Complementary Metal Oxide Semiconductor Camera Research Articles

ZEROES: Robust Derivative-Based Demodulation Method for Optical Camera Communication

Most of Optical Camera Communication (OCC) systems benefit from the rolling shutter mechanism of Complementary Metal-Oxide Semiconductor (CMOS) cameras to record the brightness evolution of the Light-Emitting Diode (LED) through dark and bright strips within images. While this technique enhances the maximum achievable data rate, the main difficulty lies in the demodulation of the signal extracted from images, subject to blooming effect. Thus, two main approaches were proposed to deal with this issue, using adaptive thresholds whose value evolves according to amplitude changes or detecting signal variations with the first-order derivative. As the second method is more robust, a new demodulation method based on the detection of the zeros of the first-order derivative of the extracted signal was proposed in this paper. Obtained results clearly show an improvement in the extracted signal demodulation compared to other methods, achieving a raw Bit Error Rate (BER) of 10−3 around 50 cm in a Line-Of-Sight scenario, and increasing the maximum communication distance by 43.5%, reaching 330 cm in the case of a Non-Line-Of-Sight transmission.

Near-infrared in vivo imaging system for dynamic visualization of lung-colonizing bacteria in mouse pneumonia.

Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although in vivo imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile in vivo pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases.

Development of a proton CT imaging system using scintillator-based range detection.

The accuracy of proton therapy and preclinical proton irradiation experiments is susceptible to proton range uncertainties, which partly stem from the inaccurate conversion between CT numbers and relative stopping power (RSP). Proton computed tomography (PCT) can reduce these uncertainties by directly acquiring RSP maps. This study aims to develop a novel PCT imaging system based on scintillator-based proton range detection for accurate RSP reconstruction. The proposed PCT system consists of a pencil-beam brass collimator with a 1mm aperture, an object stage capable of translation and 360° rotation, a plastic scintillator for dose-to-light conversion, and a complementary metal oxide semiconductor (CMOS) camera for light distribution acquisition. A calibration procedure based on Monte Carlo (MC) simulation was implemented to convert the obtained light ranges into water equivalent ranges. The water equivalent path lengths (WEPLs) of the imaged object were determined by calculating the differences in proton ranges obtained with and without the object in the beam path. To validate the WEPL calculation, measurements of WEPLs for eight tissue-equivalent inserts were conducted. PCT imaging was performed on a custom-designed phantom and a mouse, utilizing both 60 and 360 projections. The filtered back projection (FBP) algorithm was employed to reconstruct the RSP from WEPLs. Image quality was assessed based on the reconstructed RSP maps and compared to reference and simulation-based reconstructions. The differences between the calibrated and reference ranges of 110-150 MeV proton beams were within 0.18mm. The WEPLs of eight tissue-equivalent inserts were measured with accuracies better than 1%. Phantom experiments exhibited good agreement with reference and simulation-based reconstructions, demonstrating average RSP errors of 1.26%, 1.38%, and 0.38% for images reconstructed with 60 projections, 60 projections after penalized weighted least-squares algorithm denoising, and 360 projections, respectively. Mouse experiments provided clear observations of mouse contours and major tissue types. MC simulation estimated an imaging dose of 3.44 cGy for decent RSP reconstruction. The proposed PCT imaging system enables RSP map acquisition with high accuracy and has the potential to improve dose calculation accuracy in proton therapy and preclinical proton irradiation experiments.

Time-to-brightness converter (TBC): measuring photon arrival time with conventional cameras.

We introduce a new, to our knowledge, method to measure the arrival time of photons with a sub-nanosecond precision using two conventional cameras. The method exploits the finite rise/fall time of the electro-optical global shutter implemented in modern complementary metal-oxide semiconductor (CMOS) cameras. By mapping the arrival time to the normalized brightness, the time of flight (ToF) can be determined with a precision better than 0.3 ns. The method can be implemented at the pixel level of a camera and thus simultaneously provides a high spatial resolution to achieve high-performing three-dimensional (3D) imaging.

