Dynamic Temperature Control Method, System, Equipment and Medium for Laser Processing of Silicon Dioxide Wafers

Abstract

The present invention discloses a dynamic temperature control method, system, device and medium for laser processing of silica wafers, which relates to the technical field of laser processing of wafers. The method includes: obtaining a sequence of surface temperature distribution images of the wafer; obtaining a sequence of temperature change rate distribution images; obtaining a topological network of temperature analysis sample point distributions; determining a sequence of topological networks of temperature change rates of sample points; determining a topological network of temperature change rates of target associated sample points; determining a central value of temperature change rates of target associated sample points; determining target temperature control parameters; and performing dynamic optimization of temperature control during the laser processing of the target wafer according to the target temperature control parameters. It solves the technical problems in the prior art that due to uneven temperature distribution and lagging response of temperature control means, the processing control accuracy is insufficient, the wafer is damaged, and the dynamic change of the wafer processing temperature cannot be responded in real time, and achieves the technical effects of improving the control accuracy, processing controllability and reliability.

Description

Technical Field

[0001] This application relates to the technical field of wafer laser processing, and specifically relates to a dynamic temperature control method, system, device and medium for laser processing of silicon dioxide wafers. Background Art

[0002] Silicon dioxide wafers are widely used in the fields of optoelectronic devices, integrated circuits, etc., and their processing quality directly affects the performance and reliability of the devices. Due to its high precision and high efficiency, laser processing technology has become the main method for wafer microfabrication. However, in the actual processing process, the energy input of the laser to the wafer surface is likely to cause local temperature rise, resulting in thermal stress concentration on the wafer, which may cause microcracks, warping or other damages. Traditional temperature control methods mostly adopt fixed temperature control parameters or simple temperature monitoring means, which are difficult to reflect the dynamic changes of temperature in real time, resulting in problems of temperature control lag or insufficient adjustment. Especially in the scenario of high-precision laser processing, the temperature distribution of silicon dioxide wafers often shows high dynamics and non-uniformity. The existing methods cannot comprehensively perceive the temperature change law on the wafer surface and are difficult to achieve dynamic and precise control of the processing temperature. This not only limits the further improvement of processing precision, but also may cause irreversible thermal damage to the wafer, seriously affecting the product qualification rate and production efficiency.

[0003] Therefore, in the current related technologies of laser processing of silicon dioxide wafers, there are technical problems such as insufficient processing control precision, wafer damage, and inability to respond to the dynamic changes of wafer processing temperature in real time due to uneven temperature distribution and lagging reaction of temperature control means. Summary of the Invention

[0004] By providing a dynamic temperature control method, system, device and medium for laser processing of silicon dioxide wafers, this application solves the technical problems in the prior art, such as insufficient processing control precision, wafer damage, and inability to respond to the dynamic changes of wafer processing temperature in real time due to uneven temperature distribution and lagging reaction of temperature control means, and achieves the technical effects of improving control precision, processing controllability and reliability.

[0005] The present application provides a dynamic temperature control method for laser processing of silicon dioxide wafers. The method includes: using a high-frame-rate thermal imaging device to collect images of the temperature distribution on the surface of the target wafer within a preset laser processing window, obtaining a sequence of wafer surface temperature distribution images; performing adjacent temperature difference calculation on the sequence of wafer surface temperature distribution images to obtain a sequence of temperature change rate distribution images; evenly extracting temperature analysis sample points according to the area size of the target wafer to obtain a temperature analysis sample point distribution topology network, where each temperature analysis sample point includes a sample point position; indexing the sequence of temperature change rate distribution images with the sample point positions of each temperature analysis sample point in the temperature analysis sample point distribution topology network to determine a sequence of sample point temperature change rate topology networks; performing iterative mapping correlation analysis on the sequence of sample point temperature change rate topology networks in chronological order from front to back to determine a target correlation sample point temperature change rate topology network; performing centralized analysis of the target correlation sample point temperature change rate on the target correlation sample point temperature change rate topology network to determine a centralized value of the target correlation sample point temperature change rate; obtaining a temperature control parameter set of the target wafer within the preset laser processing window, optimizing the temperature control parameter set based on the centralized value of the target correlation sample point temperature change rate to determine a target temperature control parameter; and performing dynamic optimization of temperature control during the laser processing of the target wafer according to the target temperature control parameter.

