CN112966467B - Flexible PCB structure design method based on wet chemical etching combined simulation - Google Patents
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Abstract
The invention discloses a flexible PCB structure design method based on wet chemical etching joint simulation, which comprises the steps of presetting a production parameter data set according to actual production data; then, constructing an equivalent two-dimensional geometric model of the flexible PCB by using multi-physical-field finite element simulation software according to the data set, establishing a multi-physical-field simulation model, and completing the establishment of the simulation model by setting material properties, boundary conditions, initial conditions and grids; and then calling and iteratively simulating the simulation model by using a model interaction algorithm and a GA algorithm according to a preset optimization target and a code parameter set, and solidifying and reserving the optimized structure parameters obtained by simulation for production design. The invention utilizes simulation to carry out simulation calculation of the structural parameters, and combines GA algorithm to obtain more intelligent structural parameters which have more pertinence and meet the actual design requirements, thereby being a scientific design method which can greatly reduce the design cost of the test mode and improve the design time efficiency.
Description
Technical Field
The invention belongs to the technical field of electronic manufacturing, relates to an electronic manufacturing design technology driven by simulation optimization, and particularly relates to a flexible PCB structure design method based on wet chemical etching combined simulation.
Background
The flexible PCB is a printed circuit board with high quality assurance and excellent flexibility resistance, has the characteristics of thin thickness, light weight, good flexibility, high wiring density and the like, and can greatly improve the space utilization rate and the flexibility. In recent years, flexible PCB has been widely used in electronic products such as mobile phones, digital cameras, notebook computers, memory cards, etc., and still has great development potential.
The production process flow of the flexible PCB comprises I-shaped punching, coating, exposure, development, etching, film stripping, tin melting, automatic optical detection, printing ink printing, cutting, circuit detection, final cleaning and packaging, and the wet etching process is one of the main technologies of the flexible PCB manufacturing process. In this technology, how to obtain a good flexible PCB structure under the existing process conditions is a critical issue to be considered. Generally speaking, the design of the flexible PCB structure mainly considers the adjustment of parameters such as the thickness of a copper layer, the thickness of photoresist, the line width and the like, so as to obtain a good flexible PCB structure.
In the existing wet etching process, a roll continuous production mode which is easy to produce in batches is usually adopted, namely, roll-to-roll production equipment conveys a PCB substrate material, pressure spraying equipment sprays etching liquid to the substrate to perform wet etching, wherein technological parameters such as the concentration of the etching liquid, the spraying pressure, the speed of a rolling line body and the like all influence the design of structural parameters. Under the influence of the process parameters, the structure parameters are reasonably designed, so that the cost can be greatly reduced, and the product yield can be improved.
In the current process design process, the design of the structure parameters of the flexible PCB mainly adopts a trial and error method, namely, the structure parameters are directly set to continuously carry out experimental verification, the time consumption is long, and the cost is high. In addition, in the process of determining the parameters, the specific mechanism of the process is not clear, the design blindness is large, and the targeted optimization design cannot be realized.
At present, aiming at the problems, a low-cost and high-benefit parameter design model and an experimental means are lacked, wet chemical etching simulation is carried out, iterative operation is carried out by combining a GA (genetic Algorithm) algorithm, the etching mechanism of the etching liquid with controlled flow on the flexible PCB under specific structural parameters can be effectively disclosed, meanwhile, the intelligent optimization of the structural parameters is realized, and the optimized product structure meeting the actual production line production and processing is obtained.
Disclosure of Invention
Aiming at the problems of long time consumption, high cost, high blindness and the like caused by adopting a trial and error method in the conventional design process of the structural parameters of the flexible PCB, the invention provides an intelligent structural design method of the flexible PCB, which can reveal the mechanism of etching the flexible PCB by etching liquid with controlled flow and design proper structural parameters based on the mechanism, thereby achieving the purposes of saving cost, improving benefit and scientifically designing the structural parameters.
