A method and system for evaluating the high-repetition-rate laser irradiation effect on a detector

Abstract

The present invention belongs to the field of optoelectronic technology and relates to a method and system for evaluating the high-repetition-rate laser irradiation effect on a detector. The evaluation method includes: determining the high-repetition-rate laser parameter and the detector structure material property parameter; constructing a thermo-mechanical coupling transient model of the detector, and setting the heat transfer physical field and the solid mechanics physical field boundary conditions of the thermo-mechanical coupling transient model; performing mesh division on the thermo-mechanical coupling transient model, and setting the solver parameters; the solver parameters are determined by applying a pre-trained linear neural network model, specifically: inputting the laser pulse width and the laser frequency into the pre-trained linear neural network model, and calculating by applying the expression of the pre-trained linear neural network model to obtain the solver parameters; based on the solver parameters, applying the solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distribution of the detector at different irradiation times under high-repetition-rate laser irradiation. The evaluation method of the present invention has high accuracy and calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of optoelectronic technology, and in particular, to a method and system for evaluating the irradiation effect of high-repetition-rate laser on a detector. Background Art

[0002] Laser interference damage to detectors is a hot issue in the field of optoelectronic technology. The interference damage of detectors under laser irradiation is mainly caused by the combined action of melting damage and thermal stress. Therefore, obtaining the irradiation effect of the detector under laser action, specifically involving the time-evolution characteristics of the internal temperature field of the detector and the thermal stress generated by the temperature rise, has a significant supporting role in the research on the interference damage law of lasers to detectors.

[0003] Traditionally, the process of obtaining the irradiation effect of laser-irradiated detectors by experimental means is relatively complex and costly, and there is less research on the irradiation effect of high-repetition-rate lasers (i.e., a laser sequence composed of ns-order laser pulse widths and ms-order laser pulse periods) with a repetition frequency exceeding KHz. This is mainly due to the technical limitations of existing measurement means at extreme time scales. Specifically, the transient energy deposition process in the ns order far exceeds the response speed of conventional temperature and stress sensors. Limited by the thermal inertia effect and stress relaxation time, it is difficult for traditional equipment such as thermocouples, thermal imagers, and strain gauges to achieve in-situ measurement with sub-microsecond time resolution; while the ms-order pulse period requires the measurement system to have high-frequency cyclic anti-interference ability, further increasing the difficulty of experimental observation. This dual constraint of time span leads to a lack of effective characterization means for the thermo-mechanical coupling process of high-repetition-rate lasers interacting with detectors, restricting the in-depth research on the irradiation damage mechanism and law of high-repetition-rate lasers. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the irradiation effect of high-repetition-rate laser on a detector. The detector is irradiated by a high-repetition-rate laser to generate an irradiation effect, and the temperature field and stress field distributions of the detector under the high-repetition-rate laser are obtained by constructing a model and solving it. This evaluation method effectively solves the time-scale contradiction problem between the ns-order pulse width and the ms-order pulse period under the action of high-repetition-rate lasers, improves the accuracy and calculation efficiency in the evaluation research, and thus can solve the problem of evaluating the irradiation effect of high-repetition-rate laser-irradiated detectors.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for evaluating the irradiation effect of high-repetition-rate laser on a detector, and the evaluation method includes the following steps:

[0007] Determine the high-repetition-rate laser parameter and the detector structure material property parameter;

[0008] Build a thermo-mechanical coupling transient model of the detector, and set the boundary conditions of the heat transfer physical field and the solid mechanics physical field of the thermo-mechanical coupling transient model;

[0009] Perform mesh division on the thermo-mechanical coupling transient model and set the solver parameters; the solver parameters include the initial step size of the solver, the storage time step during the laser irradiation period, the storage time step during the period when the laser is not irradiated, the maximum step size constraint during the laser irradiation period, and the maximum step size constraint during the period when the laser is not irradiated; the solver parameters are determined by using a pre-trained linear neural network model. Specifically, the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency are input into the pre-trained linear neural network model, and the solver parameters are obtained by calculation using the expression of the pre-trained linear neural network model;

[0010] Use the solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distribution of the detector at different irradiation times under high-repetition-rate laser irradiation.

