A temperature distribution prediction and alarm method, system, device and medium for IC carrier boards

Abstract

This application provides a method, system, device, and medium for temperature distribution prediction and alarm of IC substrates, belonging to the field of IC substrate manufacturing technology. The method includes: collecting basic parameters of the entire IC substrate manufacturing process and preprocessing the basic parameters to obtain a standardized parameter dataset; constructing a multi-physics coupled finite element thermal analysis model based on the standardized parameter dataset; inputting the real-time collected basic parameters into the optimized finite element thermal analysis model to obtain the global temperature distribution data of the IC substrate within a preset time period, and calibrating the finite element thermal analysis model by combining it with measured temperature data; determining and correcting the alarm temperature threshold based on historical qualified production temperature data; comparing the predicted temperature distribution data with the corrected threshold to perform graded alarms. This application achieves global and accurate prediction of temperature distribution during IC substrate manufacturing, and provides timely alarms for abnormal temperatures, improving production safety.

Description

Technical Field

[0001] This application belongs to the field of IC substrate manufacturing technology, specifically relating to a method, system, equipment, and medium for predicting and alarming temperature distribution on IC substrates. Background Technology

[0002] As a key interconnect carrier between chips and PCBs, IC substrates are mostly composed of a composite structure of organic substrates and metal circuits. In production processes such as reflow soldering, curing, and packaging, the uniformity and stability of temperature distribution directly determine the solder joint strength, circuit reliability, and product lifespan.

[0003] In existing technologies, IC substrate temperature monitoring mainly relies on contact measurements, such as thermocouples, and non-contact scanning. Thermocouples provide single-point / multi-point measurements, which cannot reflect the overall temperature distribution of the substrate, and are particularly difficult to capture localized high temperatures in critical areas such as densely packed circuits and irregularly shaped structures. Infrared thermal imaging is affected by reflections from the metal circuitry on the substrate surface, resulting in measurement errors of ±3~5℃, and it cannot penetrate the substrate surface to obtain the internal medium temperature. Although the existing finite element method has been used for thermal simulation of large electronic devices, it mainly focuses on the steady-state thermal analysis of finished products and does not consider the coupling effects of changes in process parameters and environmental conditions during IC substrate production on the temperature field, leading to inaccurate prediction results.

[0004] In summary, existing technologies cannot achieve full-range, accurate, and advance prediction of temperature distribution during IC substrate production, which restricts the improvement of the quality of high-end IC substrate products. There is an urgent need for a temperature prediction and alarm technology solution that is adapted to the production scenario. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing technologies struggle to reflect the overall temperature distribution and produce inaccurate prediction results, by providing a method, system, device, and medium for predicting and alarming the temperature distribution of IC substrates.

[0006] In a first aspect, embodiments of this application provide a method for predicting and alarming the temperature distribution of an IC substrate, the method comprising the following steps: S1. Collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters; S2. Based on the standardized parameter dataset, construct a multi-physics coupled finite element thermal analysis model. The model includes geometric modeling and hybrid mesh generation, construction of a multi-field coupled physical model, dynamic boundary condition loading, and optimization of the solution algorithm. S3. Input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine with the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. S4. Based on the historical qualified production temperature data of the IC carrier board, determine the alarm temperature threshold. Correct the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters. Compare the predicted temperature distribution data with the corrected threshold to perform graded alarms.

[0007] Furthermore, the specific steps of step S1 are as follows: S11. Collect the inherent parameters of the carrier board, collect the thickness of the IC carrier board using a laser thickness gauge, and collect the layout and line width of the metal circuit using an X-ray detector; S12. Collect process parameters, including reflow soldering temperature, curing time, soldering power, and IC carrier board transfer speed through the production control system; S13. Collect environmental interference parameters: collect the temperature, humidity and airflow speed of the production environment through temperature and humidity sensors and airflow sensors; collect the measured temperature data of key points on the IC carrier board through a distributed thermocouple array; S14. Collect thermophysical parameters. Collect the thermophysical parameters of the medium layer material and each component through a material analyzer. The thermophysical parameters include thermal conductivity, specific heat capacity and density. S15. The 3σ criterion is used to remove outliers from all collected parameters, and the missing parameter data is supplemented by linear interpolation. The dimensional analysis method is used to unify the dimensions of different types of basic parameters and transform them into a standardized parameter dataset.

