An optimization method and system for the interface strength of an inkjet-printed thin film sensor

Abstract

The method and system for optimizing the interface strength of an inkjet-printed thin-film sensor provided by the present invention observe and obtain the mesoscopic organizational structure characteristics of inkjet-printed thin-film sensors with different process parameters under multi-dimensional process types, train an LSTM neural network with an inkjet process parameter matrix and three-dimensional mesoscopic organizational structure characteristics to obtain a trained neural network model; calculate and obtain the load-displacement curve of the indenter through a preset meso-mechanical model and a phase-field fracture model to obtain the true interface bonding strength, introduce the true interface bonding strength, and train the trained neural network model again to obtain a coupling structure-activity relationship of process parameters-mesoscopic structure-interface bonding strength; combine with a genetic algorithm to optimize the current interface bonding strength with the corresponding process parameters in the inkjet process parameter matrix. It shortens the parameter optimization cycle, reduces the cost of interface strength optimization, and improves the optimization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of metal processing, and particularly to a method and system for optimizing the interfacial strength of an inkjet-printed thin-film sensor. Background Art

[0002] In recent years, inkjet printing technology has been widely used in the manufacture of sensors, especially thin-film sensors. However, the performance of thin-film sensors is significantly affected by their interfacial strength. The interfacial strength is not only related to the reliability and durability of the sensor, but also affects its sensitivity and response time. Therefore, the research on the interfacial strength control technology for inkjet-printed thin films has gradually attracted attention.

[0003] Currently, the interfacial strength control technology for inkjet-printed thin-film sensors mainly focuses on the following aspects: material selection and modification; surface treatment technology; printing parameter optimization; post-treatment process; multi-layer structure design.

[0004] In the existing interfacial strength control technology for inkjet-printed thin-film sensors, it mainly relies on the experimental matrix formed by orthogonal different preparation process parameters for "trial and error" exploration. The disadvantage of this technical method is that when the types and parameters of the process increase, the scale of the experimental matrix will increase exponentially, resulting in a sharp increase in the test cost and a longer process optimization cycle.

[0005] Taking the process that has a greater impact on the interfacial strength of inkjet-printed thin-film sensors as an example, it includes 6 groups of process types such as printing speed, printing spacing, substrate temperature, laser sintering power, laser scanning spacing, and laser spot diameter. Each group of process types consists of 10 - 20 groups of process parameters, and the elements in its orthogonal test matrix can reach 10 6 to 2×10 6 . Conducting a detailed thin-film interfacial strength test on this basis requires a large amount of manpower and material resources, and whenever the material system of the thin-film sensor changes, the above-mentioned huge amount of test work needs to be repeated, and the implementation difficulty is huge.

[0006] Therefore, it is an urgent problem for those skilled in the art to provide a method and system for optimizing the interfacial strength of inkjet-printed thin-film sensors to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for optimizing the interfacial strength of an inkjet-printed thin-film sensor. This method has clear logic, is safe, effective, reliable and easy to operate, can effectively reduce the cost of optimizing the interfacial strength of inkjet-printed thin-film sensors, and improve the optimization efficiency.

[0008] Based on the above purpose, the technical solution provided by the present invention is as follows:

[0009] A method for optimizing the interface strength of an inkjet-printed thin film sensor, comprising the following steps:

[0010] Provide inkjet-printed thin film sensors with different process parameters under multi-dimensional process types;

[0011] Observe and obtain the microscopic tissue structure atlas of the inkjet-printed thin film sensor, and extract the three-dimensional microscopic tissue structure characteristics of the inkjet-printed thin film sensor after processing;

[0012] Using the inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types as input variables, and taking the three-dimensional microscopic tissue structure characteristics corresponding to the inkjet process parameter matrix as the target, train the LSTM neural network to obtain a trained neural network model;

[0013] According to the three-dimensional microscopic tissue structure characteristics corresponding to the inkjet process parameter matrix, the preset microscopic mechanical model of the inkjet-printed thin film sensor, and the phase field fracture model, calculate and obtain the load-displacement curve of the indenter, and obtain the true interface bonding strength according to the load-displacement curve of the indenter;

[0014] Using the inkjet process parameter matrix as the input variable, and taking the three-dimensional microscopic tissue structure characteristics corresponding to the inkjet process parameter matrix and the interface bonding strength as the output, train the trained neural network model again to obtain the coupling structure-activity relationship of process parameters - microscopic structure - interface bonding strength;

[0015] According to the coupling structure-activity relationship of process parameters - microscopic structure - interface bonding strength and the genetic algorithm, search for the corresponding process parameters in the inkjet process parameter matrix to optimize the current interface bonding strength.

