Sensor packaging conversion method

Abstract

The invention relates to a packaging conversion method of a sensor, and belongs to the technical field of sensor packaging, and the method comprises the steps: building a testing environment, and obtaining the physical characteristic data of a current packaging material under different testing environments based on the testing environment; a sensor performance prediction model is constructed according to the physical characteristic data of the current packaging material under different test environments, and the performance characteristic data of the sensor packaging process under the current environment data is estimated based on the sensor performance prediction model. Therefore, the performance characteristic data of the sensor packaging process under the current environment data are dynamically analyzed to obtain a dynamic analysis and evaluation result, and finally, the thickness and density of the packaging layer are dynamically adjusted based on the dynamic analysis and evaluation result. The sensor is evaluated and analyzed when being packaged in combination with the environment characteristics, and the performance characteristic data of the sensor packaging process under the current environment data can be estimated, so that the thickness and the density of the packaging layer are optimized, and the packaging conversion effect of the sensor is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor packaging, and in particular to a packaging conversion method for a sensor. Background Art

[0002] Currently, camera sensors sold on the market can be broadly categorized into two types: CSP (Compact Scheme) packaging and COB (chip-on-board) packaging. COB uses wire bonding to connect the chip's signals to the circuit board. This method requires specialized DA (Direct Array) and WB (Wire Bonding) equipment. CSP pre-packages the die into a semiconductor package similar to BGA. However, due to its smaller size, it's called a Chip Scale Package (CSP). The key difference between CSP and COB is that the CSP chip's photosensitive surface is protected by a layer of glass, while COB does not. The height of the modules produced using the two processes for the same lens differs, with COB modules being lower. During production and processing, CSP has relatively low requirements for dust spots. If dust spots still exist on the sensor surface, they can be reworked and repaired, but COB cannot. COB advantages: It can integrate the lens, photosensitive chip, ISP, and flexible circuit board together, and can be directly delivered to the assembly plant after packaging and testing. COB packaging is a more traditional method. After obtaining the wafer, the die is fixed to the PCB board using the chip-on-board method, and then a bracket and lens are added to form a module. This production method focuses on COB and yield control, so it has the advantages of a short production process, space saving, mature technology and lower cost advantages (about 10% lower). COB disadvantages: COB is easily contaminated during the production process, has high environmental requirements, high process equipment costs, large yield rate fluctuations, long process time, and cannot be repaired. If used in camera modules, it is prone to particle vibration problems during drop tests. While the production process is shortened, this also means a significant increase in the technical difficulty of module production, impacting yield. The advantages of CSP include packaging, which is completed in the front-end process. CSP packaging is suitable for ICs with a small number of pins, resulting in lower equipment costs and shorter production times. Advantages of CSP include the packaged chip size being comparable to the die size, making it suitable for portable electronic products. Furthermore, because the chip and circuit board are separated only by solder balls or bumps, the circuit path is significantly shortened and current loss is reduced. The PLCC package is a plastic chip carrier with leads. It is a type of surface mount package with a square, 32-pin design. Pins extend from the four sides of the package in a T-shape. Made of plastic, it is significantly smaller than a DIP package. The PLCC package is suitable for PCB installation and wiring using SMT (Surface Mount Technology) technology, offering advantages such as small size and high reliability. A PLCC is a CIS package that is mounted on a substrate using the COB process, then covered with a bracket and IR coating. The bottom four sides of the PLCC contain solder pads, so the PLCC can be mounted on the FPC through SMT. After SMT, the motor and lens can be assembled to make a camera module.However, when the sensor is packaged, it is often affected by environmental factors such as strong light, temperature, and humidity. This will lead to poor sealing of the sensor during the packaging process and low light transmission efficiency of the sensor. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a packaging conversion method for a sensor.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a sensor packaging conversion method, comprising the following steps:

[0006] Constructing a test environment, and based on the test environment, obtaining physical property data of the current packaging material under different test environments;

[0007] Building a sensor performance prediction model based on the physical property data of the current packaging material under the different test environments, and estimating the performance characteristic data of the sensor packaging process under the current environmental data based on the sensor performance prediction model;

[0008] Obtaining dynamic analysis and evaluation results by dynamically analyzing performance characteristic data of the sensor packaging process under the current environmental data;

[0009] The thickness and density of the encapsulation layer are dynamically adjusted based on the dynamic analysis and evaluation results.

