Efficient continuous chemical vapor deposition monitoring system for new energy material preparation

Abstract

The invention discloses an efficient continuous chemical vapor deposition monitoring system for new energy material preparation, and relates to the field of new energy material preparation, which comprises the following steps: synchronously uploading element structure parameters in the operation stage of a monitoring layer, and synchronously monitoring CVD environment information based on the monitoring layer in the CVD process of an element, the evaluation layer operates to obtain CVD environment information monitored in the monitoring layer and evaluates the balance of each area of a CVD space field based on the CVD environment information, and the identification layer operates in a rear-mounted manner to receive the balance evaluation result of each area of the CVD space field in the evaluation layer, identifies a detection target based on the evaluation result and performs CVD uniformity detection on the detection target so as to estimate the CVD qualification of the element; according to the method, by collecting element structure parameters and multi-dimensional data such as thermal imaging images and gas source pressure and flow in the CVD environment, based on a space field area balance evaluation model, parameters such as temperature, pressure, flow states and contour image similarity are integrated, and dynamic quantitative evaluation of environment homogeneity of all areas in the CVD technological process is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy material preparation, and in particular to a high-efficiency continuous chemical vapor deposition monitoring system for new energy material preparation. Background Art

[0002] Chemical vapor deposition (CVD) technology is widely used in the preparation of new energy materials. It can be used to prepare lithium battery electrode materials, fuel cell catalyst supports, and photovoltaic thin films. By precisely controlling temperature, pressure, and gas composition, uniform material deposition is achieved, improving conductivity, stability, and interfacial properties. This technology provides key support for the development of high-energy-density batteries, efficient catalysts, and low-loss photovoltaic devices.

[0003] The invention patent application with application number 202410455087.5 discloses a CVD process control system based on physical and chemical mechanisms and machine learning, including: a physical and chemical mechanism research and modeling module, which is used to establish a thin film growth theoretical model based on the physical and chemical process of CVD, and obtain key physical and chemical parameters affecting the CVD thin film growth process based on the said thin film growth theoretical model, and each key physical and chemical parameter corresponds to one or more process parameters; a data acquisition and data preprocessing module, which is used to collect and preprocess the machine equipment sensor data containing the said key physical and chemical parameters; and, in the initial training stage, collect the CVD deposited film thickness measurement value corresponding to the said machine equipment sensor data; each machine equipment sensor data corresponds to a process parameter; a machine learning model selection and training module, which is used to train the machine learning model and select the optimal model based on the data obtained by the data acquisition and data preprocessing module; a model interpretable analysis module, which is used to select the optimal model based on the optimal The model determines the key process parameters that affect the final film thickness during the CVD film growth process, as well as the way in which the key process parameters affect the final film thickness and the interaction between the key process parameters: a real-time monitoring and automatic adjustment module is used to deploy the optimal model on the CVD production line to achieve real-time monitoring and control of the CVD process. This application aims to solve the problem of "the uncontrollability caused by the complex physical and chemical mechanisms of CVD, especially the significant differences in the thickness and quality of CVD films of different batches of wafers under the condition of fixed process recipes. The existing industry method is to accept this difference. After each wafer batch completes the CVD step, the wafers are measured by sampling to obtain actual film thickness information, and then use this information to adjust the process recipe for the next batch of processes. However, this method of relying on experience adjustment and post-measurement to try to control film thickness is not only inefficient, but also cannot achieve real-time optimization of the system and automatic adjustment of the process recipe."

[0004] However, for the application of CVD technology in the preparation of new energy materials, the CVD process effect directly affects the later performance and life of new energy material components. Existing detection technologies are often dedicated to completing the quality detection of CVD components. The monitoring technology during the execution of the CVD process is relatively simple, so it is impossible to predictably evaluate the quality of the finished processed components or to make timely response strategies during the process execution.

[0005] To this end, we proposed an efficient continuous chemical vapor deposition monitoring system for the preparation of new energy materials. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the prior art, the present invention provides a high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials, which can effectively solve the problems of the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials, comprising: a monitoring layer, an evaluation layer, and an identification layer;

[0009] During the operation phase of the monitoring layer, component structural parameters are uploaded synchronously. During the CVD process of the component, CVD environmental information is synchronously monitored based on the monitoring layer. The evaluation layer operates to obtain the CVD environmental information monitored in the monitoring layer and evaluates the balance of each area of ​​the CVD spatial field based on the CVD environmental information. The identification layer then operates to receive the evaluation results of the balance of each area of ​​the CVD spatial field in the evaluation layer. Based on the evaluation results, the detection target is identified and CVD uniformity detection is performed on the detection target to estimate the CVD qualification of the component.

[0010] The evaluation layer includes a division module, an evaluation module and a normalization module. The division module is used to receive CVD environmental information and perform division processing on the CVD environmental information. The evaluation module is used to obtain the division processing result of the CVD environmental information in the division module, and based on the division processing result of the CVD environmental information, use the CVD environmental information to evaluate the balance of each area of ​​the CVD spatial field. The normalization module is used to receive the evaluation result of the balance of each area of ​​the CVD spatial field in the evaluation module and perform normalization processing on each evaluation result.

