Refrigeration type infrared detector thermal characteristic multi-parameter test system and method

Abstract

The invention relates to the technical field of detector testing, and discloses a multi-parameter testing system and method for thermal characteristics of a refrigeration type infrared detector, and the system comprises a cooperative measurement module, a coupling analysis module, an optimization testing module, and a data analysis platform. The method corresponds to the system. According to the application, the temperature field, stress distribution and local heat flow data of the to-be-tested detector can be synchronously acquired, and the sensor is calibrated in a low-temperature vacuum environment, so that data drift in an extreme environment is avoided, and the accuracy of actually measured data is ensured; a coupling simulation model is built based on a heat conduction equation, a reduced order model and a Maxwell model, parameters are corrected by means of measured data, and thermal load, thermal mismatch stress and refrigeration efficiency evaluation results are accurately output; the cold screen coating performance and the cold finger thermophysical property of the detector can be tested, and the special test blank of key components is filled up; and multi-source data is integrated to identify anomalies, a three-dimensional thermal field interface is output, the optimization design is assisted, and the test comprehensiveness and accuracy are integrally improved.

Description

Technical Field

[0001] This application relates to the field of detector testing technology, specifically a multi-parameter testing system and method for the thermal characteristics of a cooled infrared detector. Background Technology

[0002] Cooled infrared detectors, with their advantages of high sensitivity and wide spectral response range, are widely used in aerospace, precision security, medical diagnostics, and industrial inspection. Their performance is directly related to their thermal characteristics. Parameters such as the temperature field distribution of the detector components in a low-temperature vacuum working environment, thermal mismatch stress, cold screen radiative heat leakage, and cold finger thermal conductivity all affect detection accuracy, response speed, and long-term reliability. Therefore, accurate testing of these thermal characteristic parameters is a core aspect of the research, development, production, and performance optimization of cooled infrared detectors.

[0003] Currently, various testing methods exist for the thermal characteristics of cooled infrared detectors, but significant technical limitations remain in practical applications, making it difficult to meet the demands for multi-parameter collaborative evaluation and high-precision testing. Existing testing systems often focus on a single thermal characteristic parameter (such as measuring local temperature solely through thermocouples or stress solely through strain gauges), failing to simultaneously acquire temperature field, stress distribution, and local heat flow data. Due to the significant thermo-mechanical-electrical coupling effect in cooled infrared detector components (such as temperature changes causing differences in material thermal expansion, leading to thermal mismatch stress that affects cooling efficiency), single-parameter testing cannot reflect the coupling relationships between various physical quantities, resulting in one-sided thermal characteristic evaluation results, low testing accuracy, and an inability to support the overall thermal design optimization of the detector. Therefore, existing thermal characteristic testing systems for cooled infrared detectors suffer from poor multi-parameter synergy and low testing accuracy, hindering further improvements in the performance of cooled infrared detectors and the optimization of R&D efficiency.

[0004] Therefore, there is an urgent need for a comprehensive thermal characteristic testing technology for cooled infrared detectors to solve the above-mentioned technical bottlenecks. Summary of the Invention

[0005] The purpose of this application is to provide a multi-parameter testing system and method for the thermal characteristics of a cooled infrared detector, so as to solve the technical problems mentioned in the background art.

[0006] To achieve the above objectives, this application discloses the following technical solutions:

[0007] In a first aspect, this application discloses a multi-parameter testing system for the thermal characteristics of a cooled infrared detector, the system comprising:

[0008] The collaborative measurement module is configured to: take the cooled infrared detector component under test as the data acquisition object, simultaneously acquire the temperature field, stress distribution and local heat flow data of the detector component based on multiple sensors, perform self-calibration of the sensors in the low temperature vacuum working environment of the detector component, and output the calibrated measured data.

[0009] The coupling analysis module is configured to: take the measured data as input, construct a coupled simulation model of the detector component under test based on the heat conduction equation, the reduced-order model and the Maxwell model, dynamically correct the model parameters through the measured data, and output the evaluation results of the thermal load, thermal mismatch stress and cooling efficiency of the detector component.

[0010] The optimized test module is configured to: use the adjustable emissivity coating of the cold screen and the integrated cold finger of the detector component under test as test objects, test the performance of the cold screen coating and the material thermal properties of the integrated cold finger respectively, and output coating performance data and cold finger thermal property data.

[0011] The data analysis platform is configured to automatically identify abnormal data patterns and output a three-dimensional thermal field visualization interface, taking the measured data, the evaluation results, the coating performance data, and the cold index and thermal property data as inputs.

[0012] Preferably, the collaborative measurement module includes:

[0013] Multiple sensors are configured to acquire temperature field, stress distribution, and local heat flow data of the detector component under test.

[0014] The temperature reference unit is configured to: use the low-temperature vacuum environment where the detector component under test is located as a reference, and has a built-in liquid nitrogen temperature bath and temperature reference sensor to provide a standard temperature reference value and output reference temperature data.

