Photoelectric imaging device radiation damage analysis method based on dark current distribution fitting

Abstract

The invention provides a photoelectronic imaging device radiation damage analysis method and device based on dark current distribution fitting and electronic equipment. The method comprises the following steps: acquiring dark field image data of a photoelectric imaging device to be analyzed after accumulated radiation damage; determining a dark current value of each imaging unit in a selected area on the photoelectric imaging device based on the dark field image data, and generating dark current statistical distribution of the selected area; according to the energy deposition characteristics of the irradiation source, selecting a corresponding type of distribution model from at least two types of preset distribution models as a target distribution model; based on the target distribution model, dividing the dark current statistical distribution to obtain a plurality of distribution segments; each distribution section is composed of functions of Gaussian distribution and / or exponential distribution; performing function fitting on each divided distribution section, and extracting characteristic parameters representing different physical damage mechanisms; and carrying out quantitative analysis on the radiation damage of the photoelectric imaging device based on the characteristic parameters.

Description

Technical Field

[0001] This invention relates to the field of image sensor detection technology, and specifically to a method, apparatus, and electronic device for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting. Background Technology

[0002] Optoelectronic imaging devices are core components of space payload optoelectronic imaging systems. During long-term operation in orbit, space payloads are subjected to harsh space radiation environments, leading to parameter degradation and decreased imaging performance of these devices. This seriously jeopardizes the normal execution of on-orbit missions and the flight safety of space payloads. Dark current, as a core parameter of optoelectronic imaging devices, is not only a key indicator for measuring the degree of device degradation but also an important way to analyze radiation damage. By analyzing the distribution of dark current, we can analyze the radiation damage mechanisms and physical processes of optoelectronic imaging devices, providing theoretical support for radiation hardening methods and on-orbit maintenance measures.

[0003] Current radiation damage analysis of optoelectronic imaging devices mainly focuses on simulation and testing. Simulation of radiation damage in optoelectronic imaging devices is typically based on Monte Carlo methods, analytical dynamics, dynamic relaxation dynamics, and first-principles calculations. However, the simulation process is lengthy and complex, and mature technical methods have not yet been developed. Testing of radiation damage in optoelectronic imaging devices mainly relies on parameters such as mean dark current and fixed image noise. While these parameters can measure the performance degradation of optoelectronic imaging devices, they do not provide an effective means for mechanistic analysis. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus and electronic device for radiation damage analysis of photoelectric imaging devices based on dark current distribution fitting.

[0005] The first aspect of this invention provides a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting, comprising: acquiring dark-field image data of the photoelectric imaging device to be analyzed after cumulative radiation damage; determining the dark current value of each imaging unit in a selected area on the photoelectric imaging device based on the dark-field image data, and generating a dark current statistical distribution in the selected area; selecting one of at least two preset distribution models as the target distribution model according to the energy deposition characteristics of the irradiation source; the distribution model predefines the number, type, and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution; dividing the dark current statistical distribution based on the target distribution model to obtain multiple distribution segments; each distribution segment is composed of a function of Gaussian distribution and / or exponential distribution; performing function fitting on each of the divided distribution segments to extract characteristic parameters characterizing different physical damage mechanisms; and performing quantitative analysis of radiation damage to the photoelectric imaging device based on the characteristic parameters.

[0006] According to embodiments of the present invention, at least two types of distribution models include a first type of distribution model and a second type of distribution model. The first type of distribution model is used to handle the statistical distribution of dark current caused by irradiation sources with low Coulomb scattering shift effects, and the second type of distribution model is used to handle the statistical distribution of dark current caused by irradiation sources with high Coulomb scattering shift effects. The number of distribution segments defined by the first type of distribution model is less than the number of distribution segments defined by the second type of distribution model. Irradiation sources with low Coulomb scattering shift effects include gamma rays, X-rays, electrons, and low-flux protons, while irradiation sources with high Coulomb scattering shift effects include heavy ions and high-flux protons.

