Ultra-weak light detection system and method based on SPAD array and time correlation photon counting

Abstract

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-weak light detection, and in particular to an ultra-weak light detection system and method based on a SPAD array and time-correlated photon counting. Background Art

[0002] With the advancement of high-sensitivity light detection technologies such as quantum communication, bioluminescence imaging, deep space exploration, single-molecule imaging, and night vision imaging, the demand for detecting "ultra-weak light signals" is increasing. Ultra-weak light typically refers to conditions where the photon flux density incident on the detector is extremely low (even less than 1 photon per nanosecond). In these application scenarios, traditional CCD (Charge-Coupled Device) or CMOS (Complementary Metal Oxide Semiconductor) image sensors struggle to meet the performance requirements.

[0003] Single-photon detectors (SPADs) have attracted attention for their high sensitivity to extremely weak optical signals. SPADs (Single-Photon Avalanche Diodes) have become a hot topic in research and application. Operating in Geiger mode, SPADs produce an avalanche response to each incoming photon. This not only offers a high single-photon response efficiency (η) but also nanosecond-scale temporal resolution. However, they are also sensitive to interference sources such as dark current, thermal noise, and crosstalk, necessitating the implementation of noise suppression strategies to obtain a usable signal.

[0004] Furthermore, due to the extremely low intensity of ultra-weak light signals, they exhibit significant randomness, making simple integration-based readings or intensity averaging methods highly susceptible to noise contamination, leading to misjudgments or loss. To overcome these drawbacks, existing technologies employ Time-Correlated Single Photon Counting (TCSPC) technology. By comparing the time reference (trigger) of the excitation light source with the arrival time of the detection photons, TCSPC effectively distinguishes photon signals from background noise, thereby significantly improving the signal-to-noise ratio (SNR).

[0005] However, the existing technology still has the following shortcomings:

[0006] 1) SPAD devices mostly exist in the form of single-point detection or linear arrays, lacking scalable large-scale two-dimensional array design and insufficient integration.

[0007] 2) TCSPC technology requires processing large amounts of data, and existing algorithms have problems such as poor real-time performance;

[0008] 3) For low-reflectivity targets with strong light absorption characteristics (such as black silicon), the sensitivity of existing systems is insufficient and cannot meet high-precision requirements.

[0009] Therefore, it is urgent to develop a SPAD array detection system that is oriented to low-reflectivity materials, has a high degree of integration, strong anti-noise capability, and is suitable for TCSPC data processing.

[0010] Based on this, the present invention designs an ultra-weak light detection system and method based on SPAD array and time-correlated photon counting to solve the above problems. Summary of the Invention

[0011] To address the aforementioned shortcomings of existing technologies, the present invention provides an ultra-weak light detection system and method based on a SPAD array and time-correlated photon counting. This system, utilizing a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, and an adaptive convolution filter, enables highly sensitive imaging and quantitative detection of extremely low-reflectivity materials (such as black silicon). The system boasts excellent real-time performance, high temporal resolution, a high signal-to-noise ratio, robust on-chip processing capabilities, and a high degree of integration. It also addresses the bottlenecks of traditional TCSPC methods in processing large-scale photon data.

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0013] Ultra-weak light detection system based on SPAD array and time-correlated photon counting, including:

[0014] Trigger light source;

[0015] Multi-channel SPAD array detection unit, used to capture ultra-weak light signals and trigger the time-to-digital conversion module to record the arrival time of photons;

[0016] A time-to-digital conversion module, used to convert the arrival time of each photon into an accurate timestamp;

[0017] The photon arrival time analysis unit includes: a TCSPC model for calculating delay time and constructing a delay time histogram; a noise modeling and suppression module for modeling background noise and effective signal models, while introducing a weighted filter to suppress background noise; and an adaptive convolution filter for outputting effective photon events.

[0018] Data compression and fitting module for constructing two-dimensional images;

[0019] Spatial denoising filter, if it is a low reflectivity target with strong light absorption characteristics, the spatial denoising filter performs Gaussian-Bayesian filtering in the two-dimensional image space to smooth the image spatial noise;

[0020] An output unit, used for outputting a two-dimensional image result;

[0021] and a main control processing unit.

