Microstructure metal ink filling method based on atomic clusters

Abstract

The invention relates to the technical field of microstructure metal ink filling, and discloses an atomic cluster-based microstructure metal ink filling method, which comprises the following steps of: acquiring geometrical characteristics, surface energy and external condition parameters of a target microstructure, processing to obtain parameter data of each index, constructing a dynamic regulation and control system and an atomic cluster optimization model, obtaining filling path planning parameters and performing optimization control; according to the method, path planning and distribution optimization are carried out on the metal ink filling process of the target microstructure through the atomic cluster optimization model, the filling process of the complex microstructure is more accurate, and the performance reduction phenomenon caused by uneven filling or bubble residues in a traditional method is avoided; according to the method, the strategy of singly depending on the rheological characteristic of ink is abandoned, high-adaptability filling of different microstructures is achieved by comprehensively considering geometrical characteristics, surface energy and external condition parameters, and the overall performance of a device can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of microstructure metal ink filling, in particular to a microstructure metal ink filling method based on atomic clusters. BACKGROUND

[0002] In the field of microelectronics manufacturing, metal ink filling technology is a key process that utilizes liquid metal materials to achieve high-precision filling in microstructures. This technology is widely used in flexible electronics, three-dimensional integrated circuits, and micro-electro-mechanical systems. By injecting metal ink into microscale or nanoscale structures, conductive paths or functional components can be formed, meeting the needs of modern electronic devices for miniaturization, high performance, and high integration. Metal ink is usually composed of metal nanoparticles or metal organic compounds, and its filling process needs to consider the rheological properties, surface tension of the ink, and the geometry of the microstructure.

[0003] Existing metal ink filling methods mainly rely on traditional processes such as immersion, spraying, or spin coating. Although these methods can achieve metal ink filling to some extent, they have limitations when dealing with complex microstructures. For example, in high aspect ratio microstructures, traditional methods may have uneven filling, bubble retention, and other issues, affecting the performance of the final device. In addition, existing technologies have room for improvement in the composition control and deposition accuracy of the ink during the filling process, especially when dealing with multi-component ink, it is difficult to ensure uniform distribution of each component.

[0004] Although the above-mentioned existing technologies have promoted the development of metal ink filling technology to some extent, there are still some shortcomings. In particular, in the filling process of complex microstructures, how to achieve high-precision, high-uniformity filling while avoiding defects is a problem that needs to be solved. Therefore, developing a microstructure metal ink filling method based on atomic clusters is expected to bring new breakthroughs in this field. SUMMARY

[0005] The purpose of the present application is to provide a microstructure metal ink filling method based on atomic clusters, which solves the technical problems of uneven filling, bubble retention, and difficulty in adapting to multi-component ink distribution when filling complex microstructures with traditional methods.

[0006] To solve the above technical problems, the present application provides the following technical solutions: A microstructure metal ink filling method based on atomic clusters, comprising the following steps: Collecting the geometric feature parameters of the target microstructure; Collecting the surface energy parameters of the target microstructure; collecting external condition parameters of an environment where the target microstructure is located; extracting the collected parameter data and processing to obtain index parameter data; constructing a dynamic control system; obtaining the control parameter index max a within the future time threshold according to the constructed dynamic control system; constructing an atomic cluster optimization model with the control parameters within the future time threshold and the index parameter data; obtaining filling path planning parameters through the atomic cluster optimization model; optimizing and adjusting the control of the metal ink filling process in the target microstructure through the filling path planning parameters.

[0007] Preferably, in the collection of geometric feature parameters of the target microstructure: the collection of geometric feature parameters of the target microstructure is the aspect ratio parameter data of the target microstructure, the maximum curvature radius of the target microstructure and the minimum curvature radius of the target microstructure.

[0008] Preferably, in the collection of surface energy parameters of the target microstructure: the target microstructure includes at least a base material layer, an interface modification layer and a functional coating layer; The surface energy parameters of the base material layer include the maximum surface tension coefficient of all base material layers and the minimum surface tension coefficient of the base material layer; The surface energy parameters of the functional coating layer include the maximum wetting angle of all functional coating layers and the minimum wetting angle of the functional coating layer.