[0007] According to an embodiment of the present invention, in the first type of distribution model, the Gaussian distribution segment is located in the middle region of the overall distribution, and the distribution segments on both sides of the middle region include at least the distribution segments composed of exponential distributions.

[0008] According to an embodiment of the present invention, in the second type of distribution model, the Gaussian distribution segment is located in the middle region of the overall distribution, and at least two distribution segments composed of exponential distributions are continuously provided on the side of the middle region where the dark current value is larger.

[0009] According to an embodiment of the present invention, the step of acquiring dark field image data includes: performing a displacement dark field test on the photoelectric imaging device to acquire dark field image data at multiple different integration times.

[0010] According to an embodiment of the present invention, the step of determining the dark current value of each imaging unit in a selected area includes: for each imaging unit, performing a linear fit on its average gray value at different integration times, and determining the slope of the fitted line as the dark current value of the imaging unit.

[0011] According to an embodiment of the present invention, the step of performing function fitting on each distribution segment and extracting feature parameters includes: using a traversal optimization method to determine the target fitting interval and target fitting function for each distribution segment; and extracting Gaussian distribution parameters and / or exponential distribution parameters from each target fitting function as feature parameters.

[0012] According to an embodiment of the present invention, the step of quantitatively analyzing radiation damage based on characteristic parameters includes: quantifying the degree of uniform damage according to the characteristic parameters of a Gaussian distribution; quantifying the severity and range of discrete damage according to the characteristic parameters of an exponential distribution; and evaluating the contribution weight of different damage mechanisms to the overall performance degradation of the device based on the relative relationship between the characteristic parameters of Gaussian and exponential distributions.

[0013] A second aspect of the present invention provides a radiation damage analysis device for photoelectric imaging devices based on dark current distribution fitting, comprising: an acquisition module for acquiring dark field image data of the photoelectric imaging device to be analyzed after cumulative radiation damage; a statistics module for determining the dark current value of each imaging unit in a selected area on the photoelectric imaging device based on the dark field image data, and generating a dark current statistical distribution in the selected area; a selection module for selecting one of at least two preset distribution models as the target distribution model according to the energy deposition characteristics of the irradiation source; the distribution model predefines the number, type, and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution; a division module for dividing the dark current statistical distribution based on the target distribution model to obtain multiple distribution segments; each distribution segment is composed of a function of Gaussian distribution and / or exponential distribution; a fitting module for performing function fitting on each divided distribution segment to extract characteristic parameters characterizing different physical damage mechanisms; and an analysis module for quantitatively analyzing the radiation damage of the photoelectric imaging device based on the characteristic parameters.

[0014] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements each step of the radiation damage analysis method for photoelectric imaging devices based on dark current distribution fitting according to any of the above embodiments.

[0015] This invention provides a method, apparatus, and electronic device for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting. Based on differences in radiation damage, it uses mathematical methods to fit the dark current distribution of the photoelectric imaging device after radiation, extracting parameters for radiation damage mechanism analysis. This invention is highly operable and can accurately describe the dark current degradation of photoelectric imaging devices, providing an effective and feasible method for analyzing radiation damage mechanisms and physical processes. This invention is applicable to device development units, research institutes, and aerospace payload units that need to analyze radiation damage to photoelectric imaging devices. Attached Figure Description

[0016] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0017] Figure 1 A flowchart illustrating a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting according to an embodiment of the present invention is shown schematically.

[0018] Figure 2 A flowchart illustrating a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting according to another embodiment of the present invention is shown.

[0019] Figure 3 The diagram illustrates the type I dark current distribution and its fitting results according to an embodiment of the present invention;

[0020] Figure 4 The diagram illustrates the type II dark current distribution and its fitting results according to an embodiment of the present invention;

[0021] Figure 5 A schematic diagram illustrates the structural block diagram of a photoelectric imaging device radiation damage analysis apparatus based on dark current distribution fitting according to an embodiment of the present invention;

[0022] Figure 6 A block diagram illustrating an electronic device that implements a method for radiation damage analysis of photoelectric imaging devices based on dark current distribution fitting, according to an embodiment of the present invention, is shown schematically. Detailed Implementation

[0023] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0024] In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0025] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0026] Please see Figure 1 , Figure 1A flowchart illustrating a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting according to an embodiment of the present invention is shown. In this embodiment, the method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting may include steps S110 to S160.

