A simulation method and system for a preparation process of a nano-silver wire flexible transparent electrode

Abstract

The application provides a preparation process simulation method and system of a nano silver wire flexible transparent electrode, morphological estimation is performed on a process characteristic space of nano silver wire ink, network distribution characteristics and interface combination characteristics of the nano silver wire under different process conditions are obtained, evolution simulation is performed on the network distribution characteristics and the interface combination characteristics, and a morphological evolution curve of the nano silver wire in a film forming process is obtained; photoelectric performance indexes of the nano silver wire flexible transparent electrode are extracted from the morphological evolution curve, the photoelectric performance indexes are matched with a performance demand model of a target device, and then a predicted photoelectric response value of the target device under preset working conditions is generated; and the optimal predicted photoelectric response value is taken as a target, and a mechanical flexibility threshold of the electrode is taken as a constraint condition, so that a personalized preparation process scheme containing a process parameter combination and a post-processing time sequence is generated. Based on the above scheme, digital simulation optimization of the nano silver wire electrode preparation process can be realized.

Description

Technical Field

[0001] This application relates to the field of electrode preparation technology, and more specifically, to a simulation method and system for the preparation process of a flexible transparent electrode made of silver nanowires. Background Technology

[0002] The preparation of flexible transparent electrodes with silver nanowires involves uniformly covering the surface of a transparent substrate with conductive ink containing silver nanowires through methods such as slit coating, spin coating, or spraying. After solvent evaporation and annealing, the silver wires overlap to form a conductive network, thereby obtaining a transparent conductive film with both high optical transmittance and low sheet resistance.

[0003] In the current process of fabricating flexible transparent electrodes using silver nanowires, the coating process parameters are typically adjusted repeatedly through experimental trial and error. Researchers often rely on experience to set the moving speed of the slit coating head, the ink supply flow rate, and the substrate heating temperature. They then prepare numerous samples and test their photoelectric properties to correct the process conditions. However, this approach fails to quantitatively describe the coupling relationship between ink properties and process parameters, and cannot analyze the interaction between inherent ink properties such as viscosity and surface tension and equipment operating variables such as moving speed and gap height. Due to the lack of quantitative characterization of this coupling relationship, the original combination of process parameters often becomes ineffective when different batches of silver nanowire ink are changed or the coating equipment is adjusted. This necessitates extensive experimentation, resulting in a narrow process window, poor batch stability, and difficulty in reproducing the results across different production lines. This severely hinders the industrialization and promotion of flexible transparent electrodes using silver nanowires. Therefore, achieving digital simulation optimization of the silver nanowire electrode fabrication process to improve the synergistic matching accuracy of photoelectric performance and mechanical flexibility has become a major challenge for the industry. Summary of the Invention

[0004] This application provides a simulation method and system for the fabrication process of flexible transparent electrodes made of silver nanowires, which can realize digital simulation optimization of the fabrication process of silver nanowire electrodes, thereby improving the synergistic matching accuracy of photoelectric performance and mechanical flexibility.

[0005] In a first aspect, this application provides a simulation method for the fabrication process of a flexible transparent electrode made of silver nanowires, comprising: The physical properties and coating process parameters of the silver nanowire ink are collected, and then a multi-dimensional process feature space is constructed based on the coupling relationship between the physical properties and the coating process parameters. Morphological estimation based on film formation mechanism is performed on the process feature space to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions. Then, the evolution of the network distribution characteristics and interface bonding characteristics is simulated to obtain the morphological evolution curve of silver nanowires during the film formation process. The photoelectric performance indicators of the flexible transparent electrode of silver nanowire are extracted from the morphological evolution curve. The photoelectric performance indicators are matched with the performance requirements model of the target device to generate the predicted photoelectric response value of the target device under preset working conditions. With the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint, a personalized fabrication process scheme including a combination of process parameters and a post-processing sequence is generated.

[0006] In some embodiments, constructing a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters specifically includes: Principal component analysis was performed on the aforementioned physical property parameters to extract the physical property factors that affect film formation quality; The coating process parameters are divided into process windows to obtain parameter reference values ​​for different process stages; The coupling relationship between the key physical property factors and the baseline values ​​of each parameter is analyzed to construct a multi-dimensional process feature space with ink droplet spreading behavior and solvent evaporation rate as coordinate axes.

[0007] In some embodiments, morphological estimation based on film formation mechanism is performed on the process feature space to obtain the network distribution characteristics and interfacial bonding characteristics of silver nanowires under different process conditions, specifically including: The multi-dimensional process feature space is discretized into multiple process nodes, and each process node corresponds to a set of specific process conditions. For each process node, a pre-set silver wire self-assembly motion model is used to simulate the migration, rotation, and overlapping behavior of the silver wire at the solid-liquid interface. The overlap density, porosity, and substrate contact angle of the silver wires in the statistical node simulation results are used to obtain the network distribution characteristics and interface bonding characteristics of the silver wires under the corresponding process conditions of the process node, and then obtain the network distribution characteristics and interface bonding characteristics of the silver wires under different process conditions.