In order to solve the technical problem, the invention provides a multi-physical-field optimization simulation method based on a GA algorithm, which is used for carrying out simulation on the evolution process of an etching cavity of a copper film in the etching process, and establishing a simulation model under actual process parameters for carrying out optimization design on structural parameters through intelligent iterative optimization, and comprises the following steps:
s1, setting the structure parameters of the actual production data according to the requirement of the produced PCB product;
s2, constructing a flexible PCB equivalent two-dimensional geometric model according to the structural parameter data set, establishing a multi-physical-field simulation model, and setting material properties;
s3, setting boundary conditions and initial conditions of the multi-physical-field simulation model;
s4, setting production line process parameters in the multi-physical-field simulation model;
s5, performing grid division on the two-dimensional geometric model to complete the establishment of the simulation model;
s6, setting an optimization target and a code parameter set;
and S7, compiling a model interaction algorithm and a GA algorithm to call and iteratively simulate the simulation model, and solidifying and reserving the optimized structure parameters obtained by simulation for production design.
Further, in step S1, the structure parameters of the actual production data include an etched substrate thickness h, a photoresist thickness t, and a line width l.
Further, in step S2, the equivalent two-dimensional geometric model includes an etched substrate, a photoresist, and a flow field for etching.
Furthermore, the flow field area is a T-shaped etching model and consists of a first area between photoresist layers and a second area of a photoresist surface etching liquid flowing boundary, wherein the width and the height of the first area are determined by the line distance of lines and the thickness of the photoresist, the bottom of the flow field area is set as a moving boundary, etching is basically carried out on the etching along with the flowing of the etching liquid in the flow field area, and the moving boundary moves downwards to form an etching cavity.
Further, the modeling of the multi-physical-field simulation model comprises modeling of a chemical substance transfer field and a fluid flow field based on an N-S equation (Navier-Stokes equation), and describing the change of the etching cavity by deformation geometry; the setting of the material property comprises selection of the type of the etching solution and the material of the etching substrate.
Furthermore, the etching substrate is a copper film.
Further, in the step S3, the setting of the boundary condition includes setting of an inflow speed of the etching solution into the boundary, a stress of the outflow boundary, a boundary moving condition, and a boundary etching solution flux condition; the initial conditions include initial concentration of the etching solution, initial values of the model velocity field and the pressure.
Further, in step S4, the production line process parameters specifically include a spraying pressure p, an etching solution concentration c, and a line velocity v, and in order to simplify the simulation, the inlet velocity v is used in the production line process parametersiThe reaction spray pressure p, the etch time T reaction line body running speed v was used.
Further, in step S5, a grid quality threshold is set in the simulation calculation, and when the grid quality is lower than the threshold, the grid is automatically re-divided to ensure the grid quality.
Further, in step S6, according to the requirement of the process index for the small side etching amount and the deep vertical etching amount, the ratio of the minimum side etching amount to the vertical etching amount is adoptedTo optimize the goal; the setting of the code parameter set comprises the selection of the researched structure parameters, the variable number N of the GA function, the variable interval omega and the population size NpAnd the maximum number of iterations NgAnd setting a minimum vertical etch depth limit H and a maximum undercut limit W in the process recipe.
Further, in the step S7, the model interaction algorithm specifically implements calling simulation of the model and lateral erosion amount and vertical etching amount parameters obtained by returning a simulation result, where the calling simulation of the model specifically refers to assigning structural parameter variables obtained by successive iteration of the GA algorithm to the simulation model in each GA algorithm iteration, so as to implement automatic modification of the simulation model on the geometric structure and complete calculation of the simulation model; the GA algorithm specifically realizes intelligent iteration of the model interaction algorithm, except that the population (namely, structural parameter variable) of the first iteration is defined by initialization, the selection of the population (namely, structural parameter variable) of each iteration is influenced by the last iteration result and the algorithm characteristics, the structural parameter variable is transmitted to the model interaction algorithm to complete the simulation of the model, the simulation result participates in the operation of fitness calculation, selection, crossing, variation and generation of the next generation of population, the simulation is stopped when the iteration times reach the upper limit of an algebra or optimization convergence, the optimal structural parameter is output, and the obtained optimized structural parameter is solidified and reserved for production design.