[0010] Furthermore, in the sensor packaging conversion method, a test environment is constructed, and based on the test environment, physical property data of the current packaging material under different test environments is obtained, specifically:

[0011] Setting a number of environmental indicator data, controlling the test environment through the environmental control equipment based on the number of environmental indicator data, initializing the working parameter data information of each environmental control equipment, and controlling the test environment based on the working parameter data information of the environmental control equipment;

[0012] Obtain target environment data within a preset time in a test environment, set an environment data threshold range, and determine whether the target environment data within the preset time in the test environment is within the environment data threshold range;

[0013] When the target environment data in the test environment within a preset time is within the environment data threshold range, maintaining the current test environment unchanged;

[0014] When the target environment data in the test environment within the preset time is not within the environment data threshold range, the current test environment is continuously adjusted, the physical property data of the current packaging material under the test environment is counted, and the physical property data of the current packaging material under different test environments is obtained.

[0015] Furthermore, in the sensor packaging conversion method, a sensor performance prediction model is constructed based on the physical property data of the current packaging material under the different test environments, specifically:

[0016] Obtain the physical property data of the current packaging material under different test environments, and use big data to obtain the sensor performance characteristic data under different physical property data, and build a sensor performance prediction model based on deep neural network;

[0017] Introducing a multi-head attention mechanism to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data;

[0018] Constructing a directed description relationship based on the association relationship, taking the test environment as the first node, the physical property data of the packaging material as the second node, and the sensor performance characteristic data as the third node, and constructing a topological structure diagram of the first node, the second node, and the third node based on the directed description relationship;

[0019] A related adjacency matrix is ​​obtained based on the topological structure graph, and the related adjacency matrix is ​​input into the sensor performance prediction model for training to obtain a sensor performance prediction model that meets expectations.

[0020] Furthermore, in the sensor packaging conversion method, the performance characteristic data of the sensor packaging process under the current environmental data is estimated based on the sensor performance prediction model, specifically including:

[0021] Acquire working environment data information and packaging process data information of the current sensor during the packaging process, and input the working environment data information and packaging process data information of the current sensor during the packaging process into the sensor performance prediction model for prediction;

[0022] Through prediction, performance characteristic data of the sensor packaging process under the current environmental data are obtained, and the performance characteristic data of the sensor packaging process under the current environmental data are output.

[0023] Furthermore, in the sensor packaging conversion method, the performance characteristic data of the sensor packaging process under the current environmental data is dynamically analyzed to obtain a dynamic analysis evaluation result, which specifically includes:

[0024] Setting a performance characteristic data threshold of the sensor, and determining whether the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor;

[0025] When the performance characteristic data of the sensor packaging process under the current environmental data is not greater than the performance characteristic data threshold of the sensor, generating an abnormal sensor packaging process data evaluation result;

[0026] When the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor, a normal sensor packaging process data evaluation result is generated;

[0027] A dynamic analysis evaluation result is generated according to the abnormal sensor packaging process data evaluation result or the normal sensor packaging process data evaluation result, and the dynamic analysis evaluation result is output.

[0028] Furthermore, in the packaging conversion method of the sensor, the thickness and density of the packaging layer are dynamically adjusted based on the dynamic analysis and evaluation results, specifically including:

[0029] If the dynamic analysis evaluation result is an abnormal sensor packaging process data evaluation result, a genetic algorithm is introduced, a genetic generation is set based on the genetic algorithm, and inheritance is performed based on the genetic generation to uniformly increase the thickness and density of the packaging layer;

[0030] Obtaining a dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer; if the dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer is still an abnormal sensor packaging process data evaluation result, continuing to increase the thickness and density of the packaging layer until the sensor packaging process data evaluation result is no longer abnormal;

[0031] If the dynamic analysis evaluation result corresponding to the increased thickness and density of the packaging layer is not an abnormal sensor packaging process data evaluation result, performing packaging conversion according to the current thickness and density of the packaging layer;

[0032] If the dynamic analysis evaluation result is a normal sensor packaging process data evaluation result, packaging is performed according to the thickness and density of the current packaging layer.