[0016] Preferably, the step of observing and obtaining the microscopic tissue structure atlas of the inkjet-printed thin film sensor, and extracting the three-dimensional microscopic tissue structure characteristics of the inkjet-printed thin film sensor after processing, includes the following steps:

[0017] Observe inkjet-printed thin film sensors with different process parameters under multiple multi-dimensional process types to obtain corresponding multiple microscopic tissue structure atlases;

[0018] According to the adaptive threshold algorithm, convert the microscopic tissue structure atlas into a binary tissue atlas;

[0019] Based on stereology methods, convert the tissue atlas into three-dimensional microscopic tissue structure characteristics.

[0020] Preferably, obtaining the preset microscopic mechanical model of the inkjet-printed thin film sensor and the phase field fracture model is specifically: constructing the microscopic mechanical model of the inkjet-printed thin film sensor in the abaqus finite element software according to Python script programming;

[0021] The phase-field fracture model is constructed by the UEL program in the Abaqus finite element software.

[0022] Preferably, according to the three-dimensional mesoscopic organizational structure characteristics corresponding to the inkjet process parameter matrix, the preset mesoscopic mechanical model of the inkjet-printed thin film sensor, and the phase-field fracture model, the load-displacement curve of the indenter is calculated, and the true interfacial bonding strength is obtained from the load-displacement curve of the indenter, including the following steps:

[0023] Input the three-dimensional mesoscopic organizational structure characteristics corresponding to the inkjet process parameter matrix into the mesoscopic mechanical model of the inkjet-printed thin film sensor to obtain the stress value corresponding to the inkjet process parameter matrix;

[0024] Input the stress value corresponding to the inkjet process parameter matrix into the phase-field fracture model to obtain the moving distance of the indenter and the change in the vertical load, and integrate to obtain the load-displacement curve of the indenter;

[0025] Judge whether the load-displacement curve of the indenter meets the preset conditions. If so, traverse the inkjet process parameter matrix to calculate the interfacial bonding strength as the true interfacial bonding strength.

[0026] Preferably, the coupling structure-activity relationship of process parameters-mesoscopic structure-interfacial bonding strength is specifically:

[0027] There is a mapping relationship between the inkjet process parameter matrix and the three-dimensional mesoscopic organizational structure characteristics corresponding to the inkjet process parameter matrix;

[0028] There is a power function relationship between the three-dimensional mesoscopic organizational structure characteristics corresponding to the inkjet process parameter matrix and the interfacial bonding strength. The specific formula is:

[0029] ;

[0030] Where is the interfacial bonding strength, is the three-dimensional mesoscopic organizational structure characteristic, and are the weight coefficients.

[0031] Preferably, according to the coupling structure-activity relationship of process parameters-mesoscopic structure-interfacial bonding strength and the genetic algorithm, the process parameters corresponding to the inkjet process parameter matrix are optimized to optimize the current interfacial bonding strength, specifically:

[0032] Determine the optimal process parameters in the inkjet process parameter matrix according to the genetic algorithm;

[0033] Determine the optimized interfacial bonding strength according to the optimal process parameters, and update the current interfacial strength of the inkjet-printed thin-film sensor with the optimized interfacial bonding strength.

[0034] An interfacial strength optimization system for an inkjet-printed thin-film sensor, comprising:

[0035] A three-dimensional mesoscopic organizational structure feature module, configured to observe and obtain a mesoscopic organizational structure atlas of the inkjet-printed thin-film sensor, and extract the three-dimensional mesoscopic organizational structure features of the inkjet-printed thin-film sensor after processing;

[0036] A neural network training module, configured to use the inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types as input variables, and use the three-dimensional mesoscopic organizational structure features corresponding to the inkjet process parameter matrix as the target to train the LSTM neural network to obtain a trained neural network model;