[0011] The evaluation logic of the balance of each region of the CVD spatial field is expressed as follows:

[0012]

[0013] Where: f(a) is the balance representation value of CVD spatial field region a; n and m are the total number of memory metal contour images in the sub-images of set α and the total number of memory metal contour images in the sub-images of set β corresponding to CVD spatial field region a; sim(i,i+1) and sim(j,j+1) are the similarities between the i-th contour image and the i+1-th contour image, and the similarities between the j-th contour image and the j+1-th contour image; P is the historical average pressure and flow rate of the gas source end in the CVD environment; now 、H now The current average pressure and current average flow rate of the gas source end in the CVD environment; Indicates the operation of taking the maximum value in the brackets;

[0014] Among them, the larger f(a) is, the better the balance of the corresponding CVD spatial field area is. f(a) is used to comprehensively represent the balance of temperature, pressure, and flow state in the CVD spatial field area. Based on each CVD spatial field area, the corresponding balance characterization value of each CVD spatial field area is obtained. Indicates the operation of finding the average value of the following expression.

[0015] Furthermore, the monitoring layer includes an upload module, a thermal imaging module, and a perception module. The upload module is used to upload component structural parameters, construct a component three-dimensional model based on the component structural parameters, segment the component three-dimensional model to obtain two sub-component three-dimensional models, and analyze the complexity of the two sub-component three-dimensional models respectively. The thermal imaging module is used to collect thermal imaging images of the component CVD environment in real time, and the perception module is used to perceive the pressure and flow information of the gas source end in the CVD environment in real time.

[0016] Among them, after completing the complexity analysis of the sub-component three-dimensional model, the complexity analysis results are synchronously marked on the corresponding sub-component three-dimensional model. The thermal imaging images collected by the thermal imaging module and the gas source end pressure and flow information perceived by the perception module are both recorded as CVD environmental information.

[0017] Furthermore, the monitoring layer includes an upload module, a thermal imaging module, and a perception module. The upload module is used to upload component structural parameters, construct a component three-dimensional model based on the component structural parameters, segment the component three-dimensional model to obtain two sub-component three-dimensional models, and analyze the complexity of the two sub-component three-dimensional models respectively. The thermal imaging module is used to collect thermal imaging images of the component CVD environment in real time, and the perception module is used to perceive the pressure and flow information of the gas source end in the CVD environment in real time.

[0018] Among them, after completing the complexity analysis of the sub-component three-dimensional model, the complexity analysis results are synchronously marked on the corresponding sub-component three-dimensional model. The thermal imaging images collected by the thermal imaging module and the gas source end pressure and flow information perceived by the perception module are both recorded as CVD environmental information.

[0019] Furthermore, the complexity analysis logic of the sub-element three-dimensional model is expressed as:

[0020]

[0021] Where: θ is the complexity of the subcomponent 3D model; q is the total number of visible corner points of the subcomponent 3D model at the upward viewing angle relative to the CVD scenario; χ is the Gaussian curvature integral term; E is the number of model edges; F is the number of model faces; V 2 / 3 is the surface area; V is the volume; K(CS max ) is the curvature of the maximum surface of the sub-element three-dimensional model; is the area of ​​the largest curved surface of the subcomponent three-dimensional model; S all is the sum of the areas of all surfaces of the sub-component 3D model;

[0022] The larger the θ value is, the more complex the sub-component three-dimensional model is.

[0023] Furthermore, the Gaussian curvature integral term χ is calculated as follows:

[0024]

[0025] Where: v is the volume of the model bounding box; S is the surface to be integrated; K(x, y, z) is the Gaussian curvature of each point on the surface of the sub-component 3D model, and the absolute value is averaged after integration; dS represents the area element on the surface;

[0026] The model bounding box volume v is: the volume of the component 3D model bounding box multiplied by the ratio of the sub-component 3D model volume to the component 3D model volume.

[0027] Furthermore, during the operation phase of the segmentation module, the segmentation processing target is the thermal imaging image in the CVD environment information, and the thermal imaging image acquired by the thermal imaging module arranged at the top is symmetrically segmented. The segmentation line is the midline of the two thermal imaging modules arranged outside the CVD environment, and two sub-images are obtained, which are denoted as A and B. A and B are denoted as the sub-image set α;

[0028] The thermal imaging image collected by the thermal imaging module arranged outside is horizontally segmented relative to the acquisition angle of view. When performing horizontal segmentation, the span of each segmentation is equal to obtain several sub-images, which are recorded as X A (1) X A (2) XA (3), ...; X B (1) X B (2) X B (3) ...;

[0029] X A (1) Take X as an example, A (1) represents the first sub-image from top to bottom on the side of sub-image A, and so on. A (1) X A (2) X A (3), ...; X B (1) X B (2) X B (3), ...denoted as the sub-image set β;

[0030] Among them, a sub-image is selected from each of the two sub-image sets to represent a CVD space field, and the selection logic obeys: a sub-image is selected from the sub-image set β, and the sub-image in the sub-image set α corresponding to the selected sub-image name subscript is recorded as a CVD space field, and so on, to obtain the same number of CVD space field areas as the sub-images in the sub-image set β.