[0011] As a possible implementation, the pre-trained linear neural network model includes an input layer and an output layer; among them, the parameters of the output layer include the storage time step node during the laser irradiation period, the storage time step node during the period when the laser is not irradiated, the initial step size node, the maximum step size constraint node during the laser irradiation period, and the maximum step size constraint during the period when the laser is not irradiated.

[0012] As a possible implementation, the expression of the pre-trained linear neural network model is:

[0013] ;

[0014] Among them, is the weight matrix, ; is the bias vector, ; is the storage time step of the solver during the laser irradiation period; is the storage time step of the solver during the period when the laser is not irradiated; is the initial step size of the solver; is the maximum step size constraint of the solver during the laser irradiation period; is the maximum step size constraint of the solver during the period when the laser is not irradiated; is the high-repetition-rate laser pulse width; is the high-repetition-rate laser frequency.

[0015] As a possible implementation, the parameters of the input layer and the output layer are both normalized, and the normalization range is [0, 1]; the normalization expression is:

[0016] ;

[0017] Among them, is the result after normalizing the corresponding parameter values in the training dataset; is the corresponding parameter value in the training dataset; and are respectively the minimum and maximum values of the corresponding parameter in the training dataset.

[0018] As a possible implementation, and the parameters need to satisfy the physical constraint conditions: ; in the case where the prediction result does not satisfy the above constraint conditions, correction processing should be performed, and the correction method is

[0019] .

[0020] As a possible implementation, mesh generation is performed on the thermal-mechanical coupling transient model of the detector. Among them, free triangular mesh dissection is used for the upper surface of the silicon dioxide insulating layer and the lower surface of the polysilicon electrode, and swept mesh dissection is used for the remaining part of the model.

[0021] As a possible implementation, the range of the laser pulse width is: 100 ps - 500 ns, and the repetition frequency is 1K - 10K.

[0022] As a possible implementation, the process of solving the thermal-mechanical coupling transient model of the detector is as follows: Set the use of the backward difference formula to solve the time step in the solver; the initial step size of the solver, the storage time step of the solver during the laser irradiation period, the storage time step of the solver during the non-laser irradiation period, the maximum step size constraint of the solver during the laser irradiation period, and the maximum step size constraint of the solver during the non-laser irradiation period can be set according to the results of a pre-trained linear neural network model.

[0023] In a second aspect, the present invention provides a detector high-repetition-rate laser irradiation effect evaluation system, specifically including:

[0024] An evaluation parameter configuration unit for configuring evaluation parameters, where the evaluation parameters include high-repetition-rate laser parameters and detector structural material property parameters;

[0025] A detector thermal-mechanical coupling model construction unit for constructing a thermal-mechanical coupling transient model of the detector and determining the boundary conditions of the heat transfer physical field and the solid mechanics physical field of the thermal-mechanical coupling transient model;

[0026] A mesh generation unit for performing mesh generation on the thermal-mechanical coupling transient model based on the heat transfer physical field and the solid mechanics physical field;

[0027] A solver parameter configuration unit is used to configure solver parameters, which include the initial step size of the solver, the storage time step during the laser irradiation period, the storage time step during the non-laser irradiation period, the maximum step size constraint during the laser irradiation period, and the maximum step size constraint during the non-laser irradiation period. The solver parameters are determined by applying a pre-trained linear neural network model. Specifically, the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency are input into the pre-trained linear neural network model, and the solver parameters are obtained by calculation using the expression of the pre-trained linear neural network model.

[0028] A detector temperature field and stress field distribution determination unit uses a solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distributions of the detector at different irradiation times under high-repetition-rate laser irradiation.