[0033] A second aspect of the present invention provides a sensor packaging and conversion system, comprising a memory and a processor, wherein the memory comprises a sensor packaging and conversion method program, and when the sensor packaging and conversion method program is executed by the processor, the steps of any one of the sensor packaging and conversion methods are implemented.

[0034] A third aspect of the present invention provides a computer-readable storage medium, comprising a sensor packaging conversion method program, which, when executed by a processor, implements any one of the steps of the sensor packaging conversion method.

[0035] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0036] The present invention constructs a test environment and, based on the test environment, obtains the physical property data of the current packaging material under different test environments. A sensor performance prediction model is then constructed based on the physical property data of the current packaging material under different test environments. Based on the sensor performance prediction model, the performance characteristic data of the sensor packaging process under the current environmental data are estimated. Dynamic analysis of the performance characteristic data of the sensor packaging process under the current environmental data is then performed to obtain dynamic analysis and evaluation results. Finally, the thickness and density of the packaging layer are dynamically adjusted based on the dynamic analysis and evaluation results. By evaluating and analyzing the sensor packaging process in combination with environmental characteristics, the present invention can estimate the performance characteristic data of the sensor packaging process under the current environmental data, thereby optimizing the thickness and density of the packaging layer and improving the packaging conversion effect of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0038] Figure 1 Shows an overall flow chart of a sensor packaging conversion method;

[0039] Figure 2 A system block diagram of a sensor packaging conversion system is shown. DETAILED DESCRIPTION

[0040] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0042] like Figure 1 As shown, the first aspect of the present invention provides a sensor packaging conversion method, comprising the following steps:

[0043] S102: Constructing a test environment, and based on the test environment, obtaining physical property data of the current packaging material under different test environments;

[0044] S104: Building a sensor performance prediction model based on the physical property data of the current packaging material under different test environments, and estimating the performance characteristic data of the sensor packaging process under the current environmental data based on the sensor performance prediction model;

[0045] S106: Obtaining dynamic analysis and evaluation results by dynamically analyzing the performance characteristic data of the sensor packaging process under the current environmental data;

[0046] S108: Dynamically adjust the thickness and density of the encapsulation layer based on the dynamic analysis and evaluation results.

[0047] It should be noted that the present invention evaluates and analyzes the packaging of sensors in combination with environmental characteristics, and can estimate the performance characteristic data of the sensor packaging process under the current environmental data, thereby optimizing the thickness and density of the packaging layer and improving the packaging conversion effect of the sensor.

[0048] Furthermore, in the sensor packaging conversion method, a test environment is constructed, and based on the test environment, physical property data of the current packaging material under different test environments is obtained, specifically:

[0049] Setting a number of environmental indicator data, controlling the test environment through environmental control devices based on the number of environmental indicator data, initializing working parameter data information of each environmental control device, and controlling the test environment based on the working parameter data information of the environmental control device;

[0050] Obtain target environment data within a preset time in the test environment, set an environment data threshold range, and determine whether the target environment data within the preset time in the test environment is within the environment data threshold range;

[0051] When the target environment data in the test environment is within the environment data threshold range within the preset time, the current test environment is maintained unchanged;

[0052] When the target environment data in the test environment within the preset time is not within the environment data threshold range, the current test environment is continuously adjusted, the physical property data of the current packaging material under the test environment is counted, and the physical property data of the current packaging material under different test environments is obtained.

[0053] It should be noted that environmental indicator data includes light, temperature, humidity, etc., and environmental control equipment includes temperature control equipment, humidity control equipment, and light control equipment. This method can maintain the test environment within the environmental data threshold range, improve test accuracy, and thus obtain the physical property data of the current packaging material under different test environments.

