Reflow soldering process robustness optimization method and system

Abstract

The invention provides a reflow soldering process robustness optimization method and system, and relates to the field of digital manufacturing, and the method comprises the steps: building a thermal model of a PCB assembly in a reflow soldering process based on an obtained surface device structure and a substrate layout in the PCB assembly; a mechanical model of the PCB assembly is constructed, a convective heat transfer model of the reflow soldering technological process is established based on the thermal model, temperature boundary conditions are added into the mechanical model, and a mechanical-thermal joint simulation model is constructed; constructing a simulation agent model and a robustness optimization model; and based on the robustness optimization model, searching a simulation agent model by using a genetic algorithm, and performing reflow soldering process parameter robustness optimization. According to the method, the agent model of the reflow soldering technological process is constructed, rapid simulation analysis of temperature change and buckling deformation of any welding spot of the PCB assembly in the reflow soldering technological process is realized, robustness optimization of the reflow process is realized under the condition of considering uncertainty of technological parameters, and the yield of the reflow soldering process is ensured.

Description

Technical Field

[0001] The present application relates to the field of digital manufacturing technology, and in particular to a method and system for optimizing the robustness of a reflow soldering process. Background Art

[0002] With the advancement of technology, modern printed circuit board components have many types and high density, especially large-size devices such as microcontroller units (MCUs) have many I / O pins, small solder joint pitch, high density, and low welding batch yield, which are prone to electrical faults such as virtual shorts and virtual breaks. Reflow soldering is the main assembly technology for high-density printed circuit board (PCB) components. All electronic components on the PCB pass through the various temperature zones of the reflow soldering furnace in turn, and the overall heating and soldering are completed in one go. The ideal reflow soldering process control parameters can make all solder joints undergo a qualified temperature curve and produce less deformation, which is a key link to ensure the yield of PCB components. In the actual reflow soldering process of PCB components, different solder joints have different temperature response curves. Solder joints whose temperature response curves do not meet the welding requirements will not be able to achieve effective welding. PCB components will inevitably undergo thermal deformation during the reflow soldering process. The essence of solder joint interconnection is that the solder joint connects the device substrate pad and the PCB substrate pad. Excessive deformation will directly lead to welding failure or poor welding quality.

[0003] Reflow soldering is a key process in the production of PCB components. The temperature response and warpage of all solder joints must be guaranteed. The accuracy of process parameters is high. In theory, the reflow soldering process must be strictly and accurately controlled. However, during the reflow soldering process, the actual process parameters often fluctuate around the design parameters, making it difficult and costly to accurately control. The instability of various process parameters during the reflow soldering process will lead to unstable quality and yield of the reflow soldering process, and a small process window.

[0004] Based on the common (99.7% yield) quality control requirements, how to improve the robustness of the reflow process itself, achieve rapid debugging of process parameters, and ensure the yield is particularly important. The traditional reflow process debugging is based on iterative trial production, which requires repeated tests, has a long cycle and low efficiency. In addition, the fluctuation of process parameters makes process debugging difficult and the yield is low, which is contrary to the current situation of multiple models, small batches and short development cycles of electronic equipment. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present application provides a method and system for optimizing the robustness of a reflow soldering process, which solves the problems of slow parameter debugging of the current reflow soldering process and poor robustness of the reflow soldering process.

[0006] To achieve the above objectives, this application is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for optimizing the robustness of a reflow soldering process, the method comprising: Based on the surface device structure and substrate layout of the acquired PCB component, a thermal model of the PCB component during the reflow process is constructed; wherein the thermal model includes a surface device thermal model and a substrate thermal model; Construct the mechanical model of PCB components, which includes the surface device mechanical model and the substrate mechanical model; Based on the thermal model, a convection heat transfer model of the reflow soldering process is established. Based on the convection heat transfer model, temperature boundary conditions are added to the mechanical model to build a mechanical-thermal joint simulation model. Construct a simulation proxy model of the reflow process and a robustness optimization model respectively; Based on the robust optimization model, the genetic algorithm is used to search the simulation agent model to perform robust optimization of the reflow process parameters considering uncertainties, and the target solution of robust optimization is obtained to characterize the reflow process parameters.

[0007] According to the first aspect of the embodiment of the present application, in the surface device thermal model, the thermal resistances of the top and bottom surfaces of the surface device structure are determined to be respectively Thermal resistance and Thermal resistance, Thermal resistance is the thermal resistance from junction to shell node. Thermal resistance is the thermal resistance from junction to plate node.

[0008] According to the first aspect of the embodiment of the present application, the substrate thermal model is a thermal grid model of the alternating laminated structure of multiple layers of copper clad layers and glass fiber materials of the substrate in the PCB assembly; the substrate thermal model is provided with the thermal conductivity coefficient and interface thermal resistance of the copper clad layers and glass fiber materials.