[0022] Furthermore, the multi-channel SPAD array detection unit adopts a two-dimensional SPAD array detector, and each SPAD pixel structure includes an avalanche diode body and an active quenching circuit; the active quenching circuit structure is: bias voltage V BIAS One end is connected to the anode of the avalanche diode body, with a bias voltage of V BIAS The other end is connected to one end of the enable EN, the cathode of the avalanche diode is grounded, the cathode of the avalanche diode is also connected to the drain of the field effect transistor, the source of the field effect transistor is grounded, one end of the resistor R is connected to the drain of the field effect transistor, the other end of the resistor R is connected to the inverting input of the operational amplifier, and the non-inverting input of the operational amplifier is connected to the reference voltage V REF , the other end of the enable EN is connected to the output end.

[0023] Furthermore, the time-to-digital conversion module adopts a time-to-digital converter, which includes a main counter, a delay chain, a sampling latch and a Gray code converter.

[0024] Furthermore, the timestamp calculation formula is:

[0025]

[0026] Among them, N clk is the main counter value, is the system clock period, T fine Calculate the result for the delay chain;

[0027] The time resolution δt is:

[0028]

[0029] Where n is the TDC bit width.

[0030] Furthermore, the following algorithm is used in the TCSPC model:

[0031] Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is:

[0032]

[0033] Count all detected photon events and construct a delay time histogram:

[0034]

[0035] in, : Delay time histogram, : number of sampling times, : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : approximation of the Dirac delta function; : time variable in the histogram;

[0036] The following algorithms are used in the Noise Modeling and Suppression module:

[0037] Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is:

[0038]

[0039] in, : Background noise at time The probability density when : the average count rate of background noise, : the time of arrival of the photon;

[0040] Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope:

[0041]

[0042] Among them, P s (t): at time The signal probability density at : Peak time of effective signal, 1: standard deviation of the signal’s time distribution, : actual arrival time of photons;

[0043] The weighted filter W(t) is introduced to suppress the background:

[0044]

[0045] in, is the regularization term;

[0046] Use the adaptive convolution filtering algorithm in the adaptive convolution filter:

[0047] Define the time window function G(t) to enhance the repetitive signal:

[0048]

[0049] The convolution result is:

[0050]

[0051] Among them, t-τ reflects the time interval from the past moment τ to the current moment t;

[0052] When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

[0053] Furthermore, the photon arrival time analysis unit adopts a hardware collaborative acceleration structure, namely an FPGA+ASIC hybrid architecture.

[0054] Furthermore, the two-dimensional image is input into the spatial denoising filter and Gaussian-Bayesian filtering is performed using the following formula:

[0055]

[0056] in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter for controlling the decay speed of Gaussian weight, Ω is The neighborhood window is centered on , and Z is the normalization factor.

[0057] In order to better achieve the purpose of the present invention, the present invention also provides an ultra-weak light detection method based on a SPAD array and time-correlated photon counting, comprising the following steps:

[0058] Step 1: Laser pulse triggering;

[0059] Step 2: The two-dimensional SPAD array detector receives the ultra-weak light signal and triggers an avalanche, and triggers the time-to-digital conversion module to record the arrival time of the photon;

[0060] Step 3: The time-to-digital converter converts the arrival time of each photon into an accurate timestamp;

[0061] Step 4: The photon arrival time analysis unit calculates the delay time and constructs a delay time histogram through the TCSPC model. The background noise modeling and effective signal modeling are performed through the noise modeling and suppression module. A weighted filter is introduced to suppress the background, and the effective photon event is output through the adaptive convolution filter.

[0062] Step 5: Construct a two-dimensional image. If the target is a low-reflectivity target with strong light absorption characteristics (such as black silicon), the two-dimensional image is further input into the spatial denoising filter to perform Gaussian-Bayesian filtering to smooth the image spatial noise.

[0063] Step 6: Output the two-dimensional image result to the main control processing unit.