[0054] Furthermore, in the sensor packaging conversion method, a sensor performance prediction model is constructed based on the physical property data of the current packaging material under different test environments, specifically:

[0055] Obtain the physical property data of the current packaging material under different test environments, and use big data to obtain the sensor performance characteristic data under different physical property data, and build a sensor performance prediction model based on deep neural network;

[0056] A multi-head attention mechanism is introduced to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data;

[0057] A directed description relationship is constructed based on the association relationship, with the test environment as the first node, the physical property data of the packaging material as the second node, and the sensor performance characteristic data as the third node, and a topological structure diagram is constructed from the first node, the second node, and the third node based on the directed description relationship;

[0058] A related adjacency matrix is ​​obtained based on the topological structure graph, and the related adjacency matrix is ​​input into the sensor performance prediction model for training to obtain a sensor performance prediction model that meets expectations.

[0059] It should be noted that the multi-head attention mechanism is used to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data, so as to build a sensor performance prediction model that meets the expectations and predict the physical property data of the current packaging material under different environments.

[0060] Furthermore, in the sensor packaging conversion method, the performance characteristic data of the sensor packaging process under the current environmental data is estimated based on the sensor performance prediction model, specifically including:

[0061] Obtaining working environment data information and packaging process data information of the current sensor during the packaging process, and inputting the working environment data information and packaging process data information of the current sensor during the packaging process into the sensor performance prediction model for prediction;

[0062] Through prediction, the performance characteristic data of the sensor packaging process under the current environmental data is obtained, and the performance characteristic data of the sensor packaging process under the current environmental data is output.

[0063] It should be noted that packaging process data includes data such as packaging thickness and packaging density, while sensor packaging process performance characteristics include sealing, light shielding, light transmittance, and light transmission efficiency. Physical properties include data such as light transmittance and thermal conductivity.

[0064] Furthermore, in the sensor packaging conversion method, the performance characteristic data of the sensor packaging process under the current environmental data is dynamically analyzed to obtain dynamic analysis and evaluation results, specifically including:

[0065] Setting a performance characteristic data threshold of the sensor, and determining whether the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor;

[0066] When the performance characteristic data of the sensor packaging process under the current environmental data is not greater than the performance characteristic data threshold of the sensor, an abnormal sensor packaging process data evaluation result is generated;

[0067] When the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor, a normal sensor packaging process data evaluation result is generated;

[0068] A dynamic analysis evaluation result is generated according to an abnormal sensor packaging process data evaluation result or a normal sensor packaging process data evaluation result, and the dynamic analysis evaluation result is output.

[0069] Furthermore, in the sensor packaging conversion method, the thickness and density of the packaging layer are dynamically adjusted based on the dynamic analysis and evaluation results, specifically including:

[0070] If the dynamic analysis evaluation result is an abnormal sensor packaging process data evaluation result, a genetic algorithm is introduced, a genetic generation is set based on the genetic algorithm, and inheritance is performed based on the genetic generation to uniformly increase the thickness and density of the packaging layer;

[0071] Obtaining a dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer; if the dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer still indicates an abnormal sensor packaging process data evaluation result, continuing to increase the thickness and density of the packaging layer until the sensor packaging process data evaluation result no longer indicates an abnormality;

[0072] If the dynamic analysis evaluation result corresponding to increasing the thickness and density of the packaging layer is not an abnormal sensor packaging process data evaluation result, the packaging conversion is performed according to the current thickness and density of the packaging layer;

[0073] If the dynamic analysis evaluation result is a normal sensor packaging process data evaluation result, packaging is performed according to the thickness and density of the current packaging layer.

[0074] It should be noted that this method can improve the technical requirements for sensor packaging, optimize the performance of the sensor packaging, and improve the reliability of the sensor packaging.

