Metal net coating optimization method and device for semiconductor and medium
By dynamically adjusting electroplating parameters and conducting simulation tests, the problems of uneven thickness and insufficient adhesion of semiconductor metal mesh coatings were solved, thereby improving coating quality and process stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-03-27
AI Technical Summary
Traditional electroplating technology results in uneven thickness distribution of semiconductor metal mesh coating, high deviation rate, insufficient adhesion, difficulty in adapting to changes in substrate characteristics in real time, and delayed post-plating inspection, making it difficult to control process parameters in a closed loop, thus affecting device yield.
By setting initial electroplating parameters based on the characteristics of the metal mesh substrate, applying pulsed current for electroplating, collecting coating thickness data in real time, dynamically adjusting electroplating parameters, and combining simulation testing and quality assessment, an optimization report is generated to achieve real-time optimization of coating thickness uniformity and adhesion.
It reduces the coating thickness deviation rate and adhesion loss rate, improves coating quality and process stability, and achieves a dual improvement in coating thickness uniformity and adhesion.
Smart Images
Figure CN121737792A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of precision electroplating for semiconductors, and in particular to methods, apparatus and media for optimizing metal mesh plating for semiconductors. Background Technology
[0002] In semiconductor manufacturing, metal mesh serves as a core component for precision filtration, conductive connections, and shielding, requiring stringent standards such as nanoscale coating uniformity, ultra-low porosity, and reliable thermal cycling. Traditional DC electroplating suffers from a thickness distribution problem of "thick in the middle and thin at the edges" due to the shielding effect, with a deviation rate as high as 15%-25%, directly affecting device yield. Pulse electroplating parameters are fixed and cannot be adapted to changes in substrate characteristics in real time; post-plating inspection is lagging, making it difficult to control process parameters in a closed loop. Furthermore, the clean environment of semiconductors requires the plating solution to be compatible with silicon-based / metal-based materials to avoid side effects such as hydrogen embrittlement and corrosion. Existing technologies suffer from technical problems such as uneven coating thickness and insufficient adhesion due to inaccurate plating parameters. Summary of the Invention
[0003] This application provides a method, apparatus, and medium for optimizing metal mesh plating for semiconductors, which solves the technical problems of uneven plating thickness and insufficient adhesion caused by inaccurate electroplating parameters. It achieves the technical effect of improving both plating quality and process stability by dynamically adjusting electroplating parameters to reduce thickness deviation rate and adhesion loss rate.
[0004] The first aspect of this application provides a method for optimizing metal mesh plating for semiconductors. The method is applied to a metal mesh plating optimization apparatus for semiconductors, comprising: setting initial electroplating parameters based on the characteristics of the metal mesh substrate; performing multi-stage pretreatment on the metal mesh; immersing it in a plating solution; applying a pulsed current for electroplating and acquiring plating thickness data in real time; setting a plating thickness range based on the plating thickness data; synchronously and dynamically adjusting the initial electroplating parameters according to the plating thickness range to determine target electroplating parameters; performing electroplating simulation based on the target electroplating parameters; performing gradient thermal testing based on the metal mesh plating simulation data to generate adhesion test results; performing thickness detection based on the metal mesh plating simulation data to determine thickness uniformity data; and performing a quality assessment of the metal mesh plating simulation data based on the thickness uniformity data and the adhesion test results, generating a plating quality assessment report and feeding it back to the target electroplating parameters for continuous optimization.
[0005] In a possible implementation, based on the characteristics of the metal mesh substrate, initial electroplating parameters are set, and the metal mesh undergoes multi-stage pretreatment before being immersed in the plating solution. Pulsed current is applied for electroplating, and plating thickness data is collected in real time. The following processes are performed: the metal mesh is scanned using a laser scanner to obtain its three-dimensional topology; substrate identification and analysis are performed based on the three-dimensional topology to determine the metal mesh substrate characteristics; electroplating parameters are matched according to the metal mesh substrate characteristics to set the initial electroplating parameters; the metal mesh undergoes multi-stage pretreatment based on the initial electroplating parameters; plating solution immersion analysis is performed according to the pulsed current dynamic adjustment rules based on the processing results to generate initial electroplating results; the metal mesh is divided into regions according to the initial electroplating results to determine multiple sub-regions to be scanned; the multiple sub-regions to be scanned are scanned repeatedly along the width direction of the metal mesh, and the thickness is calculated based on the scanning results to obtain the plating thickness data.
[0006] In a possible implementation, a coating thickness range is set based on the coating thickness data. The initial electroplating parameters are then dynamically adjusted synchronously according to the coating thickness range to determine the target electroplating parameters. The following processes are then performed: Coating thickness values of the multiple sub-regions to be scanned are extracted based on the scanning results; thickness range parameters are defined based on the coating thickness values of the multiple sub-regions to be scanned; extreme values are identified based on the thickness range parameters; coating thickness range values are set based on the extreme values; thickness percentage analysis is performed according to the coating thickness range values to generate multiple thickness percentage data; parameter adjustment priorities are set based on the multiple thickness percentage data; the initial electroplating parameters are iterated and adjusted synchronously based on the parameter adjustment priorities to generate electroplating adjustment parameters; coating thickness verification iterations are performed based on the electroplating adjustment parameters to determine the target electroplating parameters.
[0007] In a possible implementation, the initial electroplating parameters are traversed and synchronously adjusted based on the parameter adjustment priority to generate electroplating adjustment parameters. The following processes are then performed: matching the initial electroplating parameters according to the parameter adjustment priority to generate a linkage adjustment instruction set; distributing the linkage adjustment instruction set to the electroplating equipment group via an industrial bus for parameter switching to generate equipment parameter adjustment data; adjusting the initial electroplating parameters at the equipment level according to the equipment parameter adjustment data to generate multiple equipment response data; performing full lifecycle tracing according to the multiple equipment response data to generate digital traceability parameters; and binding and storing the digital traceability parameters with the initial electroplating parameters to generate the electroplating adjustment parameters.