[0009] Preferably, in the collection of external condition parameters of the environment where the target microstructure is located, the external condition parameters include at least the temperature parameter of the environment where the target microstructure is located, the humidity parameter of the environment where the target microstructure is located and the pressure parameter of the environment where the target microstructure is located.

[0010] Preferably, the processing to obtain the index parameter data is specifically: extracting the geometric feature parameters of the target microstructure; collecting the maximum curvature radius of the target microstructure and the minimum curvature radius of the target microstructure and adding a relative floating threshold to obtain a geometric fitness index; extracting the surface energy parameters of the target microstructure; collecting the maximum surface tension coefficient of all base material layers and the minimum surface tension coefficient of the base material layer and adding a relative floating threshold to obtain a surface tension fitness index; collecting the maximum wetting angle of all functional coating layers and the minimum wetting angle of the functional coating layer and adding a relative floating threshold to obtain a wetting fitness index; extracting external condition parameters of an environment where the target microstructure is located; setting a plurality of time interval thresholds, and extracting the external condition parameters with the set time interval thresholds; collecting the external condition parameters within at least three time interval thresholds to obtain an environment adaptation index within each time interval threshold.

[0011] Preferably, in the construction of the dynamic regulation system, the dynamic regulation system comprises a parameter acquisition unit, a parameter analysis unit and a parameter verification unit; The parameter acquisition unit is electrically connected with the parameter analysis unit; The parameter analysis unit is electrically connected with the parameter verification unit.

[0012] Preferably, the parameter acquisition unit is used to collect temperature variation interval parameter data within a future time threshold, temperature variation interval parameter data time period temperature gradient parameter data, humidity parameter data within the future time threshold, and pressure parameter data within the future time threshold; The parameter analysis unit is used to analyze the filling influence in the target microstructure according to the parameter data collected by the parameter acquisition unit, and obtain a regulation parameter index max α ; The parameter verification unit is used to verify the regulation parameter index max α obtained by the parameter analysis unit, and the verification condition is to take the parameter data collected by the parameter acquisition unit as the basis, and further take the past collected parameter data influence index xmax α for verification, and the verification formula is as follows: αmax·C ≤ αmax ≤ αmax·D; In the verification formula, C is the minimum lower limit threshold, and D is the maximum lower limit threshold; If the verification formula is established, the regulation parameter index max α is confirmed, otherwise the regulation parameter index max α is invalid.

[0013] Preferably, the construction of the atomic cluster optimization model specifically comprises the following steps: First, receive the regulation parameter index max α and the geometric characteristic parameters, surface energy parameters and external condition parameters of the target microstructure, and substitute them into the atomic cluster optimization model; Extracting filling efficiency parameter data within at least five time interval thresholds as the basis; Substitute the extracted filling efficiency parameter data into the atomic cluster optimization model; Confirm the demand parameter data, substitute it into the atomic cluster optimization model, and perform optimization calculation; Through the atomic cluster optimization model, path planning and distribution optimization are performed to obtain filling path planning parameters; The obtained filling path planning parameter credibility is confirmed.

[0014] Preferably, in the filling path planning parameter credibility confirmation, the upper value credibility Sw and the lower value credibility Sd are confirmed by combining analysis of two kinds of data, and the upper value credibility Sw is calculated according to the following formula: Sw = NUMs×P + (RSSIa+ RSSIb +......)× Q; In the formula, NUMs represents a geometric fitness index in the target microstructure, RSSI represents a surface tension fitness index of a plurality of substrate material layers, P and Q are both predetermined weight values, and the sum of the weight values of P and Q is 1. The lower value credibility Sd is calculated according to the following formula: Sd = CN1 × M + αmax× N; In the formula, CN1 represents an external condition parameter, max α represents a control parameter index, M and N are both predetermined weight values, and the sum of the weight values of M and N is 1. If the upper value credibility Sw and the lower value credibility Sd verification formulas are both established, the credibility Fp is finally determined according to the first credibility Sw and the second credibility Sd according to the following formula: Fp = Sd / (Sd +Sw)。

[0015] Preferably, after obtaining the filling path planning parameter through the atomic cluster optimization model, the filling speed and the filling direction of the target microstructure are controlled according to the obtained filling path planning parameter, and the rheological properties of the metal ink are adjusted according to the filling path planning parameter.