[0029] Compared with the prior art, the beneficial effects produced by the present invention are as follows:

[0030] 1. The present invention proposes a method for evaluating the high-repetition-rate laser irradiation effect on a detector. This method has high evaluation accuracy and calculation efficiency and can solve the problem of evaluating the irradiation effect of high-repetition-rate laser irradiation on a detector.

[0031] 2. The present invention proposes a method for evaluating the high-repetition-rate laser irradiation effect on a detector. The process of obtaining the temperature field and stress field in the high-repetition-rate laser irradiation effect is simple and the cost is low. Description of the Drawings

[0032] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0033] Figure 1 is a flowchart of the method for evaluating the high-repetition-rate laser irradiation effect on a detector;

[0034] Figure 2 is a schematic diagram of the detector structure material in the embodiment of the present invention;

[0035] Figure 3 is a schematic diagram of the laser switch function in the embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of the mesh division in the embodiment of the present invention;

[0037] Figure 5 is a curve graph of the highest temperature change on the upper surface of the silicon substrate in the embodiment of the present invention;

[0038] Figure 6 is a curve graph of the maximum stress change on the upper surface of the silicon substrate in the embodiment of the present invention;

[0039] Figure 7 The curve graph of the highest temperature change on the upper surface of the light-shielding aluminum film in the embodiment of the present invention;

[0040] Figure 8 The curve graph of the maximum stress change on the upper surface of the light-shielding aluminum film in the embodiment of the present invention;

[0041] Figure 9 The temperature field distribution at t = 1633.9 us in the embodiment of the present invention;

[0042] Figure 10 The thermal stress distribution at t = 1633.9 us in the embodiment of the present invention;

[0043] Figure 11 The thermal expansion displacement distribution at t = 1633.9 us in the embodiment of the present invention. Specific embodiments

[0044] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and do not limit their order. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and terms such as "first" and "second" do not necessarily mean different.

[0045] It should be noted that in the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0046] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. The following at least one (item) or its similar expression refers to any combination of these items, including any combination of single (item) or plural items (items). For example, at least one (item) of a, b or c can represent: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0047] The embodiments of the present invention aim to provide a method and system for evaluating the high-repetition-rate laser irradiation effect on a detector, which can comprehensively and accurately capture the transient and complex physical phenomena of the detector irradiated by high-repetition-rate laser at different times by optimizing the solver parameters. The specific implementation methods are as follows:

[0048] In the first aspect, the embodiments of the present invention provide a method for evaluating the high-repetition-rate laser irradiation effect on a detector. Refer to Figure 1 , the evaluation method includes the following steps:

[0049] Determine the high-repetition-rate laser parameters and the detector structure material property parameters;

[0050] As a possible implementation, the range of the high-repetition-rate laser pulse width is: 100 ps - 500 ns, and the repetition frequency is 1K - 10K.

[0051] Exemplarily, the high-repetition-rate laser parameters are shown in Table 1 below;

[0052] Table 1 High-repetition-rate laser parameter table

[0053]

[0054] Exemplarily, the detector structure is a multi-layer structure. The detector multi-layer structure is as Figure 2 shown, from bottom to top, it is a silicon substrate, a silicon dioxide insulating layer, a polysilicon electrode, a silicon dioxide thickening layer, a light-shielding metal layer, and a microlens layer in sequence.

[0055] Exemplarily, the detector light-shielding metal layer materials include: metals such as aluminum and tungsten, and semiconductor materials such as silicon and germanium.

[0056] Taking aluminum as an example for the light-shielding layer metal and polyimide as an example for the microlens layer material, the detector structure material property parameters are shown in Table 2 below:

[0057] Table 2 Detector structure material property parameter table

[0058]

[0059] Build a thermo-mechanical coupling transient model of the detector, and set the heat transfer physical field and the mechanical physical field boundary conditions of the thermo-mechanical coupling transient model;

[0060] Exemplarily, build a thermo-mechanical coupling transient model of the detector based on COMSOL Multiphysics software.