[0008] In a possible implementation, electroplating simulation is performed based on the target electroplating parameters. Gradient thermal testing is then conducted based on the metal mesh coating simulation data to generate adhesion test results. The following processes are performed: Three-dimensional solid data of the metal mesh is constructed; finite element simulation is performed using the three-dimensional solid data in conjunction with the target electroplating parameters to generate an electroplating simulation parameter set; electroplating simulation is performed according to the electroplating simulation parameter set to generate metal mesh coating simulation data, which includes a simulated coating growth rate and a simulated predicted coating thickness; the simulated coating thickness is then meshed based on the simulated coating growth rate to construct a thickness simulation distribution cloud map; and multi-mode gradient thermal testing is performed based on the thickness simulation distribution cloud map to generate the adhesion test results.
[0009] In a possible implementation, multi-mode gradient thermal testing is performed based on the thickness simulation distribution cloud map to generate the adhesion test results. The following processing is then performed: Simulated thickness deviation is calculated based on the thickness simulation distribution cloud map to obtain the simulated thickness deviation rate; a simulated thickness deviation threshold is set; when the simulated thickness deviation rate is greater than the threshold, a strengthening thermal test is performed on the thickness simulation distribution cloud map to obtain strengthening test results; when the simulated thickness deviation rate is less than or equal to the threshold, a standard thermal test is performed on the thickness simulation distribution cloud map to obtain standard test results; step-by-step heating is performed according to the strengthening test results and the standard test results to obtain first heat-treated sample data and second heat-treated sample data; mesh stripping is performed on the first heat-treated sample data and the second heat-treated sample data, and the coating peeling area ratio is calculated based on the peeling results to determine the adhesion test results.
[0010] In a possible implementation, the metal mesh coating simulation data is quality-assessed based on the thickness uniformity data and the adhesion test results, generating a coating quality assessment report. The following processes are performed: a thickness uniformity index is defined based on the thickness uniformity data, and an adhesion index is defined based on the adhesion test results. The thickness uniformity index and the adhesion index are combined to construct a coating quality assessment matrix. Based on the coating quality assessment matrix, the metal mesh coating simulation data is graded for reliability, generating multiple reliability levels. Defect analysis is performed by overlaying the thickness simulation distribution cloud map with the multiple reliability levels. The defect core location is determined based on the defect analysis results. The defect analysis results are mapped to the thickness simulation distribution cloud map according to the defect core location to obtain a defect area location map. The defect area location map is then analyzed for coating quality to generate the coating quality assessment report.
[0011] In a possible implementation, a coating quality assessment report is generated and fed back to the target electroplating parameters for continuous optimization. The following processes are performed: Adhesion matching is performed on the target electroplating parameters based on the defect area location map; adhesion defect information is determined and the target electroplating parameters are optimized to generate a first optimization effect; thickness matching is performed on the target electroplating parameters based on the defect area location map; thickness defect information is determined and the target electroplating parameters are optimized to generate a second optimization effect; composite matching is performed on the target electroplating parameters based on the defect area location map; adhesion-thickness composite defect information is determined and the target electroplating parameters are optimized to generate a third optimization effect; the first optimization effect, the third optimization effect, and the first and third optimization effects are respectively quality verified; based on the verification results, the target electroplating parameters are continuously optimized to generate real-time optimization parameters for the target electroplating.
[0012] A second aspect of this application provides a metal mesh plating optimization apparatus for semiconductors, comprising: a data acquisition module, configured to set initial electroplating parameters based on the characteristics of the metal mesh substrate, perform multi-stage pretreatment on the metal mesh, immerse it in a plating solution, apply pulsed current for electroplating, and acquire plating thickness data in real time; a dynamic adjustment module, configured to set a plating thickness range value based on the plating thickness data, synchronously and dynamically adjust the initial electroplating parameters according to the plating thickness range value, and determine target electroplating parameters; a data testing module, configured to perform electroplating simulation based on the target electroplating parameters, perform gradient thermal testing based on the metal mesh plating simulation data, generate adhesion test results, perform thickness detection based on the metal mesh plating simulation data, and determine thickness uniformity data; and a quality assessment module, configured to perform quality assessment on the metal mesh plating simulation data based on the thickness uniformity data and the adhesion test results, generate a plating quality assessment report, and feed it back to the target electroplating parameters for continuous optimization.
[0013] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the metal mesh coating treatment optimization method provided in this application.
[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method, apparatus and medium for optimizing metal mesh plating for semiconductors provided in this application relate to the field of semiconductor precision electroplating technology. They solve the technical problems of uneven plating thickness and insufficient adhesion caused by inaccurate electroplating parameters, and achieve the technical effect of reducing thickness deviation rate and adhesion loss rate by dynamically adjusting electroplating parameters, thereby improving both plating quality and process stability. Attached Figure Description
[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings of the embodiments of this application will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.
[0016] Figure 1 A schematic flowchart of a method for optimizing metal mesh coating for semiconductors provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a metal mesh coating optimization device for semiconductors provided in an embodiment of this application. Detailed Implementation
[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.
[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0019] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or apparatuses. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.
[0020] This application provides a method for optimizing metal mesh plating for semiconductors, wherein the method is applied to a metal mesh plating optimization apparatus for semiconductors, such as... Figure 1As shown, the method includes: Step A100: Based on the characteristics of the metal mesh substrate, set the initial electroplating parameters, perform multi-stage pretreatment on the metal mesh, immerse it in the plating solution, apply pulse current for electroplating, and collect the coating thickness data in real time. In one possible implementation, the method further includes scanning the metal mesh with a laser scanner to obtain its three-dimensional topology; performing substrate identification analysis based on the three-dimensional topology to determine the substrate characteristics of the metal mesh; matching electroplating parameters according to the substrate characteristics of the metal mesh to set the initial electroplating parameters; performing multi-level preprocessing on the metal mesh according to the initial electroplating parameters; performing plating solution immersion analysis according to the processing results and pulse current dynamic adjustment rules to generate initial electroplating results; dividing the metal mesh into regions according to the initial electroplating results to determine multiple sub-regions to be scanned; reciprocating scanning of the multiple sub-regions to be scanned along the width direction of the metal mesh; calculating the thickness based on the scanning results to obtain the coating thickness data.
