Multi-parameter integrated photoelectric detection sensing system based on wide spectrum response photoelectric material

Abstract

The invention discloses a multi-parameter integrated photoelectric detection sensing system based on a wide spectrum response photoelectric material. The system relates to the photoelectric sensing technology and comprises a wide-spectrum photoelectric material pixel array, a reading circuit and a processor. The pixel array is composed of a plurality of detection units, and each detection unit comprises a plurality of sub-pixels with wavelength gradients. The readout circuit constructs spectral response distribution by collecting sub-pixel signals to determine the central wavelength and calculates the total photocurrent of the detection unit. And the processor inversely calculates the in-situ temperature of the detection unit based on the central wavelength, and performs closed-loop calibration on the total light current by using the in-situ temperature. Through the spatial spectrum mapping mechanism of the sub-pixel array and the internal self-consistent calibration algorithm, the problem of inaccurate measurement caused by ex-situ temperature compensation in the prior art is solved, and the measurement accuracy and environmental adaptability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photoelectric sensing, in particular to a multi-parameter integrated photoelectric detection sensing system of wide-spectrum photoelectric material. BACKGROUND

[0002] The multi-parameter integrated photoelectric detection sensing system of wide-spectrum photoelectric material aims to obtain multiple physical parameters such as spectral intensity and temperature of incident light at the same time. In the prior art, the photoelectric conversion efficiency of the core light sensing element of such a system is highly sensitive to its operating temperature, and the fluctuation of the ambient temperature will directly cause the measured results of the photocurrent intensity to drift significantly, resulting in parameter crosstalk, thereby seriously affecting the accuracy and reliability of the system.

[0003] To solve this problem, the prior art usually adopts an external compensation scheme, that is, an independent temperature sensor is arranged in the non-light sensing area of the sensor chip to measure a macroscopic ambient temperature, and the output signals of the entire pixel array are uniformly compensated and corrected based on this temperature. However, this external compensation scheme has the following fundamental defects: first, spatial mismatch, the independent temperature sensor cannot obtain the true micro-zone temperature of each pixel due to light or local environmental differences; second, time delay, the lag of heat conduction makes the reading of the external temperature sensor unable to reflect the instantaneous temperature change of the pixel light sensing area. These two defects together result in insufficient calibration accuracy and dynamic response capability of the existing system, which is a fundamental technical bottleneck that hinders its application to high-precision applications. SUMMARY

[0004] The present application provides a multi-parameter integrated photoelectric detection sensing system based on wide-spectrum photoelectric material, which aims to solve the problem of inaccurate and unreliable multi-parameter measurement results caused by the use of external and ex situ temperature compensation in the prior art.

[0005] In view of the above problems, the present application provides a multi-parameter integrated photoelectric detection sensing system based on wide-spectrum photoelectric material, which comprises:

[0006] a wide-spectrum photoelectric material pixel array, the pixel array is composed of a plurality of detection units arranged in an array, each detection unit contains at least two sub-pixels with different spectral response peak wavelengths;

[0007] a readout circuit connected to the pixel array, the readout circuit performs the following operations on the target detection unit within the same measurement period:

[0008] a) collecting the response signals of each sub-pixel in the target detection unit, and based on the preset spectral response peak wavelength of each sub-pixel and its response signal intensity, constructing the spectral response distribution data of the target detection unit;

[0009] b) determining the center wavelength of the spectral response peak from the spectral response distribution data by a peak finding algorithm, and calculating the total photocurrent intensity of the target detection unit;

[0010] a processor connected with the readout circuit, the processor having pre-stored therein a first relationship model and a second relationship model, the processor performing:

[0011] c) based on the first relationship model, inversely calculating the center wavelength into a pixel-level in-situ temperature representing a current micro-region temperature of the target detection unit;

[0012] d) based on the second relationship model and the pixel-level in-situ temperature, calibrating the total photocurrent intensity to calculate a calibrated photocurrent intensity that has eliminated the influence of the micro-region temperature;

[0013] e) outputting a multi-parameter result containing the calibrated photocurrent intensity and the pixel-level in-situ temperature.