Further, the specific steps in step S7 are: selecting N in variable interval for optimized structure parameterpTaking values to form an initial population, substituting the initial population and the non-optimized structural parameters into a simulation model for calculation to obtain a simulation result, namely NpThe combined data of the group lateral erosion amount and the vertical etching amount are respectively subjected to fitness calculation, selection, crossing, variation and next generation population (new population), the new population and the non-optimized structural parameters are substituted into the simulation model together for calculation, and the iteration is carried out until the maximum iteration number is reached, and the iteration is stopped to obtain NpA new population composed of optimized values of structural parameters and corresponding to each value of the structural parameters in the new populationThe ratio of the lateral erosion amount to the vertical etching amount of each group of data is calculated, and the structural parameter value in the group of data with the minimum ratio is the optimal parameter.
And replacing the optimized structural parameters, and optimizing according to the method until all the structural parameters are optimized.
The invention has the advantages that:
1) the specific etching mechanism of the wet etching is described in detail by using a multi-physical-field simulation method. Aiming at the problems of low benefit, strong blindness, no guiding principle and method and the like existing in the process circulation of trial and error, debugging and improvement of most of the structural design of the conventional flexible PCB product, the multi-physical-field simulation method for wet etching is provided, fills the principle blank of wet etching, makes the production more targeted and has guiding significance.
2) A structure design method of a flexible PCB with high efficiency and low cost is provided. The feasible structural parameters are searched by adopting a method of combining multi-physical-field simulation and an intelligent GA algorithm, so that the structural design of the flexible PCB production is carried out, unnecessary loss on a production line is reduced, and the method is a scientific design method which saves cost and improves benefits.
Drawings
FIG. 1 is a flowchart of an algorithm of a design method of a flexible PCB structure according to the present invention.
FIG. 2 is a simulation flowchart of the design method of the flexible PCB structure of the present invention.
FIG. 3 is a diagram of an equivalent two-dimensional geometric model of a wet etched flexible PCB.
Fig. 4 is a simulation result diagram, in fig. 4, the horizontal and vertical coordinates are the horizontal coordinate mark and the vertical coordinate mark of the equivalent two-dimensional geometric model, respectively, and the connecting line represents the etched surface position after etching.
Reference numerals: 1-photoresist, 2-copper film, 3-etching liquid flowing into the boundary, 4-etching liquid flowing out of the boundary, 5-copper film etching surface, 6-first area, and 7-second area.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1, the present invention provides a multi-physical field simulation method for simulating a wet chemical etching process of a flexible PCB, and a design of structural parameters is completed by combining a simulation method with a GA algorithm, the method comprising the steps of:
s1, setting the structure parameters of the actual production data according to the requirement of the produced PCB product;
s2, constructing a flexible PCB equivalent two-dimensional geometric model according to the structural parameter data set, establishing a multi-physical-field simulation model, and setting material properties;
s3, setting boundary conditions and initial conditions of the multi-physical-field simulation model;
s4, setting production line process parameters in the multi-physical-field simulation model;
s5, performing grid division on the two-dimensional geometric model to complete the establishment of the simulation model;
s6, setting an optimization target and a code parameter set;
and S7, compiling a model interaction algorithm and a GA algorithm to call and iteratively simulate the simulation model, and solidifying and reserving the optimized structure parameters obtained by simulation for production design.
In step S1, the structural parameters of the actual production data include an etched substrate thickness h, a photoresist thickness t, and a line width l.
In the embodiment, according to the actual survey record, the following values are taken for the structural parameter data: the line width is 8 μm, the photoresist thickness is 2 μm, and the copper (etched substrate) thickness is 8 μm.