[0021] Specifically, a Keyence LJ-V7000 line laser scanner (accuracy ±0.05mm) can be used to scan the metal mesh along its conveying direction at a speed of 50mm / s. This allows for the analysis of the mesh's aperture density, wire diameter, and edge reinforcing rib height. Based on this, the aperture density, wire diameter, and edge reinforcing rib height are combined in three dimensions to construct the three-dimensional topology of the metal mesh. Spectroscopic material analysis is then performed on the metal mesh based on this three-dimensional topology to determine its material composition and substrate characteristics. As shown in Table 1, electroplating parameters are matched according to these substrate characteristics. Table 1 Metal mesh substrate characteristics Matching Actions Experimental values Mesh density > 100 holes / cm Pulse frequency = 100Hz Uniformity increased by 40% Wire diameter range > 0.04 mm Total preprocessing time increased by 20% Degreasing extended to 90 seconds Based on this, the initial electroplating parameters are set; according to the initial electroplating parameters, the metal mesh undergoes multi-stage pretreatment, which means firstly, electrolytic degreasing is performed by passing a direct current through a 60°C alkaline solution, wherein the current density is 6 A / dm³. 2 The processing time is calculated as the average wire diameter × 10 seconds. Then, it is activated by ultrasonication in a 10% sulfuric acid solution and subjected to 40kHz ultrasonic vibration for 120 seconds. Finally, it is weakly etched by soaking in a 5% hydrochloric acid solution. Then, the residual acid inside the mesh is sprayed with high-pressure pure water. The spraying pressure is set to 0.5MPa, the spraying angle is 45° with the plane of the metal mesh, and the forward / reverse spraying is performed alternately in the direction of metal mesh conveying. The total time is ≥60 seconds.
[0022] Based on the processing results, the plating solution immersion analysis was carried out according to the dynamic adjustment rules of pulse current. The initial electroplating results can be obtained by adding wetting agent and brightener to the plating solution with nickel sulfamate as the main salt, applying a pulse current with a duty cycle of 20%, maintaining the plating solution temperature at 50±2℃, and a circulation flow rate of 1.0m / s.
[0023] Based on the initial electroplating results, the metal mesh can be divided into 50mm×50mm sub-regions, multiple sub-regions to be scanned are determined, and the eddy current thickness gauge is activated to perform an S-shaped path scan along the width direction of the metal mesh to collect 5×5 dot matrix data for each sub-region to be scanned, thereby obtaining the scanning results for each region. At the same time, the edge detection value of each region's scanning results is used as the numerator and the center value of each scanned sub-region is used as the denominator to calculate the thickness range and determine the coating thickness data.
[0024] Execute step A200, set the coating thickness range value based on the coating thickness data, and synchronously and dynamically adjust the initial electroplating parameters according to the coating thickness range value to determine the target electroplating parameters; In one possible implementation, the method further includes: extracting the coating thickness values of the plurality of sub-regions to be scanned based on the scanning results; dividing the coating thickness values of the plurality of sub-regions to be scanned into thickness range parameters; identifying extreme values based on the thickness range parameters; setting coating thickness interval values based on the parameter extreme values; performing thickness ratio analysis according to the coating thickness interval values to generate multiple thickness ratio data; setting parameter adjustment priorities based on the multiple thickness ratio data; and traversing the initial electroplating parameters for synchronized adjustment based on the parameter adjustment priorities to generate electroplating adjustment parameters. Specifically, based on the scanning results, eddy current thickness measurement is performed on 50mm×50mm sub-regions of the multiple sub-regions to be scanned. That is, 25 points are collected for coating thickness measurement in each sub-region to obtain coating thickness values for multiple sub-regions to be scanned. Extreme value analysis is performed on the coating thickness values of the multiple sub-regions to be scanned to extract the maximum and minimum coating thickness values in the multiple sub-regions to be scanned. Thickness range parameters are defined based on the maximum and minimum coating thickness values in the multiple sub-regions to be scanned. Extreme values are identified for the maximum and minimum coating thickness values in the multiple sub-regions to be scanned based on the thickness range parameters. The coating thickness value of each sub-region to be scanned is measured based on the extreme values of the parameters, and the thickness range is divided into three intervals, as shown in Table 2. The coating thickness interval values are set as follows: Table 2 Interval definition Coating thickness range Percentage of experimental case data thin area The coating thickness is less than the target thickness by 15%. 32% Qualified area The coating thickness data is ±10% of the target thickness. 45% Thick area Coating thickness data greater than +10% 23% Thickness ratio analysis was performed according to the coating thickness range values, as shown in the experimental case data ratio in Table 2. The proportion of each region in the metal mesh was analyzed, generating multiple thickness ratio data. Based on the multiple thickness ratio data, parameters were set to adjust the priority, including: when the proportion of thin area units is greater than 30% of the total number of units, it is marked as first-level priority; when the proportion of thick area units is greater than 25% of the total number of units, it is marked as second-level priority; when the total proportion of thin and thick areas is greater than 60%, it is marked as third-level priority.
[0025] Based on the parameter adjustment priority, the initial electroplating parameters are traversed for priority matching, and the priority matching parameters are used as the parameters to be adjusted. The electroplating parameters in the thin area, qualified area and thick area are adjusted synchronously to determine the electroplating adjustment parameters.
[0026] In one possible implementation, the method further includes: traversing the initial electroplating parameters according to the parameter adjustment priority to match them and generate a linkage adjustment instruction set; sending the linkage adjustment instruction set to the electroplating equipment group via an industrial bus to switch parameters and generate equipment parameter adjustment data; adjusting the initial electroplating parameters according to the equipment parameter adjustment data at the equipment level to generate multiple equipment response data; performing full lifecycle tracing according to the multiple equipment response data to generate digital traceability parameters; and binding and storing the digital traceability parameters with the initial electroplating parameters to generate the electroplating adjustment parameters.