[0006] In a possible implementation, when performing iterative mapping correlation analysis on the sequence of sample point temperature change rate topology networks in chronological order from front to back to determine a target correlation sample point temperature change rate topology network, the following processing is also performed: extracting a first sample point temperature change rate topology network and a second sample point temperature change rate topology network from the sequence of sample point temperature change rate topology networks, performing mapping correlation analysis according to the sample point positions to determine a first correlation sample point temperature change rate topology network; extracting a third sample point temperature change rate topology network from the sequence of sample point temperature change rate topology networks again, performing mapping correlation analysis on it with the first correlation sample point temperature change rate topology network to determine a second correlation sample point temperature change rate topology network; and performing iterative mapping correlation analysis on the sequence of sample point temperature change rate topology networks in combination with the second correlation sample point temperature change rate topology network until reaching the last sample point temperature change rate topology network in the sequence of sample point temperature change rate topology networks to obtain the target correlation sample point temperature change rate topology network.

[0007] In a possible implementation manner, the first sample point temperature change rate topology network and the second sample point temperature change rate topology network are extracted from the sample point temperature change rate topology network sequence, mapping correlation analysis is performed according to the sample point positions to determine the first associated sample point temperature change rate topology network, and the following processing is further performed: temperature inner product calculation is performed on the temperature analysis sample points located at the same sample point position in the first sample point temperature change rate topology network and the second sample point temperature change rate topology network to obtain the first sample point temperature similarity topology network; the first sample point temperature similarity topology network is traversed for similarity normalization processing to obtain the first similarity correlation matrix; the mapping correlation network layer is called to perform convolution calculation on the first similarity correlation matrix and the second sample point temperature change rate topology network to determine the first associated sample point temperature change rate topology network.

[0008] In a possible implementation manner, when the mapping correlation network layer is called, the following processing is further performed: a plurality of historical similarity correlation matrices, a plurality of historical sample point temperature change rate topology networks, and the corresponding plurality of historical associated sample point temperature change rate topology networks are obtained as training data; the network framework constructed based on the graph convolutional network is supervised and trained using the training data, and the network parameters are updated according to the training results during the training process until the training converges to obtain the trained mapping correlation network layer.

[0009] In a possible implementation manner, when the target associated sample point temperature change rate topology network is subjected to target associated sample point temperature change rate concentration analysis to determine the target associated sample point temperature change rate concentration value, the following processing is further performed: a plurality of target associated sample point temperature change rates of a plurality of temperature analysis sample points in the target associated sample point temperature change rate topology network are extracted; the plurality of target associated sample point temperature change rates are averaged to determine the target associated sample point temperature change rate average value; starting from the target associated sample point temperature change rate average value as the concentration analysis starting point, iterative analysis is performed on the plurality of target associated sample point temperature change rates according to a preset concentration bandwidth to determine the target associated sample point temperature change rate concentration value.

[0010] In a possible implementation, a parallel control configuration is performed on the multi-step control logic, and the following processing is further executed: taking the average value of the temperature change rates of the target associated sample points as the starting point for centralized analysis, iterating among the temperature change rates of the multiple target associated sample points according to a preset centralized bandwidth to obtain the iterated target associated sample point temperature change rate; respectively constructing a mean neighborhood of the average value of the temperature change rates of the target associated sample points and an iterative neighborhood of the iterated target associated sample point temperature change rate based on the preset centralized bandwidth; determining whether the data volume of the mean neighborhood is less than or equal to the data volume of the iterative neighborhood, and if so, updating the iterated target associated sample point temperature change rate as the starting point for centralized analysis, and continuing to iterate among the temperature change rates of the multiple target associated sample points according to the preset centralized bandwidth until a preset number of iterations is satisfied, and taking the iterated target associated sample point temperature change rate obtained in the last iteration as the centralized value of the temperature change rate of the target associated sample points.