[0015] The calibration execution unit is configured to: establish data communication with each sensor, periodically acquire and output the raw measured data output by the sensor, the raw measured data including measured temperature and stress data;

[0016] The drift correction unit is configured to: take the reference temperature data and the original measured data as input, calculate the deviation between the standard temperature and the measured value according to the sensor drift characteristic curve obtained from the pre-experiment, generate a correction coefficient, compensate the output value of each sensor, and output the calibrated measured data.

[0017] Preferably, the multi-sensor includes:

[0018] An infrared thermal imager is configured to: take the surface of the detector component under test as the object of imaging, acquire temperature field distribution data by capturing images of the surface, and output temperature field images and corresponding numerical data.

[0019] The strain gauge array is configured to be fixed to the surface of the encapsulation housing of the detector component under test by adhesive bonding, and to collect stress distribution data by using the structural stress of the encapsulation housing as the acquisition object, and output a stress numerical matrix.

[0020] The miniature thermocouple is configured to have its measuring end directly contact the cold end of the detector component under test and the cold head of the refrigerator, respectively, and to collect local temperature data at each contact point and output temperature time series data.

[0021] The synchronous controller is configured to use the acquisition timing of each sensor as the control object, and is electrically connected to the infrared thermal imager, strain gauge array and miniature thermocouple respectively. It controls each sensor to complete data acquisition synchronously on the same time axis, and outputs a synchronous trigger signal and the integrated original measured dataset.

[0022] Preferably, the coupling analysis module includes:

[0023] The model building unit is configured to: take the structural composition of the detector component under test as the modeling object, build a thermal resistance network model, embed the correlation equations of the thermal-mechanical-electrical properties of each layer of material in the thermal resistance network model, and output the initial coupled simulation model;

[0024] The parameter input unit is configured to: take the material properties of each component of the detector assembly under test as input objects, import the characteristic data of thermal conductivity, coefficient of linear expansion and electrical conductivity of the detector chip, cold head and packaging shell as a function of temperature, and output a set of material property parameters;

[0025] The real-time correction unit is configured to: take the calibrated measured data and the material property parameter set as input, take the measured data of temperature field and stress distribution as feedback, adjust the contact thermal resistance and interface thermal conductivity parameters in the initial coupled simulation model through iterative calculation, and output the corrected coupled simulation model after the deviation between the output of the initial coupled simulation model and the measured data meets the preset requirements.

[0026] Preferably, the coupling analysis module further includes a comprehensive evaluation unit;

[0027] The comprehensive evaluation unit is configured to: use the modified coupled simulation model as the calculation basis, input the cooling process time series data of the detector component under test, calculate and output the heat load change curve of the detector component during the cooling process, the thermal mismatch stress distribution data of different temperature ranges, and the cooling efficiency parameters of the refrigerator under various working states.

[0028] Preferably, the part of the optimized testing module used to test the performance of the cold screen coating includes:

[0029] The sample fixing device is configured to: fix the cold screen of the detector assembly to be tested and expose the cold screen coating on the cold screen;

[0030] The low-temperature vacuum chamber is configured to simulate the actual working environment of the detector component under test, provide the fixed cold screen coating with low-temperature vacuum conditions consistent with the detector during operation, and output stable test environment parameters.

[0031] The Fourier spectrometer is configured such that its probe end is aligned with the surface of the cold screen coating through the optical window of the low-temperature vacuum chamber, measures the emissivity characteristics of the cold screen coating in the infrared band, and outputs the emissivity test curve and the corresponding coating performance data.

[0032] The temperature control unit is configured to: adjust the temperature of the cold screen coating based on the working temperature range of the cold screen, simulate the temperature change of the cold screen under different working conditions, and output temperature adjustment signals and real-time temperature data.

[0033] Preferably, the part of the optimized testing module used for testing cold index and thermal properties includes:

[0034] The sample clamping mechanism is configured to clamp and fix both ends of the integrated cold finger of the detector assembly to be tested, and the axis of the cold finger is aligned with the test optical path.

[0035] The laser emitting unit is configured to: use the first end of the cold finger as the thermal excitation object, emit a short-duration laser pulse toward the end to generate local thermal excitation, and output a laser pulse signal with fixed energy;

[0036] The infrared detection unit is configured to: take the second end of the cold finger as the temperature monitoring object, record the temperature response curve of the end after thermal excitation in real time, and output temperature-time response curve data;

[0037] The data processing unit is configured to: take the temperature response data as the calculation input, calculate the thermal diffusivity of the cold index based on the known density and specific heat capacity parameters of the cold index, derive the thermal conductivity of the material prepared by the cold index, and output the thermal property test results of the cold index.