[0027] Specifically, as shown in Table 4, the initial electroplating parameters are matched by traversing the parameters according to the adjustment priority described above: Table 4 Parameter type Adjustment amount Device pointing Current density <![CDATA[Increase by 0.25 A / dm 2 > Electroplating power supply Pulse duty cycle Increase by 8% Pulse generator Plating solution flow rate Decrease by 0.2 m / s Circulating pump Based on Table 4, data is encapsulated to generate a linkage adjustment instruction set. This instruction set is then sent to the electroplating equipment group via the industrial bus for parameter switching. Simultaneously, it is broadcast to three devices—the electroplating power supply, pulse generator, and circulating pump—via a switch for response testing, as shown in Table 5. Table 5 equipment Average response time compliance rate Electroplating power supply 120ms 100% Pulse generator 85ms 100% Circulating pump 210ms 98.7% Based on Table 5, perform differential parameter analysis to generate equipment parameter adjustment numbers; then, adjust the initial electroplating parameters at the equipment level according to Table 6 based on the equipment parameter adjustment data. Table 6 equipment Adjustment method Security protection mechanism Electroplating power supply <![CDATA[Slope adjustment (0.1 A / dm 2 / s)]]> <![CDATA[Overcurrent protection (> 4A / dm 2 Automatic fusing)]]> Pulse generator Step adjustment (1% / 100ms) Duty cycle over-limit alarm (shutdown if >35%) Circulating pump Step switching Real-time feedback calibration of flow sensor The system collects the actual current density of the electroplating power supply, the actual duty cycle of the pulse generator, and the actual flow rate of the circulating pump to generate response data for multiple devices.
[0028] Further, full lifecycle tracing is performed based on the response data of the multiple devices. First, operation tracing is performed by adjusting the instruction hash value. The power firmware version and pulser serial number are determined according to the device fingerprint, and digital tracing parameters are generated. The digital tracing parameters are then bound and stored with the initial electroplating parameters. This means that the batch number and device fingerprint are bound together to generate a composite key, and the electroplating adjustment parameters are constructed. The storage structure is shown in Table 7. Table 7 Fields value Storage location initial parameters Current 3.0A, duty cycle 20% Local SQL database Adjust instruction set JSON directives IPFS Device response data Actual execution value Blockchain Digital signature Private key signing file Secure Encryption U-Shield The process of verifying and iterating the coating thickness based on the aforementioned electroplating adjustment parameters can include: a first scan with a 120-second delay for iterative adjustment: the proportion of thin areas decreases to 18% (from 32%), the proportion of thick areas increases to 28% (from 23%), the total proportion exceeding the tolerance is 46%, and a second adjustment is triggered. The second adjustment rule can include an additional increase in current density of 0.1 A / dm³. 2 →3.35A / dm 2 The electroplating time was extended by 8%, and the target electroplating parameters were determined after three iterations, as shown in Table 8. Table 8 interval Percentage before adjustment Final percentage Improvement range thin area 32% 8% 75% decrease Thick area 23% 9% 61% decrease Qualified area 45% 83% Up 84% Execute step A300: perform electroplating simulation based on the target electroplating parameters, perform gradient thermal testing based on the metal mesh coating simulation data, generate adhesion test results, and perform thickness detection based on the metal mesh coating simulation data to determine thickness uniformity data. In one possible implementation, the method further includes constructing three-dimensional solid data of a metal mesh; performing finite element simulation based on the target electroplating parameters and the three-dimensional solid data to generate an electroplating simulation parameter set; performing electroplating simulation according to the electroplating simulation parameter set to generate metal mesh coating simulation data, which includes a simulated coating growth rate and a simulated predicted coating thickness; filling the simulated predicted coating thickness with a mesh based on the simulated coating growth rate to construct a thickness simulation distribution cloud map; and performing multi-mode gradient thermal testing based on the thickness simulation distribution cloud map to generate the bonding strength test results.
[0029] Specifically, the metal mesh is laser-scanned to generate an STL model of the mesh topology. Based on this, three-dimensional solid data is constructed. Finite element simulation is performed on the three-dimensional solid data according to the target electroplating parameters. This means dividing the three-dimensional solid data into tetrahedral meshes, adding the target electroplating parameters, and performing finite element simulation of the coating to obtain electroplating simulation parameters. Electroplating simulation growth is performed on the three-dimensional solid data according to the electroplating simulation parameter set to obtain the coating simulation growth rate. Electroplating thickness is simulated and predicted on the three-dimensional solid data according to the electroplating simulation parameter set to obtain the simulated predicted value of the coating thickness.
[0030] Furthermore, based on the simulated growth rate of the coating, the simulated predicted value of the coating thickness is dynamically and adaptively filled with a high gradient region at the intersection of wire diameters and a flat region at the center of the mesh to obtain a thickness prediction matrix. The thickness prediction matrix is then mapped with RGB colors to construct a thickness simulation distribution cloud map. Multi-mode gradient thermal testing is performed based on the thickness simulation distribution cloud map to generate the bonding force test results. In one possible implementation, the method further includes calculating the simulated thickness deviation based on the simulated thickness distribution cloud map to obtain the simulated thickness deviation rate; setting a simulated thickness deviation threshold; when the simulated thickness deviation rate is greater than the simulated thickness deviation threshold, performing a strengthening thermal test on the simulated thickness distribution cloud map to obtain a strengthening test result; when the simulated thickness deviation rate is less than or equal to the simulated thickness deviation threshold, performing a standard thermal test on the simulated thickness distribution cloud map to obtain a standard test result; performing a stepped temperature increase according to the strengthening test result and the standard test result respectively to obtain first heat treatment sample data and second heat treatment sample data; performing mesh peeling on the first heat treatment sample data and the second heat treatment sample data, calculating the coating peeling area ratio based on the peeling result, and determining the adhesion test result.
[0031] Specifically, the thickness simulation distribution cloud map is first discretized into multiple 0.5mm×0.5mm grids. The deviation is calculated according to the target thickness, that is, the absolute value of the difference between the maximum thickness value in the thickness simulation distribution cloud map and the maximum thickness value in the thickness simulation distribution cloud map is divided by the target thickness, and then converted into a percentage format to obtain the simulation thickness deviation rate.
[0032] The critical value for simulation thickness deviation is set according to Table 9: Table 9 Application scenarios critical value in accordance with Ordinary industrial filter screen 8% ISO 4526 standard Aerospace / Medical Devices 5% AS9100 / ISO 13485 certification requirements When the simulated thickness deviation rate is greater than the simulated thickness deviation critical value, a strengthening thermal test is performed on the thickness simulation distribution cloud map. The strengthening thermal test includes: Pre-hydrogen removal stage: Argon gas (99.999% purity) is introduced while holding at 250℃; hydrogen content detection: from 8.2ppm to 1.5ppm (mass spectrometer data); rapid temperature rise control: infrared heater (response time ≤3s), temperature fluctuation ≤±2℃ (thermocouple closed-loop control). Based on this, sample data is recorded, including the surface oxidation color of the metal mesh, grain size, microhardness, etc., to obtain the strengthening test results.