Further, in step S2, according to the production parameter data set, simulation modeling is performed by using the COMSOL Multiphysics 5.5, which specifically includes: establishing the equivalent two-dimensional geometric model as shown in fig. 2, wherein the equivalent two-dimensional geometric model comprises a copper film 2 serving as an etching substrate, photoresist 1 and a flow field, the flow field is a T-shaped etching model and consists of a first area 6 between photoresist layers and a second area 7 of a photoresist surface etching liquid flowing boundary, wherein the width and the height of the first area 6 are determined by the line distance of lines and the thickness of the photoresist 1, and the second area 7 is a flowing boundary of spraying liquid on the photoresist surface, namely the etching liquid flowing boundary controlled to flow;
the bottom of the first region (i.e., the copper film etching surface 5) is set as a moving boundary, and as the etching liquid flows in the fluid domain, the copper film 2 is etched, and the moving boundary moves down to form an etching chamber. The top of the second region 7 is the etching solution inflow boundary 3, and the two sides are the etching solution outflow boundary 4, high-concentration CuCl2The solution enters the flow field from the inlet at a certain initial speed and flows out from the outlets at two sides.
The modeling of the multi-physical-field simulation model comprises modeling of a fluid flow field based on a chemical substance transfer field and an N-S equation (Navier-Stokes equation), describing two physical fields by a dilute substance transfer module and a laminar flow module respectively in simulation software, and describing the change of the etching cavity by a deformation geometry module. The chemical substance transfer field diffusion and convection equation in the simulation model is as follows:
in the formula, c is CuCl2T is time, u is flow rate of the etching solution, D is diffusion coefficient,
is a gradient operator; the N-S equation in the simulation model is as follows:
in the formula (2), rho is the fluid density of the etching liquid, p is the external pressure, I is a unit vector, mu is the dynamic viscosity, and F is the boundary stress.
The material properties are specifically set as follows: and selecting an acidic copper chloride solution as an etching solution, and selecting a copper film as an etching substrate material.
In step S3, the setting of the boundary condition is mainly to set the boundary shown in fig. 3, and includes the following aspects: the inlet speed of the etching solution into the boundary is 4m/s, the stress of the etching solution out of the boundary is 0, and the moving speed of the etched surface of the copper film is described by the formula (3):
in the formula (3), α is the anisotropy difference coefficient of the copper film, k is the etching reaction rate constant, and M is CuCl2Molar mass of (b), pCuDensity of copper, nxAnd nyThe x component and the y component of the normal vector of the surface of the copper film are respectively. The remaining boundaries are no-slip boundaries.
For CuCl2Solution concentration profile, etchant influent boundary CuCl2The solution concentration was 0.45mol/L, the flux relationship of the copper film etched surface was described by the equation (4), and the remaining boundary fluxes were all 0.
The setting of the initial conditions includes the following aspects: the initial concentration of the whole simulation model etching solution is 0, and the initial values of the velocity field and the pressure are 0.
In the step S4, the production line process parameters specifically include a spraying pressure p, an etching solution concentration c, and a line body velocity v, and in order to simplify the simulation, the etching solution inlet velocity v is used in the production line process parametersiThe reaction spray pressure p, the etching time T reaction linear velocity v was used.
In the embodiment, according to the actual survey record, the production line process parameter data is subjected to the following values: etching liquid inlet velocity viThe concentration c of the etching solution was 0.45mol/L and the etching time T was 120s at 4 m/s.
In step S5, a grid quality threshold is set during the simulation calculation, and when the grid quality is lower than the threshold, the grid is automatically subdivided to ensure the grid quality (a function of the comsol software).
Further, in the step S6, according to the requirements of the process index on the small side etching amount and the deep vertical etching amount, the ratio of the minimum side etching amount to the vertical etching amount is used as an optimization target, and the expression is shown in formula (5);
M＝minWr/Hrformula (5)
In formula (5), M is the optimization target, WrAnd HrThe amount of undercut and the amount of vertical etching were obtained from the simulation, respectively.