Choosing a camera and optimizing system parameters for speckle contrast optical spectroscopy

Speckle contrast optical spectroscopy (SCOS) is an emerging camera-based technique that can measure human cerebral blood flow (CBF) with high signal-to-noise ratio (SNR). At low photon flux levels typically encountered in human CBF measurements, camera noise and nonidealities could significantly impact SCOS measurement SNR and accuracy. Thus, a guide for characterizing, selecting, and optimizing a camera for SCOS measurements is crucial for the development of next-generation optical devices for monitoring human CBF and brain function. Here, we provide such a guide and illustrate it by evaluating three commercially available complementary metal–oxide–semiconductor cameras, considering a variety of factors including linearity, read noise, and quantization distortion. We show that some cameras that are well-suited for general intensity imaging could be challenged in accurately quantifying spatial contrast for SCOS. We then determine the optimal operating parameters for the preferred camera among the three and demonstrate measurement of human CBF with this selected low-cost camera. This work establishes a guideline for characterizing and selecting cameras as well as for determining optimal parameters for SCOS systems.

A Data-driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

In recent years, there has been a gradual increase in the performance of complementary metal oxide semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During the pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature–dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection.

Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

Measurement of Laminar Burning Speed of Methane/Hydrogen/Air Mixtures at High Pressures and Temperatures

Abstract Green hydrogen which could be produced from renewable sources by solar water splitting or photovoltaic electrolysis will play an important role in achieving net-zero in the near future. One possible approach will be to mix hydrogen with natural gas for power generation in gas turbine systems. It is necessary to know the physical properties of burning speed of the mixture of natural gas and hydrogen. Since natural gas is mainly made up of methane, the burning speed of mixtures of methane and hydrogen has been measured and reported in this paper. Adding hydrogen gas during the combustion of methane enhances flame stability, expands the lean flammability range, decreases pollutant emissions, and boosts the burning speed. Burning speed measurement is performed in a cylindrical and spherical chamber. The pressure rise due to combustion was measured by a pressure transducer on the top of cylindrical and spherical chambers. The Z-shaped Schlieren system, equipped with a high-speed complementary metal oxide semiconductor (CMOS) camera, obtains pictures of flame propagation. Laminar burning speed is measured exclusively for flames that have a smooth and spherical shape. In addition, burning speed is only measured for large flame radii with low stretch rates. Burning speed is calculated by a thermodynamic model with the pressure rise data as an input. Measurements cover a wide range of operating conditions. The hydrogen mole fraction is 0%, 20%, and 40%, with temperatures of 298–400 K, pressures between 0.5 and 5.5 atmospheres and equivalence ratios of 0.8, 1, and 1.2.

Design of a Scanning Module in a Confocal Microscopic Imaging System for Live-Cell Imaging

This study proposes a Nipkow-based pinhole disk laser scanning confocal microscopic imaging system for ordinary optical microscopy, fluorescence microscopy, and confocal microscopy imaging of biological samples in order to realize the dynamic experimental monitoring of space-based life science experiments and the fine observation of biological samples. Confocal microscopic imaging is mainly completed by a scanning module that is composed of a spinning disk and other components. The parameters of the spinning disk directly determine the quality of the image. During the design process, the resolution and signal-to-noise ratios caused by different pinhole diameters in the spinning disk are the main considerations. Changes and image blurring caused by crosstalk due to the pinhole arrangement and different pinhole spacings are addressed. The high photon efficiency of the new EMCCD (electron-multiplying charge-coupled device) and CMOS (complementary metal-oxide-semiconductor) camera reduces the exposure time as much as possible, reduces damage to living cells, and achieves high-speed confocal imaging. It is shown in a confocal imaging experiment with a variable magnification of 1–40× that the imaging resolution of the system can reach a maximum of 2592 × 1944, the spatial resolution can reach 1 μm, and the highest sampling frequency is 10 fps, thus meeting the design requirements for high-speed live-cell imaging.

Development and comparison of algorithms for beam stabilization in ultrashort pulsed laser equipped on a six-axis robot

We demonstrate an innovative beam stabilization concept consisting of complementary metal-oxide semiconductor cameras and piezo actuators for a six-axis articulated ultrashort pulsed laser robot system. The beam stabilization system is fixed on robot axes 4 and 5. Moving robot axis 5 in an angular range between 0° and 90°, the laser beam position coupled to the actual robot position is monitored by two cameras integrated on robot axis 5 and used for laser beam characterization and model generation. A mathematical description and models generated with machine learning methods, namely, linear regression and neural network, are compared for predicting the beam position drift as a function of robot axis motion, where the neural network model shows a low prediction tolerance of about 7 pixels. In addition, a stand-alone time-triggered beam correction algorithm is developed and implemented on the system, which shows an excellent correction performance for large beam position drifts (below 500 pixels).