[0061] Exemplarily, the thermo-mechanical coupling transient model of the detector is obtained by creating a solid heat transfer and a solid mechanics module and performing a high-repetition-rate laser irradiation thermo-mechanical coupling multi-physics field simulation.

[0062] Exemplarily, for the solid heat transfer module, the expression is:

[0063]

[0064] Among them, is the laser heat source; is the instantaneous temperature at a certain position; is the material density; is the material heat capacity; is the material thermal conductivity.

[0065] Exemplarily, for the solid mechanics module, the expression is:

[0066] ;

[0067] Among them, is the displacement at a certain position; is the acceleration term; is the internal stress term, and the expression of the internal stress S is: , is the fourth-order elastic matrix, is the thermal expansion strain, is the displacement strain.

[0068] Exemplarily, for the thermal expansion strain of the irradiated detector structural material, the relationship with the temperature change is:

[0069] ;

[0070] Among them, is the thermal expansion strain of the material; is the thermal expansion coefficient of the material; is the temperature; is the volume reference temperature.

[0071] Exemplarily, for the high-repetition-rate laser irradiated detector structural material, the material cannot conduct heat completely within the time scale of ns or less. A part of the laser beam directly passes through the microlens layer and the silicon dioxide insulating layer and irradiates the photosensitive surface of the silicon substrate. The silicon substrate can perform volume absorption of the laser. Therefore, the heat source on the silicon substrate is set as a volume heat source, and the corresponding laser energy deposition expression is:

[0072] ;

[0073] Among them, is the spot radius; t is the time; is the absorption coefficient of silicon for the laser; is the reflectivity of the photosensitive area; is the position of any point on the laser energy deposition surface; is the spot center position of the laser energy deposition surface; is a function in the direction; is the average output power of a single laser pulse; is the laser pulse switch time function, and in the embodiment, this function is specifically as Figure 3 shown, and the expression is:

[0074] ;

[0075] wherein, is the high-repetition-rate laser frequency; ; is a Gaussian pulse function with a peak value of 1 and a standard deviation of the pulse width;

[0076] Based on the aperture ratio of the detector, part of the laser irradiates on the light-shielding aluminum film. In the model, the thickness of the light-shielding aluminum film is much greater than its laser absorption depth. Therefore, the heat source on the light-shielding aluminum film is set as a surface heat source, and the corresponding laser energy deposition expression is:

[0077] ;

[0078] ;

[0079] ;

[0080] wherein, is the spot radius; is the time; is the absorption coefficient of silicon for the laser; is the reflectivity of the photosensitive area; is the position of any point on the laser energy deposition surface; is the spot center position of the laser energy deposition surface; is the absorption coefficient of aluminum for the laser; is a function in the direction; is the average output power of a single laser pulse; is the single laser pulse energy; is the laser pulse width; is the laser pulse switch time function, and in the embodiment, this function is specifically as Figure 3 shown, and the expression is:

[0081]

[0082] wherein, is the high-repetition-rate laser frequency; ; is a Gaussian pulse function with a peak value of 1 and a standard deviation of the pulse width.

[0083] Exemplarily, the expression for convective heat transfer between the surface of the detector structural material and the environment is:

[0084] ;

[0085] where is the convective heat transfer coefficient; is the ambient temperature; is the temperature;

[0086] The thermal radiation from the surface of the detector structural material to the environment, and the corresponding expression for the radiative heat flux is:

[0087] ;

[0088] where is the surface emissivity, is the Stefan constant.

[0089] Perform mesh division on the thermo-mechanical coupling transient model;

[0090] As a possible implementation, free triangular meshing is used for the upper surface of the silicon dioxide insulating layer and the lower surface of the polysilicon electrode, and swept meshing is used for the remaining part of the model.