[0027] In step S110, dark-field image data of the photoelectric imaging device to be analyzed after cumulative radiation damage is acquired. Specifically, a displacement dark-field test can be performed on the photoelectric imaging device to obtain dark-field image data at multiple different integration times. By acquiring multiple images and averaging them, random noise can be eliminated, improving the stability and accuracy of subsequent data processing.

[0028] In step S120, based on the dark-field image data, the dark current value of each imaging unit within a selected region on the photoelectric imaging device is determined, and a statistical distribution of the dark current in the selected region is generated. Specifically, for each imaging unit (i.e., pixel) within the region, its average gray value at each integration time is first calculated; then, a linear fit is performed with the integration time as the independent variable and the average gray value as the dependent variable, and the slope of the fitted line is determined as the dark current value of that imaging unit. By traversing all selected imaging units and repeating the above process, the set of dark current values ​​for all units can be obtained. Finally, frequency statistics are performed on this set of dark current values, and a dark current statistical distribution curve is plotted.

[0029] In step S130, based on the energy deposition characteristics of the irradiation source, one of the at least two preset distribution models is selected as the target distribution model. The distribution model predefines the number, type, and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution.

[0030] In some embodiments, at least two types of distribution models include a first type and a second type. The first type of distribution model is suitable for handling the statistical distribution of dark current caused by irradiation sources with low Coulomb scattering shift effects (such as gamma rays, X-rays, electrons, and low-flux protons), and it defines a relatively small number of distribution segments. For irradiation sources with low Coulomb scattering shift effects, such as gamma rays, X-rays, electrons, and low-flux protons, energy is mainly transferred to the material through secondary electrons. Because electrons have low mass, the energy transferred to the target material nuclei through Coulomb scattering is also low, and the shift effect caused by Coulomb scattering is not significant. Therefore, a simpler model, i.e., the first type of distribution model, can be selected. The second type of distribution model is suitable for handling the statistical distribution of dark current caused by irradiation sources with high Coulomb scattering shift effects (such as heavy ions and high-flux protons), and it defines a larger number of distribution segments than the first type of distribution model. Heavy ions and high-flux protons have large masses and high charges, so the Coulomb scattering effect is significant, requiring a more complex model, i.e., the second type of distribution model.

[0031] Furthermore, in the first type of distribution model, the Gaussian distribution segment is typically located in the middle region of the overall distribution, and the distribution segments on both sides of the middle region include at least one distribution segment composed of an exponential distribution. In the second type of distribution model, the Gaussian distribution segment is also located in the middle region of the overall distribution, and on the side with a larger dark current value corresponding to the middle region, at least two consecutive distribution segments composed of exponential distributions are set. These structural features are key to distinguishing the two types of damage modes.

[0032] In step S140, based on the target distribution model, the statistical distribution of dark current is divided into multiple distribution segments. Each distribution segment is mathematically composed of a Gaussian distribution function and / or an exponential distribution function, corresponding to different physical damage mechanisms.

[0033] In step S150, function fitting is performed on each of the divided distribution segments to extract characteristic parameters representing different physical damage mechanisms.

[0034] In some embodiments, a traversal optimization method can be used for fitting. Specifically, for a distribution segment, a traversal search is performed within its possible domain interval to find the interval boundary that maximizes the goodness of fit (e.g., coefficient of determination), thereby determining the best-fit interval and best-fit function for that segment. After fitting all distribution segments, parameters of the Gaussian distribution (e.g., mean μ, standard deviation σ) and parameters of the exponential distribution (e.g., attenuation coefficient λ) are extracted from each best-fit function, and these parameters are used as characteristic parameters characterizing the damage mechanism.

[0035] In step S160, the radiation damage of the photoelectric imaging device is quantitatively analyzed based on the characteristic parameters.