[0009] According to the first aspect of an embodiment of the present application, the process of constructing the aforementioned surface device mechanical model may specifically include the following steps: constructing a first finite element mesh model of a typical device with the device core and pins as core elements according to different packaging types of the device; in the first finite element mesh model, setting the mechanical properties of the device core and pins to complete the device mechanical modeling.

[0010] According to the first aspect of the embodiment of the present application, the process of constructing the mechanical model of the substrate includes: based on the substrate layout of the PCB assembly, constructing a second finite element mesh model of the PCB assembly substrate including a multilayer structure of a copper clad layer and a glass fiber material; in the second finite element mesh model, setting the mechanical properties of the copper clad layer and the glass fiber material to realize mechanical modeling of the substrate; wherein the mechanical properties include elastic modulus and Poisson's ratio.

[0011] According to the first aspect of the embodiment of the present application, the above-mentioned convection heat transfer model of the reflow soldering process is established based on the thermal model, and the temperature boundary conditions are added to the mechanical model based on the convection heat transfer model to construct a mechanical-thermal joint simulation model, which may specifically include the following steps: Determine the target process parameters including chain speed and temperature of each temperature zone during the reflow process; Based on the thermal model, the convective heat transfer model of the reflow process was established using the Flotherm tool and Ansys Icepak tool; Based on the convection heat transfer model, the thermal simulation boundary conditions are set according to the actual time of each temperature zone and the temperature of each temperature zone to perform thermal simulation of the reflow process and obtain the temperature response time history data of the PCB assembly; The temperature response time history data is used as the temperature boundary condition for mechanical simulation of the mechanical model to construct a mechanical-thermal joint simulation model.

[0012] According to a first aspect of an embodiment of the present application, the aforementioned simulation proxy model is a Kriging proxy model; the temperature response time history data includes a heating rate, a peak temperature, a high temperature holding time, and a cooling rate; and the construction process of the simulation proxy model may specifically include the following steps: Determine the target process parameters that affect the quality of the reflow soldering process as input variables; The temperature response time history data of the solder joints of PCB components and the warpage deformation at the solder joints are selected as the quantitative evaluation criteria for the quality of the reflow soldering process, and the quantitative evaluation criteria are determined as the output variables of the proxy model. For the determined input variables and output variables, multiple groups of input sample points are selected in the design space through Latin square experimental design, and the reflow process simulation calculation is performed on each sample point to obtain the output response value corresponding to the input sample point; The Kriging surrogate model is established by combining the obtained input variables and output response values.

[0013] According to the first aspect of the embodiment of the present application, the construction process of the aforementioned robustness optimization model may specifically include the following steps: Determine the target process parameters that affect the quality of the reflow soldering process as design variables; the target process parameters include deterministic process parameters and uncertain process parameters; the deterministic process parameters include the chain speed of the reflow soldering furnace, and the uncertain process parameters include the temperature of each temperature zone; Determine the warpage deformation in the quantitative evaluation criteria of reflow process quality as the optimization target; Determine the heating rate, peak temperature, high temperature holding time, cooling rate and process parameter chain speed in the quantitative evaluation criteria of reflow soldering process quality as design constraints; Among them, the heating rate, peak temperature, high temperature holding time and cooling rate obey the normal probability distribution, and the mean and variance are set according to the welding quality assurance requirements.

[0014] According to the first aspect of the embodiment of the present application, the robustness optimization model satisfies the expression: in, and is the design variable, is the reflow oven chain speed, For reflow oven Temperature in each temperature zone, is the optimization objective and characterizes the Warpage of solder joints; For the The heating rate of each solder joint, and Heating rate The mean and variance of is the maximum allowable heating rate; For the The high temperature holding time of each solder joint, and Keep time for high temperature The mean and variance of and They are the minimum allowable high temperature holding time and the maximum allowable high temperature holding time respectively; For the The cooling rate of each solder joint, and Cooling speed The mean and variance of is the maximum allowable cooling rate; For the The peak temperature of each solder joint, and The peak temperatures are The mean and variance of and is the minimum allowable peak temperature and the maximum allowable peak temperature; is the temperature of each temperature zone, and The temperature of each temperature zone The mean and variance of is the maximum allowable temperature in each temperature zone; is the chain speed, and They are the minimum and maximum allowable chain speed respectively.

[0015] In the second aspect, an embodiment of the present application provides a reflow process robustness optimization system, which includes: a thermal model construction module, a mechanical model construction module, a mechanical-thermal joint module, an agent optimization module and a search optimization module.