Single-Grating Monolithic Spatial Heterodyne Raman Spectrometer: An Investigation on the Effects of Detector Selection.

Spatial heterodyne Raman spectrometers (SHRSs) are modified forms of Michelson interferometers, except the mirrors in a Michelson interferometer are replaced with stationary diffraction gratings. This design removes the need for an entrance slit, as is the case in a dispersive spectrometer, and removes the need to scan the spectrum by using a moving mirror in a modern Michelson interferometer. In previous studies, various SHRS variants, such as free-standing two-grating SHRS, single-grating SHRS (1g-SHRS), monolithic SHRS (mSHRS), and single-grating mSHRS (1g-mSHRS), have been evaluated. However, the present study exclusively focuses on the 1g-mSHRS configuration. The 1g-mSHRS and 1g-SHRS increase the spectral range at fixed grating line density while trading off spectral resolution and resolving power. The mSHRS benefits from increased rigidity, lack of moving parts, and reduced footprint. In this study, we investigate how the choice of detector impacts the performance of the 1g-mSHRS system, with a specific focus on evaluating the performance of three types of cameras: charged-coupled device (CCD), intensified CCD (ICCD), and complementary metal-oxide-semiconductor (CMOS) cameras. These systems were evaluated using geological, organic, and inorganic samples using a 532 nm continuous wave laser for the CMOS and CCD cameras, and a 532 nm neodymium-doped yttrium aluminum garnet pulsed laser for the ICCD camera. The footprint of the 1g-mSHRS was 3.5 × 3.5 × 2.5 cm3 with a mass of 272 g or 80 g, depending on whether the monolith housing is included or not. We found that increasing the number of pixels utilized along the x-axis of the camera increases fringe visibility (FV) and optimizes the resolution (by capturing the entirety of the grating and magnifying the fringes). The number of pixels utilized in the y-axis, chip size, and dimensions, affect the signal-to-noise ratio of the systems. Additionally, we discuss the effect of pixel pitch on the recovery of Fizeau fringes, including the relationship between the Nyquist frequency, aliasing, and FV.

Deep learning-based fluorescence image correction for high spatial resolution precise dosimetry

Objective. While radiation-excited fluorescence imaging has great potential to measure absolute 2D dose distributions with high spatial resolution, the fluorescence images are contaminated by noise or artifacts due to Cherenkov light, scattered light or background noise. This study developed a novel deep learning-based model to correct the fluorescence images for accurate dosimetric application. Approach. 181 single-aperture static photon beams were delivered to an acrylic tank containing quinine hemisulfate water solution. The emitted radiation-exited optical signals were detected by a complementary metal-oxide semiconductor camera to acquire fluorescence images with 0.3 × 0.3 mm2 pixel size. 2D labels of projected dose distributions were obtained by applying forward projection calculation of the 3D dose distributions calculated by a clinical treatment planning system. To calibrate the projected dose distributions for Cherenkov angular dependency, a novel empirical Cherenkov emission calibration method was performed. Total 400-epoch supervised learning was applied to a convolutional neural network (CNN) model to predict the projected dose distributions from fluorescence images, gantry, and collimator angles. Accuracy of the calculated projected dose distributions was evaluated with that of uncorrected or conventional methods by using a few quantitative evaluation metrics. Main results. The projected dose distributions corrected by the empirical Cherenkov emission calibration represented more accurate noise-free images than the uncalibrated distributions. The proposed CNN model provided accurate projected dose distributions. The mean absolute error of the projected dose distributions was improved from 2.02 to 0.766 mm·Gy by the CNN model correction. Moreover, the CNN correction provided higher gamma index passing rates for three different threshold criteria than the conventional methods. Significance. The deep learning-based method improves the accuracy of dose distribution measurements. This technique will also be applied to optical signal denoising or Cherenkov light discrimination in other imaging modalities. This method will provide an accurate dose verification tool with high spatial resolution.