[0031] Furthermore, the normalization processing operation for the evaluation result of the evaluation module in the normalization module is:

[0032]

[0033] Where: f(a)′ is the uniformity representation value of the CVD spatial field region a after normalization; f(θ1, θ2) is the decision function; θ1 and θ2 are the calculation results of the complexity of the three-dimensional models of the two sub-elements; u is the pixel color value level in the thermal imaging image; k v is the ratio of the number of pixels of the corresponding level in the two sub-images corresponding to the CVD spatial field area a at the vth level;

[0034] Among them, the decision function takes the value of θ1 or θ2, and the complexity calculation result of the three-dimensional model of the sub-element close to the CVD space field area a is taken, k v When calculating, always compare the smaller value to the larger value, ensuring that each ratio is less than 1.

[0035] Furthermore, the identification layer includes an identification module, a detection module, and an output module. The identification module is used to traverse the normalized results of the uniformity characterization values ​​of each CVD spatial field area, identify the value of each result, and sort the results from small to large based on the size of the result value, so that the results with small result values ​​are sorted in front and the results with large result values ​​are sorted in the back. The detection module is used to sequentially detect whether the components corresponding to the CVD spatial field area of ​​each result are qualified based on the sorting of the normalized results of the uniformity characterization values ​​of each CVD spatial field area, and end when it is detected that all components corresponding to the CVD spatial field area of ​​a result are qualified, and all components corresponding to the CVD spatial field area of ​​all results in the sorting queue are judged to be qualified. The output module is used to receive the component qualification judgment result in the detection module, and output the CVD spatial field area to which the qualified component belongs as output content;

[0036] Among them, the component qualification judgment results in the detection module and the output content of the output module are the content of the component CVD qualification estimation.

[0037] Furthermore, the identification layer is operated after the component completes the CVD process. In the stage of determining whether the component is qualified, the surface of the component is bombarded by a focused electron beam, releasing characteristic X-rays with specific energy, which are then detected by an energy dispersive spectrometer to complete the determination;

[0038] When determining the ownership of a component in the CVD spatial field area, the ownership is determined by the intersection spatial area obtained by stretching two sub-images corresponding to the CVD spatial field area.

[0039] Furthermore, the division module is interactively connected to the perception module through the local area network, the perception module is interactively connected to the thermal imaging module and the upload module through the local area network, the division module is interactively connected to the evaluation module and the normalization module through the local area network, the normalization module is interactively connected to the identification module through the local area network, and the identification module is interactively connected to the detection module and the output module through the local area network.

[0040] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0041] The present invention provides a high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials. During operation, the system collects multi-dimensional data such as component structural parameters, thermal imaging images in the CVD environment, and gas source pressure and flow. Based on a spatial field regional equilibrium evaluation model, the system integrates parameters such as temperature, pressure, flow state, and contour image similarity to achieve a dynamic quantitative assessment of the environmental homogeneity of each region during the CVD process.

[0042] The system can not only analyze the spatial field balance in real time during the deposition process, but also correlate it with the complexity of the component through normalization processing, proactively identify key detection areas, and combine electron beam detection technology to detect the quality of finished components, thereby simplifying and improving the efficiency of the inspection process for mass-produced components. It effectively solves the pain points of existing technologies that are unable to predictably evaluate the quality of finished components and respond to process problems in a timely manner, and provides a full-chain intelligent solution from process monitoring to quality estimation for the preparation of new energy materials, effectively improving the controllability of the CVD process and the reliability of the quality of finished components, and laying a technical foundation for extending the service life of new energy material components and optimizing their later performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0044] Figure 1 This is a schematic diagram of the structure of a high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials;

[0045] Figure 2 Schematic diagram of the positional relationship between the thermal imaging module and the CVD chamber in the present invention;

[0046] Figure 3 This is a schematic diagram illustrating the thermal imaging image acquisition direction and segmentation principle in the present invention;

[0047] Figure 4 This is an example schematic diagram of the CVD spatial field region determination logic in the present invention.

[0048] The marks in the figure respectively indicate: 1. CVD chamber; 2. memory metal; 3. thermal imaging module; 4. example plane identification of the thermal imaging module on the same plane. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] The present invention will be further described below with reference to the embodiments.

[0051] Example:

[0052] The high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials in this embodiment is as follows: Figure 1 As shown, it includes: monitoring layer, evaluation layer, and identification layer;

[0053] During the operation phase of the monitoring layer, component structural parameters are uploaded synchronously. During the CVD process of the component, CVD environmental information is synchronously monitored based on the monitoring layer. The evaluation layer operates to obtain the CVD environmental information monitored in the monitoring layer and evaluates the balance of each area of ​​the CVD spatial field based on the CVD environmental information. The identification layer then operates to receive the evaluation results of the balance of each area of ​​the CVD spatial field in the evaluation layer. Based on the evaluation results, the detection target is identified and CVD uniformity detection is performed on the detection target to estimate the CVD qualification of the component.