[0064] Furthermore, in step 4, the following algorithm is used in the TCSPC model:

[0065] Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is:

[0066]

[0067] Count all detected photon events and construct a delay time histogram:

[0068]

[0069] in, : Delay time histogram, : number of sampling times, : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : approximation of the Dirac delta function; : time variable in the histogram;

[0070] The following algorithms are used in the Noise Modeling and Suppression module:

[0071] Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is:

[0072]

[0073] in, : Background noise at time The probability density when : the average count rate of background noise, : the time of arrival of the photon;

[0074] Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope:

[0075]

[0076] Among them, P s (t): at time The signal probability density at : Peak time of effective signal, 1: standard deviation of the signal’s time distribution, : actual arrival time of photons;

[0077] The weighted filter W(t) is introduced to suppress the background:

[0078]

[0079] in, is the regularization term;

[0080] Use the adaptive convolution filtering algorithm in the adaptive convolution filter:

[0081] Define the time window function G(t) to enhance the repetitive signal:

[0082]

[0083] The convolution result is:

[0084]

[0085] Among them, t-τ reflects the time interval from the past moment τ to the current moment t;

[0086] When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

[0087] Furthermore, in step 5, the two-dimensional image is further input into the spatial denoising filter to perform Gaussian-Bayesian filtering using the following formula:

[0088]

[0089] in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter for controlling the Gaussian weight decay speed, Ω is The neighborhood window is centered on , and Z is the normalization factor.

[0090] Compared with the existing technology, the present invention has the following beneficial effects: the present invention adopts a two-dimensional SPAD array detector (SPAD) and a time-to-digital converter (TDC) to form a detection unit, combined with a TCSPC model, a noise modeling and suppression module, and an adaptive convolution filter, etc., to effectively suppress background noise, improve the ability to identify extremely weak signals, and have good signal restoration performance under conditions of strong ambient light interference and high noise levels. It can adapt to the high absorption characteristics of low reflectivity areas (such as black silicon surfaces), significantly enhance the detection ability of weak reflected light, and achieve high-sensitivity imaging and quantitative detection of extremely low reflectivity materials (such as black silicon). The system of the present invention supports black silicon materials with a reflectivity of less than 10 -3 Effective imaging under conditions, the minimum measurable reflectivity is 7×10 -5 The system is highly integrated, supports on-chip parallel TCSPC processing, and features excellent real-time performance, high temporal resolution, high signal-to-noise ratio, and strong on-chip processing capabilities. It can adopt a hardware collaborative acceleration structure, suitable for the rapid construction of large-scale two-dimensional images. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0092] Figure 1 This is a structural diagram of the ultra-weak light detection system based on SPAD array and time-correlated photon counting of the present invention.

[0093] Figure 2 This is the structural diagram of the active quenching circuit.

[0094] Figure 3 Flowchart of the photon arrival time resolution unit.

[0095] Figure 4 This is a schematic diagram of the output results of black silicon sample testing using the present invention.

[0096] Figure 5 Schematic diagram of the chip physical layout of the two-dimensional SPAD array detector (SPAD) and time-to-digital converter (TDC). DETAILED DESCRIPTION

[0097] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0098] Example 1: Please refer to the accompanying drawings of the specification Figure 1-Figure 5 , an ultra-weak light detection system based on SPAD array and time-correlated photon counting, including:

[0099] 1. Trigger the light source, using a laser and TTL signal to trigger the laser pulse emission;

[0100] 2. Multi-channel SPAD array detection unit (SPAD for short), which uses a two-dimensional SPAD array detector to capture ultra-weak light signals and trigger the time-to-digital conversion module to record the arrival time of photons;

[0101] To achieve high integration and high spatial resolution, a 256×256 two-dimensional SPAD array detector was fabricated using a CMOS (complementary metal oxide semiconductor) compatible process. Each SPAD pixel in the two-dimensional SPAD array detector consists of an avalanche diode (AD) and an active quenching circuit (AQC).

[0102] Active quenching circuit see Figure 2 : Bias voltage V BIAS One end is connected to the anode of the avalanche diode body, with a bias voltage of V BIAS The other end is connected to one end of the enable EN, the cathode of the avalanche diode is grounded, the cathode of the avalanche diode is also connected to the drain of the field effect transistor, the source of the field effect transistor is grounded, one end of the resistor R is connected to the drain of the field effect transistor, the other end of the resistor R is connected to the inverting input of the operational amplifier, and the non-inverting input of the operational amplifier is connected to the reference voltage V REF , the other end of enable EN is connected to the output end, and the output end outputs the signal processed by the operational amplifier.