[0038] Preferably, the data analysis platform includes:

[0039] The data storage unit is configured to: classify and store the measured data, evaluation results, coating performance data, and cold index and thermal property data of cooled infrared detector components with different structures and / or different materials under various test conditions;

[0040] The data retrieval unit is configured to: take user query requirements as input, retrieve data in the data storage unit by using detector model, test temperature range and material type as keywords, and output matching historical test data;

[0041] The machine learning training unit is configured to: construct an anomaly recognition classification model using the normal and abnormal patterns corresponding to the historical test data as training samples, and iteratively optimize the model parameters using new test data as supplementary samples, and output the updated anomaly recognition model.

[0042] Preferably, the three-dimensional thermal field visualization interface includes:

[0043] The field distribution rendering unit is configured to take temperature field and stress distribution data as input, convert the temperature field data into a color cloud map and the stress distribution data into a vector arrow map, and overlay the two types of graphics on the three-dimensional structural model of the detector component under test, and output a three-dimensional thermo-mechanical field rendering map.

[0044] The interactive operation unit is configured to respond to user operations and output detailed diagrams of the heat and force distribution of key internal parts.

[0045] The report generation unit is configured to: take the measured data, the evaluation results, the coating performance data, the cold index and thermal property data, and the three-dimensional thermal-mechanical field rendering as inputs, extract key parameters, data anomalies, and packaging structure optimization suggestions during the testing process, and generate and output a standardized test report.

[0046] Secondly, this application discloses a multi-parameter testing method for the thermal characteristics of a cooled infrared detector, applied to the multi-parameter testing system for the thermal characteristics of a cooled infrared detector as described above. The method includes:

[0047] Using the cooled infrared detector assembly under test as the data acquisition object, the temperature field, stress distribution and local heat flow data of the detector assembly are collected simultaneously by multiple sensors. The sensors are self-calibrated in the low temperature vacuum working environment of the detector assembly, and the calibrated measured data are output.

[0048] Using the measured data as input, a coupled simulation model of the detector component under test is constructed based on the heat conduction equation, the reduced-order model, and the Maxwell model. At the same time, the model parameters are dynamically corrected using the measured data, and the evaluation results of the thermal load, thermal mismatch stress, and cooling efficiency of the detector component are output.

[0049] Using the adjustable emissivity coating of the cold screen and the integrated cold finger configured in the detector component under test as test objects, the performance of the cold screen coating and the material thermal properties of the integrated cold finger are tested respectively, and the coating performance data and the cold finger thermal property data are output.

[0050] Using the measured data, the evaluation results, the coating performance data, and the cold index and thermal property data as input, the system automatically identifies abnormal data patterns and outputs a three-dimensional thermal field visualization interface.

[0051] Beneficial Effects: The multi-parameter testing system and method for the thermal characteristics of cooled infrared detectors in this application, by taking the cooled infrared detector component under test as the object, synchronously collects the temperature field, stress distribution, and local heat flow data of the detector component using multiple sensors, and performs sensor self-calibration in the low-temperature vacuum working environment of the detector component, effectively ensuring the accuracy and reliability of the measured data and avoiding the interference of sensor drift on the test results under extreme environments; by using the measured data as input, a coupled simulation model of the detector component under test is constructed based on the heat conduction equation, the reduced-order model, and the Maxwell model, and the model parameters are dynamically corrected by the measured data, which can accurately output the thermal load, thermal mismatch stress, and cooling efficiency of the detector component. The evaluation results enabled precise analysis of the detector's thermo-mechanical-electrical coupling characteristics. By conducting performance and material thermophysical property tests on the adjustable emissivity coating and integrated cold finger of the detector components under test, the system output coating performance data and cold finger thermophysical property data, filling a gap in specialized testing of key detector components and improving the dimensions of thermal characteristic testing. Furthermore, using measured data, evaluation results, coating performance data, and cold finger thermophysical property data as input, the system automatically identifies data anomaly patterns and outputs a three-dimensional thermal field visualization interface. This interface intuitively presents the detector's thermo-mechanical distribution patterns, assisting technicians in accurately locating problems and optimizing the detector's packaging structure and material selection. Overall, this improves the comprehensiveness, accuracy, and practicality of thermal characteristic testing for cooled infrared detectors. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a structural block diagram of a multi-parameter thermal characteristic testing system for a cooled infrared detector provided in an embodiment of this application. Detailed Implementation

[0054] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0055] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] This embodiment provides a first aspect such as Figure 1 The multi-parameter thermal characteristic testing system for a cooled infrared detector shown includes a collaborative measurement module, a coupling analysis module, an optimization testing module, and a data analysis platform. These modules are connected via a data bus to form a closed-loop testing system, ensuring stable data exchange between them.

[0057] In detail

[0058] The collaborative measurement module is configured to: take the cooled infrared detector component under test as the data acquisition object, simultaneously acquire the temperature field, stress distribution and local heat flow data of the detector component based on multiple sensors, perform self-calibration of the sensors in the low temperature vacuum working environment of the detector component, and output the calibrated measured data.