[0008] In some embodiments, the evolution simulation of the network distribution characteristics and interface bonding characteristics to obtain the morphological evolution curve of the silver nanowires during the film formation process specifically includes: Initialize a phase field evolution model based on concentration field and fluid dynamics; The network distribution characteristics are used as the initial state of the phase field evolution model; The interface features are used as boundary constraints for this phase field evolution model. In the phase-field evolution model, the propagation rate of solvent evaporation and the influence coefficient of the Marangoni effect are set, and the topological change process of the silver wire network from wet film state to dry film state is iteratively calculated, thereby outputting the morphological evolution curve of resistivity as a function of drying time.

[0009] In some embodiments, extracting the photoelectric performance indicators of the flexible transparent electrode from the morphological evolution curve specifically includes: Geometric parameters are extracted from the endpoint state of the morphological evolution curve to obtain the average aperture and linear density of the silver wire network; The average aperture and the linear density are input into a preset optical scattering model and an electrical permeation model to calculate the transmittance and sheet resistance of the flexible transparent electrode of silver nanowires. The photoelectric performance indicators of the flexible transparent electrode made of silver nanowires are determined by the transmittance value and the sheet resistance value.

[0010] In some embodiments, matching the photoelectric performance indicators with the performance requirement model of the target device to generate the predicted photoelectric response value of the target device under preset operating conditions specifically includes: Obtain the performance requirement model of the target device at a specified operating frequency and under bending conditions. This performance requirement model defines the matching tolerance range of transmittance and sheet resistance. Substitute the transmittance and sheet resistance values ​​from the photoelectric performance indicators into the performance requirement model to determine whether they are within the matching tolerance range. If it is within the matching tolerance range, the equivalent circuit model of the target device is driven based on the photoelectric performance index to generate the predicted photoelectric response value of the target device under the preset operating conditions.

[0011] In some embodiments, the nano-silver wire ink is an ink coated with polyvinylpyrrolidone synthesized based on a polyol method.

[0012] Secondly, this application provides a simulation system for the fabrication process of a flexible transparent electrode made of silver nanowires, comprising: The acquisition module is used to acquire the physical property parameters and coating process parameters of the silver nanowire ink, and then construct a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters. The processing module is used to perform morphological estimation of the process feature space based on the film formation mechanism to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions, and then perform evolution simulation of the network distribution characteristics and interface bonding characteristics to obtain the morphological evolution curve of silver nanowires during the film formation process. The processing module is also used to extract the photoelectric performance index of the flexible transparent electrode of silver nanowire from the morphological evolution curve, match the photoelectric performance index with the performance requirement model of the target device, and then generate the predicted photoelectric response value of the target device under preset working conditions. The execution module is used to generate a personalized fabrication process scheme that includes a combination of process parameters and a post-processing sequence, with the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described simulation method for the fabrication process of flexible transparent electrodes with silver nanowires.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-mentioned simulation method for the fabrication process of flexible transparent electrodes made of silver nanowires.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a simulation method and system for the fabrication process of a flexible transparent electrode made of silver nanowires. The method involves collecting the physical properties and coating process parameters of the silver nanowire ink, and then constructing a multi-dimensional process feature space based on the coupling relationship between these parameters. Morphological estimation based on the film formation mechanism is performed on this feature space to obtain the network distribution and interface bonding characteristics of the silver nanowires under different process conditions. The evolution of these characteristics is then simulated to obtain the morphological evolution curve of the silver nanowires during film formation. The photoelectric performance indicators of the flexible transparent electrode are extracted from these curves. These indicators are then matched with the performance requirements of the target device to generate a predicted photoelectric response value for the target device under preset operating conditions. With the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint, a personalized fabrication process scheme including combinations of process parameters and post-processing sequences is generated.