[0103] Each SPAD pixel structure of the two-dimensional SPAD array detector adopts a silicon-based avalanche structure with an operating voltage of V op :

[0104] V op =V br +ΔV

[0105] Among them, V bris the avalanche breakdown voltage, ΔV is the overvoltage, and ΔV ranges from 2 to 5V.

[0106] When the SPAD reaches the breakdown condition, it generates an avalanche response and triggers the time-to-digital conversion module to record the time. When the incident photon triggers the SPAD to generate an avalanche, the voltage at the SPAD cathode drops rapidly. After the operational amplifier detects this voltage change, it immediately outputs a high level to drive the MOSFET to turn on, grounding the cathode of the SPAD, thereby quickly discharging the avalanche current and terminating the avalanche process. After the preset quenching time t q After that, the MOSFET is disconnected and the SPAD is reconnected to the bias voltage VBIAS and returns to the detection state, completing a complete quenching-reset cycle.

[0107] The quenching cycle is usually:

[0108]

[0109] in, is the junction capacitance, To restore the voltage.

[0110] The photon detection probability (PDP) of each SPAD pixel structure can be expressed as:

[0111]

[0112] Where λ is the wavelength of the photon, P in is the incident light power, P det This is the actual detection optical power.

[0113] 3. Time-to-digital conversion module, which uses a time-to-digital converter (TDC) to convert the arrival time of each photon into an accurate timestamp;

[0114] The time-to-digital converter (TDC) consists of a main counter, a delay chain, a sampling latch, and a Gray code converter;

[0115] The main counter uses the system clock as the timing reference to achieve a rough quantitative record of time.

[0116] The delay chain subdivides the system clock cycle into sub-cycle levels through a pseudo-random time delay line (TDL) or a digital phase-locked loop (DLL) structure.

[0117] Sampling latch: When the trigger signal edge arrives, it quickly samples the current state of the delay chain and converts the continuous time signal into discrete digital state information.

[0118] Gray code converter: Converts the state information of the delay chain from linear encoding to Gray code format and outputs the time value.

[0119] The timestamp calculation formula is:

[0120]

[0121] Among them, N clk is the main counter value, is the system clock period, T fine Calculate the result for the delay chain;

[0122] The time resolution δt is:

[0123]

[0124] Where n is the TDC bit width. To ensure the time resolution accuracy in the sub-nanosecond level, T is selected. clk =1 ns, n=10, then δt≈1 ps.

[0125] The time-to-digital conversion module can resolve time differences less than 10 ps.

[0126] 4. Photon arrival time analysis unit, including TCSPC model, noise modeling and suppression module and adaptive convolution filter, used to output valid photon events;

[0127] Among them, the following algorithm is used in the TCSPC model:

[0128] Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is:

[0129]

[0130] Count all detected photon events and construct a delay time histogram:

[0131]

[0132] in, : Delay time histogram, in time The count value on indicates how many photons are emitted at a time difference of was detected. : Number of samples, i.e., how many times the system performs an excitation-detection cycle. Each excitation may detect zero or one photon event. : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : An approximation to the Dirac delta function, used in The histogram is counted cumulatively. Since the actual data is discretely sampled, It is actually a time window function of finite width, often expressed as the count of a certain time bin. : The time variable in the histogram is usually quantized into time bins, and the resolution is determined by the TDC (time to digital converter).

[0133] Among them, the following algorithms are used in the noise modeling and suppression module:

[0134] Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is:

[0135]

[0136] in, : Background noise at time The probability density when . : Average count rate of background noise (unit: events / time). : The time of arrival of the photon. This is a standard exponential distribution used to describe the distribution of the time of occurrence of random events in the background noise.

[0137] Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope:

[0138]

[0139] Among them, P s (t): at time The signal probability density at : Peak time of effective signal, i.e. expected arrival time, 1: The standard deviation of the signal’s time distribution, reflecting its width, : The actual arrival time of the photon. This formula is a Gaussian (normal) distribution, simulating the distribution characteristics of real photon signals.