[0037] A true interfacial bonding strength module, configured to calculate and obtain a load-displacement curve of the indenter according to the three-dimensional mesoscopic organizational structure features corresponding to the inkjet process parameter matrix, a preset mesoscopic mechanical model of the inkjet-printed thin-film sensor, and a phase-field fracture model, and obtain the true interfacial bonding strength according to the load-displacement curve of the indenter;

[0038] A structure-activity relationship module, configured to use the inkjet process parameter matrix as an input variable, and use the three-dimensional mesoscopic organizational structure features and the interfacial bonding strength corresponding to the inkjet process parameter matrix as outputs to train the trained neural network model again to obtain a process parameter-mesoscopic structure-interfacial bonding strength coupling structure-activity relationship;

[0039] An interfacial bonding strength optimization module, according to the process parameter-mesoscopic structure-interfacial bonding strength coupling structure-activity relationship and the genetic algorithm, optimizes the current interfacial bonding strength with the process parameters corresponding to the inkjet process parameter matrix.

[0040] The method for optimizing the interfacial strength of an inkjet-printed thin-film sensor provided by the present invention is to observe and obtain the mesoscopic organizational structure features of an inkjet-printed thin-film sensor with different process parameters under multi-dimensional process types, train the LSTM neural network with the inkjet process parameter matrix and the three-dimensional mesoscopic organizational structure features to obtain a trained neural network model; calculate and obtain a load-displacement curve of the indenter through a preset mesoscopic mechanical model and a phase-field fracture model to obtain the true interfacial bonding strength, introduce the true interfacial bonding strength, and train the trained neural network model again to obtain a process parameter-mesoscopic structure-interfacial bonding strength coupling structure-activity relationship; combine the genetic algorithm to optimize the current interfacial bonding strength with the process parameters corresponding to the inkjet process parameter matrix.

[0041] Compared with the prior art, the present invention introduces the LSTM machine learning method, obtains the coupled structure-activity relationship through digital simulation, and combines the interfacial strength to optimize the inkjet printing process parameters directionally, overcoming the need for a large number of mechanical experiments in the original optimization process, shortening the parameter optimization cycle, reducing the cost of interfacial strength optimization, and improving the optimization efficiency.

[0042] The present invention also provides an interfacial strength optimization system for an inkjet printing thin film sensor. Since it belongs to the same technical concept as this method and solves the same technical problems, it should have the same beneficial effects, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of a method for optimizing the interfacial strength of an inkjet printing thin film sensor provided by an embodiment of the present invention;

[0045] Figure 2 It is a flowchart of step S2 provided by an embodiment of the present invention;

[0046] Figure 3 It is a flowchart of step S4 provided by an embodiment of the present invention;

[0047] Figure 4 It is a flowchart of step S6 provided by an embodiment of the present invention;

[0048] Figure 5 It is a schematic structural diagram of an interfacial strength optimization system for an inkjet printing thin film sensor provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0050] The embodiments of the present invention are written in a progressive manner.

[0051] An embodiment of the present invention provides a method and system for optimizing the interface strength of an inkjet-printed thin-film sensor. The main technical problem to be solved in the prior art is that detailed thin-film interface strength test experiments require a large amount of manpower and material resources, and whenever the thin-film sensor material system changes, the above-mentioned huge amount of test work needs to be repeated, and the implementation difficulty is huge.

[0052] As Figure 1 shown, a method for optimizing the interface strength of an inkjet-printed thin-film sensor includes the following steps:

[0053] S1. Provide inkjet-printed thin-film sensors with different process parameters under multi-dimensional process types;

[0054] S2. Observe and obtain the microscopic tissue structure map of the inkjet-printed thin-film sensor, and extract the three-dimensional microscopic tissue structure characteristics of the inkjet-printed thin-film sensor after processing;

[0055] S3. Use the inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types as the input variable, and use the three-dimensional microscopic tissue structure characteristics corresponding to the inkjet process parameter matrix as the target to train the LSTM neural network to obtain a trained neural network model;

[0056] S4. According to the three-dimensional microscopic tissue structure characteristics corresponding to the inkjet process parameter matrix, the preset microscopic mechanical model of the inkjet-printed thin-film sensor, and the phase-field fracture model, calculate and obtain the load-displacement curve of the indenter, and obtain the true interface bonding strength according to the load-displacement curve of the indenter;

[0057] S5. Use the inkjet process parameter matrix as the input variable, and use the three-dimensional microscopic tissue structure characteristics and interface bonding strength corresponding to the inkjet process parameter matrix as the output to train the trained neural network model again to obtain the coupling structure-effect relationship of process parameters - microscopic structure - interface bonding strength;

[0058] S6. Search for the corresponding process parameters in the inkjet process parameter matrix according to the coupling structure-effect relationship of process parameters - microscopic structure - interface bonding strength and the genetic algorithm to optimize the current interface bonding strength.