[0075] In addition, the method further comprises:

[0076] Acquire packaging material structural characteristic data of the current sensor when it is packaged, and construct a digital twin model of the packaging structure based on the packaging material structural characteristic data of the current sensor when it is packaged;

[0077] Obtaining the light transmission efficiency under different packaging material structures, and mapping the light transmission efficiency under different packaging material structures to the digital twin model of the packaging structure, simulating the light transmission efficiency of the digital twin model of the current packaging structure in the actual use environment;

[0078] Obtaining light transmission efficiency requirement information of the current sensor, and determining whether the light transmission efficiency of the digital twin model of the current packaging structure in an actual usage environment is greater than the light transmission efficiency requirement information of the current sensor;

[0079] When the optical transmission efficiency of the digital twin model of the current packaging structure in the actual use environment is greater than the optical transmission efficiency requirement information of the current sensor, the packaging material structure characteristic data of the current sensor during packaging is output;

[0080] When the optical transmission efficiency of the digital twin model of the current packaging structure in the actual usage environment is not greater than the optical transmission efficiency requirement information of the current sensor, reset the packaging material structure characteristic data of the current sensor during packaging, and optimize the structure of the packaging material until it is greater than the optical transmission efficiency requirement information of the current sensor.

[0081] It should be noted that since the packaging structure is affected by the material and thickness, the light transmission efficiency will decrease, which will lead to a decrease in sensor performance. This method can further optimize the structure of the packaging layer, including the material structure, layer structure, etc., to improve the design rationality of the packaging structure.

[0082] In addition, the method further comprises:

[0083] Obtaining a digital twin model of the packaging structure and estimated environmental data information during packaging, performing physical field simulation analysis based on the digital twin model of the packaging structure and the estimated environmental data information during packaging, and obtaining thermal stress data for each position in the packaging structure;

[0084] Setting a thermal stress data threshold, and determining whether there is a location node where thermal stress data is greater than the thermal stress data threshold based on the thermal stress data of each location in the packaging structure and the thermal stress data threshold;

[0085] When there is a position node whose thermal stress data is greater than the thermal stress data threshold, further resetting the packaging structure to reset the digital twin model of the packaging structure until there is no position node whose thermal stress data is greater than the thermal stress data threshold;

[0086] When there is no location node whose thermal stress data is greater than the thermal stress data threshold, a digital twin model of the packaging structure is output.

[0087] It should be noted that when there is a position node where the thermal stress data is greater than the thermal stress data threshold, cracks, welding slag residue and other phenomena will occur. This method can further optimize the reset packaging structure to reset the digital twin model of the packaging structure and improve the rationality of sensor packaging.

[0088] like Figure 2 As shown, the second aspect of the present invention provides a sensor packaging conversion system 4, including a memory 41 and a processor 42. The memory 41 includes a sensor packaging conversion method program. When the sensor packaging conversion method program is executed by the processor 42, the following steps are implemented:

[0089] Build a test environment and, based on the test environment, obtain the physical property data of the current packaging material under different test environments;

[0090] Build a sensor performance prediction model based on the physical property data of the current packaging material under different test environments, and estimate the performance characteristic data of the sensor packaging process under the current environmental data based on the sensor performance prediction model;

[0091] By dynamically analyzing the performance characteristic data of the sensor packaging process under the current environmental data, dynamic analysis and evaluation results are obtained;

[0092] The thickness and density of the encapsulation layer are dynamically adjusted based on the dynamic analysis and evaluation results.

[0093] It should be noted that the present invention evaluates and analyzes the packaging of sensors in combination with environmental characteristics, and can estimate the performance characteristic data of the sensor packaging process under the current environmental data, thereby optimizing the thickness and density of the packaging layer and improving the packaging conversion effect of the sensor.

[0094] Furthermore, in the sensor packaging conversion system, a test environment is constructed, and based on the test environment, the physical property data of the current packaging material under different test environments is obtained, specifically:

[0095] Setting a number of environmental indicator data, controlling the test environment through environmental control devices based on the number of environmental indicator data, initializing working parameter data information of each environmental control device, and controlling the test environment based on the working parameter data information of the environmental control device;

[0096] Obtain target environment data within a preset time in the test environment, set an environment data threshold range, and determine whether the target environment data within the preset time in the test environment is within the environment data threshold range;

[0097] When the target environment data in the test environment is within the environment data threshold range within the preset time, the current test environment is maintained unchanged;

[0098] When the target environment data in the test environment within the preset time is not within the environment data threshold range, the current test environment is continuously adjusted, the physical property data of the current packaging material under the test environment is counted, and the physical property data of the current packaging material under different test environments is obtained.