[0033] When the simulated thickness deviation rate is less than or equal to the simulated thickness deviation critical value, a standard thermal test is performed on the thickness simulation distribution cloud map. The standard thermal test refers to holding the temperature at 350℃ for 30 minutes at a heating rate of 5℃ to obtain the standard test results.
[0034] According to the strengthening test results and the standard test results, a stepped temperature increase is performed to obtain first heat treatment sample data and second heat treatment sample data. Mesh peeling is then performed on the first heat treatment sample data and the second heat treatment sample data. This can be achieved through intelligent mesh generation, i.e., laser etching of a 1mm × 1mm mesh. Thin-area strengthening refers to densifying the thin area to a 0.5mm mesh. Customized adhesive tape is used to peel the tape at a peeling angle of 90° ± 1° to generate peeling results. The percentage of coating detachment area is calculated based on the peeling results. This percentage is defined as the adhesion loss rate: an adhesion loss rate ≤ 3% indicates first-level adhesion; an adhesion loss rate of 3% to 8% indicates second-level adhesion; and an adhesion loss rate > 8% indicates unqualified adhesion. The adhesion test results are then determined.
[0035] Step A400 is executed, whereby the metal mesh coating simulation data is evaluated based on the thickness uniformity data and the adhesion test results, and a coating quality evaluation report is generated and fed back to the target electroplating parameters for continuous optimization.
[0036] In one possible implementation, the method further includes defining a thickness uniformity index based on the thickness uniformity data, defining an adhesion index based on the adhesion test results, combining the thickness uniformity index and the adhesion index to construct a coating quality assessment matrix; classifying the reliability of the metal mesh coating simulation data based on the coating quality assessment matrix to generate multiple reliability levels; performing defect analysis by superimposing the thickness simulation distribution cloud map according to the multiple reliability levels, locating the defect core based on the defect analysis results, determining the defect core location; mapping the defect analysis results to the thickness simulation distribution cloud map according to the defect core location to obtain a defect area location map; and performing coating quality analysis on the defect area location map to generate the coating quality assessment report.
[0037] Specifically, as shown in Table 10, a thickness uniformity index is defined based on the thickness uniformity data, and a bonding strength index is defined based on the bonding strength test results: Table 10 Thickness uniformity index Grading Standards Thickness uniformity Standard deviation ≤ 0.8 μm (Excellent) Grade B: 0.8μm < standard deviation ≤ 1.5μm (Pass) Grade C: Standard deviation > 1.5 μm (Unacceptable) bonding force Grade A: Loss rate ≤ 3% (Excellent) Grade B: Loss rate ≤ 8% (Pass) Grade C: Loss rate > 8% (Unacceptable) Combining the thickness uniformity index with the adhesion index means pairing the thickness uniformity index and the adhesion index according to grades A, B, and C in Table 10 to form a coating quality assessment matrix; based on the coating quality assessment matrix, the reliability of the metal mesh coating simulation data is graded, generating multiple reliability levels as shown in Table 11: Table 11 Error range Trust level Response Action Thickness difference ≤ 0.3μm and adhesion difference ≤ 2% High credibility Directly optimize the process Thickness difference ≤ 0.6 μm and adhesion difference ≤ 5% Medium credibility Local calibration simulation Beyond the above range Low credibility Reconstructing simulation boundary conditions Defect analysis is performed by superimposing the thickness simulation distribution cloud map according to multiple confidence levels. The superposition can be based on the double 80% positioning principle of multiple confidence levels to determine the defect analysis results. These results include thin-area defect data and thick-area defect data. Continuous area analysis is then performed based on the thin-area and thick-area defect data. When the continuous defect area is ≥0.25mm... 2 Lock onto the core location in time.
[0038] Based on the core location of the defect, the defect analysis results are mapped to the thickness simulation distribution cloud map and rendered using a three-color overlay, as shown in Table 12, to obtain the defect region location map: Table 12 Layers color Data source bottom layer blue Thickness simulation cloud map Middle layer red Measured thickness lattice Top floor Gold Highlight Defect core area Based on the defect area location map, the current quality of the coating is analyzed to obtain data on excessive current density. Based on the defect area location map, the boundary compensation quality of the coating is analyzed to obtain data on insufficient plating solution fluidity. The data on excessive current density is located at the edge of the metal mesh, and the data on insufficient plating solution fluidity is located at the intersection of wire diameters. All quality data are integrated to generate the coating quality assessment report.
[0039] In one possible implementation, the method further includes: performing adhesion matching based on the defect region location map fed back to the target electroplating parameters; determining adhesion defect information and optimizing the target electroplating parameters to generate a first optimization effect; performing thickness matching based on the defect region location map fed back to the target electroplating parameters; determining thickness defect information and optimizing the target electroplating parameters to generate a second optimization effect; performing composite matching based on the defect region location map fed back to the target electroplating parameters; determining adhesion-thickness composite defect information and optimizing the target electroplating parameters to generate a third optimization effect; and performing quality verification on the first optimization effect, the third optimization effect, and the first optimization effect, respectively, and continuously optimizing the target electroplating parameters based on the verification results to generate real-time optimization parameters for the target electroplating.
[0040] Specifically, according to the defect area location map, the target electroplating parameters are fed back to match the bonding force. The judgment conditions can be set first: based on the red diagonal line filling area in the defect area location map in Table 12, the target electroplating parameters are optimized for mesh edge defects based on the changes in current density, heat treatment temperature and bonding force loss rate to generate the first optimization effect.