[0008] Furthermore, in step S2, the specific steps of geometric modeling and hybrid mesh generation include: S211. Based on the inherent parameters of the preprocessed carrier board, construct a three-dimensional geometric model of the IC carrier board, including metal lines, dielectric layers, and solder joints, using a finite element simulation platform. The model size is consistent with that of the actual IC carrier board produced. S212. A hybrid meshing strategy of tetrahedral and hexahedral meshes is adopted. First, the entire three-dimensional geometric model is initially coarsely meshed. Then, the mesh is refined in critical areas sensitive to temperature changes, such as dense circuit areas and solder joints. The mesh size after refinement is 0.01~0.05mm. The mesh is coarsened in non-critical areas such as the edge of the carrier board and non-circuit areas. The mesh size after coarsening is 0.1~0.2mm. S213. Perform quality verification on the divided mesh. The verification criteria are: mesh distortion rate ≤ 5% and mesh aspect ratio ≤ 3. If the above criteria are met, construct a multi-field coupled physical model. If the above criteria are not met, return to step S212 to re-divide the mesh.

[0009] Furthermore, in step S2, the specific steps for constructing the multi-field coupled physical model are as follows: S221. Based on the pre-processed thermophysical parameters, considering the multi-field coupling effect of heat conduction, heat convection, and heat radiation, establish the transient thermal equilibrium equation of the IC carrier plate:

[0010] in, The material density of the corresponding component in the IC substrate. This refers to the specific heat capacity of the material corresponding to the component. The temperature of any node on the IC carrier board. For time, For the Laplace operator, The thermal conductivity of the material for the corresponding components. The intensity of the internal heat source corresponds to the internal heat generated during welding and curing in the IC substrate production process. S222. For multi-material composite structures, a partitioned assignment method is adopted, assigning the pre-processed thermophysical parameters of different components to different regions of the model, and correcting the thermal conductivity using the following formula:

[0011] in, For temperature T The actual thermal conductivity is as follows. Thermal conductivity at the reference temperature The thermal conductivity attenuation coefficient is... This is the reference temperature.

[0012] Furthermore, in step S2, the specific steps for optimizing the dynamic boundary condition loading and solution algorithm are as follows: S231. The pretreated process parameters are transformed into thermal load boundaries, and the pretreated environmental disturbance parameters are transformed into convective heat transfer boundaries. The convective heat transfer boundaries satisfy Newton's law of cooling.

[0013] in, The direction of the normal vector of the IC substrate surface boundary. The convective heat transfer coefficient is... The temperature at the boundary point of the IC substrate. The temperature of the production environment; S232. The updated process parameters and environmental disturbance parameters are synchronously transmitted to the finite element model to load boundary conditions in real time; S233. The Newmark-β time integration algorithm is selected to solve the transient thermal equilibrium equation, and the solution process is optimized by using GPU acceleration.

[0014] Furthermore, the specific steps of step S3 are as follows: S31. Input the pre-processed carrier board inherent parameters, process parameters, and environmental interference parameters into the optimized multi-field coupled dynamic finite element thermal analysis model, start the transient thermal analysis solution, obtain the global temperature distribution data of the IC carrier board in the next 10-60s, generate a temperature distribution cloud map, and extract the specific temperature values ​​of each node. S32. Compare the collected measured temperature data at each measuring point with the corresponding temperature distribution data predicted by the finite element thermal analysis model, record the prediction deviation for each measuring point, and form a deviation dataset. S33. With the goal of minimizing the deviation between the predicted temperature and the measured temperature, a calibration objective function is constructed using the least squares method. The formula for the objective function is:

[0015] in, For the first i The model predicts the temperature at each measuring point. For the first i The actual measured temperature at each measuring point n This represents the total number of measuring points; S34. The thermal conductivity and convective heat transfer coefficient h of each component in the correct model are calculated iteratively by objective function. The number of iterations is preset until the model prediction error stabilizes within ±0.5℃, thus completing the real-time calibration of the finite element thermal analysis model.