[0099] It should be noted that environmental indicator data includes light, temperature, humidity, etc., and environmental control equipment includes temperature control equipment, humidity control equipment, and light control equipment. This method can maintain the test environment within the environmental data threshold range, improve test accuracy, and thus obtain the physical property data of the current packaging material under different test environments.

[0100] Furthermore, in the sensor packaging conversion system, a sensor performance prediction model is constructed based on the physical property data of the current packaging material under different test environments. Specifically:

[0101] Obtain the physical property data of the current packaging material under different test environments, and use big data to obtain the sensor performance characteristic data under different physical property data, and build a sensor performance prediction model based on deep neural network;

[0102] A multi-head attention mechanism is introduced to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data;

[0103] A directed description relationship is constructed based on the association relationship, with the test environment as the first node, the physical property data of the packaging material as the second node, and the sensor performance characteristic data as the third node, and a topological structure diagram is constructed from the first node, the second node, and the third node based on the directed description relationship;

[0104] A related adjacency matrix is ​​obtained based on the topological structure graph, and the related adjacency matrix is ​​input into the sensor performance prediction model for training to obtain a sensor performance prediction model that meets expectations.

[0105] It should be noted that the multi-head attention mechanism is used to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data, so as to build a sensor performance prediction model that meets the expectations and predict the physical property data of the current packaging material under different environments.

[0106] Furthermore, in the sensor packaging conversion system, the performance characteristic data of the sensor packaging process under the current environmental data is estimated based on the sensor performance prediction model, specifically including:

[0107] Obtaining working environment data information and packaging process data information of the current sensor during the packaging process, and inputting the working environment data information and packaging process data information of the current sensor during the packaging process into the sensor performance prediction model for prediction;

[0108] Through prediction, the performance characteristic data of the sensor packaging process under the current environmental data is obtained, and the performance characteristic data of the sensor packaging process under the current environmental data is output.

[0109] It should be noted that packaging process data includes data such as packaging thickness and packaging density, while sensor packaging process performance characteristics include sealing, light shielding, light transmittance, and light transmission efficiency. Physical properties include data such as light transmittance and thermal conductivity.

[0110] Furthermore, in the sensor packaging conversion system, the performance characteristic data of the sensor packaging process under the current environmental data is dynamically analyzed to obtain dynamic analysis and evaluation results, including:

[0111] Setting a performance characteristic data threshold of the sensor, and determining whether the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor;

[0112] When the performance characteristic data of the sensor packaging process under the current environmental data is not greater than the performance characteristic data threshold of the sensor, an abnormal sensor packaging process data evaluation result is generated;

[0113] When the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor, a normal sensor packaging process data evaluation result is generated;

[0114] A dynamic analysis evaluation result is generated according to an abnormal sensor packaging process data evaluation result or a normal sensor packaging process data evaluation result, and the dynamic analysis evaluation result is output.

[0115] Furthermore, in the sensor packaging conversion system, the thickness and density of the packaging layer are dynamically adjusted based on the dynamic analysis and evaluation results, specifically including:

[0116] If the dynamic analysis evaluation result is an abnormal sensor packaging process data evaluation result, a genetic algorithm is introduced, a genetic generation is set based on the genetic algorithm, and inheritance is performed based on the genetic generation to uniformly increase the thickness and density of the packaging layer;

[0117] Obtaining dynamic analysis and evaluation results corresponding to increasing the thickness and density of the packaging layer; if the dynamic analysis and evaluation results corresponding to increasing the thickness and density of the packaging layer still indicate an abnormal sensor packaging process data evaluation result, continuing to increase the thickness and density of the packaging layer until the sensor packaging process data evaluation result no longer indicates an abnormality;

[0118] If the dynamic analysis evaluation result corresponding to increasing the thickness and density of the packaging layer is not an abnormal sensor packaging process data evaluation result, the packaging conversion is performed according to the current thickness and density of the packaging layer;

[0119] If the dynamic analysis evaluation result is a normal sensor packaging process data evaluation result, packaging is performed according to the thickness and density of the current packaging layer.