[0041] Based on the defect area location map, the target electroplating parameters are fed back for thickness matching. First, a judgment condition can be set: based on the gold flashing area (thickness deviation > ±15%) in the defect area location map in Table 12, the thickness defect information is determined. Then, the target electroplating parameters are optimized according to Table 13 to generate a second optimization effect. Table 13 Defect types Optimize actions Equipment execution Edge thickening <![CDATA[Current density - 0.15 A / dm 2 + Duty cycle - 5%]]> Variable frequency pump step speed + conveyor belt speed reduction Mesh thin layer Plating solution flow rate +0.3 m / s + extension time 10% Power slope adjustment The composite matching of the target electroplating parameters based on the defect area location map refers to setting a dual-threshold judgment mechanism, namely a composite defect flag, where the area must simultaneously meet the requirements of thickness deviation > ±15% and adhesion loss rate > 8%. The adhesion-thickness composite defect information is then used to optimize the target electroplating parameters according to Table 14, generating a third optimization effect: Table 14 Parameter thickness deviation, bonding force deviation optimization, action wire diameter intersection area +18% +12%, duty cycle +7%. Edge reinforcement zone +20% +25% Current drop of 0.25 A / dm 2 Bonding force loss rate from 14%→4% The first optimization effect, the third optimization effect, and the third optimization effect are respectively subjected to quality verification. Quality indicators can be set as follows: thickness qualified area ratio ≥ 85%, bonding force loss rate ≤ 3%, porosity ≤ 3 / cm. 2 The verification was conducted, and the target electroplating parameters were continuously optimized according to Table 15 based on the verification results, generating real-time optimized parameters for the target electroplating: Table 15 Optimization type Thickness pass rate Bonding force loss rate Process stabilization time First optimization effect 82%→ 89% 9.7%→ 3.2% 12 minutes Second optimization effect 53%→86% 5.1%→ 4.3% 8 minutes Third optimization effect 61%→94% 13%→ 2.8% 5 minutes This achievement represents a leap from "trial and error based on experience" to "digital precision" in metal mesh coating, providing technical support for high-reliability applications.
[0042] This application addresses the technical problem of uneven coating thickness and insufficient adhesion caused by inaccurate electroplating parameters. It achieves the technical effect of reducing thickness deviation rate and adhesion loss rate by dynamically adjusting electroplating parameters, thereby improving both coating quality and process stability.
[0043] In the above text, refer to Figure 1 A method for optimizing metal mesh plating for semiconductors according to embodiments of this application is described in detail. Next, reference will be made to... Figure 2 This application describes a metal mesh coating optimization apparatus for semiconductors according to embodiments thereof.
[0044] The metal mesh plating optimization device for semiconductors according to the embodiments of this application is used to solve the technical problems of uneven plating thickness and insufficient adhesion caused by inaccurate electroplating parameters. It achieves the technical effect of reducing the thickness deviation rate and adhesion loss rate by dynamically adjusting the electroplating parameters, thereby improving both plating quality and process stability.
[0045] The metal mesh coating optimization device for semiconductors includes: a data acquisition module 10, a dynamic adjustment module 20, a data testing module 30, and a quality assessment module 40.
[0046] The data acquisition module 10 is used to set initial electroplating parameters based on the characteristics of the metal mesh substrate, perform multi-stage pretreatment on the metal mesh, immerse it in the plating solution, apply pulse current for electroplating, and collect plating thickness data in real time. The dynamic adjustment module 20 is used to set a coating thickness range value based on the coating thickness data, and to synchronously and dynamically adjust the initial electroplating parameters according to the coating thickness range value to determine the target electroplating parameters. The data testing module 30 is used to perform electroplating simulation based on the target electroplating parameters, perform gradient thermal testing based on the metal mesh coating simulation data, generate adhesion test results, and perform thickness detection based on the metal mesh coating simulation data to determine thickness uniformity data. The quality assessment module 40 is used to assess the quality of the metal mesh coating simulation data based on the thickness uniformity data and the adhesion test results, and generate a coating quality assessment report to be fed back to the target electroplating parameters for continuous optimization.
[0047] The specific configuration of the data acquisition module 10 will be described in detail below. As mentioned above, after the metal mesh is pre-treated in multiple stages based on the characteristics of the metal mesh substrate, initial electroplating parameters are set, and then the metal mesh is immersed in the plating solution. Pulsed current is applied for electroplating, and plating thickness data is collected in real time. The data acquisition module 10 may further include: scanning the metal mesh with a laser scanner to obtain the three-dimensional topology of the metal mesh; performing substrate identification analysis based on the three-dimensional topology of the metal mesh to determine the characteristics of the metal mesh substrate; matching electroplating parameters according to the characteristics of the metal mesh substrate to set the initial electroplating parameters; performing multi-stage pre-treatment on the metal mesh according to the initial electroplating parameters; performing plating solution immersion analysis according to the pulse current dynamic adjustment rules based on the processing results to generate initial electroplating results; dividing the metal mesh into regions according to the initial electroplating results to determine multiple sub-regions to be scanned; reciprocating scanning of the multiple sub-regions to be scanned along the width direction of the metal mesh; calculating the thickness based on the scanning results to obtain the plating thickness data.
[0048] The specific configuration of the dynamic adjustment module 20 will be described in detail below. As mentioned above, based on the coating thickness data, a coating thickness range value is set, and the initial electroplating parameters are synchronously and dynamically adjusted according to the coating thickness range value to determine the target electroplating parameters. The dynamic adjustment module 20 may further include: extracting the coating thickness values of the multiple sub-regions to be scanned according to the scanning results; dividing the coating thickness range parameters based on the coating thickness values of the multiple sub-regions to be scanned; identifying extreme values according to the thickness range parameters; setting the coating thickness range value according to the extreme values of the parameters; performing thickness ratio analysis according to the coating thickness range value to generate multiple thickness ratio data; setting the parameter adjustment priority based on the multiple thickness ratio data; traversing the initial electroplating parameters for linkage and synchronous adjustment based on the parameter adjustment priority to generate electroplating adjustment parameters; and performing coating thickness verification iteration based on the electroplating adjustment parameters to determine the target electroplating parameters.
[0049] The specific configuration of the dynamic adjustment module 20 will be described in detail below. As mentioned above, the initial electroplating parameters are traversed and synchronously adjusted based on the parameter adjustment priority to generate electroplating adjustment parameters. The dynamic adjustment module 20 may further include: traversing the initial electroplating parameters according to the parameter adjustment priority to match and generate a linkage adjustment instruction set; sending the linkage adjustment instruction set to the electroplating equipment group through the industrial bus to switch parameters and generate equipment parameter adjustment data; adjusting the initial electroplating parameters at the equipment level according to the equipment parameter adjustment data to generate multiple equipment response data; performing full lifecycle traceability according to the multiple equipment response data to generate digital traceability parameters; and binding and storing the digital traceability parameters with the initial electroplating parameters to generate the electroplating adjustment parameters.