[0016] Specifically, the thermal model construction module is used to construct a thermal model of the PCB component during the reflow soldering process based on the surface device structure and substrate layout in the acquired PCB component; wherein the thermal model includes a surface device thermal model and a substrate thermal model; the mechanical model construction module is used to construct a mechanical model of the PCB component, and the mechanical model includes a surface device mechanical model and a substrate mechanical model; the mechanical-thermal joint module is used to establish a convection heat transfer model of the reflow soldering process based on the thermal model, add temperature boundary conditions to the mechanical model based on the convection heat transfer model, and construct a mechanical-thermal joint simulation model; the agent optimization module is used to respectively construct a simulation agent model of the reflow soldering process and a robust optimization model; the search optimization module is used to search the simulation agent model using a genetic algorithm based on the robust optimization model, perform robust optimization of the reflow soldering process parameters considering uncertainty, and obtain the target solution of the robust optimization to characterize the reflow soldering process parameters.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, a method for optimizing the robustness of a reflow soldering process in the first aspect is implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, a method for optimizing the robustness of a reflow soldering process in the aforementioned first aspect is implemented.

[0019] The present application provides a method and system for optimizing the robustness of a reflow soldering process. Compared with the prior art, the present application has the following beneficial effects: This application constructs a mechanical and thermal joint simulation model of the reflow process based on the surface device structure and substrate layout in the PCB assembly, and further constructs a simulation proxy model for mathematical simplification. Through the established robustness optimization model, the genetic algorithm is used to search the simulation proxy model, and the robustness optimization of the reflow process parameters considering uncertainty can be performed. The ideal target solution of robustness optimization is obtained by iterative analysis to characterize the reflow process parameters. This application constructs a proxy model of the reflow process, and realizes the rapid simulation analysis of the temperature change and warping deformation of any solder joint of the PCB assembly during the reflow process. Under the condition of considering the uncertainty of the process parameters, the robustness optimization of the reflow process is achieved to ensure the yield of the reflow process. The method provided by this application can quickly evaluate the manufacturability of the PCB assembly in the scheme stage of the PCB assembly, which can greatly reduce the debugging time of the reflow process and reduce the manufacturing risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a method for optimizing robustness of a reflow soldering process provided in an embodiment of the present application; Figure 2 yes Figure 1 An exemplary flow chart of S130; Figure 3 It is a schematic diagram of a reflow soldering process provided by an embodiment of the present application; Figure 4 It is a simplified thermal model schematic diagram of a PCB assembly provided in an embodiment of the present application; Figure 5 It is a simplified mechanical model schematic diagram of a PCB assembly provided in an embodiment of the present application; Figure 6 It is a structural schematic diagram of a reflow process robustness optimization system provided in an embodiment of the present application; Figure 7 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. Description of the drawings: PCB assembly 101; heater 102; heating fan 103; cooling fan 104; surface device thermal model 301; insulation surface 302; device top surface 303; device bottom surface 304; substrate thermal model 305; surface device mechanical model 401; device bottom solder joint 402; substrate mechanical model 403. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0024] The embodiments of the present application provide a method and system for optimizing the robustness of a reflow soldering process, thereby solving the problems of slow parameter debugging of the current reflow soldering process and poor robustness of the reflow soldering process.

[0025] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows: With the advancement of technology, modern printed circuit board components have many types and high density, especially large-size devices such as MCU have many I / O pins, small solder joint pitch, high density, low welding batch yield, and are prone to electrical faults such as virtual short and virtual break. Reflow soldering is the main assembly technology for high-density PCB components. All electronic components on the PCB pass through the various temperature zones of the reflow soldering furnace in turn, and the overall heating and one-time soldering are completed. The ideal reflow soldering process control parameters can make all solder joints undergo qualified temperature curves and produce less deformation, which is a key link to ensure the yield of PCB components. In the actual reflow soldering process of PCB components, different solder joint temperature response curves are different. Solder joints whose temperature response curves do not meet the welding requirements will not be able to achieve effective welding. PCB components will inevitably undergo thermal deformation during the reflow soldering process. The essence of solder joint interconnection is that the solder joint connects the device substrate pad and the PCB substrate pad. Excessive deformation will directly lead to welding failure or poor welding quality.

[0026] Reflow soldering is a key process in the production of PCB components. The temperature response and warpage of all solder joints must be guaranteed. The accuracy of process parameters is high. In theory, the reflow soldering process must be strictly and accurately controlled. However, during the reflow soldering process, the actual process parameters often fluctuate around the design parameters, making it difficult and costly to accurately control. The instability of various process parameters during the reflow soldering process will lead to unstable quality and yield of the reflow soldering process, and a small process window. Based on the commonly used (99.7% yield) quality control requirements, how to improve the robustness of the reflow process itself, achieve rapid debugging of process parameters, and ensure the yield is particularly important.

[0027] The traditional reflow process debugging is based on iterative trial production, which requires repeated tests, has a long cycle and low efficiency. The fluctuation of process parameters makes process debugging difficult and the yield rate is low, which is contrary to the current situation of multiple models, small batches and short development cycles of electronic equipment. Therefore, it is urgent to develop a reflow process robustness optimization method and system that can ensure the yield rate of the reflow process under the condition of considering the uncertainty of process parameters.