[0059] In step S1, provide inkjet-printed thin-film sensors with different process parameters under process types including printing speed, printing spacing, substrate temperature, laser sintering power, laser scanning spacing, and laser spot diameter. Select 5-10 process parameters under each process type to ensure the universality of the process optimization path and the effectiveness of the test results, as shown in Table 1:

[0060] Table 1. Different process parameters of inkjet-printed thin-film sensors under multi-dimensional process types

[0061]

[0062] In step S2, observe the structure of the inkjet-printed thin film sensor to obtain a mesoscopic tissue map. After processing, various three-dimensional mesoscopic tissue structure characteristics of the inkjet-printed thin film sensor are proposed.

[0063] In step S3, use the inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types as the input variable X, and use the corresponding three-dimensional mesoscopic tissue structure characteristics of the inkjet process parameter matrix as the target Y to train the LSTM neural network to obtain a trained neural network model.

[0064] It should be noted that the long short-term memory network (LSTM, Long Short-Term Memory) is a type of recurrent neural network in time, which is specifically designed to solve the long-term dependence problem existing in general RNNs (recurrent neural networks). All RNNs have a chain form of repeating neural network modules. In a standard RNN, this repeating structural module has a very simple structure, such as a tanh layer.

[0065] In step S4, according to the three-dimensional mesoscopic tissue structure characteristics, in combination with the preset mesoscopic mechanical model and phase field fracture model of the printed thin film sensor, calculate the load-displacement curve of the indenter head, and then obtain the true interfacial bonding strength.

[0066] In this embodiment, the critical load is determined through a micro-scratch experiment. The scratch width is determined by observing the scratch morphology using an optical microscope. The scratch depth is determined by the descending distance of the displacement sensor at the indenter head. The elastic modulus of the thin film is determined through a nano-indentation experiment. The true interfacial bonding strength can be calculated using these four parameters.

[0067] In step S5, use the inkjet process parameter matrix as the input variable X, and use the corresponding three-dimensional mesoscopic tissue structure characteristics and interfacial bonding strength of the inkjet process parameter matrix as the output Y to retrain the trained neural network model in step S3, so as to obtain the coupling structure-activity relationship of process parameters-mesoscopic structure-interfacial bonding strength.

[0068] In this embodiment, taking the porosity of the process parameters as an example, the structure-activity relationship of the porosity is due to the different porosities on the surface of the ITO thin film under different process parameters. By analyzing and statistically processing the microstructural maps of different process parameters, the porosities under different process parameters can be obtained.

[0069] In step S6, introduce a genetic algorithm and combine it with the coupling structure-activity relationship of process parameters-mesoscopic structure-interfacial bonding strength to search for the corresponding process parameters in the inkjet process parameter matrix and optimize the current interfacial bonding strength.

[0070] In this embodiment, there are five process parameters for each of the six process types. First, determine a process type, such as laser power, and select the power with the maximum interfacial bonding strength among the five laser powers. Determine this power as the optimal power, and then modify another process type, such as printing speed. After determining the optimal power, compare the five scanning speeds, and select the scanning speed corresponding to the maximum bonding strength as the optimal scanning speed. After determining the optimal power and the optimal speed, select a new process type, and so on, until the optimal interfacial strength under the six process parameters is finally determined.

[0071] As Figure 2 shown, preferably, step S2 includes the following steps:

[0072] A1. Observe the inkjet-printed thin-film sensors with different process parameters under various multi-dimensional process types to obtain corresponding multiple microscopic tissue structure maps;

[0073] A2. According to the adaptive threshold algorithm, convert the microscopic tissue structure map into a binary tissue map;

[0074] A3. Based on the stereology method, convert the tissue map into three-dimensional microscopic tissue structure features.