[0016] Compared with the prior art, the present application has the following beneficial effects: 1. The filling path of the metal ink filling process of the target microstructure is planned and optimized through the atomic cluster optimization model, the filling process of the complex microstructure is more accurate, and the performance decline phenomenon caused by uneven filling or bubble residue in the traditional method is avoided. This method discards the strategy of relying on the rheological properties of the ink alone, and realizes high adaptability filling of different microstructures by comprehensively considering the geometric characteristics, surface energy and external condition parameters, which helps to improve the overall performance of the device.

[0017] 2. In use, the geometric characteristic parameters, surface energy parameters and external condition parameters of the collected target microstructure can be used as a reference to combine the constructed dynamic control system to perform real-time control on the metal ink filling process of the target microstructure with the required parameter data, and obtain control parameter indicators. This control method has higher predictability, and the control parameter indicators are more objective. At the same time, a large amount of past data is verified to avoid filling defects caused by unmet needs. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a flowchart of the method of the present application; Figure 2 is a flowchart of the dynamic control system; Figure 3 is a logic diagram of the operation flow of the atomic cluster optimization model; Figure 4 is a calculation flowchart of the filling path planning parameter reliability confirmation. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] The present application provides a microstructure metal ink filling method based on atomic clusters, and the specific implementation is combined with the accompanying drawings Figure 1 to Figure 4 will be described in detail. In actual application, the method realizes the optimization of metal ink filling of the target microstructure through a series of steps, ensures uniform filling process without bubble residue, and can adapt to the demand of multi-component ink distribution.

[0021] First, in the parameter collection stage, the geometric characteristic parameters, surface energy parameters and external condition parameters of the target microstructure are collected. For example, the accompanying drawings Figure 1As shown, the geometric characteristic parameters of the target microstructure include aspect ratio parameter data, maximum curvature radius and minimum curvature radius. These parameters are obtained by high-precision measuring equipment and stored as initial data sets. For surface energy parameters, the target microstructure contains a substrate material layer, an interface modification layer and a functional coating layer, wherein the maximum and minimum surface tension coefficients of the substrate material layer are determined by a contact angle measuring instrument, and the maximum and minimum wetting angles of the functional coating layer are determined by a dynamic wetting analyzer. The external condition parameters include the temperature, humidity and pressure of the environment in which the target microstructure is located, which are collected in real time by environmental monitoring sensors and recorded. After the above parameter collection is completed, the data processing link is entered.

[0022] In the data processing process, first, the geometric characteristic parameters of the target microstructure are extracted, the maximum curvature radius and the minimum curvature radius are collected, and a relative floating threshold is added to obtain a geometric fitness index. Next, the surface energy parameters are extracted, the maximum and minimum surface tension coefficients of the substrate material layer are collected, and a relative floating threshold is added to obtain a surface tension fitness index. For the maximum and minimum wetting angles of the functional coating layer, the same method is used to obtain a wetting fitness index. The processing of external condition parameters is completed by setting several time interval thresholds, for example, collecting environmental parameters every 5 minutes, collecting temperature, humidity and pressure parameters in at least three time intervals, and obtaining environmental fitness indexes in each time interval. These indexes together constitute the input data of the subsequent dynamic control system.

[0023] As shown in the accompanying drawings, Figure 2 The dynamic control system is composed of a parameter collection unit, a parameter analysis unit and a parameter verification unit. The parameter collection unit is electrically connected with the parameter analysis unit, and is used to collect temperature variation interval parameter data, temperature gradient parameter data in each time period, humidity parameter data and pressure parameter data in a future time threshold. The parameter analysis unit receives data from the parameter collection unit, and analyzes the filling influence in the target microstructure according to the data, and generates a control parameter index. The control parameter index is then transmitted to the parameter verification unit, which verifies the effectiveness of the control parameter index. The verification formula is a max·C ≤ a max ≤ a max·D, wherein C is the minimum lower threshold and D is the maximum lower threshold. If the verification formula is true, the control parameter index is confirmed to be effective, otherwise it is not. This verification mechanism ensures the objectivity and accuracy of the control parameter index.