[0011] In a possible implementation, adjacent temperature difference calculation is performed on the sequence of wafer surface temperature distribution images to obtain a sequence of temperature change rate distribution images, and the following processing is further executed: obtaining a temperature difference calculation formula, where the temperature difference calculation formula is;

[0012] ;

[0013] where is the temperature change rate of the i-th pixel point in any wafer surface temperature distribution image in the sequence of wafer surface temperature distribution images, is the temperature value of the i-th pixel point in the sequence of wafer surface temperature distribution images at time, is the temperature value of the i-th pixel point in the sequence of wafer surface temperature distribution images at time, and i is an integer greater than or equal to 1; using the temperature difference calculation formula to perform adjacent temperature difference calculation on the sequence of wafer surface temperature distribution images to obtain a sequence of temperature change rate distribution images.

[0014] The present application also provides a dynamic temperature control system for laser processing of silica wafers, including: an image acquisition module, configured to acquire images of the temperature distribution on the surface of a target wafer within a preset laser processing window by using a high-frame-rate thermal imaging device, so as to obtain a sequence of images of the temperature distribution on the wafer surface; a temperature difference calculation module, configured to perform adjacent temperature difference calculation on the sequence of images of the temperature distribution on the wafer surface to obtain a sequence of images of the temperature change rate distribution; an equalization extraction module, configured to perform equalization extraction of temperature analysis sample points according to the area size of the target wafer to obtain a topological network of the distribution of temperature analysis sample points, wherein each temperature analysis sample point includes a sample point position; an image sequence retrieval module, configured to retrieve the sequence of images of the temperature change rate distribution by using the sample point positions of each temperature analysis sample point in the topological network of the distribution of temperature analysis sample points to determine a sequence of topological networks of the sample point temperature change rate; a mapping correlation analysis module, configured to perform iterative mapping correlation analysis on the sequence of topological networks of the sample point temperature change rate in sequence according to the order from front to back in time to determine a topological network of the target correlation sample point temperature change rate; a temperature change analysis module, configured to perform centralized analysis of the target correlation sample point temperature change rate on the topological network of the target correlation sample point temperature change rate to determine a centralized value of the target correlation sample point temperature change rate; a temperature control parameter determination module, configured to obtain a set of temperature control parameters of the target wafer within the preset laser processing window, and optimize the set of temperature control parameters based on the centralized value of the target correlation sample point temperature change rate to determine target temperature control parameters; a temperature control dynamic optimization module, configured to perform dynamic temperature control optimization during the laser processing of the target wafer according to the target temperature control parameters.

[0015] The present application also provides an electronic device, including: a memory, configured to store executable instructions; a processor, configured to implement the dynamic temperature control method for laser processing of silica wafers when executing the executable instructions stored in the memory.

[0016] The present application also provides a computer-readable storage medium, including: a computer program stored thereon, which implements the dynamic temperature control method for laser processing of silica wafers when executed by a processor.

[0017] The dynamic temperature control method, system, device and medium for laser processing of silica wafers proposed in this application are used to obtain a sequence of surface temperature distribution images of the wafers; obtain a sequence of temperature change rate distribution images; obtain a topological network of temperature analysis sample point distributions; determine a sequence of topological networks of sample point temperature change rates; determine a topological network of target associated sample point temperature change rates; determine the central value of the target associated sample point temperature change rate; determine the target temperature control parameters; and perform dynamic optimization of temperature control during the laser processing of the target wafer. This solves the technical problems in the prior art, such as insufficient processing control accuracy, wafer damage, and inability to respond in real time to the dynamic changes in wafer processing temperature due to uneven temperature distribution and lagging temperature control means, and achieves the technical effects of improving control accuracy, processing controllability and reliability. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0019] Figure 1 Schematic flowchart of the dynamic temperature control method for laser processing of silica wafers provided by an embodiment of the present application;

[0020] Figure 2 Schematic structural diagram of the dynamic temperature control system for laser processing of silica wafers provided by an embodiment of the present application;

[0021] Figure 3 Schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0022] Description of the reference numerals: Image acquisition module 10, temperature difference calculation module 20, equalization extraction module 30, image sequence retrieval module 40, mapping association analysis module 50, temperature change analysis module 60, temperature control parameter determination module 70, temperature control dynamic optimization module 80, input device 401, processor 402, memory 403, output device 404. Detailed Embodiments

[0023] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically lists the detailed embodiments of this application.