[0054] The monitoring layer includes an upload module, a thermal imaging module, and a perception module. The upload module is used to upload component structural parameters, build a component 3D model based on the component structural parameters, segment the component 3D model to obtain two sub-component 3D models, and analyze the complexity of the two sub-component 3D models respectively. The thermal imaging module is used to collect thermal imaging images of the component CVD environment in real time, and the perception module is used to perceive the pressure and flow information of the gas source end in the CVD environment in real time.

[0055] After the complexity analysis of the sub-component 3D model is completed, the complexity analysis results are simultaneously marked on the corresponding sub-component 3D model. The thermal imaging image collected by the thermal imaging module and the gas source end pressure and flow information sensed by the perception module are both recorded as CVD environmental information.

[0056] There are three thermal imaging modules, which are respectively deployed on the outside and top of the CVD environment. The three thermal imaging modules are in the same plane, with two on the outside and horizontal to each other, and one on the top and perpendicular to the thermal imaging image acquisition direction of the thermal imaging modules on the outside. The CVD environment corresponds to the chamber for CVD operation.

[0057] The outer wall of the CVD environment is evenly distributed with memory metal. The memory metal is symmetrically distributed when viewed from above or from the front. The memory metal deforms and recovers according to the preset shape in the preset temperature range to adapt to the CVD operating temperature change.

[0058] The complexity analysis logic of the sub-component 3D model is expressed as follows:

[0059]

[0060] Where: θ is the complexity of the subcomponent 3D model; q is the total number of visible corner points of the subcomponent 3D model at the upward viewing angle relative to the CVD scenario; χ is the Gaussian curvature integral term; E is the number of model edges; F is the number of model faces; V 2 / 3 is the surface area; V is the volume; K(CS max ) is the curvature of the maximum surface of the sub-element three-dimensional model; is the area of ​​the largest curved surface of the subcomponent three-dimensional model; S all is the sum of the areas of all surfaces of the sub-component 3D model;

[0061] Among them, the larger the θ value is, the more complex the sub-component three-dimensional model is;

[0062] The calculation formula of Gaussian curvature integral term χ is:

[0063]

[0064] Where: v is the volume of the model bounding box; S is the surface to be integrated; K(x, y, z) is the Gaussian curvature of each point on the surface of the sub-component 3D model, and the absolute value is averaged after integration; dS represents the area element on the surface;

[0065] The model bounding box volume v is: the volume of the component 3D model bounding box multiplied by the ratio of the subcomponent 3D model volume to the component 3D model volume;

[0066] The complexity of the sub-component three-dimensional model is calculated by the above logical formula, which supports the subsequent normalization processing of the evaluation results of the evaluation module in the system of this embodiment;

[0067] The evaluation layer includes a division module, an evaluation module and a normalization module. The division module is used to receive CVD environmental information and perform division processing on the CVD environmental information. The evaluation module is used to obtain the division processing results of the CVD environmental information in the division module, and based on the division processing results of the CVD environmental information, use the CVD environmental information to evaluate the balance of each area of ​​the CVD spatial field. The normalization module is used to receive the evaluation results of the balance of each area of ​​the CVD spatial field in the evaluation module and perform normalization processing on each evaluation result.

[0068] The evaluation logic of the balance of each region of the CVD spatial field is expressed as follows:

[0069]

[0070] Where: f(a) is the balance representation value of CVD spatial field region a; n and m are the total number of memory metal contour images in the sub-images of set α and the total number of memory metal contour images in the sub-images of set β corresponding to CVD spatial field region a; sim(i,i+1) and sim(j,j+1) are the similarities between the i-th contour image and the i+1-th contour image, and the similarities between the j-th contour image and the j+1-th contour image; P is the historical average pressure and flow rate of the gas source end in the CVD environment; now 、H now The current average pressure and current average flow rate of the gas source end in the CVD environment; Indicates the operation of taking the maximum value in the brackets;

[0071] Among them, the larger f(a) is, the better the balance of the corresponding CVD spatial field area is. f(a) is used to comprehensively represent the balance of temperature, pressure, and flow state in the CVD spatial field area. Based on each CVD spatial field area, the corresponding balance characterization value of each CVD spatial field area is obtained. Indicates the operation of finding the average value of the following formula;

[0072] Through the above logic formula calculation, a limited calculation logic is further provided for the uniformity characterization value of each CVD spatial field area, which further supports the identification layer operation of the system in this embodiment and ultimately completes the component CVD qualification evaluation;

[0073] During the partitioning module operation phase, the partitioning processing target is the thermal imaging image in the CVD environment information. The thermal imaging image acquired by the top thermal imaging module is symmetrically partitioned. The partitioning line is the midline of the two thermal imaging modules deployed outside the CVD environment. Two sub-images are obtained, denoted as A and B. A and B are denoted as the sub-image set α.