[0016] Furthermore, the specific steps of step S4 are as follows: S41. Based on historical qualified production temperature data of IC substrates, statistically analyze the reasonable range of temperature distribution according to different substrate types and different process stages, and determine the alarm threshold for each process stage. T th0 ; S42. The alarm threshold is corrected using the gradient boosting tree algorithm, combined with real-time preprocessed carrier board parameters, process parameters, and environmental interference parameters. The formula is as follows:

[0017] in, To correct the alarm threshold, m The number of parameter types involved in the correction. For the first j Correction weights for class parameters, For the first j The rate of change of the class parameter relative to the baseline value; S43. Compare the global temperature distribution data predicted by the finite element thermal analysis model with the corrected threshold. Tth Compare them one by one: If the temperature reaches 90% of the threshold, an alarm will be triggered, and an audible and visual alert will be issued. If the temperature exceeds the threshold but is ≤5℃, a level one alarm is triggered, and a process parameter fine-tuning signal is sent to the production control system. If the temperature exceeds the threshold by more than 5°C, a level 2 alarm will be triggered, and a production stop signal will be sent immediately. At the same time, an abnormal temperature distribution cloud map will be output, marking the location and temperature value of the hot spot area.

[0018] Secondly, embodiments of this application also provide a temperature distribution prediction and alarm system for an IC substrate, the system being used to implement the method described in the first aspect, the system comprising: The data acquisition and preprocessing module is used to collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters. The finite element thermal analysis model construction module is used to construct a multi-physics coupled finite element thermal analysis model based on a standardized parameter dataset. The model includes geometric modeling and hybrid mesh generation, construction of multi-field coupled physical model, dynamic boundary condition loading, and optimization of solution algorithm. The model prediction and calibration module is used to input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. The threshold correction and alarm module is used to determine the alarm temperature threshold based on the historical qualified production temperature data of the IC carrier board. It corrects the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters, and compares the predicted temperature distribution data with the corrected threshold to perform graded alarms.

[0019] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for predicting and alarming the temperature distribution of an IC substrate as described in the first aspect.

[0020] Fourthly, embodiments of the present invention also provide a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the method for predicting and alarming the temperature distribution of an IC substrate as described in the first aspect.

[0021] As can be seen from the above technical solutions, the present invention has the following advantages: By constructing a finite element model and combining it with multi-field coupled thermal analysis, the system achieves full-domain temperature distribution prediction for IC substrates, covering the surface, internal media, and densely packed circuit areas. Coupled with least squares calibration, the prediction error is small, solving the problems of incomplete single-point thermocouple measurements and large errors in infrared thermal imaging that cannot penetrate the surface. By real-time acquisition and preprocessing of process, environmental, and substrate parameters, and updating the boundary conditions of the finite element model, the system can predict temperature distribution changes and adapt to varying production parameter requirements. Based on historical data, a basic threshold is constructed, combined with real-time parameter dynamic correction, to achieve a three-level alarm system, effectively avoiding false alarms due to instantaneous fluctuations and missed alarms due to slow local temperature rises, thus improving alarm reliability. It overcomes the limitations of existing finite element methods, which are mostly used for steady-state thermal analysis of finished products, by constructing a dynamic thermal analysis model adapted to the thinning and multi-material composite characteristics of IC substrates. The prediction results are accurate, while simultaneously improving the production quality and intelligent management level of IC substrates. Attached Figure Description

[0022] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the temperature distribution prediction and alarm method for IC substrates used in this application.

[0024] Figure 2 This is a schematic diagram of the temperature distribution prediction and alarm system for IC substrates used in this application.