[0024] After the control parameter index is confirmed, the construction stage of the atomic cluster optimization model is entered. As shown in the accompanying drawings, Figure 3As shown, the atomic cluster optimization model receives the regulation parameter index and the geometric feature parameters, surface energy parameters and external condition parameters of the target microstructure as inputs. During model operation, first, the filling efficiency parameter data within at least five time interval thresholds are extracted as a benchmark and substituted into the model for calculation. Then, the demand parameter data are confirmed and substituted into the model for optimization calculation, and finally the filling path planning parameters are generated. The reliability confirmation process of the filling path planning parameters is as shown in the attached Figure 4 As shown, the calculation is completed through two data combination analyses. The calculation formula of the upper value reliability Sw is Sw = NUMs x P + (RSSIa + RSSIb +…) x Q, wherein NUMs is the geometric fitness index of the target microstructure, RSSI is the surface tension fitness index of several substrate material layers, P and Q are specified weight values, and the sum of the weight values of P and Q is 1. The calculation formula of the lower value reliability Sd is Sd = CN1 x M + αmax x N, wherein CN1 is the external condition parameter, αmax is the regulation parameter index, M and N are specified weight values, and the sum of the weight values of M and N is 1. If both the upper value reliability Sw and the lower value reliability Sd satisfy the verification formula, the final reliability Fp is determined, and the calculation formula is Fp = Sd / (Sd + Sw).

[0025] After the filling path planning parameters are confirmed, the filling speed and filling direction of the target microstructure are controlled according to the parameters. Specifically, the filling speed is achieved by adjusting the flow rate of the ink delivery system, and the filling direction is achieved by controlling the moving track of the nozzle. In addition, the rheological properties of the metal ink are also adjusted according to the filling path planning parameters, such as by changing the viscosity or surface tension of the ink to adapt to different filling needs. This optimized control method ensures the uniform distribution of metal ink in complex microstructures, avoiding the performance degradation phenomenon caused by uneven filling or bubble residue in traditional methods.

[0026] In practical applications, the method of the present application can be widely applied in the field of microelectronic device manufacturing. For example, in the manufacture of microfluidic chips, the target microstructure usually has a complex three-dimensional geometric shape and a multi-layer functional coating, and traditional filling methods are difficult to achieve uniform coverage. Through the method of the present application, the flow path of the ink in the microchannel can be accurately controlled to ensure good connection of the metal ink between different layers. In addition, in the manufacture of flexible electronic devices, the substrate material layer of the target microstructure may be composed of flexible polymers, and its surface energy parameters are significantly different from those of rigid materials. The method of the present application considers the geometric features, surface energy and external condition parameters to achieve high adaptability of the flexible substrate filling, significantly improving the overall performance of the device; The dynamic regulation system realizes data transmission and information interaction between each unit through electrical connection. The parameter acquisition unit is responsible for real-time acquisition of environmental parameters and transmission of data to the parameter analysis unit. The parameter analysis unit generates regulation parameter indicators after analyzing the received data and transmits them to the parameter verification unit. The parameter verification unit feeds back effective data to the atomic cluster optimization model after verifying the regulation parameter indicators. This modular design not only improves the running efficiency of the system, but also enhances the scalability of the system, facilitating subsequent function upgrade or parameter adjustment.

[0027] The running logic of the atomic cluster optimization model is shown in the accompanying Figure 3 After receiving the regulation parameter indicators, geometric feature parameters, surface energy parameters and external condition parameters, the model performs path planning and distribution optimization in turn. In the path planning process, the model generates a preliminary filling path according to the geometric fitness index and surface tension fitness index of the target microstructure. Then, in the distribution optimization stage, the model further optimizes the filling path in combination with the external condition parameters and regulation parameter indicators to ensure uniform distribution and no bubble residue of the metal ink in the target microstructure. This two-stage optimization strategy significantly improves the accuracy and reliability of the filling process.