[0028] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] The following first introduces a method for optimizing the robustness of a reflow soldering process provided in an embodiment of the present application.

[0030] A schematic diagram of a process for optimizing robustness of a reflow soldering process provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the reflow process robustness optimization method may include the following steps S110 - S150 .

[0031] S110. Based on the acquired surface device structure and substrate layout in the PCB assembly, a thermal model of the PCB assembly during the reflow process is constructed; wherein the thermal model includes a surface device thermal model and a substrate thermal model.

[0032] S120, constructing a mechanical model of the PCB assembly, the mechanical model including a surface device mechanical model and a substrate mechanical model.

[0033] S130. Based on the thermal model, a convection heat transfer model of the reflow soldering process is established. Based on the convection heat transfer model, temperature boundary conditions are added to the mechanical model to construct a mechanical-thermal joint simulation model.

[0034] S140, respectively constructing a simulation proxy model of the reflow soldering process and a robustness optimization model.

[0035] S150. Based on the robust optimization model, a genetic algorithm is used to search for a simulation agent model to perform robust optimization of the reflow process parameters considering uncertainty, and a robust optimization target solution is obtained to characterize the reflow process parameters.

[0036] It should be noted that the reflow soldering process analyzed in this embodiment is as follows Figure 3 As shown in , the reflow oven involved in the process includes 10 heating temperature zones and 3 cooling temperature zones. The PCB components mainly include substrates and devices on the surface. In this embodiment, only a large-size BGA surface-mount device is considered. During the reflow process, the PCB components are conveyed by the reflow oven conveyor chain at a set speed. The reflow oven in the furnace body heats or cools the PCB components by fan blowing.

[0037] The above is a specific implementation method of a reflow soldering process robustness optimization method provided in an embodiment of the present application. It can be understood that the present application constructs a thermal model and a mechanical model of the reflow soldering process based on the surface device structure and the substrate layout in the PCB assembly. According to the thermal model, a convection heat transfer model of the reflow soldering can be established. After obtaining the convection heat transfer model, the temperature boundary conditions can be added to the mechanical model to obtain a mechanical-thermal joint simulation model; after obtaining the mechanical-thermal joint simulation model, a simulation agent model is further constructed to simplify the mathematics. By using the established robust optimization model and searching the simulation agent model with a genetic algorithm, the robust optimization of the reflow soldering process parameters considering uncertainty can be performed, and the ideal target solution for robust optimization is obtained by iterative analysis to characterize the reflow soldering process parameters.

[0038] Based on this, the present application constructs a proxy model of the reflow soldering process, and realizes the rapid simulation analysis of the temperature change and warping deformation of any solder joint of the PCB assembly during the reflow soldering process. Under the condition of considering the uncertainty of process parameters, the robustness optimization of the reflow process is realized to ensure the yield of the reflow soldering process. The method provided by the present application can quickly evaluate the manufacturability of the PCB assembly at the scheme stage of the PCB assembly, which can greatly reduce the debugging time of the reflow soldering process and reduce manufacturing risks; the robustness optimization method of the reflow soldering process based on quality control provided by the present application provides a very promising and feasible technical path.

[0039] In some embodiments, in the surface device thermal model, the thermal resistances of the top and bottom surfaces of the surface device structure are determined to be Thermal resistance and Thermal resistance, Thermal resistance is the thermal resistance from junction to shell node. Thermal resistance is the thermal resistance from junction to board node. The substrate thermal model is a thermal grid model of the alternating laminated structure of multiple copper clad layers and glass fiber materials in the substrate of the PCB assembly; the substrate thermal model is provided with the thermal conductivity and interface thermal resistance of the copper clad layers and glass fiber materials.

[0040] In the embodiment of the present application, a simplified thermal model of a large-size BGA surface mount device is constructed as follows: Figure 4 As shown, the device is simplified into a rectangular structure, and the thermal resistances of the top and bottom surfaces of the device are set as Thermal resistance and Thermal resistance, the device is set as an insulating interface around. The shell node is the part of the device package that is in direct contact with the thermal interface material connected to the heat sink, the junction node is where the chip power consumption is generated, and the board node is the part directly connected to the bottom of the package, that is, the PCB board. Thermal resistance and Thermal resistance can be obtained by testing according to JEDEC standards or by consulting the device product manual. In addition, according to the layout design of the PCB substrate, a thermal grid model of the alternating laminated structure of multiple layers of copper clad layers and glass fiber materials of the PCB substrate can be established, and thermal properties such as thermal conductivity coefficient and interface thermal resistance of the copper clad layers and glass fiber materials can be set.