[0014] The technical scheme provided by the present application has at least the following technical effects: the present application uses the spectral response characteristics of a pixel itself as an internal, real-time and in-situ thermometer, and uses the temperature information to synchronously correct the photocurrent intensity of the same pixel by means of an internal self-consistent closed-loop calibration mechanism. This scheme fundamentally overcomes the spatial mismatch and time delay defects inherent in the external compensation scheme in the prior art, can accurately calibrate the real micro-region temperature of a pixel with zero delay, thereby greatly improving the measurement accuracy, repeatability and long-term stability of the entire system in a complex environment, and simplifying the system structure. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A structure schematic diagram of a multi-parameter integrated photoelectric detection and sensing system based on a wide-spectrum response photoelectric material is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0016] The above technical scheme will be described in detail below in combination with the drawings and specific embodiments, so as to better understand the above technical scheme. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments for explaining the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only the parts related to the present application are shown in the drawings, not all.

[0017] Please refer to Figure 1, a multi-parameter integrated optoelectronic detection sensing system based on a wide-spectrum optoelectronic material, the system comprising:

[0018] a wide-spectrum optoelectronic material pixel array, the pixel array consisting of a plurality of detection units arranged in an array, each of the detection units containing at least two sub-pixels with different spectral response peak wavelengths;

[0019] a readout circuit connected to the pixel array, the readout circuit performing the following operations on a target detection unit in a same measurement cycle:

[0020] a) collecting response signals of each sub-pixel in the target detection unit, and constructing spectral response distribution data of the target detection unit based on the preset spectral response peak wavelength of each sub-pixel and the response signal intensity thereof;

[0021] b) determining the center wavelength of the spectral response peak from the spectral response distribution data by a peak searching algorithm, and calculating the total photocurrent intensity of the target detection unit;

[0022] a processor connected to the readout circuit, the processor having a first relationship model and a second relationship model pre-stored therein, the processor performing:

[0023] c) inversely calculating the center wavelength into a pixel-level in-situ temperature representing the current micro-region temperature of the target detection unit based on the first relationship model;

[0024] d) calibrating the total photocurrent intensity based on the second relationship model and the pixel-level in-situ temperature to calculate a calibrated photocurrent intensity that has eliminated the influence of the micro-region temperature;

[0025] e) outputting a multi-parameter result containing the calibrated photocurrent intensity and the pixel-level in-situ temperature.

[0026] The physical implementation of the system of the present application comprises three main parts: a wide-spectrum optoelectronic material pixel array, a readout circuit and a processor.

[0027] The wide-spectrum optoelectronic material pixel array is the light sensing front end of the system. Unlike the traditional single-pixel design, the pixel array in the present embodiment is logically divided into a plurality of detection units. For example, the array consists of 1024x1024 detection units, each of which physically occupies a 3x3 micro-region and contains 9 independent sub-pixels. The center-to-center distance of the detection units is controlled in the range of 5-10 microns to balance the spatial resolution and the light sensing area.

[0028] Each sub-pixel in the detection unit is constructed as a Fabry-Perot microcavity structure to generate a response signal with wavelength selectivity. The microcavity structure is deposited on a silicon-based substrate from bottom to top by a semiconductor process. The bottom is a distributed Bragg reflector composed of 10 to 20 pairs of alternately deposited silicon dioxide and titanium dioxide films. Subsequently, a layer of wide-spectrum photovoltaic material resonant cavity layer with precisely controlled thickness as the photosensitive region is grown on the bottom distributed Bragg reflector by chemical vapor deposition or molecular beam epitaxy technology, for example, a lead sulfide quantum dot film with a thickness of 200 to 500 nanometers. Finally, a top distributed Bragg reflector composed of 5 to 10 pairs of the same material is deposited on the resonant cavity layer. The optical thickness of the resonant cavity layer determines the central wavelength of the resonant enhanced absorption of the microcavity structure, thereby forming a response peak on the spectral response curve of the sub-pixel. In order to provide a high signal-to-noise ratio tracking target for the subsequent peak searching algorithm, the full width at half maximum of the response peak can be controlled to be less than 10 nanometers.