[0036] In some embodiments, the degree of relatively uniform overall dark current rise and pixel-to-pixel uniformity degradation caused by the total ionization dose effect can be assessed based on the characteristic parameters of the Gaussian distribution (such as the mean μ and standard deviation σ). Then, based on the characteristic parameters of the exponential distribution (such as the attenuation coefficient λ), the severity and impact range of discretely distributed high dark current hotspot pixels caused by displacement damage (such as nuclear scattering) can be assessed. Finally, based on the relative relationship between the characteristic parameters of the Gaussian and exponential distributions (such as the proportion of pixels covered by each distribution, amplitude comparison, etc.), the relative contributions of different physical damage mechanisms to the overall device performance degradation are comprehensively analyzed, thereby gaining a deeper understanding of the composition and main causes of damage.

[0037] The proposed method for radiation damage analysis of photoelectric imaging devices based on dark current distribution fitting in this invention involves conducting cumulative radiation damage experiments and dark-field tests to obtain the dark current of each pixel in the analysis area and performing frequency statistics on the dark current. Depending on the differences in cumulative radiation damage test conditions, dark current distribution fitting is performed in two scenarios: piecewise fitting based on differences in dark current distribution caused by different physical processes; first, the rationality of the piecewise fitting is determined based on mathematical principles; then, an ergonomic approach is used to find the fitting method with the optimal coefficient of determination, determining the optimal domain and the optimal fitting function, ultimately achieving overall dark current distribution fitting. This invention can fit the dark current distribution of photoelectric imaging devices after radiation damage, compensating for the shortcomings of radiation damage analysis methods for photoelectric imaging devices, providing a new method for further research, and is highly operable.

[0038] The method described above will be further illustrated below with reference to a specific embodiment. Please refer to [link / reference]. Figures 2-4 , Figure 2 A flowchart illustrating a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting according to another embodiment of the present invention is shown. Figure 3 The diagram illustrates the type I dark current distribution and its fitting results according to an embodiment of the present invention. Figure 4 The diagram illustrates the type II dark current distribution and its fitting results according to an embodiment of the present invention.

[0039] As an example, the photoelectric imaging device in this embodiment is a scientific-grade complementary metal-oxide-semiconductor (CMOS) image sensor (resolution 2048×2048). It was subjected to low-flux proton irradiation (simulating a low-LET radiation environment) and Ta ion irradiation (simulating a high-LET radiation environment). After irradiation, the sensor underwent dark-field testing. Figure 2 As shown, the radiation damage analysis method for photoelectric imaging devices based on dark current distribution fitting in this embodiment may include the following steps:

[0040] Step a: After subjecting the test sample (CMOS image sensor) to low-flux proton irradiation and Ta ion irradiation, perform dark-field testing, acquiring multiple (e.g., 20) dark-field images at each integration time.

[0041] Step b: Calculate the average gray value of pixels in a specified region of the dark field image at each integration time. For example, the average gray value μ of the pixel in the m-th row and n-th column at a certain integration time. mn The grayscale value μ of the pixel can be obtained from the 20 images acquired during this integration time. mn1 μ mn2、 ..., μ mn20 Calculated using the following formula:

[0042]

[0043] Step c: Using the integration time selected for the shifted dark field test as the independent variable, the average pixel gray value μ mn Plot a curve for the dependent variable, fit the curve to a linear function, and the slope of the fit is the dark current value of that pixel.

[0044] Step d: Perform steps b to c on all pixels within the specified area to obtain the dark current values ​​of all pixels within the specified area.

[0045] Step e: Count the frequency of dark current values ​​for all pixels in the region and plot the dark current distribution curve.

[0046] Step f: Based on different experimental conditions, the dark current distribution curves are divided into two types, I and II: Type I corresponds to an irradiation source that can be... 60 Type II irradiation sources include Co-γ rays, X-rays, electrons, and low-flux protons. Heavy ions and high-flux protons can also be used. In this embodiment, the dark current distribution curve from the low-flux proton experiment is classified as Type I, and the curve from the Ta ion experiment is classified as Type II.