[0028] The reliability confirmation process of the filling path planning parameters is shown in the accompanying Figure 4 Through the calculation of the upper value reliability Sw and the lower value reliability Sd and the determination of the final reliability Fp, the scientificity and reliability of the filling path planning parameters are ensured. In practical application, this confirmation process not only improves the accuracy of the filling path planning, but also provides an important reference for subsequent process optimization.

[0029] In summary, the specific embodiments of the present application describe the complete process from parameter acquisition to filling path optimization. Through the synergistic effect of the dynamic regulation system and the atomic cluster optimization model, accurate control of the metal ink filling process of complex microstructures is achieved.

[0030] In order to better enable relevant persons in the art to fully understand and implement the present application, the following further supplements the specific implementation principles of the present application in conjunction with a specific application scenario.

[0031] In the manufacture of flexible electronics, the target microstructure is usually composed of a flexible polymer substrate material layer, an interface modification layer, and a functional coating layer. Its geometric feature parameters include a microchannel structure with a depth-to-width ratio of 5:1, a maximum curvature radius of 0.8 millimeters, and a minimum curvature radius of 0.2 millimeters. These data are obtained by a high-precision three-dimensional scanner and stored as the initial geometric feature dataset of the target microstructure. At the same time, the maximum surface tension coefficient of the substrate material layer is measured to be 72 millinewtons per meter, and the minimum surface tension coefficient is 35 millinewtons per meter using a contact angle measuring instrument; the maximum wetting angle of the functional coating layer is 120 degrees, and the minimum wetting angle is 60 degrees measured by a dynamic wetting analyzer. The temperature of the environment where the target microstructure is located is 25 degrees Celsius, the humidity is 45%, and the pressure is 1 standard atmosphere, which are collected in real time by an environmental monitoring sensor. The above parameters constitute the input data basis of the dynamic regulation system.

[0032] In the data processing phase, first, the maximum curvature radius and the minimum curvature radius of the target microstructure are collected and a relative floating threshold of ±0.05 millimeters is added to obtain the geometric fitness index. Next, the maximum surface tension coefficient and the minimum surface tension coefficient of the substrate material layer are collected and a relative floating threshold of ±5 millinewtons per meter is added to generate the surface tension fitness index. For the maximum wetting angle and the minimum wetting angle of the functional coating layer, the same floating threshold of ±5 degrees is used to obtain the wetting fitness index. The external condition parameters are processed by collecting environmental data every 5 minutes, extracting the temperature, humidity, and pressure parameters in at least three time intervals, and calculating the corresponding environmental fitness indexes. These indexes are collectively used as the input data of the dynamic regulation system.

[0033] The parameter collection unit in the dynamic regulation system is responsible for collecting temperature variation interval data, temperature gradient data in each time period, humidity data, and pressure data within the next 10 minutes in real time, and passing these data to the parameter analysis unit. The parameter analysis unit generates the regulation parameter index αmax according to the received data, combined with the geometric features and surface energy parameters of the target microstructure. Subsequently, the parameter verification unit verifies the received regulation parameter index αmax, and the verification formula is αmax·C ≤ αmax ≤ αmax·D, where C is 0.8 and D is 1.2. If the verification formula is true, the regulation parameter index αmax is confirmed to be valid; otherwise, it is invalid. This mechanism ensures the objectivity and accuracy of the regulation parameter index.

[0034] After the confirmation of the regulation parameter index a max, the model enters the running phase of the atomic cluster optimization. The model receives a max, the geometric feature parameters of the target microstructure, the surface energy parameters, and the external condition parameters as inputs. First, the filling efficiency parameter data of the past five time intervals are extracted as the benchmark and substituted into the model for preliminary calculation. Then, the demand parameter data, such as the viscosity range of the metal ink being 5-10 centipoise and the surface tension range being 30-50 millinewtons per meter, are confirmed and substituted into the model for optimization calculation. The model generates a preliminary filling path according to the geometric fitness index and the surface tension fitness index of the target microstructure, and then optimizes the distribution of the filling path in combination with the external condition parameters and the regulation parameter index a max, to finally generate the filling path planning parameters.