[0029] The key is that the optical thickness of the resonant cavity layer of the 9 sub-pixels in the same detection unit is designed to have a preset gradient distribution of microns or nanometers. For example, the central response wavelengths of sub-pixel 1 to sub-pixel 9 are designed to be different values, covering the wavelength range in which the target spectral peak may change due to temperature drift (e.g. 3.3 to 3.4 microns). Through this spatial structural difference, the detection unit can capture the light intensity response at different wavelengths at one time, thereby realizing spatial-to-time spectral sampling.

[0030] The readout circuit and the processor of the system constitute a data acquisition and processing unit. The readout circuit is a special integrated circuit integrated on the same chip as the pixel array through flip-chip technology, which internally includes an address decoder, a transimpedance amplifier array and an analog-to-digital converter. The processor selects a field programmable gate array or a digital signal processor, which is connected to the readout circuit through a mobile industry processor interface. This hardware configuration ensures that the data flow path from the generation of analog photocurrent by the pixel array, to the conversion of the analog photocurrent into digital signals by the readout circuit, to the calculation by the processor is low-latency.

[0031] On the basis of the above hardware, the internal self-consistent calibration process of the system begins to execute. The first stage of this process is the synchronous acquisition and extraction of the original photoelectric signal, which is completed by the readout circuit under the timing control of the processor. The core task of this stage is to provide two necessary input quantities that are accurately synchronized in time for subsequent data processing: the central wavelength of the spectral response peak and the total photocurrent intensity.

[0032] To achieve this goal, the readout circuit performs a measurement cycle on each target pixel in the pixel array, which contains two parallel steps. The first step of this measurement cycle is to construct the spectral distribution and determine the center wavelength. The readout circuit collects the response signal intensity of all sub-pixels in the detection unit respectively. Since each sub-pixel corresponds to a different wavelength, this set of data directly constitutes the discrete spectral response distribution of the detection unit at the current time. After the processor receives the data, it performs a preset peak-finding algorithm (such as Gaussian fitting) to fit the discrete data of the sub-pixels into a continuous curve, thereby determining the high-precision center wavelength of the spectral response peak.

[0033] In this step, as a preferred embodiment, in order to improve the robustness of the system in extremely weak light environment, the processor adds a step of validity threshold judgment after calculating the center wavelength and before calculating the temperature. That is: the processor checks whether the fitted spectral peak intensity is greater than the preset valid signal threshold. If it is greater than the threshold, it means that the ambient light is sufficient and the peak-finding result is reliable, and the processor continues to perform the subsequent temperature calculation; if it is not greater than the threshold, it means that the current light is insufficient, and the processor will ignore the current peak-finding result and directly use the pixel-level in-situ temperature calculated in the last measurement cycle to prevent parameter jumps caused by dark current noise.

[0034] In the same measurement cycle, the second step is to measure the total photocurrent intensity of the detection unit. The readout circuit (or the digital logic of the processor) sums up the response signal intensity of all sub-pixels in the detection unit. This sum value represents the total light energy received by the detection unit as the raw photocurrent signal to be calibrated. The transimpedance amplifier in the readout circuit converts the photo-generated current generated by the target pixel in the preset integration time into a voltage signal, and then this voltage signal is digitized by the analog-to-digital converter to obtain a value representing the total photocurrent intensity. The determination of the center wavelength and the measurement of the total photocurrent intensity are placed in the same integration and readout cycle to ensure that these two physical quantities reflect the physical state of the pixel at the same time, thereby providing a data basis for the subsequent zero-delay calibration.

[0035] The second stage of the flow is a closed-loop process of internal self-consistent calibration, which is performed by the processor. The prerequisite for the execution of this stage is that the first relationship model and the second relationship model are stored in the processor.