[0091] Exemplarily, the thermo-mechanical coupling transient model of the detector is a three-dimensional model, as Figure 4 shown. This model is a cuboid with dimensions of 200um × 200um × 35um. Among them, the silicon substrate and the silicon dioxide insulating layer are 200um × 200um × 30um and 200um × 200um × 1um respectively. The polysilicon electrode, the silicon dioxide thickening layer, and the light-shielding metal layer all have dimensions of 20um × 20um × 1um and are equally spaced in a 9×9 array on the upper surface of the silicon dioxide insulating layer, with a spacing of 2.5um; the thickness of the uppermost microlens layer is 1um.

[0092] Set the solver parameters. The solver parameters include the initial step size of the solver, the storage time step during the laser irradiation period, the storage time step during the non-laser irradiation period, the maximum step size constraint during the laser irradiation period, and the maximum step size constraint during the non-laser irradiation period; the solver parameters are determined by applying a pre-trained linear neural network model. Specifically: input the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency into the pre-trained linear neural network model, and calculate the solver parameters by applying the expression of the pre-trained linear neural network model;

[0093] As a possible implementation, the pre-trained linear neural network model includes an input layer and an output layer; among them, the parameters of the input layer include the high-repetition-rate laser pulse width node and the high-repetition-rate laser frequency node; the parameters of the output layer include the storage time step node for the laser irradiation period, the storage time step node for the non-laser-irradiation period, the initial step size node, the maximum step size constraint node for the laser irradiation period, and the maximum step size constraint for the non-laser-irradiation period.

[0094] As a possible implementation, the expression of the pre-trained linear neural network model is:

[0095] ;

[0096] Among them, is the weight matrix, ; is the bias vector, ; is the storage time step of the solver during the laser irradiation period; is the storage time step of the solver during the non-laser-irradiation period; is the initial step size of the solver; is the maximum step size constraint of the solver during the laser irradiation period; is the maximum step size constraint of the solver during the non-laser-irradiation period; is the high-repetition-rate laser pulse width; is the high-repetition-rate laser frequency.

[0097] As a possible implementation, the parameters of the input layer and the output layer are both normalized, and the normalization range is [0, 1]; the normalization expression is:

[0098] ;

[0099] Among them, is the result after normalizing the corresponding parameter value in the training dataset; is the corresponding parameter value in the training dataset; and are the minimum and maximum values of the corresponding parameter in the training dataset, respectively.

[0100] As a possible implementation, , and parameters need to satisfy the physical constraint conditions: ; In the case where the prediction result does not meet the above constraint conditions, correction processing should be performed, and the correction method is:

[0101] ; .

[0102] As a possible implementation, the initial step size of the solver, the storage time step of the solver during the laser irradiation section, the storage time step of the solver during the non-laser-irradiated time period, the maximum step size constraint of the solver during the laser irradiation time period, and the maximum step size constraint of the solver during the non-laser-irradiated time period can be set according to the results of a pre-trained linear neural network model.

[0103] Apply the solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distribution of the detector at different irradiation times under high-repetition-rate laser irradiation;

[0104] After the solution is completed, post-process the model, call the data of the datasets such as the cut lines, cross-sections, or cut points to be monitored, and use the plotting group to draw the change diagrams of the corresponding physical quantities such as temperature, stress, and thermal expansion displacement.

[0105] Such as Figures 5 to 8 shown, in this embodiment, the upper surfaces of the silicon substrate and the light-shielding aluminum film are selected, and the curves of their maximum temperature and maximum stress are respectively drawn in the one-dimensional plotting group. As Figure 9 、 10 and Figure 11 shown, in this embodiment, a three-dimensional plotting group is added to draw the temperature field distribution diagram, the thermal stress distribution diagram, and the thermal expansion displacement distribution diagram of the detector at t = 1633.9 us. The figure well shows the changes in the physical properties of the detector, effectively solves the problem of the time scale contradiction between the ns-level pulse width and the ms-level pulse period under the action of high-repetition-rate laser in the simulation, improves the accuracy and calculation efficiency of the simulation evaluation, and thus can solve the problem of evaluating the irradiation effect of high-repetition-rate laser irradiating the detector.