[0016] Therefore, in this application, the goal is to achieve the optimal predicted photoelectric response value, with the mechanical flexibility threshold of the electrode as a constraint, to generate a personalized fabrication process scheme that includes combinations of process parameters and post-processing sequences. First, by determining the morphological evolution curve, the dynamic evolution law of the silver wire network microstructure can be obtained, thus providing data support for establishing a quantitative mapping relationship between process parameters and film quality. This curve fully records the continuous change trajectory of silver wire overlap density, pore distribution, and interface contact angle during the transition from wet film to dry film, elevating process simulation from static empirical judgment to dynamic mechanism analysis. This allows process optimization to be based on traceable and quantifiable microstructure evolution, thereby ensuring the accuracy and reliability of subsequent performance simulations. Then, by determining the predicted photoelectric response value, a quantitative evaluation of the matching between electrode performance and device requirements can be obtained, thus providing a basis for personalized process schemes. The inversion solution provides a clear target orientation. This response value transforms the microscopic network structure parameters into macroscopic device performance, establishing a two-layer mapping relationship from basic performance indicators such as transmittance and sheet resistance to application-level performance indicators such as gain and response speed. By matching and analyzing with the performance requirement model of the target device, the gap between the current process scheme and the ideal performance can be accurately identified. This prevents process optimization from blindly pursuing the extreme value of a single performance indicator, but rather seeks the optimal balance of overall performance for specific application scenarios. A quantitative evaluation bridge is built between process parameters and device performance, ensuring that the optimization direction is always aligned with actual application needs. This ensures that the final generated process scheme can simultaneously meet the synergistic requirements of optoelectronic performance and mechanical flexibility. In summary, based on the above scheme, digital simulation optimization of the silver nanowire electrode fabrication process can be achieved, thereby improving the synergistic matching accuracy of optoelectronic performance and mechanical flexibility. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a simulation method for fabrication of flexible transparent electrodes with silver nanowires according to some embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining photoelectric performance indicators according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a simulation system for the fabrication process of flexible transparent electrodes with silver nanowires according to some embodiments of this application; Figure 4This is a schematic diagram of the structure of a computer device for simulating the fabrication process of flexible transparent electrodes with silver nanowires, according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart illustrating a simulation method for the fabrication process of a flexible transparent electrode made of silver nanowires according to some embodiments of this application. The simulation method for the fabrication process of the flexible transparent electrode made of silver nanowires mainly includes the following steps: In step 101, the physical properties and coating process parameters of the silver nanowire ink are collected, and then a multi-dimensional process feature space is constructed based on the coupling relationship between the physical properties and the coating process parameters.

[0021] It should be noted that, in this application, the nano-silver wire ink is an ink coated with polyvinylpyrrolidone synthesized based on the polyol method; the physical property parameters are quantitative indicators describing the physicochemical properties of the nano-silver wire ink itself; and the coating process parameters are controllable variables that define the operating conditions of the coating equipment and the environmental conditions.

[0022] In practice, firstly, the physical properties of the silver nanowire ink are collected. This includes measuring the viscosity of the ink using a rotational rheometer at 25 degrees Celsius, measuring the surface tension of the ink using a surface tension meter with the pendant drop method, measuring the percentage of solid content of the silver nanowires in the ink using the gravimetric method, and measuring the absorbance value of the ink's dispersion stability using an ultraviolet spectrophotometer. Then, the coating process parameters are collected. This includes reading the moving speed of the slit coating head using an encoder, setting the ink supply flow rate using a high-precision injection pump, monitoring the heating temperature of the substrate using an infrared thermometer, and adjusting the gap height between the coating head and the substrate using a micrometer.

[0023] In some embodiments, constructing a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters can be achieved through the following steps: Principal component analysis was performed on the aforementioned physical property parameters to extract the physical property factors that affect film formation quality; The coating process parameters are divided into process windows to obtain parameter reference values ​​for different process stages; The coupling relationship between the key physical property factors and the baseline values ​​of each parameter is analyzed to construct a multi-dimensional process feature space with ink droplet spreading behavior and solvent evaporation rate as coordinate axes.

[0024] It should be noted that in this application, the physical property factor is a new variable that characterizes the film-forming driving force hidden in the original physical property parameters; the parameter reference value is the standard operating value that the coating equipment should maintain under different process stages; and the multi-dimensional process feature space is a two-dimensional characterization coordinate system used to directly link process control conditions with the microscopic behavior of the coating film.

[0025] In specific implementation, firstly, principal component analysis is performed on the physical property parameters to extract the physical property factors affecting film quality. This can be achieved as follows: Principal component analysis is performed on the viscosity, surface tension, solid content percentage, and dispersion stability absorbance values ​​among the physical property parameters. Specifically, the covariance matrix of the four parameters is calculated, and the eigenvalues ​​and corresponding eigenvectors of the matrix are solved. After sorting the eigenvalues ​​from largest to smallest, the two eigenvectors with a cumulative contribution rate exceeding 85% are selected as principal components. These two principal components are the extracted physical property factors. The first principal component is mainly interpreted as a factor related to fluid flowability, and the second principal component is mainly interpreted as a factor related to interface stability. These two factors are used as physical property factors representing the essential characteristics of the ink. Then, the coating process parameters are divided into process windows to obtain the parameter benchmark values ​​for different process stages. This can be achieved as follows: The process windows are divided on the moving speed, supply flow rate, heating temperature, and gap height among the coating process parameters. Specifically, according to the time sequence of the coating process, the entire coating process is divided into the start-up stage, the steady-state coating stage, and the finishing stage. In the startup phase, the moving speed and supply flow rate are set to low values ​​as the baseline parameters for this phase. In the steady-state coating phase, both the moving speed and supply flow rate are set to preset target values, while the heating temperature and gap height are set to constant values ​​as the baseline parameters for this phase. In the finishing phase, the supply flow rate is gradually reduced to zero while the moving speed remains constant as the baseline parameters for this phase. This results in three sets of baseline parameters covering the entire coating process. Finally, the coupling relationship between the key physical property factors and the various baseline parameters is analyzed to construct a multi-dimensional process feature space with droplet spreading behavior and solvent evaporation rate as coordinate axes. This can be achieved as follows: Correlation analysis is performed between the first and second physical property factors and the baseline values ​​of various parameters in the start-up, steady-state coating, and finish-up stages, respectively. Specifically, the matching coefficient between the first physical property factor and the baseline value of the steady-state stage moving speed is calculated. This matching coefficient characterizes the synergy between ink flowability and coating speed. Simultaneously, the matching coefficient between the second physical property factor and the baseline value of the steady-state stage heating temperature is calculated. This matching coefficient characterizes the synergy between ink interface stability and drying temperature. The calculated matching coefficients are used as coordinate variables, with the first matching coefficient as the horizontal axis and the second matching coefficient as the vertical axis, to construct a two-dimensional process feature space. Each point in this process feature space represents a matching state between ink properties and process conditions. The horizontal axis represents the ink droplet spreading behavior on the substrate, and the vertical axis represents the solvent evaporation rate during the drying process. This two-dimensional coordinate system, with ink droplet spreading behavior and solvent evaporation rate as the axes, serves as a multi-dimensional process feature space for morphological estimation.