[0036] The first relationship model is a data model of the drift relationship between the center wavelength and the pixel-level in-situ temperature. The establishment process of this model is completed before the system is shipped: the pixel array or a single pixel is placed in a temperature-controllable test environment, and a stable light source is used for irradiation; at a plurality of discrete temperature set points, the spectral response peak center wavelength corresponding to each temperature point is measured and recorded by the readout circuit and the processor, thereby obtaining a set of (center wavelength, temperature) data pairs; finally, the set of data pairs is converted into a function or a lookup table by polynomial fitting or piecewise linear interpolation method, and the function or the lookup table is stored in the non-volatile memory of the processor as the first relationship model.

[0037] The second relationship model is a data model of the function relationship between the photoelectric conversion efficiency of the photoelectric material and the temperature. The establishment process of this model is synchronized with the establishment process of the first relationship model. At each of the above-mentioned temperature set points, the total photocurrent intensity output by the pixel is recorded simultaneously, thereby obtaining a set of (total photocurrent intensity, temperature) data pairs. By dividing the total photocurrent intensity measured at each temperature point by the total photocurrent intensity measured at a preset reference temperature (for example, 25 degrees Celsius), the change factor of the photoelectric conversion efficiency at different temperatures is calculated. The obtained series of (change factor, temperature) data pairs are also converted into a function or a lookup table, thereby forming the second relationship model and storing it.

[0038] When the system is working normally, after the processor receives the center wavelength and the total photocurrent intensity collected in a measurement period from the readout circuit, real-time reverse calculation is started. The processor first calls the stored first relationship model, and takes the currently measured center wavelength value as the input variable. If the first relationship model is a function, the processor directly performs function operation; if it is a lookup table, the processor performs table lookup and linear interpolation operation, and calculates the pixel-level in-situ temperature corresponding to the center wavelength value.

[0039] After the processor calculates the pixel-level in-situ temperature, accurate calibration of the photocurrent intensity is immediately performed. The processor calls the stored second relationship model, takes the just-calculated pixel-level in-situ temperature as the input variable, and obtains the change factor of the photoelectric conversion efficiency at the current temperature by function calculation or lookup table interpolation. Finally, the processor divides the total photocurrent intensity measured in the same measurement period by the change factor, thereby obtaining the calibrated photocurrent intensity which has eliminated the influence of the current micro-region temperature of the pixel.

[0040] The last step of the processing flow is the final output of the calibrated multi-parameter results. The processor will package the calculated calibrated photocurrent intensity and pixel-level in-situ temperature into a data frame. This data frame can be output to the host computer or other data processing unit through an interface such as universal asynchronous receiver-transmitter or internal integrated circuit bus. A feasible output data format is to represent the calibrated photocurrent intensity and pixel-level in-situ temperature as 32-bit floating-point numbers, and attach the coordinate information and time stamp of the pixel.

[0041] In one specific embodiment for high-precision gas leakage detection of a mobile platform.

[0042] The system of the present application is applied to a methane gas leakage inspection module carried on a UAV. The goal of this inspection module is to conduct non-contact detection on the complex pipeline network of a petrochemical plant. Methane gas has a characteristic absorption peak in the mid-infrared band near 3.3 microns.

[0043] The implementation of the sensing system of the present application in this scenario is as follows: the pixel array of the system adopts a Fabry-Perot microcavity structure, and the optical thickness of the resonant cavity layer is designed to produce the strongest spectral response peak at 3.3 microns. The first relationship model and the second relationship model stored in the processor of the system are obtained through the aforementioned calibration process.

[0044] During the inspection flight, the sensing system carried by the UAV continuously scans along the pipeline. When the UAV experiences the same rapid movement from the high-temperature region to the normal-temperature region as described above, the self-consistent calibration flow in the system runs in real time. In each measurement period, the readout circuit synchronously acquires the center wavelength of the 3.3 micron spectral response peak and the total photocurrent intensity. As the micro-region temperature of the pixel rapidly decreases together with the UAV, the center wavelength of its spectral response peak will experience a drift that can be instantaneously detected. The processor immediately calls the first relationship model to inversely calculate the current real, already-decreased micro-region temperature of the pixel with zero delay from the drift value of this center wavelength. Subsequently, the processor calls the second relationship model to use this real micro-region temperature value to accurately calibrate the total photocurrent intensity obtained in the same measurement, completely eliminating the signal drift caused by the temperature drop.