[0041] In some embodiments, the construction process of the aforementioned surface device mechanical model may specifically include the following steps: according to different packaging types of the device, a first finite element mesh model of a typical device is constructed with the device core and pins as core elements; in the first finite element mesh model, the mechanical properties of the device core and pins are set to complete the device mechanical modeling.

[0042] The process of constructing the mechanical model of the substrate includes: constructing a second finite element mesh model of the PCB component substrate including a multilayer structure of a copper clad layer and a glass fiber material based on the substrate layout of the PCB component; in the second finite element mesh model, setting the mechanical properties of the copper clad layer and the glass fiber material to realize mechanical modeling of the substrate; wherein the mechanical properties include elastic modulus and Poisson's ratio.

[0043] In the embodiments of the present application, it can be understood that Figure 5 As shown in the figure, the mechanical model of PCB components mainly includes two parts: the mechanical model of surface devices and the mechanical model of substrates. According to the structural characteristics of large-size BGA devices, a simplified finite element mesh model of typical devices is constructed with device plastic encapsulation materials, internal chips, device substrates, and BGA solder ball arrays as core elements, and the material elastic modulus, Poisson's ratio and other mechanical properties of device plastic encapsulation materials, internal chips, device substrates and BGA solder ball arrays are set to achieve device mechanical modeling. Based on the PCB component substrate layout, a finite element mesh model of a PCB component substrate containing a multilayer structure of copper clad layers and glass fiber materials is constructed, and the elastic modulus, Poisson's ratio and other mechanical properties of copper clad layers and glass fiber materials are set to achieve substrate mechanical modeling.

[0044] In some embodiments, Figure 2 As shown, the above-mentioned convection heat transfer model of the reflow soldering process is established based on the thermal model, and the temperature boundary conditions are added to the mechanical model based on the convection heat transfer model to construct a mechanical-thermal joint simulation model, which can specifically include the following steps: S210, determining target process parameters including chain speed and temperature of each temperature zone during the reflow process.

[0045] S220. Based on the thermal model, the convective heat transfer model of the reflow soldering process was established using the Flotherm tool and Ansys Icepak tool.

[0046] S230. Based on the convection heat transfer model, the thermal simulation boundary conditions are set according to the actual passing time of each temperature zone and the temperature of each temperature zone to perform thermal simulation of the reflow soldering process and obtain the temperature response time history data of the PCB assembly.

[0047] S240, using the temperature response time history data as the temperature boundary condition for mechanical simulation of the mechanical model, and constructing a mechanical-thermal joint simulation model.

[0048] In the embodiments of the present application, it can be understood that the present application determines the key process parameters such as the chain speed and the temperature of each temperature zone in the reflow process, and uses tools such as Flotherm and Ansys Icepak to establish a convection heat transfer model of the reflow process based on the constructed thermal model of the PCB assembly, and sets the thermal simulation boundary conditions according to the actual time of each temperature zone and the temperature of the temperature zone, so as to realize the thermal simulation of the reflow process of the PCB assembly, thereby obtaining the temperature response time history data of the PCB assembly. The obtained temperature response time history data is used as the temperature boundary condition of the mechanical simulation, and the mechanical simulation of the reflow process of the PCB assembly is realized by using tools such as Hypermesh, Ansys, Abaqus, etc., so as to obtain the warping deformation data of the PCB assembly.

[0049] In some embodiments, the aforementioned simulation proxy model is a Kriging proxy model; the temperature response time history data includes a heating rate, a peak temperature, a high temperature holding time, and a cooling rate; and the construction process of the simulation proxy model may specifically include the following steps: S310, determining target process parameters that affect the quality of the reflow soldering process as input variables.

[0050] S320, selecting the temperature response time history data of the solder joints of the PCB components and the warpage deformation at the solder joints as the quantitative evaluation criteria for the quality of the reflow soldering process, and determining the quantitative evaluation criteria as the output variables of the proxy model.

[0051] S330. For the determined input variables and output variables, multiple groups of input sample points are selected in the design space through Latin square experimental design, and the reflow process simulation calculation is performed on each sample point to obtain the output response value corresponding to the input sample point.

[0052] S340. Establish a Kriging proxy model by combining the obtained input variables and output response values.

[0053] In the embodiments of the present application, it can be understood that the present application determines the main process parameters that affect the quality of the reflow soldering process as input variables. Taking into account the operability in the actual process, the chain speed of the reflow soldering furnace, the temperature set in each temperature zone, etc. are selected as the main reflow soldering process parameters, and the reflow soldering process quality evaluation criteria are determined as the output variables of the proxy model. Taking into account the causes of the quality defects of the reflow soldering process of PCB components, the temperature response time history data of the solder joints of the PCB components and the warping deformation at the solder joints are selected as the quantitative evaluation criteria for the quality of the reflow soldering process. For the determined input variables and output responses, through the Latin square experimental design, multiple groups of input sample points are selected in the design space, and the reflow soldering process simulation calculation is performed on each sample point to obtain the output response value corresponding to the input sample point. The Kriging proxy model is established in combination with the obtained input variables and output response sample points.