[0047] Step g: For the dark current distribution in Type I (low-flux proton) experiments, it is divided into the following 5 parts (distribution segments) according to the Type I distribution model, in ascending order of dark current: ① Distribution 1 (exponential distribution), ② Distribution 2 (superposition of exponential and Gaussian distributions), ③ Distribution 3 (Gaussian distribution), ④ Distribution 4 (superposition of Gaussian and exponential distributions), and ⑤ Distribution 5 (exponential distribution). The function types corresponding to each distribution segment and the physical processes they reflect can be found in Table 1.

[0048] Table 1

[0049]

[0050] Steps h~k are specific fitting operations for the type I distribution.

[0051] Step h: For the dark current distribution in low-flux proton experiments, since distributions 1 and 2 may not exist, the domain of distribution 3 (Gaussian distribution segment) can be determined first. The x-coordinate corresponding to the maximum ordinate value of the dark current distribution is determined as the midpoint of the domain of distribution 3. Based on the symmetry of the Gaussian distribution, the minimum domain of distribution 3 is the midpoint and one point to its left and right. The radius of the maximum domain is the distance from the minimum x-coordinate value to the midpoint. By traversing from the minimum domain to the maximum domain, the fit with the coefficient of determination closest to 1 is found, thus determining the domain of distribution 3 and the best fitting function.

[0052] Step i: For the dark current distribution in low-flux proton experiments, based on the exponential distribution, the required number of samples for fitting is ≥3, and the existence of distribution 1 and distribution 2 is determined.

[0053] Step j: For the dark current distribution of the low-flux proton experiment, the best fit of distribution 5 is determined by traversal. Its minimum domain is the x-coordinate and the two points to its left. The maximum domain can be determined as the x-coordinate value corresponding to the first occurrence of the minimum y-coordinate value on the right side from the position where the Gaussian distribution fails.

[0054] Step k: For the dark current distribution in low-flux proton experiments, based on the fitting results of distributions 1, 3, and 5, determine the optimal domain and best fit for distributions 2 and 4. Experimental data and fitting results can be obtained as follows: Figure 3 As shown.

[0055] Step 1: For the dark current distribution in the Ta ion experiment, based on the second type of distribution model, the dark current distribution curve can be divided into the following 7 parts, ordered from smallest to largest dark current: ① Distribution 1 (exponential distribution), ② Distribution 2 (superposition of exponential and Gaussian distributions), ③ Distribution 3 (Gaussian distribution), ④ Distribution 4 (superposition of Gaussian and exponential distributions), ⑤ Distribution 5 (exponential distribution), ⑥ Distribution 6 (superposition of exponential and exponential distributions), ⑦ Distribution 7 (exponential distribution). The function types corresponding to each distribution segment and the physical processes they reflect can be found in Table 2. The method for determining the optimal domain and fitting function for each distribution segment can refer to the traversal optimization principle in steps h~k, which will not be elaborated here. Experimental data and fitting results can be obtained as follows: Figure 4 As shown, a more complex distribution pattern was successfully analyzed.

[0056] Table 2

[0057]

[0058] Step m: Extract the Gaussian distribution parameters (μ, σ) and exponential distribution parameters (λ) corresponding to each distribution segment from the fitting results for Type I and Type II distributions, respectively. Based on these characteristic parameters, conduct quantitative analysis of radiation damage to photoelectric imaging devices, such as assessing the contribution of different damage mechanisms and predicting performance degradation trends.

[0059] This embodiment provides a radiation damage analysis method for photoelectric imaging devices based on dark current distribution fitting. By conducting cumulative radiation damage experiments on the photoelectric imaging device, offline testing is used to acquire output images of the device at different exposure times. The dark current of each imaging unit in a specified area of ​​the photoelectric imaging device array is calculated, and its frequency distribution is statistically analyzed to plot a dark current distribution image. For different experimental conditions, this embodiment provides two dark current distribution fitting algorithms based on different physical processes: For 60For Co-γ rays, X-rays, and low-flux protons, a 5-region fitting algorithm combining exponential and Gaussian distributions is used; for high-flux protons and heavy ions, a 7-region fitting algorithm combining exponential and Gaussian distributions is used. After fitting, feature parameters can be extracted using Gaussian and exponential distributions to quantitatively analyze radiation damage to photoelectric imaging devices. The radiation damage analysis method for photoelectric imaging devices described in this embodiment is applicable to various image sensors, can accurately mathematically describe radiation damage to photoelectric imaging devices, provides data support for quantitative analysis of radiation damage, and has advantages such as good fitting effect and strong operability.