[0045] Through this internal self-consistent closed-loop calibration, the output of the calibrated photocurrent intensity of the sensing system of the present application can remain highly stable even when experiencing severe environmental temperature changes, and only responds to real changes in methane gas concentration, thereby avoiding false alarms caused by temperature changes and significantly improving the accuracy and reliability of gas leakage detection of a mobile platform in a dynamic and complex environment.

[0046] The above-described embodiments are only some of the embodiments of the present application, but not all the embodiments. It should be noted that the present application is not limited to the above-described embodiments. Any modification, equivalent replacement, variation, or improvement made by any person of ordinary skill in the art within the concept and principle of the present application, without creative labor, based on the above-described embodiments, should fall into the protection scope of the claims of the present application.

Claims

1. A multi-parameter integrated photoelectric detection and sensing system based on broadband spectral response photoelectric materials, characterized in that, The system includes: A broadband optoelectronic material pixel array, wherein the pixel array is composed of a plurality of detector units arranged in an array, and each detector unit contains at least two sub-pixels with different peak wavelengths of spectral response; The readout circuit, connected to the pixel array, performs the following operations on the target detection unit within the same measurement cycle: a) Collect the response signals of each sub-pixel in the target detection unit, and construct the spectral response distribution data of the target detection unit based on the preset spectral response peak wavelength and response signal intensity of each sub-pixel; b) Using a peak-finding algorithm, determine the center wavelength of the spectral response peak from the spectral response distribution data, and calculate the total photocurrent intensity of the target detection unit; A processor, connected to the readout circuit, pre-stores a first relational model and a second relational model. The processor executes: c) Based on the first relationship model, the center wavelength is back-calculated into a pixel-level in-situ temperature characterizing the current micro-area temperature of the target detection unit; d) Based on the second relationship model and the pixel-level in-situ temperature, the total photocurrent intensity is calibrated to calculate the calibrated photocurrent intensity after eliminating the influence of the micro-area temperature; e) Output multi-parameter results including the calibrated photocurrent intensity and the pixel-level in-situ temperature.

2. The multi-parameter integrated photoelectric detection and sensing system based on broadband spectral response photoelectric materials according to claim 1, characterized in that, The processor is also configured to perform the following before executing step c): Determine whether the peak intensity of the spectral response peak determined by the readout circuit is greater than the preset effective signal threshold; if yes, proceed to step c); if no, maintain the pixel-level in-situ temperature of the previous measurement cycle.

3. The multi-parameter integrated photoelectric detection and sensing system based on broadband response photoelectric materials according to claim 1, characterized in that, The readout circuit calculates the total photocurrent intensity of the target detection unit by summing the response signal intensities of all sub-pixels within the target detection unit.

4. The multi-parameter integrated photoelectric detection and sensing system based on broadband spectral response photoelectric materials according to claim 1, characterized in that, Each of the sub-pixels within the detection unit contains a Fabry-Perot microcavity structure, and the thickness of the microcavity resonant layer of different sub-pixels within the same detection unit exhibits a preset gradient distribution.

5. The multi-parameter integrated photoelectric detection and sensing system based on broadband spectral response photoelectric materials according to claim 1, characterized in that, The peak-finding algorithm is either the centroid algorithm or the Gaussian fitting algorithm.

6. The multi-parameter integrated photoelectric detection and sensing system based on broadband response photoelectric materials according to claim 1, characterized in that, The first relationship model is a data model of the drift relationship between the center wavelength and the pixel-level in-situ temperature.

7. The multi-parameter integrated photoelectric detection and sensing system based on broadband response photoelectric materials according to claim 1, characterized in that, The second relationship model is a data model of the functional relationship between the photoelectric conversion efficiency of optoelectronic materials and temperature.

Images