[0025] Figure 3 This is a schematic diagram of the electronic device described in this application. Detailed Implementation

[0026] The various embodiments of the present invention will be described more fully in the detailed steps of the method for predicting and alarming temperature distribution on an IC substrate, which will be described in detail below. The present invention may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present invention to the specific embodiments disclosed herein, but rather the present invention should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the present invention.

[0027] It should be understood that, when used in this specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0029] To make the objectives, features, and advantages of this invention more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solutions protected by this invention. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0030] Please see Figure 1 The diagram shows a flowchart of a method for predicting and alarming the temperature distribution of an IC substrate. This method includes: S1. Collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters; S2. Based on the standardized parameter dataset, construct a multi-physics coupled finite element thermal analysis model. The model includes geometric modeling and hybrid mesh generation, construction of a multi-field coupled physical model, dynamic boundary condition loading, and optimization of the solution algorithm. S3. Input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine with the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. S4. Based on the historical qualified production temperature data of the IC carrier board, determine the alarm temperature threshold. Correct the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters. Compare the predicted temperature distribution data with the corrected threshold to perform graded alarms.

[0031] As a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, another method for predicting and alarming the temperature distribution of IC carrier boards is provided. This method includes the following steps: S1. Collect basic parameters for the entire IC substrate production process and preprocess these parameters to obtain a standardized parameter dataset; the basic parameters include inherent substrate parameters, process parameters, environmental interference parameters, and thermophysical parameters; the specific steps of step S1 are as follows: S11. Collect the inherent parameters of the carrier board, collect the thickness of the IC carrier board using a laser thickness gauge, and collect the layout and line width of the metal circuit using an X-ray detector; S12. Collect process parameters, including reflow soldering temperature, curing time, soldering power, and IC carrier board transfer speed through the production control system; S13. Collect environmental interference parameters: collect the temperature, humidity and airflow speed of the production environment through temperature and humidity sensors and airflow sensors; collect the measured temperature data of key points on the IC carrier board through a distributed thermocouple array; S14. Collect thermophysical parameters. Collect the thermophysical parameters of the medium layer material and each component through a material analyzer. The thermophysical parameters include thermal conductivity, specific heat capacity and density. S15. The 3σ criterion is used to remove outliers from all collected parameters, and the missing parameter data is supplemented by linear interpolation. The dimensional analysis method is used to unify the dimensions of different types of basic parameters and transform them into a standardized parameter dataset.

[0032] In some embodiments, the FCCSP chip carrier reflow soldering production station is equipped with the following equipment: a laser thickness gauge (accuracy 0.001mm) to acquire the carrier thickness; an X-ray detector to acquire the metal circuit layout; a production control system to acquire reflow soldering temperature, soldering power, and transmission speed in real time; temperature and humidity sensors and airflow sensors to acquire environmental parameters; and a 16-channel distributed thermocouple array (sampling frequency 10Hz) arranged at key locations such as carrier solder joints and areas with dense circuitry. All equipment is connected to a data processing terminal via industrial Ethernet to achieve synchronous parameter acquisition and preprocessing.

[0033] S2. Based on the standardized parameter dataset, construct a multi-physics coupled finite element thermal analysis model. This model includes geometric modeling and hybrid mesh generation, construction of the multi-physics coupled physical model, dynamic boundary condition loading, and solution algorithm optimization. The specific steps of geometric modeling and hybrid mesh generation in step S2 include: S211. Based on the inherent parameters of the preprocessed carrier board, construct a three-dimensional geometric model of the IC carrier board, including metal lines, dielectric layers, and solder joints, using a finite element simulation platform. The model size is consistent with that of the actual IC carrier board produced. S212. A hybrid meshing strategy of tetrahedral and hexahedral meshes is adopted. First, the entire three-dimensional geometric model is initially coarsely meshed. Then, the mesh is refined in critical areas sensitive to temperature changes, such as dense circuit areas and solder joints. The mesh size after refinement is 0.01~0.05mm. The mesh is coarsened in non-critical areas such as the edge of the carrier board and non-circuit areas. The mesh size after coarsening is 0.1~0.2mm. S213. Perform quality verification on the divided mesh. The verification criteria are: mesh distortion rate ≤ 5% and mesh aspect ratio ≤ 3. If the above criteria are met, construct a multi-field coupled physical model. If the above criteria are not met, return to step S212 to re-divide the mesh.