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

Using a fast hybrid pixel detector for dose-efficient diffraction imaging beam-sensitive organic molecular thin films

We discuss the benefits and showcase the applications of using a fast, hybrid-pixel detector (HPD) for 4D-STEM experiments and emphasize that in diffraction imaging the structure of molecular nano-crystallites in organic solar cell thin films with a dose-efficient modality 4D-scanning confocal electron diffraction (4D-SCED). With 4D-SCED, spot diffraction patterns form from an interaction area of a few nm while the electron beam rasters over the sample, resulting in high dose effectiveness yet highly demanding on the detector in frame speed, sensitivity, and single-pixel count rate. We compare the datasets acquired with 4D-SCED using a fast HPD with those using state-of-the-art complementary metal-oxide-semiconductor (CMOS) cameras to map the in-plane orientation of π-stacking nano-crystallites of small molecule DRCN5T in a blend of DRCN5T: PC71BM after solvent vapor annealing. The high-speed CMOS camera, using a scintillator optimized for low doses, showed impressive results for electron sensitivity and low noise. However, the limited speed restricted practical experimental conditions and caused unintended damage to small and weak nano-crystallites. The fast HPD, with a speed three orders of magnitude higher, allows a much higher probe current yet a lower total dose on the sample, and more scan points cover a large field of view in less time. A lot more faint diffraction signals that correspond to just a few electron events are detected. The improved performance of direct electron detectors opens more possibilities to enhance the characterization of beam-sensitive materials using 4D-STEM techniques.

Scanning darkfield high-resolution microendoscope for label-free microvascular imaging.

Characterization of microvascular changes during neoplastic progression has the potential to assist in discriminating precancer and early cancer from benign lesions. Here, we introduce a novel high-resolution microendoscope that leverages scanning darkfield reflectance imaging to characterize angiogenesis without exogenous contrast agents. Scanning darkfield imaging is achieved by coupling programmable illumination with a complementary metal-oxide semiconductor (CMOS) camera rolling shutter, eliminating the need for complex optomechanical components and making the system portable, low-cost (<$5,500) and simple to use. Imaging depth is extended by placing a gradient-index (GRIN) lens at the distal end of the imaging fiber to resolve subepithelial microvasculature. We validated the capability of the scanning darkfield microendoscope to visualize microvasculature at different anatomic sites in vivo by imaging the oral cavity of healthy volunteers. Images of cervical specimens resected for suspected neoplasia reveal distinct microvascular patterns in columnar and squamous epithelium with different grades of precancer, indicating the potential of scanning darkfield microendoscopy to aid in efforts to prevent cervical cancer through early diagnosis.

Passive machine vision-based defect classification in tungsten inert gas welding on SS304 using AI-based gradient descent algorithm

In modern digitization, safety industries demand flaw-free and high-integrity welds, due to part localization on high uncertainty makes automation a challenging task. Integrating robotic welding with high-value manufacturing sector makes volume rise through pre-programmed repetitive performance on desired welding space. Further, the establishment of fully automated robotic-based welding operations with different configurations on controlled localization is the least available due to unviable cost. Hence, the proposed work concentrates on internet of things (IoT)-driven robotic tungsten inert gas (TIG) welding on stainless steel (SS) 304 by incorporating online programming (OP) with visual control schemes to classify the nature of weld quality using an artificial neural network. Continuous sensory-guided techniques with IoT high-level operator interface affords automated welding planning by using feature matching strategies. Area scan-based complementary metal oxide semiconductor (CMOS) cameras have been used to capture passive vision real-time images of the weld species for defect classification. In real-time, discrete reference workpiece and image feature extraction processes seem to be complicated in an unstructured welding environment. Hence, the present idea will enhance the efficiency of high product variance, but the accuracy of the automation relies on the weld image database. The proposed method (a) adaptively regulates its welding parameter variation on the welding trajectory path, (b) adapts and produces precise good weld workpieces with a 92% production rate with flaw-free welds and (c) initiates the automatic tuning of robot kinematics through IoT closed-loop online external control strategy. The proposed research experimental results confirm that fully automated IoT-driven robotic TIG welding affords good welding with an 88% improved quality rate through online passive vision-based image feature analysis.

A Novel Method for Angioscopic Imaging and Visualizing the Skull Base Using Complementary Metal Oxide Semiconductor Cameras.