[0050] The specific configuration of the data testing module 30 will be described in detail below. As mentioned above, electroplating simulation is performed according to the target electroplating parameters, gradient thermal testing is performed based on the metal mesh coating simulation data, and adhesion test results are generated. The data testing module 30 may further include: constructing three-dimensional solid data of the metal mesh; performing finite element simulation according to the target electroplating parameters and the three-dimensional solid data to generate an electroplating simulation parameter set; performing electroplating simulation according to the electroplating simulation parameter set to generate metal mesh coating simulation data, which includes the coating simulation growth rate and the coating thickness simulation prediction value; filling the coating thickness simulation prediction value with a grid based on the coating simulation growth rate to construct a thickness simulation distribution cloud map; and performing multi-mode gradient thermal testing based on the thickness simulation distribution cloud map to generate the adhesion test results.
[0051] The specific configuration of the data testing module 30 will be described in detail below. As mentioned above, the data testing module 30 can further include: calculating the simulated thickness deviation based on the simulated thickness distribution cloud map to obtain the simulated thickness deviation rate; setting a simulated thickness deviation threshold; when the simulated thickness deviation rate is greater than the simulated thickness deviation threshold, performing a strengthening thermal test on the simulated thickness distribution cloud map to obtain a strengthening test result; when the simulated thickness deviation rate is less than or equal to the simulated thickness deviation threshold, performing a standard thermal test on the simulated thickness distribution cloud map to obtain a standard test result; performing a stepped temperature increase according to the strengthening test result and the standard test result to obtain first heat-treated sample data and second heat-treated sample data; performing mesh peeling on the first heat-treated sample data and the second heat-treated sample data, calculating the coating peeling area ratio based on the peeling result, and determining the adhesion test result.
[0052] The specific configuration of the quality assessment module 40 will be described in detail below. As mentioned above, the quality assessment module 40 performs a quality assessment on the metal mesh coating simulation data based on the thickness uniformity data and the adhesion test results, generating a coating quality assessment report. The quality assessment module 40 may further include: defining a thickness uniformity index based on the thickness uniformity data, defining an adhesion index based on the adhesion test results, combining the thickness uniformity index and the adhesion index to construct a coating quality assessment matrix; classifying the metal mesh coating simulation data based on the coating quality assessment matrix to generate multiple credibility levels; performing defect analysis by overlaying the thickness simulation distribution cloud map with the multiple credibility levels, locating the defect core based on the defect analysis results; mapping the defect analysis results to the thickness simulation distribution cloud map according to the defect core location to obtain a defect area location map; and performing coating quality analysis on the defect area location map to generate the coating quality assessment report. The specific configuration of the quality assessment module 40 will be described in detail below. As mentioned above, the quality assessment module 40 generates a coating quality assessment report and feeds it back to the target electroplating parameters for continuous optimization. The quality assessment module 40 may further include: performing adhesion matching according to the defect area location map fed back to the target electroplating parameters, determining adhesion defect information, and optimizing the target electroplating parameters to generate a first optimization effect; performing thickness matching according to the defect area location map fed back to the target electroplating parameters, determining thickness defect information, and optimizing the target electroplating parameters to generate a second optimization effect; performing composite matching according to the defect area location map fed back to the target electroplating parameters, determining adhesion-thickness composite defect information, and optimizing the target electroplating parameters to generate a third optimization effect; and performing quality verification on the first optimization effect, the first optimization effect, and the third optimization effect respectively, and continuously optimizing the target electroplating parameters based on the verification results to generate real-time optimization parameters for the target electroplating.
[0053] The metal mesh plating optimization apparatus for semiconductors provided in this application embodiment can execute the metal mesh plating optimization method for semiconductors provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.
[0054] In embodiment three, this application also provides a computer-readable storage medium that can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the metal mesh plating optimization method for semiconductors in the embodiments of this application, thereby implementing the above-mentioned metal mesh plating optimization method for semiconductors.
[0055] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this application.
[0056] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method for optimizing metal mesh coatings for semiconductors, characterized in that, The method includes: Based on the characteristics of the metal mesh substrate, the initial electroplating parameters are set, the metal mesh is pretreated in multiple stages and then immersed in the plating solution. Pulsed current is applied for electroplating and the coating thickness data is collected in real time. Based on the coating thickness data, a coating thickness range value is set, and the initial electroplating parameters are dynamically adjusted synchronously according to the coating thickness range value to determine the target electroplating parameters. Electroplating simulation is performed based on the target electroplating parameters. Gradient thermal testing is performed based on the metal mesh coating simulation data to generate adhesion test results. Thickness detection is performed based on the metal mesh coating simulation data to determine thickness uniformity data. Based on the thickness uniformity data and the adhesion test results, the quality of the metal mesh coating simulation data is evaluated, and a coating quality evaluation report is generated and fed back to the target electroplating parameters for continuous optimization.
2. The method for optimizing metal mesh plating for semiconductors as described in claim 1, characterized in that, Based on the characteristics of the metal mesh substrate, initial electroplating parameters are set. After multi-stage pretreatment of the metal mesh, it is immersed in the plating solution, electroplating is performed by applying pulsed current, and the coating thickness data is collected in real time. The method includes: The three-dimensional topological structure of the metal mesh is obtained by scanning the metal mesh with a laser scanner. Substrate identification and analysis are performed based on the three-dimensional topology of the metal mesh to determine the characteristics of the metal mesh substrate; Electroplating parameters are matched according to the characteristics of the metal mesh substrate, and the initial electroplating parameters are set. The metal mesh is pre-treated in multiple stages according to the initial electroplating parameters. Based on the treatment results, the immersion analysis of the plating solution is performed according to the dynamic adjustment rule of pulse current to generate the initial electroplating result. Based on the initial electroplating results, the metal mesh is divided into regions to determine multiple sub-regions to be scanned. The plurality of sub-regions to be scanned are reciprocated along the width direction of the metal mesh, and the thickness is calculated based on the scanning results to obtain the coating thickness data.