[0034] In step S2, the specific steps for constructing the multi-field coupled physical model are as follows: S221. Based on the pre-processed thermophysical parameters, considering the multi-field coupling effect of heat conduction, heat convection, and heat radiation, establish the transient thermal equilibrium equation of the IC carrier plate:

[0035] in, The material density of the corresponding component in the IC substrate. This refers to the specific heat capacity of the material corresponding to the component. The temperature of any node on the IC carrier board. For time, For the Laplace operator, The thermal conductivity of the material for the corresponding components. The intensity of the internal heat source corresponds to the internal heat generated during welding and curing in the IC substrate production process. S222. For multi-material composite structures, a partitioned assignment method is adopted, assigning the pre-processed thermophysical parameters of different components to different regions of the model, and correcting the thermal conductivity using the following formula:

[0036] in, For temperature T The actual thermal conductivity is as follows. Thermal conductivity at the reference temperature The thermal conductivity attenuation coefficient is... This is the reference temperature.

[0037] In step S2, the specific steps for optimizing the dynamic boundary condition loading and solution algorithm are as follows: S231. The pretreated process parameters are transformed into thermal load boundaries, and the pretreated environmental disturbance parameters are transformed into convective heat transfer boundaries. The convective heat transfer boundaries satisfy Newton's law of cooling.

[0038] in, The direction of the normal vector of the IC substrate surface boundary. The convective heat transfer coefficient is... The temperature at the boundary point of the IC substrate. The temperature of the production environment; S232. The updated process parameters and environmental disturbance parameters are synchronously transmitted to the finite element model to load boundary conditions in real time; S233. The Newmark-β time integration algorithm is selected to solve the transient thermal equilibrium equation, and the solution process is optimized by using GPU acceleration.

[0039] In some embodiments, a three-dimensional finite element model of the FCCSP carrier board is constructed based on the Abaqus platform: the carrier board size is 50mm×50mm×0.2mm, the metal line width is 20μm, and the dielectric layer is BT resin; a hybrid mesh is used, with a mesh size of 0.02mm in critical areas and 0.15mm in non-critical areas, for a total of approximately 800,000 elements; the thermal conductivity of BT resin (0.25W / (m·K)) and the thermal conductivity of copper lines (401W / (m·K)) are input, and temperature dependence characteristics are set; the reflow soldering preheating stage temperature (180℃) is used as the heat load boundary, and the ambient airflow velocity (1m / s) is converted into the convective heat transfer coefficient (20W / (m·K)). 2 The Newmark-β algorithm was used to solve the problem (K), and the time for a single prediction was 0.8 seconds after GPU acceleration.

[0040] S3. Input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC carrier within a preset time period, and combine the measured temperature data to calibrate the finite element thermal analysis model using the least squares method; the specific steps of step S3 are as follows: S31. Input the pre-processed carrier board inherent parameters, process parameters, and environmental interference parameters into the optimized multi-field coupled dynamic finite element thermal analysis model, start the transient thermal analysis solution, obtain the global temperature distribution data of the IC carrier board in the next 10-60s, generate a temperature distribution cloud map, and extract the specific temperature values ​​of each node. S32. Compare the collected measured temperature data at each measuring point with the corresponding temperature distribution data predicted by the finite element thermal analysis model, record the prediction deviation for each measuring point, and form a deviation dataset. S33. With the goal of minimizing the deviation between the predicted temperature and the measured temperature, a calibration objective function is constructed using the least squares method. The formula for the objective function is:

[0041] in, For the first iThe model predicts the temperature at each measuring point. For the first i The actual measured temperature at each measuring point n This represents the total number of measuring points; S34. The thermal conductivity and convective heat transfer coefficient h of each component in the correct model are calculated iteratively by objective function. The number of iterations is preset until the model prediction error stabilizes within ±0.5℃, thus completing the real-time calibration of the finite element thermal analysis model.