[0059] In this embodiment, the collaborative measurement module includes:

[0060] Multiple sensors are configured to acquire temperature field, stress distribution, and local heat flow data of the detector component under test.

[0061] The temperature reference unit is configured to: use the low-temperature vacuum environment where the detector component under test is located as a reference, and has a built-in liquid nitrogen temperature bath and temperature reference sensor to provide a standard temperature reference value and output reference temperature data.

[0062] The calibration execution unit is configured to: establish data communication with each sensor, periodically acquire and output the raw measured data output by the sensor, including measured temperature and stress data;

[0063] The drift correction unit is configured to: take reference temperature data and original measured data as input, calculate the deviation between the standard temperature and the measured value based on the sensor drift characteristic curve obtained from the pre-experiment, generate a correction coefficient, compensate the output value of each sensor, and output the calibrated measured data.

[0064] Based on the above, by clarifying the complete calibration process in the collaborative measurement module, namely, the isothermal reference unit providing a standard temperature reference under low-temperature vacuum conditions, the calibration execution unit periodically acquiring raw sensor data, and the drift correction unit compensating for errors based on pre-experimental drift curves, the problem of data deviation caused by the lack of a targeted calibration mechanism for sensors under low-temperature vacuum conditions in existing tests is solved. This further improves the accuracy of the output data of the collaborative measurement module and lays a data foundation for the reliability of subsequent coupling analysis and evaluation results.

[0065] Furthermore, the multi-sensor includes:

[0066] An infrared thermal imager is configured to: take the surface of the detector component under test as the object of imaging, acquire temperature field distribution data by capturing images of the surface, and output temperature field images and corresponding numerical data.

[0067] The strain gauge array is configured to be fixed to the surface of the encapsulation housing of the detector component under test by adhesive bonding, and to collect stress distribution data by using the structural stress of the encapsulation housing as the acquisition object, and output a stress numerical matrix.

[0068] The miniature thermocouple is configured to have its measuring end directly contact the cold end of the detector component under test and the cold head of the refrigerator, respectively, and to collect local temperature data at each contact point and output temperature time series data.

[0069] The synchronous controller is configured to use the acquisition timing of each sensor as the control object, and is electrically connected to the infrared thermal imager, strain gauge array and miniature thermocouple respectively. It controls each sensor to synchronously complete data acquisition on the same time axis, and outputs a synchronous trigger signal and the integrated original measured dataset (including temperature field, stress distribution and local heat flow data).

[0070] Based on the above, by refining the multi-sensor infrared thermal imager to specifically collect the detector surface temperature field, the strain gauge array to collect the packaging shell stress, and the miniature thermocouple to collect the local temperature of the cold junction and cold head, and by using a synchronous controller to ensure the consistency of the acquisition sequence of each sensor, the problem of incomplete parameter acquisition by a single sensor and the distortion of coupling relationship analysis caused by asynchronous data sequence of multiple sensors in the existing test is solved. It realizes the accurate synchronous acquisition of multi-dimensional thermal characteristic parameters of the detector, and provides comprehensive raw data support for multi-parameter coupling evaluation.

[0071] The coupling analysis module is configured to: take measured data as input, construct a coupled simulation model of the detector component under test based on the heat conduction equation, the reduced-order model and the Maxwell model, dynamically correct the model parameters through measured data, and output the evaluation results of the thermal load, thermal mismatch stress and cooling efficiency of the detector component.

[0072] In this embodiment, the coupling analysis module includes:

[0073] The model building unit is configured to: take the structural composition of the detector component under test (detector chip, refrigerator cold head, packaging shell) as the modeling object, build a thermal resistance network model, embed the correlation equations of the thermal-mechanical-electrical properties of each layer of material in the thermal resistance network model, and output the initial coupled simulation model;

[0074] The parameter input unit is configured to: take the material properties of each component of the detector assembly under test as input objects, import the characteristic data of thermal conductivity, coefficient of linear expansion and electrical conductivity of the detector chip, cold head and packaging shell as a function of temperature, and output a set of material property parameters;

[0075] The real-time correction unit is configured to take the calibrated measured data and material property parameter set as input, take the measured data of temperature field and stress distribution as feedback, adjust the contact thermal resistance and interface thermal conductivity parameters in the initial coupled simulation model through iterative calculation, and output the corrected coupled simulation model after the deviation between the output of the initial coupled simulation model and the measured data meets the preset requirements.

[0076] Based on the above, a thermal resistance network model is constructed using the model building unit based on the actual three-layer structure of the detector, and the thermo-mechanical-electrical characteristic correlation equation is embedded. Combined with the reduced-order model, the computational workload is simplified. The parameter input unit imports the thermal conductivity, linear expansion coefficient, and other properties of the material as a function of temperature, ensuring that the model closely matches the actual material properties. The real-time correction unit uses the calibrated measured data as feedback to iteratively adjust parameters such as contact thermal resistance and interface thermal conductivity. This solves the problems of existing coupled simulation models, such as complex computational workload, failure to consider the temperature dependence of materials, and large deviation from actual operating conditions. It improves the accuracy and practicality of the coupled simulation model and provides an accurate model foundation for the evaluation of the detector's thermal characteristics.