[0035] The reliability confirmation process of the filling path planning parameters is shown in FIG. 4. The calculation formula of the upper value reliability Sw is Sw = NUMs x P + (RSSIa + RSSIb +...) x Q, where NUMs is the geometric fitness index of the target microstructure, RSSI is the surface tension fitness index of several substrate material layers, and P and Q are 0.6 and 0.4, respectively. The calculation formula of the lower value reliability Sd is Sd = CN1 x M + a max x N, where CN1 is the external condition parameter, a max is the regulation parameter index, and M and N are 0.7 and 0.3, respectively. If both the upper value reliability Sw and the lower value reliability Sd satisfy the verification formula, the final reliability Fp is determined, and the calculation formula is Fp = Sd / (Sd + Sw). This process ensures the scientificity and reliability of the filling path planning parameters. Figure 4 After the confirmation of the filling path planning parameters, the filling speed and the filling direction of the target microstructure are controlled according to the parameters. The filling speed is achieved by adjusting the flow rate of the ink delivery system, for example, setting the flow rate to 0.5 milliliters per second. The filling direction is achieved by controlling the movement trajectory of the nozzle, for example, moving along the central axis of the microchannel at a constant speed. In addition, the rheological properties of the metal ink are adjusted according to the filling path planning parameters, for example, by adding an appropriate amount of surfactant to reduce the surface tension of the ink to 40 millinewtons per meter to meet the wetting requirements of the target microstructure. This optimized control method ensures the uniform distribution of the metal ink in the complex microchannel, avoiding the performance degradation phenomenon caused by uneven filling or bubble residue in the traditional method.

[0036]

[0037] ​In practical applications, the above method significantly improves the overall performance of flexible electronic devices. For example, in the manufacture of flexible sensors, the base material layer of the target microstructure is composed of polydimethylsiloxane (PDMS), which has a significantly different surface energy parameter from rigid materials. By considering the geometric characteristics, surface energy and external condition parameters comprehensively, the present application realizes high adaptability filling of flexible substrates, ensuring good connection of metal ink in the microchannel. In addition, during the multi-component ink filling process, through the synergistic action of the dynamic regulation system and the atomic cluster optimization model, precise distribution control of different component inks is realized, significantly improving the conductive performance and mechanical stability of the device.

[0038] In summary, the present application realizes precise control of the metal ink filling process of complex microstructures by a complete process from parameter acquisition to filling path optimization, combined with the synergistic action of the dynamic regulation system and the atomic cluster optimization model. This method not only applies to flexible electronic device manufacturing, but also can be widely used in the fields of microfluidic chips, three-dimensional integrated circuits, etc., providing important support for the development of microelectronic manufacturing technology.

[0039] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for filling microstructured metallic ink based on atomic clusters, characterized in that, Includes the following steps: S1: Collect the geometric feature parameters of the target microstructure; S2: Collect the surface energy parameters of the target microstructure; S3: Collect external condition parameters of the environment in which the target microstructure is located; S4: Extract the parameter data collected in S1 to S3 and process it to obtain the parameter data of each indicator. S5: Construct a dynamic control system; S6: Obtain the control parameter index max α within the future time threshold based on the constructed dynamic control system; S7: Construct an optimization model for atomic clusters using the control parameters and various index parameter data within the future time threshold; S8: Obtain filling path planning parameters through the atomic cluster optimization model; S9: The process of filling metallic ink into the target microstructure is optimized and controlled by filling path planning parameters.

2. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In S1: the geometric feature parameters of the target microstructure are collected as aspect ratio, maximum radius of curvature and minimum radius of curvature of the target microstructure.

3. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In S2: the target microstructure includes at least a substrate material layer, an interface modification layer, and a functional coating; The surface energy parameters of the substrate material layer include the maximum surface tension coefficient of all substrate material layers and the minimum surface tension coefficient of the substrate material layer. The surface energy parameters of the functional coatings include the maximum wetting angle of all functional coatings and the minimum wetting angle of the functional coatings.

4. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In S3: the external condition parameters include at least the temperature, humidity and pressure parameters of the environment in which the target microstructure is located.

5. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In step S4: the processing to obtain the data for each indicator parameter is specifically as follows: S4.1: Extract the geometric feature parameters of the target microstructure; S4.2: Combine the maximum and minimum radii of curvature of the target microstructure and add a relative floating threshold to obtain the geometric fit index; S4.3: Extract the surface energy parameters of the target microstructure; S4.4: Collect the maximum and minimum surface tension coefficients of all substrate material layers and add a relative floating threshold to obtain the surface tension fit index. S4.5: Combine the maximum and minimum wetting angles of all functional coatings and add a relative floating threshold to obtain the wetting compatibility index; S4.6: Extract the external condition parameters of the environment in which the target microstructure is located; S4.7: Set several time interval thresholds to extract external condition parameters based on the set time interval thresholds; S4.8: By aggregating external condition parameters within at least three time interval thresholds, an environmental adaptability index is obtained for each time interval threshold.

6. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In S5: the dynamic control system includes a parameter acquisition unit, a parameter analysis unit, and a parameter verification unit; The parameter acquisition unit and the parameter analysis unit are electrically connected; The parameter analysis unit and the parameter verification unit are electrically connected.

7. The method for filling microstructured metallic ink based on atomic clusters according to claim 6, characterized in that: The parameter acquisition unit is used to collect parameter data of temperature change range within a future time threshold, temperature gradient parameter data of each time period, humidity parameter data, and pressure parameter data. The function of the parameter analysis unit is to analyze the impact of the parameter data collected by the parameter acquisition unit on the filling of the target microstructure and obtain the control parameter index max α. The function of the parameter verification unit is to verify the control parameter index max α obtained by the parameter analysis unit. The verification condition is based on the parameter data collected by the parameter acquisition unit, and then verified by the influence index xmax α of the previously collected parameter data. The verification formula is as follows: αmax·C ≤ αmax ≤ αmax·D; In the verification formula, C is the minimum lower threshold and D is the maximum lower threshold. If the formula is valid, the control parameter index max α is confirmed; otherwise, the control parameter index max α is invalid.

8. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In step S7: the construction of the atomic cluster optimization model specifically includes the following steps: S7.1: Receive the control parameter index max α, as well as the geometric characteristic parameters, surface energy parameters, and external condition parameters of the target microstructure, and substitute them into the atomic cluster optimization model; S7.2: Extract fill efficiency parameter data within at least five time interval thresholds as a baseline; S7.3: Substitute the extracted filling efficiency parameter data into the atomic cluster optimization model; S7.4: Confirm the required parameter data, substitute it into the atomic cluster optimization model, and perform optimization calculations; S7.5: Path planning and distribution optimization are performed using the atomic cluster optimization model to obtain the filling path planning parameters; S7.6: Confirm the reliability of the obtained fill path planning parameters.

9. A method for filling microstructured metallic ink based on atomic clusters according to claim 8, characterized in that: In the confidence verification of the filling path planning parameters, two types of data are combined and analyzed to confirm the upper confidence level Sw and the lower confidence level Sd. The formula for calculating the upper confidence level Sw is as follows: Sw = NUMs×P + (RSSIa+ RSSIb +......)× Q; In the formula, NUMs represents the geometric fit index in the target microstructure, RSSI represents the surface tension fit index of several substrate material layers, P and Q are both specified weight values, and the sum of the weight values ​​of P and Q is 1. The formula for calculating the confidence level Sd of the lower value is as follows: Sd = CN1 × M + αmax × N; In the formula, CN1 represents the external condition parameter, αmax represents the control parameter index, M and N are both specified weight values, and the sum of the weight values ​​of M and N is 1; If the verification formulas for both the upper confidence level Sw and the lower confidence level Sd are valid, then the final confidence level Fp is determined, as shown in the following formula: Fp = Sd / (Sd + Sw).

10. The method for filling microstructured metallic ink based on atomic clusters according to claim 1, characterized in that, In S9: the filling path planning parameters are obtained through the atomic cluster optimization model, the filling speed and filling direction of the target microstructure are controlled according to the obtained filling path planning parameters, and the rheological properties of the metal ink are adjusted according to the filling path planning parameters.

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