[0075] In step A1, observe the surface morphology and internal structure of the inkjet-printed thin-film sensor by SEM and FIB to obtain multiple microscopic tissue structure maps;

[0076] It should be noted that the scanning electron microscope (SEM) is an observation method between the transmission electron microscope and the optical microscope. It uses a very narrow and highly energetic electron beam to scan the sample, and through the interaction between the electron beam and the substance, various physical information is excited. These information are collected, amplified, and re-imaged to achieve the purpose of characterizing the microscopic morphology of the substance. The resolution of a new type of scanning electron microscope can reach 1 nm; the magnification can be continuously adjusted up to 300,000 times and above; and it has a large depth of field, a large field of view, and a good three-dimensional imaging effect. In addition, when combined with other analytical instruments, the scanning electron microscope can perform micro-area composition analysis of substances while observing the microscopic morphology. The scanning electron microscope has a wide range of applications in the research of rock and soil, graphite, ceramics, and nanomaterials. Therefore, the scanning electron microscope plays a significant role in the field of scientific research;

[0077] Focused Ion Beam (FIB) technology is a micro-cutting technology that uses an electrostatic lens to focus an ion beam into a very small size. The particle beam of commercial FIB systems is mostly extracted from a liquid metal ion source. Since gallium has a low melting point, low vapor pressure, and good oxidation resistance, the metal material in the liquid metal ion source is mostly gallium (Ga). Applying an external electric field (Suppressor) at the top of the ion column to the liquid metal ion source can cause the liquid metal or alloy to form a fine tip. Adding a negative electric field (Extractor) to pull the metal or alloy at the tip can then extract the ion beam, which is then focused by an electrostatic lens. After passing through a series of variable apertures (Automatic Variable Aperture, AVA), the size of the ion beam can be determined. Then, an E×B mass analyzer is used to screen out the required ion species. Finally, the ion beam is focused on the sample and scanned through an octupole deflector and an objective lens. The ion beam bombards the sample, and the secondary electrons and ions generated are collected and imaged, or physical collisions are used to achieve cutting or grinding.

[0078] In step A2, using an adaptive threshold algorithm, the above-mentioned fine-scale tissue map is converted into a binary map. Based on this, information such as the void shape and area in the map is statistically analyzed.

[0079] The pseudo-code of the adaptive threshold algorithm is as follows:

[0080] I1 = imread(strcat(p,f)); % Input the original image

[0081] PS = 5.5; % The actual width of each pixel value, unit: nm

[0082] I1 = I1(:,:,1);

[0083] level = graythresh(I1);

[0084] I1 = im2bw(I1,level);

[0085] The threshold is calculated through code based on the micro-scale tissue map.

[0086] In step A3, based on stereology methods, the two-dimensional tissue map information obtained in the above steps is converted into three-dimensional structural features, obtaining fine-scale structural feature information such as void geometric shape and porosity.

[0087] It should be noted that stereology is a mathematical method that uses two-dimensional cross-sections or projection images to obtain three-dimensional structural information. Through strict mathematical methods, it obtains information from cross-sections (such as two-dimensional cross-sections or one-dimensional intercept lines) with a smaller dimension than the actual tissue to quantitatively describe the three-dimensional parameters of the actual tissue.

[0088] Preferably, a meso-mechanical model and a phase-field fracture model of the inkjet printing thin film sensor are obtained, specifically: a meso-mechanical model of the inkjet printing thin film sensor is constructed in the abaqus finite element software according to Python script programming;

[0089] The phase-field fracture model is constructed through the uel program in the abaqus finite element software.

[0090] In the actual application process, a meso-mechanical model of the inkjet printing thin film sensor is constructed in the abaqus finite element software through Python script programming, and the phase-field fracture model is constructed through the uel program in the abaqus finite element software;

[0091] ABAQUS is a powerful finite element software for engineering simulation, and the scope of problems it solves ranges from relatively simple linear analysis to many complex non-linear problems. ABAQUS includes a rich library of elements that can simulate arbitrary geometries. It also has a library of various types of material models that can simulate the properties of typical engineering materials, including metals, rubbers, polymer materials, composite materials, reinforced concrete, compressible hyperelastic foam materials, and geological materials such as soil and rock. As a general simulation tool, in addition to being able to solve a large number of structural (stress / displacement) problems, ABAQUS can also simulate many problems in other engineering fields, such as heat conduction, mass diffusion, thermoelectric coupling analysis, acoustic analysis, geomechanics analysis (fluid seepage / stress coupling analysis), and piezoelectric medium analysis.