[0060] Please see Figure 5 , Figure 5 The schematic diagram illustrates a structural block diagram of a photoelectric imaging device radiation damage analysis apparatus 500 based on dark current distribution fitting according to an embodiment of the present invention. The apparatus 500 can be integrated into an electronic device and may include:

[0061] The acquisition module 510 is used to acquire dark-field image data of the photoelectric imaging device to be analyzed after cumulative radiation damage;

[0062] The statistics module 520 is used to determine the dark current value of each imaging unit in a selected area on the photoelectric imaging device based on dark field image data, and to generate the dark current statistical distribution of the selected area.

[0063] The selection module 530 is used to select one of the at least two preset distribution models as the target distribution model based on the energy deposition characteristics of the irradiation source. The distribution model predefines the number, type, and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution.

[0064] The partitioning module 540 is used to partition the statistical distribution of dark current based on the target distribution model, resulting in multiple distribution segments; each distribution segment is composed of a function of Gaussian distribution and / or exponential distribution.

[0065] The fitting module 550 is used to perform function fitting on each of the divided distribution segments and extract feature parameters that characterize different physical damage mechanisms.

[0066] Analysis module 560 is used to perform quantitative analysis of radiation damage to photoelectric imaging devices based on characteristic parameters.

[0067] For details on the specifics of each of the above modules, please refer to the implementation details of the radiation damage analysis method for photoelectric imaging devices based on dark current distribution fitting mentioned earlier, which will not be repeated here.

[0068] Please see Figure 6 , Figure 6A block diagram illustrating an electronic device that implements a method for radiation damage analysis of photoelectric imaging devices based on dark current distribution fitting, according to an embodiment of the present invention, is shown schematically.

[0069] like Figure 6 As shown, the electronic device 600 includes: a memory 610, a processor 620, and a computer program stored in the memory 610 and executable on the processor. When the processor executes the program, it implements the aforementioned... Figure 1 The illustrated embodiment describes a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting.

[0070] Furthermore, the electronic device also includes at least one input device 630 and at least one output device 640. The memory 610, processor 620, input device 630, and output device 640 are connected via a bus 650. Specifically, the input device 630 may be a camera, touch panel, physical buttons, or mouse, etc. The output device 640 may specifically be a display screen.

[0071] The memory 610 can be a high-speed random access memory (RAM) or a non-volatile memory, such as a disk storage device. The memory 610 is used to store a set of executable program code, and the processor 620 is coupled to the memory 610.

[0072] Furthermore, embodiments of the present invention also provide a computer-readable storage medium, which may be disposed in the electronic device described in the above embodiments, and the computer-readable storage medium may be as described above. Figure 6 The electronic device in the illustrated embodiment. A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the aforementioned... Figure 1 The illustrated embodiment describes a method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting. Furthermore, the computer storage medium can also be any medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0073] It should be noted that the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0074] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product.

[0075] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0076] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed, and is not intended to limit the scope of the claimed invention, but merely to illustrate preferred embodiments of the invention. Those skilled in the art should understand that the scope of the invention is not limited to the specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A method for analyzing radiation damage to photoelectric imaging devices based on dark current distribution fitting, characterized in that, include: Acquire dark-field image data of the optoelectronic imaging device to be analyzed after cumulative radiation damage; Based on the dark field image data, the dark current value of each imaging unit in a selected area on the photoelectric imaging device is determined, and the dark current statistical distribution of the selected area is generated. Based on the energy deposition characteristics of the irradiation source, one of the at least two preset distribution models is selected as the target distribution model; the distribution model predefines the number, type and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution; Based on the target distribution model, the statistical distribution of dark current is divided into multiple distribution segments; each distribution segment is composed of a function of Gaussian distribution and / or exponential distribution. Function fitting is performed on each of the defined distribution segments to extract characteristic parameters representing different physical damage mechanisms; Based on the aforementioned characteristic parameters, a quantitative analysis of radiation damage to the photoelectric imaging device is performed.