[0140] The weighted filter W(t) is introduced to suppress the background:

[0141]

[0142] in, is the regularization term;

[0143] Among them, the adaptive convolution filtering (ATCF) algorithm is used in the adaptive convolution filter:

[0144] Define the time window function G(t) to enhance the repetitive signal:

[0145]

[0146] The convolution result is:

[0147]

[0148] Among them, t-τ reflects the time interval from the past moment τ to the current moment t;

[0149] When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

[0150] Furthermore, the photon arrival time resolution unit utilizes a hardware-coordinated acceleration structure, namely a hybrid FPGA + ASIC architecture. The ASIC is used to implement front-end precision timing tasks such as SPAD array integration, TDC logic (including the main counter, delay chain, etc.), and high-speed timestamp generation, while also performing preliminary noise threshold screening. The FPGA is responsible for processing logic such as TCSPC algorithm implementation, histogram construction, background modeling and filtering, adaptive convolution, image compression, and system data scheduling. The two collaborate via a high-speed interface to achieve real-time processing and image output of large-scale parallel detection data. The FPGA is used for timing control and real-time data processing, while the ASIC is used to embed the SPAD and TDC core logic to improve data throughput. The hardware-coordinated acceleration structure is suitable for the rapid construction of large-scale two-dimensional images.

[0151] 5. Data Compression and Fitting Module (DCF), used to construct two-dimensional images;

[0152] 6. Spatial denoising filter, used to perform Gaussian-Bayesian filtering in two-dimensional image space to smooth image spatial noise and enhance imaging effects;

[0153] Use the following formula:

[0154]

[0155] in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter that controls the decay speed of Gaussian weights and determines the degree of smoothing (the larger the value, the stronger the smoothing). Ω is the standard deviation parameter that controls the decay speed of Gaussian weights and determines the degree of smoothing (the larger the value, the stronger the smoothing). The neighborhood window centered on is usually a window of size The area of ​​​​Z is the normalization factor, which makes the sum of all weights equal to 1, ensuring that the output intensity value is reasonable.

[0156] 7. Output unit, used for outputting two-dimensional image results;

[0157] 8. A main control processing unit (MCU), configured to send the two-dimensional image result to the display unit for display, and to send the two-dimensional image result to the storage unit through the data interface for storage.

[0158] Example 2: Figure 1-Figure 5 As shown, the ultra-weak light detection method based on SPAD array and time-correlated photon counting includes the following steps:

[0159] Step 1: Laser pulse triggering;

[0160] Step 2: The two-dimensional SPAD array detector receives the ultra-weak light signal and triggers an avalanche, and triggers the time-to-digital conversion module to record the arrival time of the photons;

[0161] Each SPAD pixel structure of the two-dimensional SPAD array detector includes an avalanche diode (AD) and an active quenching circuit (AQC).

[0162] Active quenching circuit see Figure 2 : Bias voltage V BIAS One end is connected to the anode of the avalanche diode body, with a bias voltage of V BIAS The other end is connected to one end of the enable EN, the cathode of the avalanche diode is grounded, the cathode of the avalanche diode is also connected to the drain of the field effect transistor, the source of the field effect transistor is grounded, one end of the resistor R is connected to the drain of the field effect transistor, the other end of the resistor R is connected to the inverting input of the operational amplifier, and the non-inverting input of the operational amplifier is connected to the reference voltage V REF , the other end of enable EN is connected to the output end, and the output end outputs the signal processed by the operational amplifier.

[0163] Each SPAD pixel structure of the two-dimensional SPAD array detector adopts a silicon-based avalanche structure with an operating voltage of V op :

[0164] V op =V br +ΔV

[0165] Among them, V br is the avalanche breakdown voltage, ΔV is the overvoltage, and ΔV ranges from 2 to 5V.