[0054] In some embodiments, the construction process of the aforementioned robustness optimization model may specifically include the following steps: S410, determining the target process parameters that affect the quality of the reflow soldering process as design variables; the target process parameters include deterministic process parameters and uncertain process parameters; the deterministic process parameters include the chain speed of the reflow soldering furnace, and the uncertain process parameters include the temperature of each temperature zone.

[0055] S420, determining the warpage deformation in the quantitative evaluation criteria of the reflow process quality as an optimization target.

[0056] S430. Determine the heating rate, peak temperature, high temperature holding time, cooling rate and process parameter chain speed in the quantitative evaluation criteria for the reflow soldering process quality as design constraints; wherein the heating rate, peak temperature, high temperature holding time and cooling rate obey the normal probability distribution, and the mean and variance are set according to the welding quality assurance requirements.

[0057] In the embodiments of the present application, it can be understood that the present application distinguishes between deterministic process parameters and uncertain process parameters according to the controllability of process parameters in process practice, and selects the deterministic process parameter as the chain speed of the reflow oven; the uncertain process parameter is selected as the temperature of each temperature zone, and it is believed that the actual temperature of each temperature zone of the reflow oven obeys the normal probability distribution, and its mean and variance are determined according to measured data or experience. The present application sets the warpage deformation in the quantitative evaluation criteria of the reflow process quality as the optimization target. It is believed that the maximum temperature obeys the normal probability distribution, and its mean and variance are set according to the welding quality assurance requirements. In the analysis, the warpage deformation values ​​of two typical solder joints at the center and edge of large-size BGA devices are extracted as optimization targets.

[0058] In addition, this application sets the heating rate, peak temperature, high temperature holding time, cooling rate, etc. in the quantitative evaluation criteria of the reflow process quality and the process parameter chain speed as design constraints. It is believed that the heating rate, peak temperature, high temperature holding time, cooling rate, etc. obey the normal probability distribution, and their mean and variance are set according to the welding quality assurance requirements. In the analysis, the heating rate, peak temperature, high temperature holding time, and cooling rate values ​​of two typical solder joints at the center and edge of the device are extracted as design constraints.

[0059] In some embodiments, the robustness optimization model satisfies the expression: in, and is the design variable, is the reflow oven chain speed, For reflow oven Temperature in each temperature zone, is the optimization objective and characterizes the Warpage of solder joints; For the The heating rate of each solder joint, and Heating rate The mean and variance of is the maximum allowable heating rate; For the The high temperature holding time of each solder joint, and Keep time for high temperature The mean and variance of and They are the minimum allowable high temperature holding time and the maximum allowable high temperature holding time respectively; For the The cooling rate of each solder joint, and Cooling speed The mean and variance of is the maximum allowable cooling rate; For the The peak temperature of each solder joint, and The peak temperatures are The mean and variance of and is the minimum allowable peak temperature and the maximum allowable peak temperature; is the temperature of each temperature zone, and The temperature of each temperature zone The mean and variance of is the maximum allowable temperature in each temperature zone; is the chain speed, and They are the minimum and maximum allowable chain speed respectively.

[0060] It is understandable that the maximum or minimum allowable value of the constraint condition is obtained through experience, process manual or experiment. This application is based on the established robust optimization model and uses genetic algorithm to search for the proxy model to obtain Robust optimization optimizes the optimal solution, that is, obtains the optimal reflow soldering process parameters. Determine whether the optimization objectives and constraints meet the requirements. If they meet the requirements, the optimization is terminated to obtain the optimal parameters of the reflow soldering process. If they do not meet the requirements, reselect the design variables or design constraints and perform iterative optimization.

[0061] In some embodiments, the present application provides a reflow process robustness optimization system 500, such as Figure 6 As shown, the reflow process robustness optimization system 500 may include the following modules: The thermal model building module 510 is used to build a thermal model of the PCB assembly during the reflow process based on the surface device structure and substrate layout of the acquired PCB assembly; wherein the thermal model includes a surface device thermal model and a substrate thermal model; A mechanical model building module 520 is used to build a mechanical model of the PCB assembly, the mechanical model including a surface device mechanical model and a substrate mechanical model; The mechanical-thermal joint module 530 is used to establish a convection heat transfer model of the reflow soldering process based on the thermal model, add temperature boundary conditions to the mechanical model based on the convection heat transfer model, and construct a mechanical-thermal joint simulation model; The proxy optimization module 540 is used to construct a simulation proxy model of the reflow process and a robustness optimization model respectively; The search optimization module 550 is used to search the simulation agent model based on the robust optimization model using a genetic algorithm to perform robust optimization of the reflow process parameters considering uncertainty, and obtain a robust optimization target solution to characterize the reflow process parameters.