[0026] In step 102, morphological estimation based on film formation mechanism is performed on the process feature space to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions. Then, the evolution of the network distribution characteristics and interface bonding characteristics is simulated to obtain the morphological evolution curve of silver nanowires during the film formation process.

[0027] In some embodiments, morphological estimation of the process feature space based on the film formation mechanism can be performed to obtain the network distribution characteristics and interfacial bonding characteristics of silver nanowires under different process conditions. This can be achieved by the following steps: The multi-dimensional process feature space is discretized into multiple process nodes, and each process node corresponds to a set of specific process conditions. For each process node, a pre-set silver wire self-assembly motion model is used to simulate the migration, rotation, and overlapping behavior of the silver wire at the solid-liquid interface. The overlap density, porosity, and substrate contact angle of the silver wires in the statistical node simulation results are used to obtain the network distribution characteristics and interface bonding characteristics of the silver wires under the corresponding process conditions of the process node, and then obtain the network distribution characteristics and interface bonding characteristics of the silver wires under different process conditions.

[0028] It should be noted that, in this application, process nodes are discretized coordinate points used to locate a single controllable experimental condition within the feature space; network distribution characteristics are geometric indicators used to describe the spatial density and structural regularity of the silver nanowires on the substrate plane; and interface bonding characteristics are physical indicators used to describe the adhesion strength and contact morphology at the interface between the silver nanowires and the substrate.

[0029] In specific implementation, firstly, the multi-dimensional process feature space is discretized into multiple process nodes. Each process node corresponds to a specific set of process conditions, which can be achieved in the following way: the two-dimensional process feature space constructed with ink droplet spreading behavior as the horizontal axis and solvent evaporation rate as the vertical axis is subjected to grid discretization. Specifically, ten representative points are uniformly selected from the minimum to the maximum value on the horizontal axis, and ten representative points are also uniformly selected from the minimum to the maximum value on the vertical axis. One hundred grid points are formed by the intersection of the horizontal and vertical axes. Each grid point is defined as a process node, and each process node corresponds to a specific set of process conditions, for example, located at the zero point of the horizontal axis. The node at position 0.7 on the vertical axis specifically represents a combination of process parameters with moderate to low droplet spreading ability and relatively fast solvent evaporation rate. Then, for each process node, the migration, rotation, and overlapping behavior of the silver wire at the solid-liquid interface can be simulated using a pre-built silver wire self-assembly motion model. This can be achieved as follows: For each process node, the pre-built silver wire self-assembly motion model is sequentially called for node simulation. This model, based on Brownian dynamics, treats each silver nanowire as a rod-shaped unit composed of multiple rigid segments. For the process node currently being simulated, the droplet spreading coefficient and solvent evaporation rate corresponding to that process node are used as... Environmental parameters are input into the model, which then calculates the thermal random force, fluid drag force, and van der Waals attraction between silver wires at the solid-liquid interface. By iteratively solving the equations of motion, the model updates the spatial coordinates and angular orientation of each silver wire, thus reproducing the dynamic behavior of silver wires settling towards the substrate, rotating on the surface, and approaching each other to form overlaps. Finally, the overlap density, porosity, and substrate contact angle of the silver wires in the statistical node simulation results are analyzed to obtain the network distribution characteristics and interfacial bonding characteristics of the silver nanowires under the corresponding process conditions. Therefore, the network distribution characteristics and interfacial bonding characteristics of the silver nanowires under different process conditions can be achieved in the following way: [Further details about the process are needed for accurate translation]. The simulation results of the nodes are statistically analyzed. Specifically, the total number of connection points formed by the intersection of silver lines within a unit area of ​​the simulation region is counted, and this value is divided by the area of ​​the simulation region to obtain the overlap density. The average spacing between all silver lines in the simulation region is counted, and this distance is taken as the line gap. The orientation distribution of the silver lines in the simulation region on the plane is analyzed, and it is calculated whether the orientation angles are concentrated in a certain direction. The degree of concentration is taken as the orientation consistency. The set of the obtained overlap density, line gap, and orientation consistency is taken as the network distribution characteristics. The angle between the axis of all silver lines in direct contact with the substrate and the substrate plane is counted, and the average value of all angles is taken as the substrate contact angle.The contact area formed between the silver wire surface and the substrate surface due to van der Waals forces is statistically analyzed. This area value is taken as the effective contact area. The set of substrate contact angle and effective contact area is taken as the interface bonding feature. Through this method, the network distribution feature dataset and interface bonding feature of each process node can be obtained.

