High concentration factor diffractive microlenses integrated with CMOS single-photon avalanche diode detector arrays for fill-factor improvement.

Large-format single-photon avalanche diode (SPAD) arrays often suffer from low fill-factors-the ratio of the active area to the overall pixel area. The detection efficiency of these detector arrays can be vastly increased with the integration of microlens arrays designed to concentrate incident light onto the active areas and may be refractive or diffractive in nature. The ability of diffractive optical elements (DOEs) to efficiently cover a square or rectangular pixel, combined with their capability of working as fast lenses (i.e., ∼f/3) makes them versatile and practical lens designs for use in sparse photon applications using microscale, large-format detector arrays. Binary-mask-based photolithography was employed to fabricate fast diffractive microlenses for two designs of 32×32 SPAD detector arrays, each design having a different pixel pitch and fill-factor. A spectral characterization of the lenses is performed, as well as analysis of performance under different illumination conditions from wide- to narrow-angle illumination (i.e., f/2 to f/22 optics). The performance of the microlenses presented exceeds previous designs in terms of both concentration factor (i.e., increase in light collection capability) and lens speed. Concentration factors greater than 33× are achieved for focal lengths in the substrate material as short as 190µm, representing a microlens f-number of 3.8 and providing a focal spot diameter of <4µm. These results were achieved while retaining an extremely high degree of performance uniformity across the 1024 devices in each case, which demonstrates the significant benefits to be gained by the implementation of DOEs as part of an integrated detector system using SPAD arrays with very small active areas.

Wavelength-time coding for multispectral 3D imaging using single-photon LiDAR.

Single-photon multispectral light detection and ranging (LiDAR) approaches have emerged as a route to color reconstruction and enhanced target identification in photon-starved imaging scenarios. In this paper, we present a three-dimensional imaging system based on a time-of-flight approach which is capable of simultaneous multispectral measurements using only one single-photon detector. Unlike other techniques, this approach does not require a wavelength router in the receiver channel. By observing multiple wavelengths at each spatial location, or per pixel (four discrete visible wavelengths are used in this work), we can obtain a single waveform with wavelength-to-time mapped peaks. The time-mapped peaks are created by the known chromatic group delay dispersion in the laser source's optical fiber, resulting in temporal separations between these peaks being in the region of 200 to 1000 ps, in this case. A multispectral single waveform algorithm was proposed to fit these multiple peaked LiDAR waveforms, and then reconstruct the color (spectral response) and depth profiles for the entire image. To the best of our knowledge, this is the first dedicated computational method operating in the photon-starved regime capable of discriminating multiple peaks associated with different wavelengths in a single pixel waveform and reconstructing spectral responses and depth.

High-resolution depth profiling using a range-gated CMOS SPAD quanta image sensor.

A CMOS single-photon avalanche diode (SPAD) quanta image sensor is used to reconstruct depth and intensity profiles when operating in a range-gated mode used in conjunction with pulsed laser illumination. By designing the CMOS SPAD array to acquire photons within a pre-determined temporal gate, the need for timing circuitry was avoided and it was therefore possible to have an enhanced fill factor (61% in this case) and a frame rate (100,000 frames per second) that is more difficult to achieve in a SPAD array which uses time-correlated single-photon counting. When coupled with appropriate image reconstruction algorithms, millimeter resolution depth profiles were achieved by iterating through a sequence of temporal delay steps in synchronization with laser illumination pulses. For photon data with high signal-to-noise ratios, depth images with millimeter scale depth uncertainty can be estimated using a standard cross-correlation approach. To enhance the estimation of depth and intensity images in the sparse photon regime, we used a bespoke clustering-based image restoration strategy, taking into account the binomial statistics of the photon data and non-local spatial correlations within the scene. For sparse photon data with total exposure times of 75 ms or less, the bespoke algorithm can reconstruct depth images with millimeter scale depth uncertainty at a stand-off distance of approximately 2 meters. We demonstrate a new approach to single-photon depth and intensity profiling using different target scenes, taking full advantage of the high fill-factor, high frame rate and large array format of this range-gated CMOS SPAD array.

Metasurface for characterization of the polarization state of light.

The miniaturization of measurement systems currently used to characterize the polarization state of light is limited by the bulky optical components used such as polarizers and waveplates. We propose and experimentally demonstrate a simple and compact approach to measure the ellipticity and handedness of the polarized light using an ultrathin (40 nm) gradient metasurface. A completely polarized light beam is decomposed into a left circularly polarized beam and a right circularly polarized beam, which are steered in two directions by the metasurface consisting of nanorods with spatially varying orientations. By measuring the intensities of the refracted light spots, the ellipticity and handedness of various incident polarization states are characterized at a range of wavelengths and used to determine the polarization information of the incident beam. To fully characterize the polarization state of light, an extra polarizer can be used to measure the polarization azimuth angle of the incident light.

Fill-factor improvement of Si CMOS single-photon avalanche diode detector arrays by integration of diffractive microlens arrays.

Single-photon avalanche diode (SPAD) detector arrays generally suffer from having a low fill-factor, in which the photo-sensitive area of each pixel is small compared to the overall area of the pixel. This paper describes the integration of different configurations of high efficiency diffractive optical microlens arrays onto a 32 × 32 SPAD array, fabricated using a 0.35 µm CMOS technology process. The characterization of SPAD arrays with integrated microlens arrays is reported over the spectral range of 500-900 nm, and a range of f-numbers from f/2 to f/22. We report an average concentration factor of 15 measured for the entire SPAD array with integrated microlens array. The integrated SPAD and microlens array demonstrated a very high uniformity in overall efficiency.