[0106] In a second aspect, the present invention provides a system for evaluating the high-repetition-rate laser irradiation effect of a detector, specifically including:

[0107] An evaluation parameter configuration unit for configuring evaluation parameters, where the evaluation parameters include high-repetition-rate laser parameters and detector structure material property parameters;

[0108] A detector thermo-mechanical coupling model construction unit for constructing a thermo-mechanical coupling transient model of the detector and determining the boundary conditions of the heat transfer physical field and the solid mechanics physical field of the thermo-mechanical coupling transient model;

[0109] A meshing unit for meshing the thermo-mechanical coupling transient model based on the heat transfer physical field and the solid mechanics physical field;

[0110] A solver parameter configuration unit is used to configure solver parameters, which include the initial step size of the solver, the storage time step of the laser irradiation period, the storage time step of the non-laser irradiation period, the maximum step size constraint of the laser irradiation period, and the maximum step size constraint of the non-laser irradiation period. The solver parameters are determined by applying a pre-trained linear neural network model. Specifically, the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency are input into the pre-trained linear neural network model, and the solver parameters are obtained by calculating using the expression of the pre-trained linear neural network model.

[0111] A detector temperature field and stress field distribution determination unit applies the solver to solve the thermo-mechanical coupling transient model to obtain the temperature field and stress field distribution of the detector at different irradiation moments under high-repetition-rate laser irradiation.

[0112] Although the present invention has been described in connection with various embodiments, however, in the process of implementing the claimed invention, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the drawings, the disclosure content, and the like. In the specification, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions listed in the specification. Certain measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0113] Although the present invention has been described in connection with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present invention. Accordingly, the present specification and the drawings are merely exemplary descriptions of the present invention and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for evaluating the high-repetition-rate laser irradiation effect on a detector, characterized in that, The evaluation method includes the following steps: Determine the high-repetition-rate laser parameters and the detector structure material property parameters; Construct a thermo-mechanical coupling transient model of the detector, and set the heat transfer physical field and the boundary conditions of the solid mechanics physical field of the thermo-mechanical coupling transient model; wherein, in the heat transfer physical field of the thermo-mechanical coupling transient model, the heat source set on the substrate is a volume heat source, and the heat source set on the light-shielding aluminum film is a surface heat source; Perform mesh division on the thermo-mechanical coupling transient model, and set the solver parameters; the solver parameters include the initial step size of the solver, the storage time step during the laser irradiation period, the storage time step during the non-laser irradiation period, the maximum step size constraint during the laser irradiation period, and the maximum step size constraint during the non-laser irradiation period; the solver parameters are determined by using a pre-trained linear neural network model. Specifically, the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency are input into the pre-trained linear neural network model, and the solver parameters are obtained by calculation using the expression of the pre-trained linear neural network model; the pre-trained linear neural network model includes an input layer and an output layer; wherein, the parameters of the input layer include the high-repetition-rate laser pulse width node and the high-repetition-rate laser frequency node; the parameters of the output layer include the storage time step node during the laser irradiation period, the storage time step node during the non-laser irradiation period, the initial step size node, the maximum step size constraint node during the laser irradiation period, and the maximum step size constraint during the non-laser irradiation period; the expression of the pre-trained linear neural network model is: ； Among them, is the weight matrix, ; is the bias vector, ; is the storage time step of the solver in the laser irradiation section; is the storage time step of the solver in the time period without laser irradiation; is the initial step size of the solver; is the maximum step size constraint of the solver in the laser irradiation time period; is the maximum step size constraint of the solver in the time period without laser irradiation; is the high-repetition-rate laser pulse width; is the high-repetition-rate laser frequency; Use the solver to solve the thermo-mechanical coupling transient model to obtain the temperature and stress node parameters of the detector at different irradiation moments under high-repetition-rate laser irradiation, and analyze the temperature and stress node parameters to obtain the temperature field and stress field distribution of the detector.