The setting of the code parameter set comprises the selection of the researched structure parameters, the variable number N of the GA function, the variable interval omega and the population size NpAnd the maximum number of iterations NgAnd setting a minimum vertical etching depth limit H and a maximum etching amount limit W in the process index;
in the present embodiment, the code parameter set is set to: selecting the photoresist thickness as a structural parameter to be researched and optimized, selecting other structural parameters in a structural parameter data set of actual production data, wherein the variable quantity N of the GA function is 1, and the value interval of the variable photoresist thickness is omega [2μm,4μm ═ 2μm [ ]]Population size NpMaximum number of iterations N of 3gThe minimum vertical etching depth limit H is 8 μm, and the maximum etching amount limit W is 2 μm, 3.
In the step S7, the MATLAB 2020a is used to complete the writing of the model interaction algorithm and the GA algorithm. The model interaction algorithm specifically realizes the calling simulation of the model and the lateral erosion W obtained by returning the simulation resultrVertical etching amount parameter HrThe calling simulation of the model specifically refers to N obtained by assigning successive iteration of the GA algorithm to the simulation model in each GA algorithm iterationpDifferent photoresist thickness wxRealizing automatic change of the simulation model on the geometric structure, completing the calculation of the simulation model to obtain NpCombining the amount of lateral etching with the amount of vertical etching;the GA algorithm realizes intelligent iteration of the model interaction algorithm, except that a first generation population (namely, a structure parameter variable) is defined by initialization, each generation of population (namely, the structure parameter variable) is influenced by the last iteration result and the algorithm characteristic, and the structure parameter variable serving as the population is transmitted to the N of the model interaction algorithm completion modelpSub-simulation, NpAnd (3) the group simulation result participates in the operation of fitness calculation, selection, crossing, variation and generation of a next generation population (namely, next iteration variable value), the simulation is stopped when the iteration times reach an upper limit of an algebra or optimization convergence, the optimal structural parameter is output, and the obtained optimized structural parameter is solidified and reserved for production design.
Specifically, in this embodiment, the GA algorithm is defined according to initialization of NpSelecting N in the variable interval omega of the photoresist thicknesspA different thickness value (e.g. w)1＝2，w2＝3，w34) to form a first generation population, respectively substituting different photoresist thickness values and other structural parameters (thickness h and line width l of an etched substrate) into a simulation model through a model interaction algorithm to calculate Np(3) Then, obtain Np(3) Combining the group lateral etching amount and the vertical etching amount, and calculating the fitness of each group of combinations to obtain NpSelecting, crossing, mutating and generating next generation population (namely next iteration variable value), if the iteration times does not reach the maximum times, substituting the generated new population and other structural parameters (the thickness h and the line width l of the etched substrate) into the simulation model for calculation, executing according to the steps until the maximum iteration times is reached or the algorithm converges in advance, terminating the iteration, and performing N through an optimization targetp(3) Optimizing the group data, in particular, calculating Np(3) And (3) determining the ratio of the lateral etching amount to the vertical etching amount of the group data, wherein the data with the minimum ratio is the optimal data, and the photoresist thickness corresponding to the data is the optimal scheme. According to the mode, other structural parameters (such as the thickness h and the line width l of the etched substrate) can be optimized one by one, the optimal structural parameter group is finally obtained, and the structural parameter data is solidified and reserved for production.