[0092] As Figure 3 shown, preferably, step S4 includes the following steps:

[0093] B1. Input the three-dimensional meso-structural characteristics corresponding to the inkjet process parameter matrix into the meso-mechanical model of the inkjet printing thin film sensor to obtain the stress value corresponding to the inkjet process parameter matrix;

[0094] B2. Input the stress value corresponding to the inkjet process parameter matrix into the phase-field fracture model to obtain the moving distance of the indenter and the change in the vertical load, and integrate to obtain the load-displacement curve of the scriber;

[0095] B3. Determine whether the load-displacement curve of the scriber meets the preset conditions. If so, traverse the inkjet process parameter matrix to calculate the interface bonding strength as the true interface bonding strength.

[0096] From step B1 to step B3, the meso-mechanical model of the inkjet-printed thin film sensor calculates the corresponding stress values based on the three-dimensional meso-structure characteristics; then the stress values are input into the phase field fracture model, which is written by the uel subroutine in abaqus. The crack propagation is controlled by setting the magnitude of the stress. When the stress reaches the set initial value, the crack propagates. When the crack propagates to the interface between the thin film and the substrate, it is considered that the thin film has failed. At this time, the distance that the scratching head can move and the change in the vertical load are obtained, which is the load-displacement curve image. The load-displacement curve is compared with the pre-stored image in the database. If the comparison is consistent, the inkjet process parameter matrix is traversed, and the calculated interface bonding strength is used to represent the true interface bonding strength. If not, it returns to the LSTM model training step to retrain the LSTM model.

[0097] Preferably, the coupling structure-activity relationship of process parameters-meso-structure-interface bonding strength is specifically as follows:

[0098] There is a mapping relationship between the inkjet process parameter matrix and the three-dimensional meso-structure characteristics corresponding to the inkjet process parameter matrix;

[0099] There is a power function relationship between the three-dimensional meso-structure characteristics corresponding to the inkjet process parameter matrix and the interface bonding strength. The specific formula is:

[0100] ;

[0101] Among them, is the interface bonding strength, is the three-dimensional meso-structure characteristic, and are the weight coefficients.

[0102] In the actual application process, the inkjet process parameter matrix is formed by the mutual combination of different process parameters under multi-dimensional process types. The inkjet process parameter matrix and the corresponding three-dimensional meso-structure characteristics are obtained through step S2. Therefore, there is a mapping relationship between the inkjet process parameter matrix and the corresponding three-dimensional meso-structure characteristics; there is a power function relationship between the three-dimensional meso-structure characteristics corresponding to the inkjet process parameter matrix and the interface bonding strength, and the weight coefficients a and b need to be confirmed by fitting.

[0103] In this embodiment, for the microstructural atlas of ITO thin films with different process parameters, the machine learning method is used to statistically analyze the surface porosity (x), and the scratch experiment is used to calculate the interface bonding strength of different process parameters. The fitting parameters a and b are calculated using this formula.

[0104] As Figure 4 shown, preferably, step S6 is specifically as follows:

[0105] C1. Determine the optimal process parameters in the inkjet process parameter matrix according to the genetic algorithm;

[0106] C2. Determine the optimized interfacial bonding strength according to the optimal process parameters, and update the current interfacial strength of the inkjet printed thin film sensor with the optimized interfacial bonding strength.

[0107] In steps C1 to C2, the optimal process parameters are determined in the inkjet process parameter matrix through the genetic algorithm. Through the coupling structure-activity relationship of process parameters - mesoscopic structure - interfacial bonding strength, the optimized interfacial bonding strength is correspondingly obtained, and the current interfacial strength of the inkjet printed thin film sensor is updated with the optimized interfacial strength.