Underwater depth imaging using time-correlated single-photon counting.

A depth imaging system, based on the time-of-flight approach and the time-correlated single-photon counting (TCSPC) technique, was investigated for use in highly scattering underwater environments. The system comprised a pulsed supercontinuum laser source, a monostatic scanning transceiver, with a silicon single-photon avalanche diode (SPAD) used for detection of the returned optical signal. Depth images were acquired in the laboratory at stand-off distances of up to 8 attenuation lengths, using per-pixel acquisition times in the range 0.5 to 100 ms, at average optical powers in the range 0.8 nW to 950 µW. In parallel, a LiDAR model was developed and validated using experimental data. The model can be used to estimate the performance of the system under a variety of scattering conditions and system parameters.

Nicotinamide mononucleotide based hyaluronic acid methacryloyl hybrid hydrogel regulating stem cells fate for bone regeneration via SIRT1/RUNX2 signaling.

Efficient bone reconstruction, especially of the critical size after bone damage, remains a challenge in the clinic. Bone marrow mesenchymal stem cell (BMSC) osteogenic differentiation is considered as a promising strategy for bone repair. Nicotinamide adenine dinucleotide (NAD+) regulating BMSC fate and cellular function enhance osteogenesis, but is hardly delivered and lack of targeting. Herein, a novel and biocompatible scaffold was fabricated to locally deliver a precursor of NAD+, nicotinamide mononucleotide (NMN) to the bone defect site, and its bone repair capability and healing mechanism were clarified. NMN-based hyaluronic acid methacryloyl hybrid hydrogel scaffold (denoted as NMN/HAMA) was prepared via photopolymerization. In vitro RT-qPCR analysis, western blotting, Elisa and alizarin red S staining assays demonstrated that the NMN/HAMA hybrid hydrogel regulated BMSCs cellular function in favour of osteogenic differentiation and mineralization by upregulating the mRNA and proteins expression of the osteogenic genes type I pro-collagen (Col-1), bone morphogenic protein 4 (BMP4), and runt-related transcription factor 2 (RUNX2) via the SIRT1 pathway. Implantation of such hybrid hydrogels significantly enhanced bone regeneration in rodent critical calvarial defect models. Furthermore, restoration of the bone defect with NMN administration was inhibited in Prx1 Cre+; SIRT1flox/flox mice, confirming that the NMN/HAMA hybrid hydrogel scaffold promoted bone regeneration via the SIRT1-RUNX2 pathway. These results imply that NMN-based scaffold may be a promising and economic strategy for the treatment of bone defects.

Kilometer-range depth imaging at 1,550 nm wavelength using an InGaAs/InP single-photon avalanche diode detector.

We have used an InGaAs/InP single-photon avalanche diode detector module in conjunction with a time-of-flight depth imager operating at a wavelength of 1550 nm, to acquire centimeter resolution depth images of low signature objects at stand-off distances of up to one kilometer. The scenes of interest were scanned by the transceiver system using pulsed laser illumination with an average optical power of less than 600 µW and per-pixel acquisition times of between 0.5 ms and 20 ms. The fiber-pigtailed InGaAs/InP detector was Peltier-cooled and operated at a temperature of 230 K. This detector was used in electrically gated mode with a single-photon detection efficiency of about 26% at a dark count rate of 16 kilocounts per second. The system's overall instrumental temporal response was 144 ps full width at half maximum. Measurements made in daylight on a number of target types at ranges of 325 m, 910 m, and 4.5 km are presented, along with an analysis of the depth resolution achieved.

Kilometer-range, high resolution depth imaging via 1560 nm wavelength single-photon detection.

This paper highlights a significant advance in time-of-flight depth imaging: by using a scanning transceiver which incorporated a free-running, low noise superconducting nanowire single-photon detector, we were able to obtain centimeter resolution depth images of low-signature objects in daylight at stand-off distances of the order of one kilometer at the relatively eye-safe wavelength of 1560 nm. The detector used had an efficiency of 18% at 1 kHz dark count rate, and the overall system jitter was ~100 ps. The depth images were acquired by illuminating the scene with an optical output power level of less than 250 µW average, and using per-pixel dwell times in the millisecond regime.

Learning Non-Local Spatial Correlations To Restore Sparse 3D Single-Photon Data.

This paper presents a new algorithm for the learning of spatial correlation and non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime (fewer or less than one photon per pixel) or with a reduced number of scanned spatial points (pixels). The algorithm alternates between three steps: (i) extract multi-scale information, (ii) build a robust graph of non-local spatial correlations between pixels, and (iii) the restoration of depth and reflectivity images. A non-uniform sampling approach, which assigns larger patches to homogeneous regions and smaller ones to heterogeneous regions, is adopted to reduce the computational cost associated with the graph. The restoration of the 3D images is achieved by minimizing a cost function accounting for the multi-scale information and the non-local spatial correlation between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Various results based on simulated and real Lidar data show the benefits of the proposed algorithm that improves the quality of the estimated depth and reflectivity images, especially in the photon-starved regime or when containing a reduced number of spatial points.

Lidar Waveform-Based Analysis of Depth Images Constructed Using Sparse Single-Photon Data.

This paper presents a new Bayesian model and algorithm used for depth and reflectivity profiling using full waveforms from the time-correlated single-photon counting measurement in the limit of very low photon counts. The proposed model represents each Lidar waveform as a combination of a known impulse response, weighted by the target reflectivity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded through prior distributions that account for the different parameter constraints and their spatial correlation among the image pixels. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target reflectivity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.