3. The method for optimizing metal mesh plating for semiconductors as described in claim 2, characterized in that, Based on the coating thickness data, a coating thickness range is set, and the initial electroplating parameters are dynamically adjusted synchronously according to the coating thickness range to determine the target electroplating parameters. The method includes: Based on the scanning results, the coating thickness values of the multiple sub-regions to be scanned are extracted, and thickness range parameters are defined based on the coating thickness values of the multiple sub-regions to be scanned. Extreme values are identified based on the thickness range parameters, and the coating thickness range is set based on the extreme values of the parameters. Based on the coating thickness range, a thickness ratio analysis is performed to generate multiple thickness ratio data points. Parameters are then set and priority is adjusted based on these multiple thickness ratio data points. Based on the parameter adjustment priority, the initial electroplating parameters are traversed and adjusted synchronously to generate electroplating adjustment parameters; Based on the electroplating adjustment parameters, the coating thickness is verified iteratively to determine the target electroplating parameters.
4. The method for optimizing metal mesh plating for semiconductors as described in claim 3, characterized in that, Based on the parameter adjustment priority, the initial electroplating parameters are traversed and adjusted synchronously to generate electroplating adjustment parameters. The method includes: According to the parameter adjustment priority, the initial electroplating parameters are traversed and matched to generate a set of linkage adjustment instructions; The linkage adjustment instruction set is sent to the electroplating equipment group via the industrial bus to switch parameters and generate equipment parameter adjustment data. The initial electroplating parameters are adjusted at the device level according to the device parameter adjustment data, generating multiple device response data. Perform full lifecycle tracing based on the response data of the multiple devices to generate digital traceability parameters; The electroplating adjustment parameters are generated by binding and storing the digital traceability parameters with the initial electroplating parameters.
5. The method for optimizing metal mesh coating for semiconductors as described in claim 1, characterized in that, Electroplating simulation is performed based on the target electroplating parameters. Gradient thermal testing is then conducted based on the metal mesh coating simulation data to generate adhesion test results. The method includes: Construct three-dimensional solid data of the metal mesh, and perform finite element simulation according to the target electroplating parameters and the three-dimensional solid data to generate an electroplating simulation parameter set; Electroplating simulation is performed according to the electroplating simulation parameter set to generate metal mesh coating simulation data, which includes the simulated growth rate of the coating and the simulated predicted value of the coating thickness. Based on the simulated growth rate of the coating, the simulated predicted value of the coating thickness is filled with a grid to construct a simulated thickness distribution cloud map. Multi-mode gradient thermal testing is performed based on the thickness simulation distribution cloud map to generate the bonding force test results.
6. The method for optimizing metal mesh coating for semiconductors as described in claim 5, characterized in that, The bonding force test results are generated by performing multi-mode gradient thermal testing based on the thickness simulation distribution cloud map, the method including: Based on the thickness simulation distribution cloud map, the simulated thickness deviation is calculated to obtain the simulated thickness deviation rate. A critical value for simulated thickness deviation is set. When the simulated thickness deviation rate is greater than the critical value, a strengthening thermal test is performed on the simulated thickness distribution cloud map to obtain the strengthening test results. When the simulated thickness deviation rate is less than or equal to the simulated thickness deviation critical value, a standard thermal test is performed on the thickness simulation distribution cloud map to obtain the standard test result. According to the enhanced test results and the standard test results, step temperature increase was performed respectively to obtain the first heat treatment sample data and the second heat treatment sample data; Mesh peeling is performed on the first heat-treated sample data and the second heat-treated sample data. The percentage of coating peeling area is calculated based on the peeling results to determine the adhesion test results.
7. The method for optimizing metal mesh coating for semiconductors as described in claim 5, characterized in that, The quality of the metal mesh coating simulation data is evaluated based on the thickness uniformity data and the adhesion test results, and a coating quality evaluation report is generated. The method includes: A thickness uniformity index is defined based on the thickness uniformity data, and an adhesion index is defined based on the adhesion test results. The thickness uniformity index and the adhesion index are combined to construct a coating quality evaluation matrix. Based on the coating quality assessment matrix, the credibility of the metal mesh coating simulation data is graded to generate multiple credibility levels. Defect analysis is performed by superimposing the thickness simulation distribution cloud map according to the multiple confidence levels, and the core location of the defect is determined based on the defect analysis results. The defect analysis results are mapped to the thickness simulation distribution cloud map according to the core location of the defect to obtain the defect area location map; The defect area location map is analyzed for coating quality to generate a coating quality assessment report.
8. The method for optimizing metal mesh coating for semiconductors as described in claim 7, characterized in that, The method includes generating a coating quality assessment report and feeding it back to the target electroplating parameters for continuous optimization. According to the defect area location map, the target electroplating parameters are fed back to perform bonding force matching, and the bonding force defect information is determined to optimize the target electroplating parameters to generate the first optimization effect. Based on the defect area location map, the target electroplating parameters are fed back for thickness matching. The thickness defect information is determined and the target electroplating parameters are optimized to generate a second optimization effect. Based on the defect area location map, the target electroplating parameters are fed back for composite matching. The bonding force-thickness composite defect information is determined and the target electroplating parameters are optimized to generate a third optimization effect. The first optimization effect, the third optimization effect, and the first optimization effect are respectively verified for quality. Based on the verification results, the target electroplating parameters are continuously optimized to generate real-time optimization parameters for the target electroplating.
9. A parameter detection system based on bearing machining, characterized in that, The system is used to implement the metal mesh coating optimization method for semiconductors according to any one of claims 1-8, comprising: The data acquisition module is used to set initial electroplating parameters based on the characteristics of the metal mesh substrate, perform multi-stage pretreatment on the metal mesh, immerse it in the plating solution, apply pulse current for electroplating, and collect coating thickness data in real time. The dynamic adjustment module is used to set a coating thickness range value based on the coating thickness data, and to synchronously and dynamically adjust the initial electroplating parameters according to the coating thickness range value to determine the target electroplating parameters. The data testing module is used to perform electroplating simulation based on the target electroplating parameters, perform gradient thermal testing based on the metal mesh coating simulation data, generate adhesion test results, and perform thickness detection based on the metal mesh coating simulation data to determine thickness uniformity data. The quality assessment module is used to assess the quality of the metal mesh coating simulation data based on the thickness uniformity data and the adhesion test results, and generate a coating quality assessment report to be fed back to the target electroplating parameters for continuous optimization.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method for optimizing metal mesh coating for semiconductors as described in any one of claims 1 to 8.