[0120] It should be noted that this method can improve the technical requirements for sensor packaging, optimize the performance of the sensor packaging, and improve the reliability of the sensor packaging.

[0121] In addition, the system also includes:

[0122] Acquire packaging material structural characteristic data of the current sensor when it is packaged, and construct a digital twin model of the packaging structure based on the packaging material structural characteristic data of the current sensor when it is packaged;

[0123] Obtaining the light transmission efficiency under different packaging material structures, and mapping the light transmission efficiency under different packaging material structures to the digital twin model of the packaging structure, simulating the light transmission efficiency of the digital twin model of the current packaging structure in the actual use environment;

[0124] Obtaining light transmission efficiency requirement information of the current sensor, and determining whether the light transmission efficiency of the digital twin model of the current packaging structure in an actual usage environment is greater than the light transmission efficiency requirement information of the current sensor;

[0125] When the optical transmission efficiency of the digital twin model of the current packaging structure in the actual use environment is greater than the optical transmission efficiency requirement information of the current sensor, the packaging material structure characteristic data of the current sensor during packaging is output;

[0126] When the optical transmission efficiency of the digital twin model of the current packaging structure in the actual usage environment is not greater than the optical transmission efficiency requirement information of the current sensor, reset the packaging material structure characteristic data of the current sensor during packaging, and optimize the structure of the packaging material until it is greater than the optical transmission efficiency requirement information of the current sensor.

[0127] It should be noted that since the packaging structure is affected by the material and thickness, the light transmission efficiency will decrease, which will lead to a decrease in sensor performance. This method can further optimize the structure of the packaging layer, including the material structure, layer structure, etc., to improve the design rationality of the packaging structure.

[0128] In addition, the system also includes:

[0129] Obtaining a digital twin model of the packaging structure and estimated environmental data information during packaging, performing physical field simulation analysis based on the digital twin model of the packaging structure and the estimated environmental data information during packaging, and obtaining thermal stress data for each position in the packaging structure;

[0130] Setting a thermal stress data threshold, and determining whether there is a location node where thermal stress data is greater than the thermal stress data threshold based on the thermal stress data of each location in the packaging structure and the thermal stress data threshold;

[0131] When there is a position node whose thermal stress data is greater than the thermal stress data threshold, further resetting the packaging structure to reset the digital twin model of the packaging structure until there is no position node whose thermal stress data is greater than the thermal stress data threshold;

[0132] When there is no location node whose thermal stress data is greater than the thermal stress data threshold, a digital twin model of the packaging structure is output.

[0133] It should be noted that when there is a position node where the thermal stress data is greater than the thermal stress data threshold, cracks, welding slag residue and other phenomena will occur. This method can further optimize the reset packaging structure to reset the digital twin model of the packaging structure and improve the rationality of sensor packaging.

[0134] A third aspect of the present invention provides a computer-readable storage medium, comprising a sensor packaging conversion method program, which, when executed by a processor, implements any one of the steps of the sensor packaging conversion method.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0136] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0137] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0138] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0139] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0140] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A sensor packaging conversion method, characterized in that: The following steps are involved: Constructing a test environment, and based on the test environment, obtaining physical property data of the current packaging material under different test environments; Building a sensor performance prediction model based on the physical property data of the current packaging material under the different test environments, and estimating the performance characteristic data of the sensor packaging process under the current environmental data based on the sensor performance prediction model; Obtaining dynamic analysis and evaluation results by dynamically analyzing performance characteristic data of the sensor packaging process under the current environmental data; The thickness and density of the encapsulation layer are dynamically adjusted based on the dynamic analysis and evaluation results.

2. The sensor packaging conversion method according to claim 1, characterized in that: Construct a test environment, and based on the test environment, obtain the physical property data of the current packaging material under different test environments, specifically: Setting a number of environmental indicator data, controlling the test environment through the environmental control equipment based on the number of environmental indicator data, initializing the working parameter data information of each environmental control equipment, and controlling the test environment based on the working parameter data information of the environmental control equipment; Obtain target environment data within a preset time in a test environment, set an environment data threshold range, and determine whether the target environment data within the preset time in the test environment is within the environment data threshold range; When the target environment data in the test environment within a preset time is within the environment data threshold range, maintaining the current test environment unchanged; When the target environment data in the test environment within the preset time is not within the environment data threshold range, the current test environment is continuously adjusted, the physical property data of the current packaging material under the test environment is counted, and the physical property data of the current packaging material under different test environments is obtained.