[0042] In some embodiments, the thermophysical parameters and solution algorithm parameters of the finite element model are calibrated once every 5,000 sets of new production data (including parameters, temperature measurements, and alarm response results); the correction weight of the adaptive threshold is adjusted monthly based on the alarm accuracy (target ≥ 99%) to ensure continuous adaptation of the model to actual production.

[0043] S4. Based on the historical qualified production temperature data of the IC carrier board, determine the alarm temperature threshold. Correct the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters. Compare the predicted temperature distribution data with the corrected threshold to perform graded alarms.

[0044] The specific steps of step S4 are as follows: S41. Based on historical qualified production temperature data of IC substrates, statistically analyze the reasonable range of temperature distribution according to different substrate types and different process stages, and determine the alarm threshold for each process stage. T th0 ; S42. The alarm threshold is corrected using the gradient boosting tree algorithm, combined with real-time preprocessed carrier board parameters, process parameters, and environmental interference parameters. The formula is as follows:

[0045] in, To correct the alarm threshold, m The number of parameter types involved in the correction. For the first j Correction weights for class parameters, For the first j The rate of change of the class parameter relative to the baseline value; S43. Compare the global temperature distribution data predicted by the finite element thermal analysis model with the corrected threshold. T th Compare them one by one: If the temperature reaches 90% of the threshold, an alarm will be triggered, and an audible and visual alert will be issued. If the temperature exceeds the threshold but is ≤5℃, a level one alarm is triggered, and a process parameter fine-tuning signal is sent to the production control system. If the temperature exceeds the threshold by more than 5°C, a level 2 alarm will be triggered, and a production stop signal will be sent immediately. At the same time, an abnormal temperature distribution cloud map will be output, marking the location and temperature value of the hot spot area.

[0046] In some embodiments, for the reflow soldering stage of the FCCSP carrier board, based on 50,000 historical data sets, a base threshold of 185°C for the preheating stage and a base threshold of 250°C for the peak soldering stage are determined. When the real-time collected soldering power increases from 300W to 330W, the peak soldering threshold is corrected to 262.5°C using a gradient boosting tree algorithm. When the ambient humidity increases from 40%RH to 55%RH (exceeding the baseline), the preheating threshold is corrected to 180.5°C. When the model predicts that the temperature of a solder joint in the peak soldering stage is 265°C (exceeding the corrected threshold by 2.5°C), a level one alarm is triggered, and a power reduction signal is sent to the production control system. If the predicted temperature rises to 270°C (exceeding the threshold by 7.5°C), a level two alarm is immediately triggered, the reflow soldering process is suspended, and an abnormal temperature cloud map is displayed on the monitoring terminal, marking the hot spot locations.

[0047] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] Please see Figure 2 The diagram shows a temperature distribution prediction and alarm system for IC carrier boards. The system includes: The data acquisition and preprocessing module is used to collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters. The finite element thermal analysis model construction module is used to construct a multi-physics coupled finite element thermal analysis model based on a standardized parameter dataset. The model includes geometric modeling and hybrid mesh generation, construction of multi-field coupled physical model, dynamic boundary condition loading, and optimization of solution algorithm. The model prediction and calibration module is used to input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. The threshold correction and alarm module is used to determine the alarm temperature threshold based on the historical qualified production temperature data of the IC carrier board. It corrects the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters, and compares the predicted temperature distribution data with the corrected threshold to perform graded alarms.

[0049] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0050] In the embodiments of this application, such as Figure 3 As shown, processor 101 can be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such implementations can be implemented within a controller. For software implementations, implementations such as processes or functions can be implemented with separate software modules that allow the performance of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language, and the software code can be stored in memory and executed by the controller.

[0051] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0052] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0053] This application also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the DHCP-based node detection method.