[0062] According to an embodiment of the present application, any multiple modules among the thermal model building module 510, the mechanical model building module 520, the mechanical-thermal joint module 530, the proxy optimization module 540, and the search optimization module 550 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0063] In other optional embodiments, the reflow process robustness optimization system 500 may also include a central processing module 560, which is used to send control instructions to other modules and coordinate the actions of other modules. The thermal model construction module 510, the mechanical model construction module 520, the mechanical-thermal joint module 530, the proxy optimization module 540 and the search optimization module 550 are all connected to the central processing module 560 and accept control instructions from the central processing module 560.

[0064] Figure 6 Each module in the system shown has the function of implementing each step in the aforementioned reflow process robustness optimization method and can achieve its corresponding technical effect. For the sake of concise description, it will not be repeated here.

[0065] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 7 shown.

[0066] The electronic device may include a processor 610 and a memory 620 storing computer program instructions.

[0067] Specifically, the processor 610 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0068] The memory 620 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 620 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 620 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 620 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 620 is a non-volatile solid-state memory.

[0069] The memory 620 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 620 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any one of the reflow process robustness optimization methods in the above-mentioned embodiments.

[0070] The processor 610 reads and executes the computer program instructions stored in the memory 620 to implement any one of the reflow process robustness optimization methods in the above embodiments.

[0071] In one example, the electronic device may further include a communication interface 630 and a bus 600. Figure 7 As shown, the processor 610, the memory 620, and the communication interface 630 are connected via a bus 600 and communicate with each other.

[0072] The communication interface 630 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application. Bus 600 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 600 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0073] In addition, in combination with a reflow soldering process robustness optimization method in the above embodiment, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the reflow soldering process robustness optimization methods in the above embodiment is implemented.

[0074] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0075] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0076] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0077] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0078] In summary, compared with the prior art, this application has the following beneficial effects: 1. After obtaining the mechanical and thermal joint simulation model of the reflow soldering process, this application further constructs a simulation agent model to simplify the mathematics and physics, thereby realizing the rapid simulation analysis of the temperature change and warping deformation of any solder joint of the PCB component during the reflow soldering process; considering the uncertainty of process parameters, the robustness optimization of the reflow process is achieved to ensure the yield of the reflow soldering process.

[0079] 2. The method provided by the present application can quickly evaluate the manufacturability of PCB components in the design stage of PCB components, which can greatly reduce the debugging time of reflow soldering process and reduce manufacturing risks. Robust optimization of the reflow process for quality control provides a promising and feasible technical path.

[0080] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the robustness of a reflow soldering process, characterized in that: include: Based on the surface device structure and substrate layout of the acquired PCB assembly, a thermal model of the PCB assembly during the reflow process is constructed; wherein the thermal model includes a surface device thermal model and a substrate thermal model; Constructing a mechanical model of the PCB assembly, wherein the mechanical model includes a surface device mechanical model and a substrate mechanical model; Based on the thermal model, a convection heat transfer model of the reflow soldering process is established, and based on the convection heat transfer model, temperature boundary conditions are added to the mechanical model to construct a mechanical-thermal joint simulation model; Construct a simulation proxy model of the reflow process and a robustness optimization model respectively; Based on the robust optimization model, a genetic algorithm is used to search the simulation agent model, and robust optimization of reflow process parameters considering uncertainty is performed to obtain a target solution of robust optimization to characterize the reflow process parameters.

2. A method for optimizing the robustness of a reflow soldering process as claimed in claim 1, characterized in that: In the surface device thermal model, the thermal resistances of the top and bottom surfaces of the surface device structure are determined to be Thermal resistance and Thermal resistance, the Thermal resistance is the thermal resistance from junction to shell node. Thermal resistance is the thermal resistance from the junction to the plate node; The substrate thermal model is a thermal grid model of the alternating laminated structure of multiple copper clad layers and glass fiber materials of the substrate in the PCB assembly; the substrate thermal model is provided with the thermal conductivity coefficient and interface thermal resistance of the copper clad layers and the glass fiber materials.

3. A method for optimizing the robustness of a reflow soldering process as claimed in claim 1, characterized in that: The process of constructing the surface device mechanical model includes: According to different packaging types of devices, the first finite element mesh model of typical devices is constructed with the device core and pins as the core elements; In the first finite element mesh model, mechanical properties of the device core and pins are set to complete device mechanical modeling; The construction process of the substrate mechanical model includes: Based on the substrate layout of the PCB assembly, construct a second finite element mesh model of the PCB assembly substrate including a copper clad layer and a glass fiber material multilayer structure; In the second finite element mesh model, the mechanical properties of the copper cladding layer and the glass fiber material are set to realize the mechanical modeling of the substrate; wherein the mechanical properties include elastic modulus and Poisson's ratio.