[0166] When the SPAD reaches the breakdown condition, it generates an avalanche response and triggers the time-to-digital conversion module to record the time. When the incident photon triggers the SPAD to generate an avalanche, the voltage at the SPAD cathode drops rapidly. After the operational amplifier detects this voltage change, it immediately outputs a high level to drive the MOSFET to turn on, grounding the cathode of the SPAD, thereby quickly discharging the avalanche current and terminating the avalanche process. After the preset quenching time t q After that, the MOSFET is disconnected and the SPAD is reconnected to the bias voltage VBIAS and returns to the detection state, completing a complete quenching-reset cycle.

[0167] The quenching cycle is usually:

[0168]

[0169] in, is the junction capacitance, To restore the voltage.

[0170] The photon detection probability (PDP) of each SPAD pixel structure can be expressed as:

[0171]

[0172] Where λ is the wavelength of the photon, P in is the incident light power, P det This is the actual detection optical power.

[0173] Step 3: The time-to-digital converter (TDC) converts the arrival time of each photon into an accurate timestamp;

[0174] The time-to-digital converter (TDC) consists of a main counter, a delay chain, a sampling latch, and a Gray code converter;

[0175] The main counter uses the system clock as the timing reference to achieve a rough quantitative record of time.

[0176] The delay chain subdivides the system clock cycle into sub-cycle levels through a pseudo-random time delay line (TDL) or a digital phase-locked loop (DLL) structure.

[0177] Sampling latch: When the trigger signal edge arrives, it quickly samples the current state of the delay chain and converts the continuous time signal into discrete digital state information.

[0178] Gray code converter: Converts the state information of the delay chain from linear encoding to Gray code format and outputs the time value.

[0179] The timestamp calculation formula is:

[0180]

[0181] Among them, N clk is the main counter value, is the system clock period, T fine Calculate the result for the delay chain;

[0182] The time resolution δt is:

[0183]

[0184] Where n is the TDC bit width. To ensure the time resolution accuracy in the sub-nanosecond level, T is selected. clk =1 ns, n=10, then δt≈1 ps.

[0185] The time-to-digital conversion module can resolve time differences less than 10 ps.

[0186] Step 4: The photon arrival time analysis unit calculates the delay time and constructs a delay time histogram through the TCSPC model. The background noise modeling and effective signal modeling are performed through the noise modeling and suppression module. A weighted filter is introduced to suppress the background, and the effective photon event is output through the adaptive convolution filter.

[0187] Among them, the following algorithm is used in the TCSPC model:

[0188] Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is:

[0189]

[0190] Count all detected photon events and construct a delay time histogram:

[0191]

[0192] in, : Delay time histogram, : number of sampling times, : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : approximation of the Dirac delta function; : time variable in the histogram;

[0193] Among them, the following algorithms are used in the noise modeling and suppression module:

[0194] Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is:

[0195]

[0196] in, : Background noise at time The probability density when : the average count rate of background noise, : the time of arrival of the photon;

[0197] Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope:

[0198]

[0199] Among them, P s (t): at time The signal probability density at : Peak time of effective signal, 1: standard deviation of the signal’s time distribution, : actual arrival time of photons;

[0200] The weighted filter W(t) is introduced to suppress the background:

[0201]

[0202] in, is the regularization term;

[0203] Among them, the adaptive convolution filtering (ATCF) algorithm is used in the adaptive convolution filter:

[0204] Define the time window function G(t) to enhance the repetitive signal:

[0205]

[0206] The convolution result is:

[0207]

[0208] Among them, t-τ reflects the time interval from the past moment τ to the current moment t;

[0209] When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

[0210] Step 5: Construct a two-dimensional image. If the target is a low-reflectivity target with strong light absorption characteristics (such as black silicon), the two-dimensional image is further input into the spatial denoising filter to perform Gaussian-Bayesian filtering to smooth the image spatial noise and enhance the imaging effect.

[0211] Use the following formula:

[0212]

[0213] in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter for controlling the decay speed of Gaussian weight, Ω is The neighborhood window is centered on , and Z is the normalization factor.

[0214] Step 6: Output the two-dimensional image result to the main control processing unit (MCU). The two-dimensional image result can be sent to the display unit for display, or sent to the storage unit for storage through the data interface.