2. The method for evaluating the high-repetition-rate laser irradiation effect of a detector according to claim 1, characterized in that, The parameters of the input layer and the output layer are both processed by normalization, and the normalization range is [0, 1]; the normalization expression is: ； Among them, is the result after normalizing the corresponding parameter values in the training dataset; is the corresponding parameter value in the training dataset; and are the minimum and maximum values of the corresponding parameter in the training dataset, respectively.

3. The method for evaluating the high-repetition-rate laser irradiation effect of a detector according to claim 1, wherein , and The parameters need to meet the physical constraint conditions: ; In the case where the prediction result does not meet the above constraint conditions, corrective measures should be taken, and the corrective method is: ; .

4. The method for evaluating the high-repetition-rate laser irradiation effect of a detector according to claim 1, wherein For the mesh division, free triangular meshing is used for the upper surface of the silicon dioxide insulating layer and the lower surface of the polysilicon electrode, and swept meshing is used for the remaining part of the model.

5. The method for evaluating the high-repetition-rate laser irradiation effect of a detector according to claim 1, wherein The range of the high-repetition-rate laser pulse width is: 100 ps - 500 ns, and the repetition frequency is 1K - 10K.

6. The method for evaluating the high-repetition-rate laser irradiation effect of a detector according to claim 1, characterized in that, Set the use of the backward difference formula to solve the time step in the solver; the initial step size of the solver, the storage time step of the solver during the laser irradiation period, the storage time step of the solver during the non-laser irradiation period, the maximum step size constraint of the solver during the laser irradiation period, and the maximum step size constraint of the solver during the non-laser irradiation period are all set according to the results of the pre-trained linear neural network model.

7. A high-repetition-rate laser irradiation effect evaluation system for a detector, characterized in that Including: An evaluation parameter configuration unit for configuring evaluation parameters, where the evaluation parameters include high-repetition-rate laser parameters and detector structure material property parameters; A detector thermo-mechanical coupling model construction unit for constructing a thermo-mechanical coupling transient model of the detector and determining the boundary conditions of the heat transfer physical field and the solid mechanics physical field of the thermo-mechanical coupling transient model; A meshing division unit for performing meshing division on the thermo-mechanical coupling transient model based on the heat transfer physical field and the solid mechanics physical field; A solver parameter configuration unit is used to configure solver parameters, and the solver parameters include the initial step size of the solver, the storage time step of the laser irradiation period, the storage time step of the non-laser irradiation period, the maximum step size constraint of the laser irradiation period, and the maximum step size constraint of the non-laser irradiation period; the solver parameters are determined by applying a pre-trained linear neural network model. Specifically, the high-repetition-rate laser pulse width and the high-repetition-rate laser frequency are input into the pre-trained linear neural network model, and the solver parameters are obtained by calculation using the expression of the pre-trained linear neural network model; the pre-trained linear neural network model includes an input layer and an output layer; among them, the parameters of the input layer include a high-repetition-rate laser pulse width node and a high-repetition-rate laser frequency node; the parameters of the output layer include a storage time step node of the laser irradiation period, a storage time step node of the non-laser irradiation period, an initial step size node, a maximum step size constraint node of the laser irradiation period, and a maximum step size constraint of the non-laser irradiation period; the expression of the pre-trained linear neural network model is: ； Among them, is the weight matrix, ; is the bias vector, ; is the storage time step of the solver in the laser irradiation section; is the storage time step of the solver in the time period when the laser is not irradiated; is the initial step size of the solver; is the maximum step size constraint of the solver in the laser irradiation time period; is the maximum step size constraint of the solver in the time period when the laser is not irradiated; is the high-repetition-rate laser pulse width; is the high-repetition-rate laser frequency; A detector temperature field and stress field distribution determination unit applies a solver to solve the thermo-mechanical coupling transient model to obtain the temperature and stress node parameters of the detector at different irradiation times under high-repetition-rate laser irradiation, and analyzes the temperature and stress node parameters to obtain the detector temperature field and stress field distribution.

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