[0077] Furthermore, the coupling analysis module also includes a comprehensive evaluation unit;

[0078] The comprehensive evaluation unit is configured to: use the modified coupled simulation model as the calculation basis, input the time series data of the cooling process of the detector component under test, calculate and output the heat load change curve of the detector component during the cooling process, the thermal mismatch stress distribution data of different temperature ranges, and the cooling efficiency parameters of the refrigerator under various working states.

[0079] Based on the above, the comprehensive evaluation unit, based on the modified coupled simulation model, outputs the heat load change curve of the detector cooling process, the thermal mismatch stress distribution data of different temperature ranges, and the cooling efficiency parameters of the refrigerator in each state. This solves the problem of the existing evaluation results being general and lacking specific indicators, and enables the evaluation of the detector's thermal characteristics to shift from qualitative to quantitative. It provides clear data for technicians to accurately locate the peak heat load, stress concentration area, and cooling efficiency shortcomings, and directly supports the optimization of the detector's thermal design.

[0080] The optimized test module is configured to: use the adjustable emissivity coating of the cold screen and the integrated cold finger of the detector component under test as test objects, test the performance of the cold screen coating and the material thermal properties of the integrated cold finger respectively, and output coating performance data and cold finger thermal property data.

[0081] In this embodiment, the part of the optimized testing module used to test the performance of the cold screen coating includes:

[0082] The sample fixing device is configured to: fix the cold screen of the detector assembly to be tested and expose the cold screen coating on the cold screen;

[0083] The low-temperature vacuum chamber is configured to simulate the actual working environment of the detector component under test, provide the fixed cold screen coating with low-temperature vacuum conditions consistent with the detector during operation, and output stable test environment parameters.

[0084] The Fourier spectrometer is configured such that its probe end is aligned with the surface of the cold screen coating through the optical window of the low-temperature vacuum chamber, measures the emissivity characteristics of the cold screen coating in the infrared band, and outputs the emissivity test curve and the corresponding coating performance data.

[0085] The temperature control unit is configured to adjust the temperature of the cold screen coating based on the operating temperature range of the cold screen, simulate the temperature changes of the cold screen under different operating conditions, and output temperature adjustment signals and real-time temperature data.

[0086] Based on the above, by using a sample fixing device to ensure that the test state of the cold screen coating is consistent with reality, a low-temperature vacuum chamber to simulate the real working environment of the detector, a Fourier spectrometer to accurately measure the infrared emissivity of the coating, and a temperature control unit to simulate different working temperatures of the cold screen, the problem of existing tests being unable to simulate actual working conditions for testing the performance of the cold screen coating is solved. This enables accurate performance testing of the adjustable emissivity coating of the cold screen in real working scenarios, providing direct data for coating emissivity optimization and cold screen radiation heat leakage control.

[0087] In this embodiment, the part of the optimized testing module used for testing cold and hot physical properties includes:

[0088] The sample clamping mechanism is configured to clamp and fix both ends of the integrated cold finger of the detector assembly to be tested, and the axis of the cold finger is aligned with the test optical path.

[0089] The laser emitting unit is configured to: use the first end of the cold finger as the thermal excitation object, emit a short-duration laser pulse toward the end to generate local thermal excitation, and output a laser pulse signal with fixed energy;

[0090] The infrared detection unit is configured to: take the second end of the cold finger as the temperature monitoring object, record the temperature response curve of the end after thermal excitation in real time, and output temperature-time response curve data;

[0091] The data processing unit is configured to: take temperature response data as the calculation input, calculate the thermal diffusivity of the cold index based on the known density and specific heat capacity parameters of the cold index, derive the thermal conductivity of the material prepared by the cold index, and output the thermal property test results of the cold index.

[0092] Based on the above, the sample clamping mechanism ensures that the axis is aligned with the optical path during cold finger testing, the laser emission unit provides short-term thermal excitation, the infrared detection unit records the temperature response in real time, and the data processing unit derives the thermal conductivity by combining density and specific heat capacity. This solves the problems of long testing cycles and inability to simulate actual working conditions in existing cold finger thermal property tests. By using the laser pulse method, the thermal conductivity of cold finger materials can be tested quickly and accurately, providing reliable thermal property data for the selection and structural optimization of cold finger composite materials.

[0093] The data analysis platform is configured to automatically identify data anomaly patterns and output a three-dimensional thermal field visualization interface, taking measured data, evaluation results, coating performance data, and cold index and thermal property data as inputs.