[0030] In some embodiments, the evolutionary simulation of the network distribution characteristics and interface bonding characteristics to obtain the morphological evolution curve of the silver nanowires during the film formation process can be achieved by the following steps: Initialize a phase field evolution model based on concentration field and fluid dynamics; The network distribution characteristics are used as the initial state of the phase field evolution model; The interface features are used as boundary constraints for this phase field evolution model. In the phase-field evolution model, the propagation rate of solvent evaporation and the influence coefficient of the Marangoni effect are set, and the topological change process of the silver wire network from wet film state to dry film state is iteratively calculated, thereby outputting the morphological evolution curve of resistivity as a function of drying time.

[0031] It should be noted that the phase-field evolution model in this application is a numerical simulation method based on thermodynamic principles, describing the evolution of microstructure in time and space. In this technical solution, the phase-field evolution model abstracts the film formation process of silver nanowires into a problem of continuous field variable changes, and tracks the dynamic reconstruction of the silver wire network by solving partial differential equations. First, the phase-field evolution model introduces a concentration field variable to distinguish between the solid silver wires and the liquid solvent, and controls the movement of the two-phase interface through the free energy function. Second, it couples fluid dynamics equations to calculate the influence of liquid flow caused by solvent evaporation on the position of the silver wires, where the solvent propagation velocity determines the drying time, and the influence coefficient of the Marangoni effect reflects the fluid flow behavior from the low-tension region to the high-tension region due to the surface tension gradient. The phase-field evolution model simulates the topological changes of silver wires as they approach, overlap, or separate during solvent evaporation by iteratively calculating the changes in the concentration field and velocity field, and outputs a resistivity curve characterizing conductivity as a function of drying time as a morphological evolution curve of resistivity with drying time.

[0032] In step 103, the photoelectric performance index of the flexible transparent electrode of silver nanowire is extracted from the morphological evolution curve, and the photoelectric performance index is matched with the performance requirement model of the target device to generate the predicted photoelectric response value of the target device under preset working conditions.

[0033] In some embodiments, the photoelectric performance indicators of the flexible transparent electrode made of silver nanowires are extracted from the morphological evolution curve, with reference to... Figure 2The diagram is a flowchart illustrating the determination of photoelectric performance indicators in some embodiments of this application. In this embodiment, the determination of photoelectric performance indicators can be achieved using the following steps: In step 1031, geometric parameters are extracted from the endpoint state of the morphological evolution curve to obtain the average aperture and linear density of the silver wire network. In step 1032, the average aperture and the linear density are input into the preset optical scattering model and electrical permeation model to calculate the transmittance and sheet resistance of the flexible transparent electrode of silver nanowire. In step 1033, the photoelectric performance indicators of the flexible transparent electrode of silver nanowire are determined by the transmittance value and the sheet resistance value.

[0034] It should be noted that, in this application, the average pore size and linear density are microscopic morphological parameters used to quantitatively describe the geometric structure of the silver nanowire network; the transmittance and sheet resistance values ​​are core indicators used to quantitatively evaluate the photoelectric performance of the flexible transparent electrode of silver nanowire; and the photoelectric performance indicators are performance evaluation standards that characterize the balance between optical transparency and conductivity of the flexible transparent electrode of silver nanowire.