[0108] As Figure 5 shown, an interfacial strength optimization system for an inkjet printed thin film sensor includes:

[0109] A three-dimensional mesoscopic organizational structure feature module, which is used to observe and obtain the mesoscopic organizational structure atlas of the inkjet printed thin film sensor, and extract the three-dimensional mesoscopic organizational structure features of the inkjet printed thin film sensor after processing;

[0110] A neural network training module, which is used to use the inkjet process parameter matrix formed by the combination of different process parameters under multiple-dimensional process types as input variables, and the three-dimensional mesoscopic organizational structure features corresponding to the inkjet process parameter matrix as the target to train the LSTM neural network to obtain a trained neural network model;

[0111] A true interfacial bonding strength module, which is used to calculate and obtain the load-displacement curve of the indenter according to the three-dimensional mesoscopic organizational structure features corresponding to the inkjet process parameter matrix, a preset mesoscopic mechanical model of the inkjet printed thin film sensor, and a phase field fracture model, and obtain the true interfacial bonding strength according to the load-displacement curve of the indenter;

[0112] A structure-activity relationship module, which is used to use the inkjet process parameter matrix as an input variable, and the three-dimensional mesoscopic organizational structure features and interfacial bonding strength corresponding to the inkjet process parameter matrix as outputs to train the trained neural network model again to obtain the coupling structure-activity relationship of process parameters - mesoscopic structure - interfacial bonding strength;

[0113] An interfacial bonding strength optimization module, which optimizes the current interfacial bonding strength according to the coupling structure-activity relationship of process parameters - mesoscopic structure - interfacial bonding strength and the genetic algorithm for the process parameters corresponding in the inkjet process parameter matrix.

[0114] In the actual application process, a three-dimensional mesoscopic organizational structure feature module, a neural network training module, a real interface bonding strength module, a structure-property relationship module, and an interface bonding strength optimization module are also set in the inkjet printing film sensor interface strength optimization system. The functions of the various modules in this system correspond to the various steps in the implementation method, which will not be elaborated here.

[0115] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0116] In addition, in each embodiment of the present invention, the various functional modules can all be integrated in one processor, or each module can be separately used as a device alone, or two or more modules can be integrated in one device; the various functional modules in each embodiment of the present invention can be implemented in the form of hardware, or can be implemented in the form of a combination of hardware and software functional units.

[0117] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed through program instructions and related hardware. The foregoing program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, the steps including the above method embodiments are executed; and the foregoing storage media include: various media such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0118] It should be understood that in this application, if "system", "device", "unit" and / or "module" are used, it is only a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, then the word can be replaced by other expressions.

[0119] As shown in this application and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. An element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device that includes the element.

[0120] If a flowchart is used in this application, the flowchart is used to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the operations before or after may not necessarily be executed precisely in order. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0121] The above has introduced in detail a method and system for optimizing the interface strength of an inkjet printing film sensor provided by the present invention. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the interface strength of an inkjet printed thin film sensor, characterized in that: The steps include: Provide inkjet printed thin film sensors with different process parameters under multi-dimensional process types; Observe and obtain the microstructure map of the inkjet printed thin film sensor, and extract the three-dimensional microstructure characteristics of the inkjet printed thin film sensor after processing; An inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types is used as an input variable, and a three-dimensional microstructure feature corresponding to the inkjet process parameter matrix is ​​used as a target to train the LSTM neural network to obtain a trained neural network model; According to the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix, the preset inkjet printing thin film sensor micromechanics model and the phase field fracture model, the load displacement curve of the scratching head is calculated and obtained, and the real interface bonding strength is obtained according to the load displacement curve of the scratching head; Taking the inkjet process parameter matrix as input variables, taking the three-dimensional microstructure characteristics and the interface bonding strength corresponding to the inkjet process parameter matrix as outputs, the trained neural network model is trained again to obtain the process parameter-microstructure-interface bonding strength coupled structure-activity relationship; According to the process parameter-microstructure-interface bonding strength coupled structure-activity relationship and genetic algorithm, searching for corresponding process parameters in the inkjet process parameter matrix to optimize the current interface bonding strength; The process parameter-microstructure-interface bonding strength coupling structure-activity relationship is specifically: There is a mapping relationship between the inkjet process parameter matrix and the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix; The three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix and the interface bonding strength are in a power function relationship, and the specific formula is: ； in, is the interface bonding strength, It is the three-dimensional microstructure feature. and is the weight coefficient.