[0074] The thermal imaging image collected by the thermal imaging module arranged outside is horizontally segmented relative to the acquisition angle of view. When performing horizontal segmentation, the span of each segmentation is equal to obtain several sub-images, which are recorded as X A (1) X A (2) X A (3), ...; X B (1) X B (2) X B (3) ...;

[0075] X A (1) Take X as an example, A (1) represents the first sub-image from top to bottom on the side of sub-image A, and so on. A (1) X A (2) XA (3), ...; X B (1) X B (2) X B (3), ...denoted as the sub-image set β;

[0076] Among them, a sub-image is selected from each of the two sub-image sets to represent a CVD space field, and the selection logic follows: a sub-image is selected from the sub-image set β, and the sub-image in the sub-image set α corresponding to the selected sub-image name subscript is recorded as a CVD space field, and so on, to obtain the same number of CVD space field regions as the sub-images in the sub-image set β;

[0077] The normalization processing operation for the evaluation results of the evaluation module in the normalization module is:

[0078]

[0079] Where: f(a)′ is the uniformity representation value of the CVD spatial field region a after normalization; f(θ1, θ2) is the decision function; θ1 and θ2 are the calculation results of the complexity of the three-dimensional models of the two sub-elements; u is the pixel color value level in the thermal imaging image; k v is the ratio of the number of pixels of the corresponding level in the two sub-images corresponding to the CVD spatial field area a at the vth level;

[0080] Among them, the decision function takes the value of θ1 or θ2, and the complexity calculation result of the three-dimensional model of the sub-element close to the CVD space field area a is taken, k v When calculating, always compare the smaller value to the larger value, ensuring that each ratio is less than 1;

[0081] The above formula further defines the operating logic of the normalization module and normalizes the uniformity characterization value of the CVD spatial field area to facilitate the detection module in the system recognition layer to determine the detection target element;

[0082] The identification layer includes an identification module, a detection module, and an output module. The identification module is used to traverse the normalized results of the uniformity characterization values ​​of each CVD spatial field area, identify the value of each result, and sort the results from small to large based on the size of the result value, so that the results with small result values ​​are sorted in front and the results with large result values ​​are sorted in the back. The detection module is used to sequentially detect whether the components corresponding to the CVD spatial field area of ​​each result are qualified based on the sorting of the normalized results of the uniformity characterization values ​​of each CVD spatial field area. The detection module ends when it is detected that all components corresponding to the CVD spatial field area of ​​a result are qualified, and all components corresponding to the CVD spatial field area of ​​all results in the sorting queue are judged to be qualified. The output module is used to receive the qualified component judgment result in the detection module, and output the CVD spatial field area to which the qualified component belongs as the output content;

[0083] Among them, the component qualification judgment results in the detection module and the output content of the output module operation are the content of the component CVD qualification estimation;

[0084] The identification layer is operated after the component completes the CVD process. During the component qualification determination stage, the electron beam is focused and bombarded on the component surface, releasing characteristic X-rays with specific energy. The X-rays are then detected by an energy dispersive spectrometer to complete the determination.

[0085] When determining the ownership of a component in the CVD space field area, the ownership is determined by the intersection space area obtained by stretching the two sub-images corresponding to the CVD space field area;

[0086] The division module is interactively connected to the perception module through the local area network, the perception module is interactively connected to the thermal imaging module and the upload module through the local area network, the division module is interactively connected to the evaluation module and the normalization module through the local area network, the normalization module is interactively connected to the recognition module through the local area network, and the recognition module is interactively connected to the detection module and the output module through the local area network.

[0087] In this embodiment, the upload module runs to upload component structural parameters, constructs a component three-dimensional model based on the component structural parameters, divides the component three-dimensional model to obtain two sub-component three-dimensional models, and analyzes the complexity of the two sub-component three-dimensional models respectively. The thermal imaging module collects thermal imaging images of the component CVD environment in real time, and the perception module synchronously perceives the pressure and flow information of the gas source end in the CVD environment. The division module runs post-processing to receive the CVD environment information and divide the CVD environment information. The evaluation module further obtains the division processing result of the CVD environment information in the division module, and based on the division processing result of the CVD environment information, uses the CVD environment information to evaluate the balance of each region of the CVD spatial field. The normalization module then receives the evaluation result of the balance of each region of the CVD spatial field in the evaluation module, and normalizes each evaluation result. And through the identification module, the normalized results of the balance characterization values ​​of each CVD space field area are traversed, the values ​​of each result are identified, and the results are sorted from small to large based on the size of the result value, so that the results with small result values ​​are sorted in front and the results with large result values ​​are sorted in the back. The detection module is synchronously based on the sorting of the normalized results of the balance characterization values ​​of each CVD space field area, and sequentially detects whether the components corresponding to the CVD space field area of ​​each result are qualified. It ends when it is detected that all components corresponding to the CVD space field area of ​​a result are qualified, and all components corresponding to the CVD space field area of ​​all results after the result in the sorting queue are judged to be qualified. Finally, the component qualification judgment result in the detection module is received through the output module, and the CVD space field area to which the qualified components belong is output as the output content.