3. The sensor packaging conversion method according to claim 1, characterized in that: A sensor performance prediction model is constructed based on the physical property data of the current packaging material under the different test environments, specifically: Obtain the physical property data of the current packaging material under different test environments, and use big data to obtain the sensor performance characteristic data under different physical property data, and build a sensor performance prediction model based on deep neural network; Introducing a multi-head attention mechanism to clarify the correlation between the physical property data of the current packaging material under different test environments and the sensor performance characteristic data under different physical property data; Constructing a directed description relationship based on the association relationship, taking the test environment as the first node, the physical property data of the packaging material as the second node, and the sensor performance characteristic data as the third node, and constructing a topological structure diagram of the first node, the second node, and the third node based on the directed description relationship; A related adjacency matrix is ​​obtained based on the topological structure graph, and the related adjacency matrix is ​​input into the sensor performance prediction model for training to obtain a sensor performance prediction model that meets expectations.

4. The sensor packaging conversion method according to claim 1, characterized in that: Estimating the performance characteristic data of the sensor packaging process under the current environmental data based on the sensor performance prediction model specifically includes: Acquire working environment data information and packaging process data information of the current sensor during the packaging process, and input the working environment data information and packaging process data information of the current sensor during the packaging process into the sensor performance prediction model for prediction; Through prediction, performance characteristic data of the sensor packaging process under the current environmental data are obtained, and the performance characteristic data of the sensor packaging process under the current environmental data are output.

5. The sensor packaging conversion method according to claim 1, characterized in that: By dynamically analyzing the performance characteristic data of the sensor packaging process under the current environmental data, a dynamic analysis evaluation result is obtained, specifically including: Setting a performance characteristic data threshold of the sensor, and determining whether the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor; When the performance characteristic data of the sensor packaging process under the current environmental data is not greater than the performance characteristic data threshold of the sensor, generating an abnormal sensor packaging process data evaluation result; When the performance characteristic data of the sensor packaging process under the current environmental data is greater than the performance characteristic data threshold of the sensor, a normal sensor packaging process data evaluation result is generated; A dynamic analysis evaluation result is generated according to the abnormal sensor packaging process data evaluation result or the normal sensor packaging process data evaluation result, and the dynamic analysis evaluation result is output.

6. The sensor packaging conversion method according to claim 1, characterized in that: Dynamically adjusting the thickness and density of the encapsulation layer based on the dynamic analysis and evaluation results specifically includes: If the dynamic analysis evaluation result is an abnormal sensor packaging process data evaluation result, a genetic algorithm is introduced, a genetic generation is set based on the genetic algorithm, and inheritance is performed based on the genetic generation to uniformly increase the thickness and density of the packaging layer; Obtaining a dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer; if the dynamic analysis and evaluation result corresponding to increasing the thickness and density of the packaging layer is still an abnormal sensor packaging process data evaluation result, continuing to increase the thickness and density of the packaging layer until the sensor packaging process data evaluation result is no longer abnormal; If the dynamic analysis evaluation result corresponding to the increased thickness and density of the packaging layer is not an abnormal sensor packaging process data evaluation result, performing packaging conversion according to the current thickness and density of the packaging layer; If the dynamic analysis evaluation result is a normal sensor packaging process data evaluation result, packaging is performed according to the thickness and density of the current packaging layer.

7. A sensor packaging and conversion system, characterized in that: The sensor packaging conversion method comprises a memory and a processor, wherein the memory comprises a sensor packaging conversion method program, and when the sensor packaging conversion method program is executed by the processor, the steps of the sensor packaging conversion method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that The invention comprises a sensor packaging conversion method program, which, when executed by a processor, implements the steps of the sensor packaging conversion method according to any one of claims 1 to 6.

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