[0035] In specific implementation, firstly, geometric parameters are extracted from the endpoint state of the morphological evolution curve to obtain the average aperture and linear density of the silver wire network. This can be achieved as follows: obtain the morphological evolution curve showing the change in resistivity with drying time, locate the endpoint state corresponding to the maximum drying time at the end of the curve, and for the silver wire network image at this endpoint state, use an image recognition algorithm to statistically analyze the diameter distribution of the blank areas enclosed by the silver wires in the network, and calculate the average diameter of all blank areas as the average aperture; simultaneously, randomly select multiple straight lines crossing the image, and count the number of intersection points between each straight line and the silver wires. The linear density is obtained by dividing the number of intersection points by the line length. The average aperture and linear density are used as the basic input parameters for subsequent optical and electrical performance calculations. Then, the average aperture and linear density are input into a preset optical scattering model and an electrical permeation model to calculate the transmittance and sheet resistance of the flexible transparent silver nanowire electrode. This can be achieved by inputting the average aperture and linear density into a preset optical scattering model based on Mie scattering theory, which treats the silver wire network as a periodic lattice structure and calculates the ratio of incident light scattered and reflected on the silver wire surface as the transmittance. Numerical values ​​are then input into a preset electro-percolation model. This model, based on percolation theory, treats the silver wire network as a grid composed of randomly distributed conductive rods. By statistically analyzing the probability of forming a continuous conductive path per unit area and combining this with the inherent resistance of a single silver wire, the equivalent resistance of electrons passing through the entire network is calculated. This equivalent resistance is then converted into a sheet resistance value per unit area. The transmittance value, expressed as a percentage, and the sheet resistance value, expressed in ohms per square block, are used as quantitative evaluation results of the electrode's photoelectric performance. Finally, the flexibility and transparency of the silver nanowires are determined using the transmittance value and the sheet resistance value. The photoelectric performance index of the electrode can be achieved as follows: the transmittance and sheet resistance values ​​are normalized to convert both into dimensionless scores between zero and one. Then, the weighted sum of these two scores is calculated. The weighting coefficient of the transmittance score is set to be related to the target application field. For example, the transmittance weight is set to a higher value in display devices and the sheet resistance weight is set to a higher value in solar cells. The weighted sum is defined as the photoelectric performance index, which is used to comprehensively measure the photoelectric performance of the flexible transparent electrode of silver nanowire. The photoelectric performance index score will be used as the final output electrode performance evaluation standard.

[0036] In some embodiments, matching the photoelectric performance indicators with the performance requirement model of the target device to generate the predicted photoelectric response value of the target device under preset operating conditions can be achieved by the following steps: Obtain the performance requirement model of the target device at a specified operating frequency and under bending conditions. This performance requirement model defines the matching tolerance range of transmittance and sheet resistance. Substitute the transmittance and sheet resistance values ​​from the photoelectric performance indicators into the performance requirement model to determine whether they are within the matching tolerance range. If it is within the matching tolerance range, the equivalent circuit model of the target device is driven based on the photoelectric performance index to generate the predicted photoelectric response value of the target device under the preset operating conditions.

[0037] It should be noted that, in this application, the performance requirement model is a matching rule library that defines the specific optical and electrical performance requirements of the target device for the electrode material; the matching tolerance range is a qualified range standard used to determine whether the photoelectric performance of the electrode meets the application requirements of the device; and the predicted photoelectric response value is a key performance parameter used to quantitatively evaluate the working performance of the target device under the drive of the specified electrode.

[0038] In practical implementation, firstly, the performance requirement model of the target device under a specified operating frequency and bending state is obtained. This performance requirement model defines the matching tolerance range of transmittance and sheet resistance, which can be achieved in the following way: For the application scenario of the target device, such as using electrodes in a flexible touch screen, with a specified operating frequency of 100 Hz and a bending state of repeated bending with a curvature radius of 5 mm, the parameter ranges requiring electrode transmittance of no less than 88% and sheet resistance of no more than 150 ohms per square are extracted by consulting the design specifications of the touch screen. These two ranges are jointly defined as the transmittance and sheet resistance. A matching tolerance range is established, and a performance requirement model is constructed using the set of matching tolerance ranges. Then, the transmittance and sheet resistance values ​​from the photoelectric performance indicators are substituted into the performance requirement model to determine whether they are within the matching tolerance range. This can be achieved in the following way: Substitute the transmittance and sheet resistance values ​​from the photoelectric performance indicators into the performance requirement model, perform a matching judgment, compare the actual transmittance value with the lower threshold of 88%, and compare the actual sheet resistance value with the upper threshold of 150 ohms per square. Only when the transmittance value is greater than or equal to 88% and the sheet resistance value is within the upper threshold of 150 ohms per square is the matching tolerance range determined. The photoelectric performance index is considered to be within the matching tolerance range only when it is less than or equal to 150 ohms per square. For example, if the transmittance of an electrode sample is 90% and the sheet resistance is 120 ohms per square, it is considered to be within the range. If the transmittance is 85% and the sheet resistance is 130 ohms per square, it is considered to be outside the range because the transmittance does not meet the standard. Finally, if it is within the matching tolerance range, the predicted photoelectric response value of the target device under preset operating conditions can be generated based on the equivalent circuit model of the target device driven by the photoelectric performance index. This can be achieved in the following way: if the photoelectric performance index is determined to be within the range... Within the matching tolerance range, the photoelectric performance index is used as the input parameter to drive the equivalent circuit model of the target device. The equivalent circuit model simplifies the electrodes into a network composed of resistors and capacitors. The resistance value in the network is calculated based on the input sheet resistance value, and the parasitic capacitance value is calculated based on the device structure. Then, AC circuit simulation is performed in combination with the specified operating frequency of 100 Hz. The ratio of the output signal to the input signal is calculated as the gain value, and the time delay from the input to the output signal is calculated as the response speed. The parameter combination composed of the gain value and the response speed is used as the predicted photoelectric response value of the target device under the preset operating conditions.