2. The method for optimizing the interface strength of an inkjet printed thin film sensor according to claim 1, characterized in that: The method of observing and obtaining the microstructure map of the inkjet printed thin film sensor and extracting the three-dimensional microstructure characteristics of the inkjet printed thin film sensor after processing comprises the following steps: Observe the inkjet-printed thin film sensors with different process parameters under the multi-dimensional process type to obtain corresponding multiple micro-organization structure maps; According to an adaptive threshold algorithm, the mesoscopic tissue structure map is converted into a binary tissue map; Based on stereological methods, the tissue atlas is converted into three-dimensional microscopic tissue structural features.

3. The inkjet printed thin film sensor interface strength optimization method according to claim 1, characterized in that: Obtaining a preset mesoscopic mechanical model and phase field fracture model of an inkjet-printed thin film sensor, specifically: constructing the mesoscopic mechanical model of the inkjet-printed thin film sensor in abaqus finite element software according to Python script programming; The phase field fracture model is constructed using the uel program in the abaqus finite element software.

4. The method for optimizing the interface strength of an inkjet printed thin film sensor according to claim 3, characterized in that: The method comprises the following steps: calculating and obtaining a load displacement curve of a scratching head according to the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix, a preset micromechanical model of an inkjet printed thin film sensor, and a phase field fracture model, and obtaining a true interface bonding strength according to the load displacement curve of the scratching head: Inputting the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix into the micromechanical model of the inkjet printed thin film sensor to obtain the stress value corresponding to the inkjet process parameter matrix; Inputting the stress value corresponding to the inkjet process parameter matrix into the phase field fracture model, obtaining the moving distance of the indenter and the vertical load change, and integrating to obtain the load displacement curve of the scratching head; It is determined whether the load displacement curve of the scratching head meets a preset condition. If so, the inkjet process parameter matrix is ​​traversed to calculate the interface bonding strength as the real interface bonding strength.

5. The method for optimizing the interface strength of an inkjet printed thin film sensor according to claim 1, characterized in that: According to the process parameter-microstructure-interface bonding strength coupling structure-activity relationship and genetic algorithm, the corresponding process parameters in the inkjet process parameter matrix are used to optimize the current interface bonding strength, specifically: Determining optimal process parameters in the inkjet process parameter matrix according to the genetic algorithm; The optimized interface bonding strength is determined according to the optimal process parameters, and the current interface strength of the inkjet printed thin film sensor is updated with the optimized interface bonding strength.

6. An inkjet printed thin film sensor interface strength optimization system, characterized in that: include: A three-dimensional microstructure feature module is used to observe and obtain the microstructure map of the inkjet-printed thin film sensor, and extract the three-dimensional microstructure features of the inkjet-printed thin film sensor after processing; A neural network training module is used to form an inkjet process parameter matrix formed by combining different process parameters under multi-dimensional process types as input variables, and to train the LSTM neural network with the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix as a target to obtain a trained neural network model; A real interface bonding strength module is used to calculate and obtain the load displacement curve of the scratching head according to the three-dimensional microscopic structural characteristics corresponding to the inkjet process parameter matrix, the preset inkjet printing thin film sensor microscopic mechanical model and the phase field fracture model, and obtain the real interface bonding strength according to the load displacement curve of the scratching head; A structure-activity relationship module is used to use the inkjet process parameter matrix as an input variable, and the three-dimensional microstructure characteristics and the interface binding strength corresponding to the inkjet process parameter matrix as outputs, and to train the trained neural network model again to obtain the process parameter-microstructure-interface binding strength coupled structure-activity relationship; An interface bonding strength optimization module, which optimizes the current interface bonding strength according to the process parameter-microstructure-interface bonding strength coupling structure-activity relationship and genetic algorithm, and the corresponding process parameters in the inkjet process parameter matrix; The process parameter-microstructure-interface bonding strength coupling structure-activity relationship is specifically: There is a mapping relationship between the inkjet process parameter matrix and the three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix; The three-dimensional microstructure characteristics corresponding to the inkjet process parameter matrix and the interface bonding strength are in a power function relationship, and the specific formula is: ； in, is the interface bonding strength, It is the three-dimensional microstructure feature. and is the weight coefficient.

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