot (light spot) at single-photon level have important applications in many fields. This report demonstrates a method for measuring focal spot size at the single-photon level indirectly. This method utilizes Silicon Photomultiplier (SiPM) as the single-photon sensitive detectors, combined with a nano-positioning stage. The approach involves one- or two-dimensional space scanning and a deconvolution operation, which enable evaluations of the size and spatial distribution of the focal spot formed by a single-photon-level pulsed laser. The results indicate that the average full width at half maximum of the focal spot is about 0.657 μm, which is close to the nominal resolution of the objective lens of the microscope (i.e. 0.42 μm). The proposed method has two key advantages: (1) it can measure focal spot at the single-photon level, and (2) the focal spot can easily be aligned with the detector because the array area of the Geiger mode avalanche photodiode (Gm-APD) cells in SiPM is usually on the order of square millimeter, and there is no need to put an optical slit, knife edge, or pinhole in front of the detector. The method described herein is applicable in weak focal spot detection related fields.

Long-Distance Vehicle Dynamic Detection and Positioning Based on Gm-APD Lidar and LIDAR-YOLO

Aiming at the problems of long-distance night dynamic object detection in military operations, such as difficult imaging of traditional equipment, a small number of object pixels, a large difference in object size at different distances, and poor real-time performance. A long-distance vehicle detection method based on the LIDAR-YOLO network for Gm-APD (Geiger mode avalanche photodiode) lidar image is proposed. This method is improved based on YOLOv5s, which can detect long-distance vehicles and accurately locate motion trajectories in real-time. Firstly, the intensity image I of the lidar is preprocessed and combined with the distance image R, the BC_IIR image is obtained, and the vehicle detection data set is established. Then, according to the obvious vehicle features in the BC_IIR image, the low-complexity backbone network LIDARNet is redesigned, and several attention mechanism modules are added. The network feature extraction ability is enhanced by comparing and selecting the CA attention mechanism module. In the network prediction layer, context-aware feature map fusion is added to improve the detection precision of vehicles at different distances. Next, a GIOU loss is introduced to improve the detection precision of occluded vehicles. Finally, the method can depict the trajectory of the vehicle in 3D space based on distance information. After testing, the network achieves a detection speed of 62.67 frames per second (FPS), recall rate (R) = 98.19%, precision rate (P) = 95.53%, and F1 is 0.9675. The results show that the algorithm has better performance than other methods.