2. The analytical method according to claim 1, characterized in that, The at least two types of distribution models include a first type of distribution model and a second type of distribution model. The first type of distribution model is used to handle the statistical distribution of dark current caused by an irradiation source with a low Coulomb scattering shift effect, and the second type of distribution model is used to handle the statistical distribution of dark current caused by an irradiation source with a high Coulomb scattering shift effect. The number of distribution segments defined by the first type of distribution model is less than the number of distribution segments defined by the second type of distribution model. The irradiation source with low Coulomb scattering shift effect includes gamma rays, X-rays, electrons, and low-flux protons, while the irradiation source with high Coulomb scattering shift effect includes heavy ions and high-flux protons.

3. The analytical method according to claim 2, characterized in that, In the first type of distribution model, the Gaussian distribution segment is located in the middle region of the overall distribution, and the distribution segments on both sides of the middle region contain at least the distribution segments composed of exponential distributions.

4. The analytical method according to claim 2, characterized in that, In the second type of distribution model, the Gaussian distribution segment is located in the middle region of the overall distribution, and on the side of the middle region where the dark current value is larger, at least two distribution segments composed of exponential distribution are continuously set.

5. The analytical method according to claim 1, characterized in that, The steps for acquiring the dark field image data include: The photoelectric imaging device was subjected to a shift dark field test to obtain dark field image data at multiple different integration times.

6. The analytical method according to claim 5, characterized in that, The steps for determining the dark current value of each imaging unit within a selected region include: For each imaging unit, a linear fit is performed on its average gray value at different integration times, and the slope of the fitted line is determined as the dark current value of the imaging unit.

7. The analytical method according to claim 1, characterized in that, The steps of performing function fitting on each of the distribution segments and extracting the feature parameters include: A traversal optimization method is used to determine the target fitting interval and target fitting function for each distribution segment; Gaussian distribution parameters and / or exponential distribution parameters are extracted from each of the target fitting functions and used as the feature parameters.

8. The analytical method according to claim 1, characterized in that, The steps for quantitative analysis of radiation damage based on characteristic parameters include: The degree of uniform damage is quantified based on the characteristic parameters of the Gaussian distribution; Based on the characteristic parameters of the exponential distribution, the severity and extent of discrete damage are quantified. Based on the relative relationship between the characteristic parameters of the Gaussian distribution and the exponential distribution, the contribution weight of different damage mechanisms to the overall performance degradation of the optoelectronic imaging device is evaluated.

9. A radiation damage analysis device for photoelectric imaging devices based on dark current distribution fitting, characterized in that, include: The acquisition module is used to acquire dark-field image data of the photoelectric imaging device to be analyzed after cumulative radiation damage; The statistics module is used to determine the dark current value of each imaging unit in a selected area on the photoelectric imaging device based on the dark field image data, and to generate the dark current statistical distribution of the selected area. The selection module is used to select one of at least two preset distribution models as the target distribution model based on the energy deposition characteristics of the irradiation source; the distribution model predefines the number, type and combination of distribution segments corresponding to different physical damage mechanisms in the dark current statistical distribution. The partitioning module is used to partition the dark current statistical distribution based on the target distribution model to obtain multiple distribution segments; each distribution segment is composed of a function of Gaussian distribution and / or exponential distribution. The fitting module is used to perform function fitting on each of the divided distribution segments to extract feature parameters that characterize different physical damage mechanisms. An analysis module is used to perform quantitative analysis of radiation damage to the photoelectric imaging device based on the characteristic parameters.

10. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements each step of the method for radiation damage analysis of photoelectric imaging devices based on dark current distribution fitting as described in any one of claims 1 to 8.

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