4. A method for optimizing the robustness of a reflow soldering process as claimed in claim 1, characterized in that: The method of establishing a convection heat transfer model of the reflow soldering process based on the thermal model, adding temperature boundary conditions to the mechanical model based on the convection heat transfer model, and constructing a mechanical-thermal joint simulation model includes: Determine the target process parameters including chain speed and temperature of each temperature zone during the reflow process; Based on the thermal model, a convection heat transfer model of the reflow soldering process is established using Flotherm tool and Ansys Icepak tool; Based on the convection heat transfer model, the thermal simulation boundary conditions are set according to the actual passing time of each temperature zone and the temperature of each temperature zone to perform thermal simulation of the reflow soldering process and obtain the temperature response time history data of the PCB assembly; The temperature response time history data is used as the temperature boundary condition for mechanical simulation of the mechanical model to construct a mechanical-thermal joint simulation model.

5. A method for optimizing the robustness of a reflow soldering process as claimed in claim 4, characterized in that: The simulation proxy model is a Kriging proxy model; the temperature response time history data includes a heating rate, a peak temperature, a high temperature holding time and a cooling rate; the construction process of the simulation proxy model includes: Determining the target process parameters affecting the quality of the reflow soldering process as input variables; Selecting the temperature response time history data of the solder joints of the PCB components and the warpage deformation at the solder joints as the quantitative evaluation criteria for the quality of the reflow soldering process, and determining the quantitative evaluation criteria as the output variables of the proxy model; For the determined input variables and output variables, multiple groups of input sample points are selected in the design space through Latin square experimental design, and the reflow process simulation calculation is performed on each sample point to obtain the output response value corresponding to the input sample point; The obtained input variables and the output response values ​​are combined to establish a Kriging surrogate model.

6. A method for optimizing the robustness of a reflow soldering process as claimed in claim 4, characterized in that: The construction process of the robust optimization model includes: Determine the target process parameters that affect the quality of the reflow soldering process as design variables; the target process parameters include deterministic process parameters and uncertain process parameters; the deterministic process parameters include the chain speed of the reflow soldering furnace, and the uncertain process parameters include the temperature of each temperature zone; Determine the warpage deformation in the quantitative evaluation criteria of reflow process quality as the optimization target; Determine the heating rate, peak temperature, high temperature holding time, cooling rate and process parameter chain speed in the quantitative evaluation criteria of reflow soldering process quality as design constraints; The heating rate, the peak temperature, the high temperature holding time and the cooling rate obey a normal probability distribution, and the mean and variance are set according to welding quality assurance requirements.

7. A method for optimizing the robustness of a reflow soldering process as claimed in claim 6, characterized in that: The robust optimization model satisfies the expression: in, and is the design variable, is the reflow oven chain speed, For reflow oven Temperature in each temperature zone, is the optimization objective and characterizes the Warpage of solder joints; For the The heating rate of each solder joint, and Heating rate The mean and variance of is the maximum allowable heating rate; For the High temperature holding time of each solder joint, and Keep time for high temperature The mean and variance of and They are the minimum allowable high temperature holding time and the maximum allowable high temperature holding time respectively; For the The cooling rate of each solder joint, and Cooling speed The mean and variance of is the maximum allowable cooling rate; For the The peak temperature of each solder joint, and The peak temperatures are The mean and variance of and is the minimum allowable peak temperature and the maximum allowable peak temperature; is the temperature of each temperature zone, and The temperature of each temperature zone The mean and variance of is the maximum allowable temperature in each temperature zone; is the chain speed, and They are the minimum and maximum allowable chain speed respectively.

8. A reflow process robustness optimization system, characterized in that: include: A thermal model building module, used to build a thermal model of the PCB assembly during the reflow process based on the surface device structure and substrate layout in the acquired PCB assembly; wherein the thermal model includes a surface device thermal model and a substrate thermal model; A mechanical model building module, used to build a mechanical model of the PCB assembly, wherein the mechanical model includes a surface device mechanical model and a substrate mechanical model; A mechanical-thermal joint module is used to establish a convection heat transfer model of the reflow soldering process based on the thermal model, add temperature boundary conditions to the mechanical model based on the convection heat transfer model, and construct a mechanical-thermal joint simulation model; The agent optimization module is used to construct the simulation agent model of the reflow process and the robustness optimization model respectively; The search optimization module is used to search the simulation agent model based on the robust optimization model using a genetic algorithm to perform robust optimization of the reflow process parameters considering uncertainty, and obtain a target solution for robust optimization to characterize the reflow process parameters.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, a method for optimizing the robustness of a reflow soldering process as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, a method for optimizing the robustness of a reflow soldering process as described in any one of claims 1 to 7 is implemented.

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