[0039] In step 104, with the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint, a personalized fabrication process scheme including the combination of process parameters and the post-processing sequence is generated.

[0040] It should be noted that, in this application, the mechanical flexibility threshold is a standard used to limit the electrode's ability to undergo extreme deformation without functional failure under dynamic service conditions; the personalized preparation process scheme is a set of full-process parameter control instructions used to guide the production of a specified batch of flexible transparent nanowire electrodes.

[0041] In some embodiments, to achieve the optimal predicted photoresponse value as the objective and with the mechanical flexibility threshold of the electrode as the constraint, a personalized fabrication process scheme including process parameter combinations and post-processing timing can be generated in the following manner: First, the predicted photoresponse value of the target device under preset operating conditions is taken as the optimization objective, specifically aiming to maximize the gain value and minimize the response speed. Simultaneously, the mechanical flexibility threshold of the electrode is obtained from the mechanical reliability test specifications of the target device. This threshold is specifically defined as the maximum allowable deformation capability of the electrode after repeated bending 10,000 times with a curvature radius of three millimeters, where the sheet resistance growth rate does not exceed 20%. Next, in the established mapping relationship database between process nodes and network distribution characteristics and interface bonding characteristics, candidate process nodes that simultaneously meet the mechanical flexibility threshold constraint are selected. Specifically, mechanical finite element simulation is performed on the network distribution characteristics and interface bonding characteristics corresponding to each process node to calculate the stress distribution at the silver wire overlap point when the electrode is subjected to bending. Only when the maximum... A node is considered to meet the mechanical flexibility threshold only when the stress value is lower than the interfacial bonding strength of the silver wire and the porosity can accommodate the deformation release. Then, in the set of candidate process nodes that meet the mechanical flexibility threshold constraint, the photoelectric performance index score is used as the evaluation criterion. The optimal process node with the highest photoelectric performance index score is found through traversal search or optimization algorithm. The ink droplet spreading coefficient and solvent evaporation rate corresponding to the optimal node are used as the target process conditions. Finally, the optimal node is back-searched into the multi-dimensional process feature space, and the viscosity value, migration speed, surface tension value and gap height corresponding to the node are restored through coordinate mapping. These parameters are summarized into a coating process parameter combination. At the same time, based on the solvent evaporation rate value corresponding to the node and the drying time determined by the morphological evolution curve, a post-processing sequence including pre-drying temperature, annealing temperature and annealing holding time is formulated. The coating process parameter combination and post-processing sequence are integrated into a complete process plan document, which serves as a personalized preparation process plan for this batch of nano-silver wire ink and target device requirements.

[0042] In another aspect, in some embodiments, this application provides a simulation system for the fabrication process of flexible transparent electrodes made of silver nanowires, with reference to... Figure 3 The figure is a schematic diagram of the fabrication process simulation system for flexible transparent electrodes made of silver nanowires according to some embodiments of this application. The fabrication process simulation system for flexible transparent electrodes made of silver nanowires includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire the physical property parameters and coating process parameters of the nano-silver wire ink, and then construct a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters. Processing module 202, in this application, is used to perform morphological estimation of the process feature space based on the film formation mechanism to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions, and then perform evolution simulation of the network distribution characteristics and interface bonding characteristics to obtain the morphological evolution curve of silver nanowires during the film formation process. It should be noted that the processing module 202 is also used to extract the photoelectric performance index of the flexible transparent electrode of silver nanowire from the morphological evolution curve, match the photoelectric performance index with the performance requirement model of the target device, and then generate the predicted photoelectric response value of the target device under preset working conditions. The execution module 203 in this application is mainly used to generate a personalized preparation process scheme that includes a combination of process parameters and a post-processing sequence, with the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint.

[0043] The foregoing detailed examples of simulation methods and systems for fabrication processes of flexible transparent electrodes using silver nanowires provided in this application. It is understood that the corresponding apparatus, in order to achieve the aforementioned functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specified application, but such implementation should not be considered beyond the scope of this application.

[0044] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the above-described simulation method for the fabrication process of flexible transparent electrodes with silver nanowires.

[0045] In some embodiments, reference Figure 4The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the computer device used to simulate the fabrication process of flexible transparent electrodes made of silver nanowires according to an embodiment of this application. The simulation method for fabrication process of flexible transparent electrodes made of silver nanowires described in the above embodiments can be achieved through… Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0046] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0047] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0048] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0049] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0050] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0051] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described simulation method for the fabrication process of flexible transparent electrodes made of silver nanowires.

[0054] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0055] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A simulation method for the fabrication process of a flexible transparent electrode made of silver nanowires, characterized in that, Includes the following steps: The physical properties and coating process parameters of the silver nanowire ink are collected, and then a multi-dimensional process feature space is constructed based on the coupling relationship between the physical properties and the coating process parameters. Morphological estimation based on film formation mechanism is performed on the process feature space to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions. Then, the evolution of the network distribution characteristics and interface bonding characteristics is simulated to obtain the morphological evolution curve of silver nanowires during the film formation process. The photoelectric performance indicators of the flexible transparent electrode of silver nanowire are extracted from the morphological evolution curve. The photoelectric performance indicators are matched with the performance requirements model of the target device to generate the predicted photoelectric response value of the target device under preset working conditions. With the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint, a personalized fabrication process scheme including a combination of process parameters and a post-processing sequence is generated.