[0215] Furthermore, since the black silicon surface has an extremely low reflectivity of about 10 -3 , the following detection optimization methods can also be introduced:

[0216] (1) Laser beam incident angle in Set close to the surface normal to reduce scattering loss;

[0217] (2) Increase the number of sampling times N and adopt the time superposition strategy to improve the statistical reliability of photon accumulation.

[0218] The present invention uses a two-dimensional SPAD array detector (SPAD) and a time-to-digital converter (TDC) to form a detection unit, combined with a TCSPC model, a noise modeling and suppression module, and an adaptive convolution filter, etc., to effectively suppress background noise, improve the ability to identify extremely weak signals, and have good signal restoration performance under conditions of strong ambient light interference and high noise levels. It can adapt to the high absorption characteristics of low-reflectivity areas (such as black silicon surfaces), significantly enhance the detection ability of weak reflected light, and achieve high-sensitivity imaging and quantitative detection of extremely low-reflectivity materials (such as black silicon). The system of the present invention supports black silicon materials with a reflectivity of less than 10 -3 Effective imaging under conditions, the minimum measurable reflectivity is 7×10 -5The system is highly integrated, supports on-chip parallel TCSPC processing, and features excellent real-time performance, high temporal resolution, high signal-to-noise ratio, and strong on-chip processing capabilities. It can adopt a hardware collaborative acceleration structure, suitable for the rapid construction of large-scale two-dimensional images.

[0219] Experimental Example 1: The performance indicators of the system of the present invention are shown in Table 1.

[0220] Table 1

[0221]

[0222] Experimental Example 2: Black silicon low-reflection sample test experiment.

[0223] Laser: pulse width 100 ps, ​​repetition rate 10 MHz;

[0224] Black silicon target reflectivity: 0.07%;

[0225] Detector: system of the present invention;

[0226] Comparison system: single-channel time-correlated photon counting (TCSPC) technology + photomultiplier tube (PMT).

[0227] The results are shown in Table 2.

[0228] Table 2

[0229]

[0230] It can be seen that the present invention can still accurately reconstruct the photon time distribution spectrum under ultra-weak signals, proving its applicability under low reflectivity conditions.

[0231] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. Ultra-weak light detection system based on SPAD array and time-correlated photon counting, characterized by: include: Trigger light source; Multi-channel SPAD array detection unit, used to capture ultra-weak light signals and trigger the time-to-digital conversion module to record the arrival time of photons; A time-to-digital conversion module, used to convert the arrival time of each photon into a timestamp; The photon arrival time analysis unit includes: a TCSPC model for calculating delay time and constructing a delay time histogram; a noise modeling and suppression module for modeling background noise and effective signal models, while introducing a weighted filter to suppress background noise; and an adaptive convolution filter for outputting effective photon events. Data compression and fitting module for constructing two-dimensional images; Spatial denoising filter, if it is a low reflectivity target with strong light absorption characteristics, the spatial denoising filter performs Gaussian-Bayesian filtering in the two-dimensional image space to smooth the image spatial noise; An output unit, used for outputting a two-dimensional image result; and a main control processing unit.

2. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 1, characterized in that: The multi-channel SPAD array detection unit adopts a two-dimensional SPAD array detector. Each SPAD pixel structure includes an avalanche diode body and an active quenching circuit. The active quenching circuit structure is: bias voltage V BIAS One end is connected to the anode of the avalanche diode body, with a bias voltage of V BIAS The other end is connected to one end of the enable EN, the cathode of the avalanche diode is grounded, the cathode of the avalanche diode is also connected to the drain of the field effect transistor, the source of the field effect transistor is grounded, one end of the resistor R is connected to the drain of the field effect transistor, the other end of the resistor R is connected to the inverting input of the operational amplifier, and the non-inverting input of the operational amplifier is connected to the reference voltage V REF , the other end of the enable EN is connected to the output end.

3. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 1, characterized in that: The time-to-digital conversion module adopts a time-to-digital converter, which includes a main counter, a delay chain, a sampling latch and a Gray code converter.

4. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 3, characterized in that: The timestamp calculation formula is: Among them, N clk is the main counter value, is the system clock period, T fine Calculate the result for the delay chain; The time resolution δt is: Where n is the TDC bit width.

5. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 4, characterized in that: The following algorithm is used in the TCSPC model: Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is: Count all detected photon events and construct a delay time histogram: in, : Delay time histogram, : number of sampling times, : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : approximation of the Dirac delta function; :time; The following algorithms are used in the Noise Modeling and Suppression module: Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is: in, : Background noise at time The probability density when : average counting rate of background noise; Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope: Among them, P s (t): at time The signal probability density at : Peak time of effective signal, 1: Standard deviation of the signal’s time distribution; The weighted filter W(t) is introduced to suppress the background: in, is the regularization term; Use the adaptive convolution filtering algorithm in the adaptive convolution filter: Define the time window function G(t) to enhance the repetitive signal: The convolution result is: Among them, t-τ reflects the time interval from the past moment τ to the current moment t; When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

6. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 1, characterized in that: The photon arrival time analysis unit adopts a hardware collaborative acceleration structure, namely FPGA+ASIC hybrid architecture.

7. The ultra-weak light detection system based on SPAD array and time-correlated photon counting according to claim 5, characterized in that: Gaussian-Bayesian filtering is performed in the two-dimensional image input spatial denoising filter using the following formula: in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter for controlling the Gaussian weight decay speed, Ω is The neighborhood window is centered on , and Z is the normalization factor.

8. Ultra-weak light detection method based on SPAD array and time-correlated photon counting, characterized in that: The following steps are involved: Step 1: Laser pulse triggering; Step 2: The two-dimensional SPAD array detector receives the ultra-weak light signal and triggers an avalanche, and triggers the time-to-digital conversion module to record the arrival time of the photons; Step 3: The time-to-digital converter converts the arrival time of each photon into a timestamp; Step 4: The photon arrival time analysis unit calculates the delay time and constructs a delay time histogram through the TCSPC model. The background noise modeling and effective signal modeling are performed through the noise modeling and suppression module. A weighted filter is introduced to suppress the background, and the effective photon event is output through the adaptive convolution filter. Step 5: Construct a two-dimensional image. If the target is a low-reflectivity target with strong light absorption characteristics, the two-dimensional image is further input into the spatial denoising filter to perform Gaussian-Bayesian filtering to smooth the image spatial noise. Step 6: Output the two-dimensional image result to the main control processing unit.

9. The ultra-weak light detection method based on SPAD array and time-correlated photon counting according to claim 8, characterized in that: In step 4, the following algorithm is used in the TCSPC model: Assume that the time of the i-th excitation light emission is t i （0） , the photon detection time is t i （k） , the delay time is: Count all detected photon events and construct a delay time histogram: in, : Delay time histogram, : number of sampling times, : No. In the sampling, the detected The arrival time of photons The time reference of this excitation The delay time between : approximation of the Dirac delta function; :time; The following algorithms are used in the Noise Modeling and Suppression module: Background noise modeling: Assume that the background noise is a Poisson process with an average count rate λ b , its probability density function is: in, : Background noise at time The probability density when : the average count rate of background noise, : the time of arrival of the photon; Effective signal modeling: Assume that the effective signal peak is near t0 and the model is a Gaussian envelope: Among them, P s (t): at time The signal probability density at : Peak time of effective signal, 1: Standard deviation of the signal’s time distribution; The weighted filter W(t) is introduced to suppress the background: in, is the regularization term; Use the adaptive convolution filtering algorithm in the adaptive convolution filter: Define the time window function G(t) to enhance the repetitive signal: The convolution result is: Among them, t-τ reflects the time interval from the past moment τ to the current moment t; When S(t) ≥ θ, the photon event is considered to be a valid photon event, and θ is the signal extraction threshold.

10. The ultra-weak light detection method based on SPAD array and time-correlated photon counting according to claim 9, characterized in that: In step 5, the two-dimensional image is further input into the spatial denoising filter to perform Gaussian-Bayesian filtering using the following formula: in, is the grayscale value or intensity value of the current pixel, is the position of the current pixel, I(i, j) is the pixel in the neighborhood The grayscale value or intensity value, 2 is the standard deviation parameter for controlling the Gaussian weight decay speed, Ω is The neighborhood window is centered on , and Z is the normalization factor.

Images