Complementary metal oxide semiconductor (CMOS) electrode arrays are a novel technology for miniaturized endoscopes; however, its use for neurointervention is yet to be investigated. In this proof-of-concept study, we aimed to demonstrate the feasibility of CMOS endoscopes in a canine model by providing direct visualization of the endothelial surface, deploying stents and coils, and accessing the spinal subdural space and skull base. Using 3 canine models, standard guide catheters were introduced into the internal carotid and vertebral arteries through the transfemoral route using fluoroscopy. A 1.2-mm CMOS camera was delivered through the guide catheter to inspect the endothelium. Next, the camera was introduced alongside standard neuroendovascular devices including coils and stents to provide direct visualization of their deployment within the endothelium during fluoroscopy. One canine was used for skull base and extravascular visualization. A lumbar laminectomy was performed, and the camera was navigated within the spinal subdural space until the posterior circulation intracranial vasculature was visualized. We successfully visualized the endothelial surface and performed several endovascular procedures such as deployment of coils and stents under direct endovascular, angioscopic vision. We also demonstrated a proof of concept for accessing the skull base and posterior cerebral vasculature using CMOS cameras through the spinal subdural space. This proof-of-concept study demonstrates the feasibility of CMOS camera technology to directly visualize endothelium, perform common neuroendovascular procedures, and access the base of the skull in a canine model.

An integrated 3D printed radioluminescent-based phantom for quality assurance on a robotic-arm linac

Objective. To develop, characterize and improve upon a high-resolution 3D printed radioluminescence-based imaging phantom for quality assurance (QA) of a robotic arm linear accelerator. Approach. A phantom was constructed which consisted of a scintillating sheet, fiducial markers, a low-cost complementary metal-oxide semiconductor camera and a 3D printed light–tight enclosure. The camera, equipped with a 12 mm lens, was angled 45 degrees from the horizontal axis with a direct line of sight of the scintillating sheet. A perspective image transformation with optical distortion correction was employed to obtain beam’s eye view images for different collimators. Beam profiles, Iris™ field size, multileaf collimator leaf positioning and central laser-radiation field coincidence QA tests were performed and compared against data obtained with gafchromic film. The phantom’s short-term stability, sensitivity to changes in output, field size and leaf positioning were also assessed. Main Results. The limiting resolution of the optical system was measured to be ∼0.26 mm. Field size, as measured by the radioluminescence system for Iris apertures, agreed to within 0.2 mm of the values measured using film. The imaging system was sensitive to field size changes well below 0.2 mm and output changes as small as 1 monitor unit (MU). For the optical setup, the mean leaf deviation error for banks X1 and X2 was 0.21 and 0.17 mm at 800 mm source to axis distance, whereas the mean difference for the film dataset was 0.16 mm and 0.22 mm for banks X1 and X2, respectively. The optical system was able to detect leaf positioning errors as small as 0.2 mm. Compared with film data, excellent agreement was seen for relative central axis beam profiles for 10 mm and 5 mm beams. Significance. The phantom presented here is an alternative to film and electronic portal imager devices, due to its low-cost, portability, and high spatial and temporal resolution.

Slicing Newton spheres with a two-camera 3D imaging system.

We demonstrate a simple approach to achieve three-dimensional ion momentum imaging. The method employs two complementary metal-oxide-semiconductor cameras in addition to a standard microchannel plates/phosphor screen imaging detector. The two cameras are timed to measure the decay of luminescence excited by ion hits to extract the time of flight. The achieved time resolution is better than 10ns, which is mainly limited by camera jitters. A better than 5ns resolution can be achieved when the jitter is suppressed.

Toward High‐Throughput Single‐Spin Optically Detected Magnetic Resonance Spectroscopy with Spatially Programmable Laser Illumination

AbstractThe single nitrogen‐vacancy (NV) centers in diamond are shown as a quantum sensor with broad applications in nanoscale sensing of magnetic field, electric field, temperature, and strain. However, the basic technique, namely, optically detected magnetic resonance to manipulate and measure single NV centers needs to be highly sped up for broad and practical applications. Here, this work shows a parallel optically detected magnetic resonance (ODMR) platform of single NV centers including the programmable excitation laser spots generated by a digital micromirror device, a four‐channel uniform microwave delivery, and the fluorescence detector based on scientific complementary metal–oxide–semiconductor camera. In the viewing field of 26 µm, hundreds of distinguishable fluorescent spots are addressed and 413 NV center spots are recognized. The magnetic resonance spectrum, Rabi oscillation, and spin echo of each NV center spot are measured in parallel. As a result, a preliminary speedup of ≈20‐fold is achieved compared to the confocal‐based ODMR and can be further extended to thousands of folds after updating the digital micromirror device and camera.