2. The method as described in claim 1, characterized in that, Constructing a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters specifically includes: Principal component analysis was performed on the aforementioned physical property parameters to extract the physical property factors that affect film formation quality; The coating process parameters are divided into process windows to obtain parameter reference values ​​for different process stages; The coupling relationship between the key physical property factors and the baseline values ​​of each parameter is analyzed to construct a multi-dimensional process feature space with ink droplet spreading behavior and solvent evaporation rate as coordinate axes.

3. The method as described in claim 1, characterized in that, Morphological estimation based on film formation mechanism is performed on the process feature space to obtain the network distribution characteristics and interfacial bonding characteristics of silver nanowires under different process conditions, specifically including: The multi-dimensional process feature space is discretized into multiple process nodes, and each process node corresponds to a set of specific process conditions. For each process node, a pre-set silver wire self-assembly motion model is used to simulate the migration, rotation, and overlapping behavior of the silver wire at the solid-liquid interface. The overlap density, porosity, and substrate contact angle of the silver wires in the statistical node simulation results are used to obtain the network distribution characteristics and interface bonding characteristics of the silver wires under the corresponding process conditions of the process node, and then obtain the network distribution characteristics and interface bonding characteristics of the silver wires under different process conditions.

4. The method as described in claim 1, characterized in that, The evolutionary simulation of the network distribution characteristics and interface bonding characteristics yielded the morphological evolution curves of the silver nanowires during the film formation process, specifically including: Initialize a phase field evolution model based on concentration field and fluid dynamics; The network distribution characteristics are used as the initial state of the phase field evolution model; The interface features are used as boundary constraints for this phase field evolution model. In the phase-field evolution model, the propagation rate of solvent evaporation and the influence coefficient of the Marangoni effect are set, and the topological change process of the silver wire network from wet film state to dry film state is iteratively calculated, thereby outputting the morphological evolution curve of resistivity as a function of drying time.

5. The method as described in claim 1, characterized in that, The photoelectric performance indicators of the flexible transparent electrode made from the morphological evolution curve specifically include: Geometric parameters are extracted from the endpoint state of the morphological evolution curve to obtain the average aperture and linear density of the silver wire network; The average aperture and the linear density are input into a preset optical scattering model and an electrical permeation model to calculate the transmittance and sheet resistance of the flexible transparent electrode with silver nanowires. The photoelectric performance indicators of the flexible transparent electrode made of silver nanowires are determined by the transmittance value and the sheet resistance value.

6. The method as described in claim 1, characterized in that, Matching the aforementioned photoelectric performance indicators with the performance requirement model of the target device to generate the predicted photoelectric response value of the target device under preset operating conditions specifically includes: Obtain the performance requirement model of the target device at a specified operating frequency and under bending conditions. This performance requirement model defines the matching tolerance range of transmittance and sheet resistance. Substitute the transmittance and sheet resistance values ​​from the photoelectric performance indicators into the performance requirement model to determine whether they are within the matching tolerance range. If it is within the matching tolerance range, the equivalent circuit model of the target device is driven based on the photoelectric performance index to generate the predicted photoelectric response value of the target device under the preset operating conditions.

7. The method as described in claim 1, characterized in that, The nano-silver wire ink is an ink coated with polyvinylpyrrolidone, synthesized using a polyol method.

8. A simulation system for the fabrication process of a flexible transparent electrode made of silver nanowires, characterized in that, include: The acquisition module is used to acquire the physical property parameters and coating process parameters of the silver nanowire ink, and then construct a multi-dimensional process feature space based on the coupling relationship between the physical property parameters and the coating process parameters. The processing module is used to perform morphological estimation of the process feature space based on the film formation mechanism to obtain the network distribution characteristics and interface bonding characteristics of silver nanowires under different process conditions, and then perform evolution simulation of the network distribution characteristics and interface bonding characteristics to obtain the morphological evolution curve of silver nanowires during the film formation process. The processing module is also used to extract the photoelectric performance index of the flexible transparent electrode of silver nanowire from the morphological evolution curve, match the photoelectric performance index with the performance requirement model of the target device, and then generate the predicted photoelectric response value of the target device under preset working conditions. The execution module is used to generate a personalized fabrication process scheme that includes a combination of process parameters and a post-processing sequence, with the goal of achieving the optimal predicted photoelectric response value and the mechanical flexibility threshold of the electrode as a constraint.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the simulation method for the fabrication process of the flexible transparent electrode of the silver nanowire as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the simulation method for the fabrication process of the flexible transparent electrode made of silver nanowires as described in any one of claims 1 to 7.

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