[0094] In this embodiment, it is feasible to display the output results of the data analysis platform on a user interface (including but not limited to mobile phones, tablets, desktop computers, etc.), allowing technicians to view the three-dimensional thermal field interface. The data analysis platform includes:

[0095] The data storage unit is configured to: classify and store the measured data, evaluation results, coating performance data, and cold index and thermal property data of cooled infrared detector components with different structures and / or different materials under various test conditions;

[0096] The data retrieval unit is configured to: take user query requirements as input, retrieve data in the data storage unit by detector model, test temperature range and material type as keywords, and output matching historical test data;

[0097] The machine learning training unit is configured to: use normal patterns (such as stable temperature field, uniform stress distribution) and abnormal patterns (such as sudden change in cold end heat capacity, stress concentration area) corresponding to historical test data as training samples to build an anomaly recognition classification model, and use new test data as supplementary samples to iteratively optimize the model parameters and output the updated anomaly recognition model.

[0098] Based on the above, the data storage unit categorizes and stores multi-source test data from detectors of different structures and materials, enabling structured data management and reuse. The data retrieval unit supports quick searches by keywords such as model, temperature, and material, improving data retrieval efficiency. The machine learning training unit trains anomaly recognition models using historical data and iteratively optimizes them, solving the problems of chaotic test data management, reliance on manual anomaly recognition, and low accuracy in existing systems. This improves the data reusability and intelligent anomaly recognition capabilities of the data analysis platform, providing historical data references and more accurate anomaly judgments for subsequent tests.

[0099] Furthermore, the three-dimensional thermal field visualization interface includes:

[0100] The field distribution rendering unit is configured to take temperature field and stress distribution data as input, convert the temperature field data into a color cloud map and the stress distribution data into a vector arrow map, and overlay the two types of graphics on the three-dimensional structural model of the detector component under test, and output a three-dimensional thermo-mechanical field rendering map.

[0101] The interactive operation unit is configured to respond to user operations (rotation, scaling, and sectioning) on ​​the user interaction terminal and output detailed diagrams of the heat and force distribution of key internal parts (such as the contact surface between the chip and the cold head, and the corners of the package shell).

[0102] The report generation unit is configured to take measured data, evaluation results, coating performance data, cold index and thermal property data, and three-dimensional thermo-mechanical field rendering as inputs, extract key parameters (such as minimum cooling temperature and maximum stress value), data anomalies, and packaging structure optimization suggestions during the testing process, and generate and output a standardized test report.

[0103] Based on the above, the field distribution rendering unit converts temperature field and stress distribution data into intuitive color cloud maps and vector arrow diagrams, solving the problem of weak data visualization; the interactive operation unit supports rotation, zoom, and cross-section to view internal details, solving the problem of not being able to intuitively observe the internal thermal-mechanical distribution of the detector; the report generation unit automatically extracts key parameters, anomalies, and optimization suggestions to generate standardized reports, solving the problems of cumbersome report generation and inconsistent information, and improving the overall readability and ease of use of test results, assisting technicians in quickly locating problems and formulating optimization solutions.

[0104] In summary, the multi-parameter thermal characteristic testing system for the cooled infrared detector in this embodiment achieves synchronous acquisition of temperature field, stress distribution, and local heat flow data of the detector components under test, as well as sensor self-calibration under low-temperature vacuum conditions through a collaborative measurement module. This solves the problems of asynchronous testing of multiple parameters and data drift at low temperatures in existing systems. The coupling analysis module constructs a coupled simulation model based on the heat conduction equation, a reduced-order model, and the Maxwell model, and dynamically corrects the parameters, achieving accurate evaluation of the detector's thermo-mechanical-electric coupling characteristics and avoiding the one-sidedness of single-parameter evaluation. The optimization testing module conducts specialized tests on the detector's built-in cold screen coating and integrated cold finger, filling the gap in thermal characteristic testing of key components. The data analysis platform integrates multi-source data to achieve anomaly identification and three-dimensional thermal field visualization, solving the problems of single data analysis methods and unintuitive results. Overall, it achieves comprehensive and accurate testing of multiple thermal characteristics of the detector, providing reliable support for detector optimization design.

[0105] This embodiment provides a method for multi-parameter testing of the thermal characteristics of a cooled infrared detector in a second aspect, applied to the multi-parameter testing system for the thermal characteristics of a cooled infrared detector as described above. The method includes the following steps:

[0106] Using the cooled infrared detector assembly under test as the data acquisition object, the temperature field, stress distribution and local heat flow data of the detector assembly are collected simultaneously by multiple sensors. The sensors are self-calibrated in the low temperature vacuum working environment of the detector assembly, and the calibrated measured data are output.

[0107] Using measured data as input, a coupled simulation model of the detector component under test is constructed based on the heat conduction equation, the reduced-order model, and the Maxwell model. At the same time, the model parameters are dynamically corrected through measured data, and the evaluation results of the thermal load, thermal mismatch stress, and cooling efficiency of the detector component are output.