In the embodiment, COMSOL and MATLAB are subjected to interactive simulation, after 3 generations of iterative operation (internal iteration), 3 times of simulation models are called for each iterative operation, 9 times of data exchange are carried out, the obtained optimal photoresist thickness is 2.254 μm, the corresponding lateral etching amount and the vertical etching amount are respectively 1.790 μm and 8.145 μm, the lateral etching amount is less than 2 μm, the vertical etching amount is greater than 8 μm, the limitation requirement of the process index is met, the simulated etching effect corresponding to the parameter is shown in FIG. 3, and the parameter is cured and reserved for production design.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A flexible PCB structure design method based on wet chemical etching joint simulation is characterized by comprising the following steps:
s1, setting a structural parameter data set according to the actual production data;
s2, constructing an equivalent two-dimensional geometric model of the flexible PCB according to the structural parameter data set, establishing a multi-physical-field simulation model, and setting material properties;
s3, setting boundary conditions and initial conditions of the multi-physical-field simulation model;
s4, setting production line process parameters in the multi-physical-field simulation model;
s5, carrying out grid division on the equivalent two-dimensional geometric model to complete the establishment of the simulation model;
s6, setting an optimization target and a code parameter set;
s7, compiling a model interaction algorithm and a GA algorithm to call and iteratively simulate the simulation model, and solidifying and reserving optimized structure parameters obtained by simulation for production design;
in step S2, the equivalent two-dimensional geometric model includes an etched substrate, a photoresist, and a flow field; the flow field area is a T-shaped etching model and consists of first areas between photoresist layers and second areas of the flowing boundary of etching liquid on the surface of the photoresist;
in step S7, the model interaction algorithm specifically implements single-call simulation of the model and lateral erosion amount and vertical etching amount parameters obtained by returning a simulation result, wherein the single-call simulation of the model specifically refers to assigning structural parameter variables obtained by successive iteration of a GA algorithm to the simulation model, thereby implementing automatic modification of the simulation model on a geometric structure and completing single calculation of the simulation model; the GA algorithm specifically realizes intelligent iteration of the model interaction algorithm, except that the first iteration is defined by initialization, the selection of structural parameter variables of each iteration is influenced by the last iteration result and algorithm characteristics, the structural parameter variables are transmitted to the model interaction algorithm to complete single simulation of the model, the simulation result is used as a population individual to participate in fitness calculation, selection, crossing, variation and operation of next generation population generation, when the iteration times reach an upper limit of an algebra or optimization convergence, the simulation is stopped, the optimal structural parameters are output, and the obtained optimized structural parameters are solidified and reserved for production design.
2. The flexible PCB board structure design method of claim 1, wherein: in step S1, the structural parameters of the actual production data include the thickness of the etched substratehThickness of photoresisttSum line widthl。
3. The flexible PCB board structure design method of claim 1, wherein: in step S2, the modeling of the multi-physics simulation model includes modeling of the chemical transfer field and the fluid flow field based on the N-S equation, and the change of the etching cavity is described by the deformation geometry.
4. The flexible PCB board structure design method of claim 1, wherein: in step S2, the setting of the material property includes selecting the type of the etching solution and the material of the etching substrate.
5. The flexible PCB board structure design method of claim 1, wherein: in step S3, the setting of the boundary condition includes setting of an inflow speed of the etching solution into the boundary, a stress of the outflow boundary, a boundary movement condition, and a boundary etching solution flux condition; the initial conditions include initial concentration of the etching solution, initial values of the model velocity field and the pressure.
6. The flexible PCB board structure design method of claim 1, wherein: in step S4, the production line process parameters specifically include the spray pressure p, the concentration c of the etching solution, and the line velocity v, and for simplifying the simulation, the inlet velocity v of the etching solution is used in the production line process parametersiInstead of the shower pressure p, the etching time T is used instead of the line body velocity v.
7. The flexible PCB board structure design method of claim 1, wherein: in step S5, a grid quality threshold is set during the simulation calculation, and when the grid quality is lower than the threshold, the grid is automatically re-divided to ensure the grid quality.
8. The flexible PCB board structure design method of claim 1, wherein: in step S6, according to the requirements of the process index on the small side etching amount and the deep vertical etching amount, the ratio of the minimum side etching amount to the vertical etching amount is used as an optimization target; the setting of the code parameter set comprises the selection of the researched structure parameters, the variable number N of the GA function, the variable interval omega and the population size NpAnd the maximum number of iterations NgAnd setting a minimum vertical etch depth limit H and a maximum undercut limit W in the process recipe.
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| CN116680842B (en) * | 2023-08-04 | 2023-10-20 | 天河超级计算淮海分中心 | Rolling type confluence ring structural parameter simulation optimizing method, equipment and medium |
| CN117276145B (en) * | 2023-11-23 | 2024-02-20 | 江苏永鼎股份有限公司 | Chip etching process optimization method and system |
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