[0054] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0055] In a storage medium, a readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting and alarming temperature distribution on an IC carrier board, characterized in that, The method includes the following steps: S1. Collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters; S2. Based on the standardized parameter dataset, construct a multi-physics coupled finite element thermal analysis model. The model includes geometric modeling and hybrid mesh generation, construction of a multi-field coupled physical model, dynamic boundary condition loading, and optimization of the solution algorithm. S3. Input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine with the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. S4. Based on the historical qualified production temperature data of the IC carrier board, determine the alarm temperature threshold. Correct the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters. Compare the predicted temperature distribution data with the corrected threshold to perform graded alarms.

2. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Collect the inherent parameters of the carrier board, collect the thickness of the IC carrier board using a laser thickness gauge, and collect the layout and line width of the metal circuit using an X-ray detector; S12. Collect process parameters, including reflow soldering temperature, curing time, soldering power, and IC carrier board transfer speed through the production control system; S13. Collect environmental interference parameters: collect the temperature, humidity and airflow speed of the production environment through temperature and humidity sensors and airflow sensors; collect the measured temperature data of key points on the IC carrier board through a distributed thermocouple array; S14. Collect thermophysical parameters. Collect the thermophysical parameters of the medium layer material and each component through a material analyzer. The thermophysical parameters include thermal conductivity, specific heat capacity and density. S15. The 3σ criterion is used to remove outliers from all collected parameters, and the missing parameter data is supplemented by linear interpolation. The dimensional analysis method is used to unify the dimensions of different types of basic parameters and transform them into a standardized parameter dataset.

3. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 1, characterized in that, In step S2, the specific steps of geometric modeling and hybrid mesh generation include: S211. Based on the inherent parameters of the preprocessed carrier board, construct a three-dimensional geometric model of the IC carrier board, including metal lines, dielectric layers, and solder joints, using a finite element simulation platform. The model size is consistent with that of the actual IC carrier board produced. S212. A hybrid meshing strategy of tetrahedrons and hexahedrons is adopted. First, the entire three-dimensional geometric model is initially coarsely meshed. Then, the mesh is refined in the dense circuit area and the critical area where the temperature of the solder joint is sensitive. After refinement, the mesh size is 0.01~0.05mm. The mesh is coarsened in the non-critical areas of the board edge and non-circuit area. After coarsening, the mesh size is 0.1~0.2mm. S213. Perform quality verification on the divided mesh. The verification criteria are: mesh distortion rate ≤ 5% and mesh aspect ratio ≤ 3. If the above criteria are met, construct a multi-field coupled physical model. If the above criteria are not met, return to step S212 to re-divide the mesh.

4. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 1, characterized in that, In step S2, the specific steps for constructing the multi-field coupled physical model are as follows: S221. Based on the pre-processed thermophysical parameters, considering the multi-field coupling effect of heat conduction, heat convection, and heat radiation, establish the transient thermal equilibrium equation of the IC carrier plate: in, The material density of the corresponding component in the IC substrate. This refers to the specific heat capacity of the material corresponding to the component. The temperature of any node on the IC carrier board. For time, For the Laplace operator, The thermal conductivity of the material for the corresponding components. The intensity of the internal heat source corresponds to the internal heat generated during welding and curing in the IC substrate production process. S222. For multi-material composite structures, a partitioned assignment method is adopted, assigning the pre-processed thermophysical parameters of different components to different regions of the model, and correcting the thermal conductivity using the following formula: in, For temperature T The actual thermal conductivity is as follows. Thermal conductivity at the reference temperature The thermal conductivity attenuation coefficient is... This is the reference temperature.

5. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 1, characterized in that, In step S2, the specific steps for optimizing the dynamic boundary condition loading and solution algorithm are as follows: S231. The pretreated process parameters are transformed into thermal load boundaries, and the pretreated environmental disturbance parameters are transformed into convective heat transfer boundaries. The convective heat transfer boundaries satisfy Newton's law of cooling. in, The direction of the normal vector of the IC substrate surface boundary. The convective heat transfer coefficient is... The temperature at the boundary point of the IC substrate. The temperature of the production environment; S232. The updated process parameters and environmental disturbance parameters are synchronously transmitted to the finite element model to load boundary conditions in real time; S233. The Newmark-β time integration algorithm is selected to solve the transient thermal equilibrium equation, and the solution process is optimized by using GPU acceleration.

6. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 2, characterized in that, The specific steps of step S3 are as follows: S31. Input the pre-processed carrier board inherent parameters, process parameters, and environmental interference parameters into the optimized multi-field coupled dynamic finite element thermal analysis model, start the transient thermal analysis solution, obtain the global temperature distribution data of the IC carrier board in the next 10-60s, generate a temperature distribution cloud map, and extract the specific temperature values ​​of each node. S32. Compare the collected measured temperature data at each measuring point with the corresponding temperature distribution data predicted by the finite element thermal analysis model, record the prediction deviation for each measuring point, and form a deviation dataset. S33. To minimize the deviation between the predicted and measured temperatures, a calibration objective function is constructed using the least squares method. The formula for the objective function is as follows: in, For the first i The model predicts the temperature at each measuring point. For the first i The actual measured temperature at each measuring point n This represents the total number of measuring points; S34. The thermal conductivity and convective heat transfer coefficient h of each component in the correct model are calculated iteratively by objective function. The number of iterations is preset until the model prediction error stabilizes within ±0.5℃, thus completing the real-time calibration of the finite element thermal analysis model.

7. The method for predicting and alarming temperature distribution on an IC carrier board according to claim 1, characterized in that, The specific steps of step S4 are as follows: S41. Based on historical qualified production temperature data of IC substrates, statistically analyze the reasonable range of temperature distribution according to different substrate types and different process stages, and determine the alarm threshold for each process stage. T th0 ; S42. The alarm threshold is corrected using the gradient boosting tree algorithm, combined with real-time preprocessed carrier board parameters, process parameters, and environmental interference parameters. The formula is as follows: in, To correct the alarm threshold, m The number of parameter types involved in the correction. For the first j Correction weights for class parameters, For the first j The rate of change of the class parameter relative to the baseline value; S43. Compare the global temperature distribution data predicted by the finite element thermal analysis model with the corrected threshold. T th Compare them one by one: If the temperature reaches 90% of the threshold, an alarm will be triggered, and an audible and visual alert will be issued. If the temperature exceeds the threshold but is ≤5℃, a level one alarm is triggered, and a process parameter fine-tuning signal is sent to the production control system. If the temperature exceeds the threshold by more than 5°C, a level 2 alarm will be triggered, and a production stop signal will be sent immediately. At the same time, an abnormal temperature distribution cloud map will be output, marking the location and temperature value of the hot spot area.

8. A temperature distribution prediction and alarm system for IC carrier boards, characterized in that, The system is used to implement the method as described in any one of claims 1-7, the system comprising: The data acquisition and preprocessing module is used to collect basic parameters of the entire IC substrate production process and preprocess the basic parameters to obtain a standardized parameter dataset; the basic parameters include inherent parameters of the substrate, process parameters, environmental interference parameters, and thermophysical parameters. The finite element thermal analysis model construction module is used to construct a multi-physics coupled finite element thermal analysis model based on a standardized parameter dataset. The model includes geometric modeling and hybrid mesh generation, construction of multi-field coupled physical model, dynamic boundary condition loading, and optimization of solution algorithm. The model prediction and calibration module is used to input the real-time collected basic parameters into the optimized finite element thermal analysis model, solve for the global temperature distribution data of the IC substrate within a preset time period, and combine the measured temperature data to calibrate the finite element thermal analysis model using the least squares method. The threshold correction and alarm module is used to determine the alarm temperature threshold based on the historical qualified production temperature data of the IC carrier board. It corrects the alarm temperature threshold by combining the gradient boosting tree algorithm with the real-time collected basic parameters, and compares the predicted temperature distribution data with the corrected threshold to perform graded alarms.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps of the method for predicting and alarming the temperature distribution of an IC substrate as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting and alarming the temperature distribution of an IC substrate as described in any one of claims 1 to 7.

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