[0108] The adjustable emissivity coating of the cold screen and the integrated cold finger configured in the detector component under test are used as test objects. The performance of the cold screen coating and the material thermal properties of the integrated cold finger are tested respectively, and the coating performance data and the cold finger thermal property data are output.

[0109] Using measured data, evaluation results, coating performance data, and cold index and thermal property data as input, the system automatically identifies abnormal data patterns and outputs a three-dimensional thermal field visualization interface.

[0110] It should be noted that the multi-parameter thermal characteristic test method of the cooled infrared detector in this embodiment corresponds to the aforementioned multi-parameter thermal characteristic test system of the cooled infrared detector. Therefore, the parts not specifically described in the multi-parameter thermal characteristic test method of the cooled infrared detector in this embodiment (including but not limited to specific implementation means, technical effects, etc.) can be referred to the relevant descriptions in the aforementioned multi-parameter thermal characteristic test system of the cooled infrared detector, and will not be repeated here.

[0111] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0112] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A multi-parameter testing system for the thermal characteristics of a cooled infrared detector, characterized in that, The system includes: The collaborative measurement module is configured to: take the cooled infrared detector component under test as the data acquisition object, simultaneously acquire the temperature field, stress distribution and local heat flow data of the detector component based on multiple sensors, perform self-calibration of the sensors in the low temperature vacuum working environment of the detector component, and output the calibrated measured data. The coupling analysis module is configured to: take the measured data as input, construct a coupled simulation model of the detector component under test based on the heat conduction equation, the reduced-order model and the Maxwell model, dynamically correct the model parameters through the measured data, and output the evaluation results of the thermal load, thermal mismatch stress and cooling efficiency of the detector component. The optimized test module is configured to: use the adjustable emissivity coating of the cold screen and the integrated cold finger of the detector component under test as test objects, test the performance of the cold screen coating and the material thermal properties of the integrated cold finger respectively, and output coating performance data and cold finger thermal property data. The data analysis platform is configured to automatically identify abnormal data patterns and output a three-dimensional thermal field visualization interface, taking the measured data, the evaluation results, the coating performance data, and the cold index and thermal property data as inputs.

2. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 1, characterized in that, The collaborative measurement module includes: Multiple sensors are configured to acquire temperature field, stress distribution, and local heat flow data of the detector component under test. The temperature reference unit is configured to: use the low-temperature vacuum environment where the detector component under test is located as a reference, and has a built-in liquid nitrogen temperature bath and temperature reference sensor to provide a standard temperature reference value and output reference temperature data. The calibration execution unit is configured to: establish data communication with each sensor, periodically acquire and output the raw measured data output by the sensor, the raw measured data including measured temperature and stress data; The drift correction unit is configured to: take the reference temperature data and the original measured data as input, calculate the deviation between the standard temperature and the measured value according to the sensor drift characteristic curve obtained from the pre-experiment, generate a correction coefficient, compensate the output value of each sensor, and output the calibrated measured data.

3. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 2, characterized in that, The multi-sensor includes: An infrared thermal imager is configured to: take the surface of the detector component under test as the object of imaging, acquire temperature field distribution data by capturing images of the surface, and output temperature field images and corresponding numerical data. The strain gauge array is configured to be fixed to the surface of the encapsulation housing of the detector component under test by adhesive bonding, and to collect stress distribution data by using the structural stress of the encapsulation housing as the acquisition object, and output a stress numerical matrix. The miniature thermocouple is configured to have its measuring end directly contact the cold end of the detector component under test and the cold head of the refrigerator, respectively, and to collect local temperature data at each contact point and output temperature time series data. The synchronous controller is configured to use the acquisition timing of each sensor as the control object, and is electrically connected to the infrared thermal imager, strain gauge array and miniature thermocouple respectively. It controls each sensor to complete data acquisition synchronously on the same time axis, and outputs a synchronous trigger signal and the integrated original measured dataset.

4. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 1, characterized in that, The coupling analysis module includes: The model building unit is configured to: take the structural composition of the detector component under test as the modeling object, build a thermal resistance network model, embed the correlation equations of the thermal-mechanical-electrical properties of each layer of material in the thermal resistance network model, and output the initial coupled simulation model; The parameter input unit is configured to: take the material properties of each component of the detector assembly under test as input objects, import the characteristic data of thermal conductivity, coefficient of linear expansion and electrical conductivity of the detector chip, cold head and packaging shell as a function of temperature, and output a set of material property parameters; The real-time correction unit is configured to: take the calibrated measured data and the material property parameter set as input, take the measured data of temperature field and stress distribution as feedback, adjust the contact thermal resistance and interface thermal conductivity parameters in the initial coupled simulation model through iterative calculation, and output the corrected coupled simulation model after the deviation between the output of the initial coupled simulation model and the measured data meets the preset requirements.

5. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 4, characterized in that, The coupling analysis module also includes a comprehensive evaluation unit; The comprehensive evaluation unit is configured to: use the modified coupled simulation model as the calculation basis, input the cooling process time series data of the detector component under test, calculate and output the heat load change curve of the detector component during the cooling process, the thermal mismatch stress distribution data of different temperature ranges, and the cooling efficiency parameters of the refrigerator under various working states.

6. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 1, characterized in that, The optimized testing module includes the following components for testing the performance of the cold screen coating: The sample fixing device is configured to: fix the cold screen of the detector assembly to be tested and expose the cold screen coating on the cold screen; The low-temperature vacuum chamber is configured to simulate the actual working environment of the detector component under test, provide the fixed cold screen coating with low-temperature vacuum conditions consistent with the detector during operation, and output stable test environment parameters. The Fourier spectrometer is configured such that its probe end is aligned with the surface of the cold screen coating through the optical window of the low-temperature vacuum chamber, measures the emissivity characteristics of the cold screen coating in the infrared band, and outputs the emissivity test curve and the corresponding coating performance data. The temperature control unit is configured to: adjust the temperature of the cold screen coating based on the working temperature range of the cold screen, simulate the temperature change of the cold screen under different working conditions, and output temperature adjustment signals and real-time temperature data.

7. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 1, characterized in that, The optimized testing module includes the part used to test cold and hot physical properties, which includes: The sample clamping mechanism is configured to clamp and fix both ends of the integrated cold finger of the detector assembly to be tested, and the axis of the cold finger is aligned with the test optical path. The laser emitting unit is configured to: use the first end of the cold finger as the thermal excitation object, emit a short-duration laser pulse toward the end to generate local thermal excitation, and output a laser pulse signal with fixed energy; The infrared detection unit is configured to: take the second end of the cold finger as the temperature monitoring object, record the temperature response curve of the end after thermal excitation in real time, and output temperature-time response curve data; The data processing unit is configured to: take the temperature response data as the calculation input, calculate the thermal diffusivity of the cold index based on the known density and specific heat capacity parameters of the cold index, derive the thermal conductivity of the material prepared by the cold index, and output the thermal property test results of the cold index.

8. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 1, characterized in that, The data analysis platform includes: The data storage unit is configured to: classify and store the measured data, evaluation results, coating performance data, and cold index and thermal property data of cooled infrared detector components with different structures and / or different materials under various test conditions; The data retrieval unit is configured to: take user query requirements as input, retrieve data in the data storage unit by using detector model, test temperature range and material type as keywords, and output matching historical test data; The machine learning training unit is configured to: construct an anomaly recognition classification model using the normal and abnormal patterns corresponding to the historical test data as training samples, and iteratively optimize the model parameters using new test data as supplementary samples, and output the updated anomaly recognition model.

9. The multi-parameter thermal characteristic testing system for a cooled infrared detector according to claim 8, characterized in that, The three-dimensional thermal field visualization interface includes: The field distribution rendering unit is configured to take temperature field and stress distribution data as input, convert the temperature field data into a color cloud map and the stress distribution data into a vector arrow map, and overlay the two types of graphics on the three-dimensional structural model of the detector component under test, and output a three-dimensional thermo-mechanical field rendering map. The interactive operation unit is configured to respond to user operations and output detailed diagrams of the heat and force distribution of key internal parts. The report generation unit is configured to: take the measured data, the evaluation results, the coating performance data, the cold index and thermal property data, and the three-dimensional thermal-mechanical field rendering as inputs, extract key parameters, data anomalies, and packaging structure optimization suggestions during the testing process, and generate and output a standardized test report.

10. A method for multi-parameter testing of the thermal characteristics of a cooled infrared detector, applied to the multi-parameter testing system for the thermal characteristics of a cooled infrared detector as described in any one of claims 1-9, characterized in that, The method includes: Using the cooled infrared detector assembly under test as the data acquisition object, the temperature field, stress distribution and local heat flow data of the detector assembly are collected simultaneously by multiple sensors. The sensors are self-calibrated in the low temperature vacuum working environment of the detector assembly, and the calibrated measured data are output. Using the measured data as input, a coupled simulation model of the detector component under test is constructed based on the heat conduction equation, the reduced-order model, and the Maxwell model. At the same time, the model parameters are dynamically corrected using the measured data, and the evaluation results of the thermal load, thermal mismatch stress, and cooling efficiency of the detector component are output. Using the adjustable emissivity coating of the cold screen and the integrated cold finger configured in the detector component under test as test objects, the performance of the cold screen coating and the material thermal properties of the integrated cold finger are tested respectively, and the coating performance data and the cold finger thermal property data are output. Using the measured data, the evaluation results, the coating performance data, and the cold index and thermal property data as input, the system automatically identifies abnormal data patterns and outputs a three-dimensional thermal field visualization interface.

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