[0088] Through the above-mentioned embodiment, the system provides effective monitoring services for new energy material components during the CVD process. By evaluating the CVD environment, it effectively assists staff in controlling and maintaining the CVD environment, thereby improving the quality of components produced by the CVD process by improving the accuracy of the CVD environment. At the same time, combined with the monitoring data, targeted testing is performed on the final components, and specific detection logic is used to quickly distinguish between qualified and unqualified components, providing effective support services for the CVD process preparation link of new energy material components.

[0089] See also Figure 2 As shown, the positional relationship among the CVD chamber 1, the memory metal 2, and the thermal imaging module 3 is shown based on the markings in the figure, and the thermal imaging module 3 is further shown in what posture it is distributed in the same plane through the example plane marking of the thermal imaging module.

[0090] See also Figure 3As shown, the segmentation result of the thermal imaging image is shown by the horizontal arrow indication, and the acquisition direction of the thermal imaging image is shown by referring to the small arrow indication on the left side of the horizontal arrow to correspond to the acquisition direction of each thermal imaging module 3;

[0091] See also Figure 4 As shown, based on the arrows in the figure, the evolution process of the two sub-images is used to finally obtain the CVD spatial field area of ​​the example;

[0092] In summary, during the operation of the system in the above embodiment, by collecting component structural parameters and thermal imaging images, gas source pressure and flow and other multi-dimensional data in the CVD environment, based on the spatial field regional balance evaluation model, comprehensive temperature, pressure, flow state and contour image similarity and other parameters, a dynamic quantitative evaluation of the environmental homogeneity of each region in the CVD process is achieved. The system can not only analyze the spatial field balance in real time during the deposition process, but also associate it with the complexity of the component through normalization processing, proactively identify key detection areas, and combine electron beam detection technology to detect the quality of finished component products, thereby simplifying and improving the efficiency of the inspection process of mass-produced components, effectively solving the pain points of the existing technology that cannot predictably evaluate the quality of finished component products and respond to process problems in a timely manner, and providing a full-chain intelligent solution from process monitoring to quality estimation for the preparation of new energy materials, effectively improving the controllability of the CVD process and the reliability of the quality of finished component products, and laying a technical foundation for extending the service life of new energy material components and optimizing their later performance.

[0093] Application scenario simulation at the specific implementation level:

[0094] 1. Application Scenarios

[0095] Take the continuous chemical vapor deposition (CVD) process for lithium battery cathode materials (such as lithium iron phosphate) as an example. When coating nanoparticles with a conductive layer (such as a carbon layer) using CVD technology, traditional processes face challenges such as difficulty controlling the uniformity of the spatial environment and delayed quality inspection, resulting in poor product consistency. This system uses real-time monitoring and analysis to accurately assess the stability of the deposition environment, improving coating uniformity and improving the yield rate of finished products.

[0096] 2. System Deployment and Core Functions

[0097] (1) Monitoring layer: multi-dimensional data collection

[0098] Component model construction:

[0099] Upload the structural parameters of lithium iron phosphate particles (such as particle size and surface morphology), build a three-dimensional model and split it into two sub-models: "core" and "coating".

[0100] Analyze the complexity of the sub-models (such as the number of surface edges, surface curvature and other geometric features), mark the complexity differences, and provide a reference for subsequent regional assessment.

[0101] Environmental parameter monitoring:

[0102] Thermal field monitoring: Two thermal imaging modules are deployed horizontally outside the CVD chamber, and one is deployed vertically on the top to capture temperature distribution images of the chamber from different perspectives (such as horizontal and vertical thermal field differences) in real time.

[0103] Physical perception: Special memory metals (such as nickel-titanium alloy) are symmetrically arranged on the outer wall of the cavity. They deform with temperature changes within a preset temperature range. The uniformity of the temperature field is judged by observing the consistency of the metal deformation (the more consistent the deformation, the more uniform the temperature field).

[0104] Gas monitoring: Real-time perception of pressure and flow fluctuations of the incoming gas source (such as the input stability of methane and carbon dioxide).

[0105] (2) Evaluation layer: spatial field balance analysis

[0106] Regional division:

[0107] The top thermal imaging image is divided into two left and right sub-images along the midline of the cavity, and the outer thermal imaging image is divided horizontally into several sub-regions (such as upper, middle, and lower). Each sub-image corresponds to a specific spatial field area of ​​the cavity (such as upper left, lower right, etc.).

[0108] Comprehensive assessment:

[0109] Compare the deformation similarity of the memory metal in each area (such as whether the metal shape changes at adjacent moments are consistent) and the difference between the historical average and current values ​​of the gas parameters to determine the balance of temperature, pressure, and flow in the area. For example:

[0110] Areas with consistent metal deformation and small fluctuations in gas parameters are judged to have “excellent” balance;

[0111] Areas with disordered deformation and large parameter fluctuations are judged to have "poor" balance.

[0112] Normalized results:

[0113] Combining the differences in complexity of the sub-models (e.g., a more complex inner core structure may affect deposition uniformity) and the distribution characteristics of thermal imaging pixels, the balance assessment results of each area are weighted to generate an intuitive comprehensive score (e.g., 1-10 points, with higher scores indicating a more stable environment).

[0114] (III) Identification layer: component qualification determination

[0115] Prioritization:

[0116] Sort the regions by their balance scores from low to high (prioritizing regions with poor environmental stability).

[0117] Quality inspection:

[0118] For the areas ranked higher, the surface of the component is bombarded by focusing an electron beam, and the energy dispersive spectrometer is used to detect indicators such as the coating thickness and composition uniformity to determine whether the component is qualified.

[0119] Qualified logic: If a component in a certain area is qualified, then all components in that area and all subsequent areas are judged to be qualified, quickly locking the qualified production range.

[0120] Result output:

[0121] Mark the cavity position corresponding to the qualified area (such as the lower right and middle), and prompt the process personnel to adjust the parameters of the unqualified area (such as increasing the gas flow in the area and optimizing the heating power).

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An efficient continuous chemical vapor deposition monitoring system for the preparation of new energy materials, characterized by: include: Monitoring layer, evaluation layer, and identification layer; During the operation phase of the monitoring layer, component structural parameters are uploaded synchronously. During the CVD process of the component, CVD environmental information is synchronously monitored based on the monitoring layer. The evaluation layer operates to obtain the CVD environmental information monitored in the monitoring layer and evaluates the balance of each area of ​​the CVD spatial field based on the CVD environmental information. The identification layer then operates to receive the evaluation results of the balance of each area of ​​the CVD spatial field in the evaluation layer. Based on the evaluation results, the detection target is identified and CVD uniformity detection is performed on the detection target to estimate the CVD qualification of the component. The evaluation layer includes a division module, an evaluation module and a normalization module. The division module is used to receive CVD environmental information and perform division processing on the CVD environmental information. The evaluation module is used to obtain the division processing result of the CVD environmental information in the division module, and based on the division processing result of the CVD environmental information, use the CVD environmental information to evaluate the balance of each area of ​​the CVD spatial field. The normalization module is used to receive the evaluation result of the balance of each area of ​​the CVD spatial field in the evaluation module and perform normalization processing on each evaluation result. The evaluation logic of the balance of each region of the CVD spatial field is expressed as follows: Where: f(a) is the balance representation value of CVD spatial field region a; n and m are the total number of memory metal contour images in the sub-images of set α and the total number of memory metal contour images in the sub-images of set β corresponding to CVD spatial field region a; sim(i,i+1) and sim(j,j+1) are the similarities between the i-th contour image and the i+1-th contour image, and the similarities between the j-th contour image and the j+1-th contour image; P is the historical average pressure and flow rate of the gas source end in the CVD environment; now 、H now The current average pressure and current average flow rate of the gas source end in the CVD environment; Indicates the operation of taking the maximum value in the brackets; Among them, the larger f(a) is, the better the balance of the corresponding CVD spatial field area is. f(a) is used to comprehensively represent the balance of temperature, pressure, and flow state in the CVD spatial field area. Based on each CVD spatial field area, the corresponding balance characterization value of each CVD spatial field area is obtained. Indicates the operation of finding the average value of the following expression.

2. The high-efficiency continuous chemical vapor deposition monitoring system for the preparation of new energy materials according to claim 1 is characterized in that: The monitoring layer includes an upload module, a thermal imaging module, and a perception module. The upload module is used to upload component structural parameters, construct a component three-dimensional model based on the component structural parameters, segment the component three-dimensional model to obtain two sub-component three-dimensional models, and analyze the complexity of the two sub-component three-dimensional models respectively. The thermal imaging module is used to collect thermal imaging images of the component CVD environment in real time, and the perception module is used to perceive the pressure and flow information of the gas source end in the CVD environment in real time. Among them, after completing the complexity analysis of the sub-component three-dimensional model, the complexity analysis results are synchronously marked on the corresponding sub-component three-dimensional model. The thermal imaging images collected by the thermal imaging module and the gas source end pressure and flow information perceived by the perception module are both recorded as CVD environmental information.

3. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 2, characterized in that: Three thermal imaging modules are provided, and the three thermal imaging modules are respectively deployed on the outside and top of the CVD environment, and the three thermal imaging modules are in the same plane, with two being provided on the outside and being horizontal to each other, and one being provided on the top and having a thermal imaging image acquisition direction perpendicular to that of the thermal imaging modules provided on the outside. The CVD environment corresponds to a chamber for CVD operation; The outer wall of the CVD environment is evenly distributed with memory metal. The memory metal deployed on the outer wall of the CVD environment is symmetrically distributed when viewed from above and from the front. The memory metal deforms and recovers in a preset shape according to temperature changes within a preset temperature range that adapts to CVD operating temperature changes.

4. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 2, characterized in that: The complexity analysis logic of the sub-element three-dimensional model is expressed as follows: Where: θ is the complexity of the subcomponent 3D model; q is the total number of visible corner points of the subcomponent 3D model at the upward viewing angle relative to the CVD scenario; χ is the Gaussian curvature integral term; E is the number of model edges; F is the number of model faces; V 2 / 3 is the surface area; V is the volume; K(CS max ) is the curvature of the maximum surface of the sub-element three-dimensional model; is the area of ​​the largest curved surface of the subcomponent three-dimensional model; S all is the sum of the areas of all surfaces of the sub-component 3D model; The larger the θ value is, the more complex the sub-component three-dimensional model is.

5. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 4, characterized in that: The calculation formula of the Gaussian curvature integral term χ is: Where: v is the volume of the model bounding box; S is the surface to be integrated; K(x, y, z) is the Gaussian curvature of each point on the surface of the sub-component 3D model, and the absolute value is averaged after integration; dS represents the area element on the surface; The model bounding box volume v is: the volume of the component 3D model bounding box multiplied by the ratio of the sub-component 3D model volume to the component 3D model volume.

6. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 1, characterized in that: During the operation phase of the segmentation module, the segmentation processing target is the thermal imaging image in the CVD environment information. The thermal imaging image acquired by the thermal imaging module arranged at the top is symmetrically segmented. The segmentation line is the midline of the two thermal imaging modules arranged outside the CVD environment, and two sub-images are obtained, which are denoted as A and B. A and B are denoted as the sub-image set α; The thermal imaging image collected by the thermal imaging module arranged outside is horizontally segmented relative to the acquisition angle of view. When performing horizontal segmentation, the span of each segmentation is equal to obtain several sub-images, which are recorded as X A (1) X A (2) X A (3), ...; X B (1) X B (2) X B (3), ...; X A (1) Take X as an example, A (1) represents the first sub-image from top to bottom on the side of sub-image A, and so on. A (1) X A (2) X A (3), ...; X B (1) X B (2) X B (3), ...denoted as the sub-image set β; Among them, a sub-image is selected from each of the two sub-image sets to represent a CVD space field, and the selection logic obeys: a sub-image is selected from the sub-image set β, and the sub-image in the sub-image set α corresponding to the selected sub-image name subscript is recorded as a CVD space field, and so on, to obtain the same number of CVD space field areas as the sub-images in the sub-image set β.

7. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 1, characterized in that: The normalization processing operation for the evaluation result of the evaluation module in the normalization module is: Where: f(a)′ is the uniformity representation value of the CVD spatial field region a after normalization; f(θ1, θ2) is the decision function; θ1 and θ2 are the calculation results of the complexity of the three-dimensional models of the two sub-elements; u is the pixel color value level in the thermal imaging image; k v is the ratio of the number of pixels of the corresponding level in the two sub-images corresponding to the CVD spatial field area a at the vth level; Among them, the decision function takes the value of θ1 or θ2, and the complexity calculation result of the three-dimensional model of the sub-element close to the CVD space field area a is taken, k v When calculating, always compare the smaller value to the larger value, ensuring that each ratio is less than 1.

8. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 1, characterized in that: The identification layer includes an identification module, a detection module, and an output module. The identification module is used to traverse the normalized results of the uniformity characterization values ​​of each CVD spatial field area, identify the value of each result, and sort the results from small to large based on the size of the result value, so that the results with small result values ​​are sorted in front and the results with large result values ​​are sorted in the back. The detection module is used to sequentially detect whether the components corresponding to the CVD spatial field area of ​​each result are qualified based on the sorting of the normalized results of the uniformity characterization values ​​of each CVD spatial field area. The identification layer ends when it is detected that all components corresponding to the CVD spatial field area of ​​a result are qualified, and the result and all components corresponding to the CVD spatial field area of ​​all results in the sorting queue are judged to be qualified. The output module is used to receive the qualified component judgment result in the detection module, and output the CVD spatial field area to which the qualified component belongs as output content; Among them, the component qualification judgment results in the detection module and the output content of the output module are the content of the component CVD qualification estimation.

9. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 8, characterized in that: The identification layer is operated after the component completes the CVD process. In the stage of judging whether the component is qualified, the surface of the component is bombarded by a focused electron beam, releasing characteristic X-rays with specific energy, which are then detected by an energy dispersive spectrometer to complete the judgment; When determining the ownership of a component in the CVD spatial field area, the ownership is determined by the intersection spatial area obtained by stretching two sub-images corresponding to the CVD spatial field area.

10. The high-efficiency continuous chemical vapor deposition monitoring system for preparing new energy materials according to claim 1, characterized in that: The division module is interactively connected to the perception module through the local area network, the perception module is interactively connected to the thermal imaging module and the upload module through the local area network, the division module is interactively connected to the evaluation module and the normalization module through the local area network, the normalization module is interactively connected to the identification module through the local area network, and the identification module is interactively connected to the detection module and the output module through the local area network.

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