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe this application in detail in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0025] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first / second" merely distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0026] As Figure 1 shown, the embodiments of this application provide a dynamic temperature control method for laser processing of silicon dioxide wafers, including:

[0027] Step S100: Use a high-frame-rate thermal imaging device to collect images of the temperature distribution on the surface of the target wafer within a preset laser processing window, and obtain a sequence of images of the temperature distribution on the wafer surface.

[0028] Preferably, during the laser processing, a thermal imaging device capable of quickly capturing temperature changes is used to monitor the temperature of the wafer surface at a high frame rate (e.g., dozens to hundreds of frames per second), and the temperature distribution in the processing area is recorded in real time. Specifically, the high-frame-rate thermal imaging device is a device based on infrared detection technology, which can calculate the temperature by detecting the infrared radiation intensity on the wafer surface. The high frame rate means that the device can quickly and continuously capture images of the temperature distribution, which is suitable for capturing the dynamic characteristics of the rapidly changing heat during laser processing. For example, typical devices include infrared thermal imagers or high-speed infrared cameras; the preset laser processing window refers to the target area range set according to the processing technology requirements, that is, the area where the laser processing head acts on the wafer surface; the wafer surface temperature distribution image is a series of infrared images obtained by the thermal imaging device, and each pixel point represents the temperature value at a certain position on the wafer surface. This image reflects the spatial distribution of the temperature on the wafer surface at different time points, forming a temperature field. The image acquisition process is carried out at a high frame rate, and the continuously acquired multi-frame temperature distribution images are arranged in time to form a time series, that is, the wafer surface temperature distribution image sequence, which records the dynamic changes of the wafer surface temperature during the processing.

[0029] Step S200, perform adjacent temperature difference calculation on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0030] Preferably, during the laser processing, by performing mathematical processing on the continuously acquired temperature distribution images, the change rate of the temperature between adjacent time points is calculated, thereby generating a new image sequence to reflect the dynamic process of the temperature change on the wafer surface. Specifically, the differential calculation refers to calculating the difference in the temperature values of each pixel point in two consecutive frames of the image sequence. By calculating the temperature change amount of each pixel point, a distribution image reflecting the temperature change rate can be obtained. The pixel value in each frame of the image represents the temperature change rate (i.e., the temperature change amount per unit time) at a certain position on the wafer surface. This image sequence can intuitively reflect the speed of the temperature change on the wafer surface during the processing and the regional distribution of the change. Through the image sequence of the temperature change rate, the dynamic evolution of the temperature field during the laser processing can be more comprehensively understood, which can reveal the rapid heating or cooling of the local heating area and provide richer information than the simple temperature distribution.

[0031] Step S200 further includes step S210 of obtaining a temperature difference calculation formula, where the temperature difference calculation formula is;

[0032] ;

[0033] where is the temperature change rate of the i-th pixel point in any wafer surface temperature distribution image in the wafer surface temperature distribution image sequence, is the temperature value of the i-th pixel in the i-th image of the wafer surface temperature distribution image sequence at time, is the temperature value of the i-th pixel in the i-th image of the wafer surface temperature distribution image sequence at time, and i is an integer greater than or equal to 1; Step S220, perform adjacent temperature difference calculation on the wafer surface temperature distribution image sequence by using the temperature difference calculation formula to obtain a temperature change rate distribution image sequence.

[0034] Preferably, the temperature difference calculation formula is;

[0035] ;

[0036] wherein, is the temperature change rate of the i-th pixel in any wafer surface temperature distribution image in the wafer surface temperature distribution image sequence, is the temperature value of the i-th pixel in the i-th image of the wafer surface temperature distribution image sequence at time, is the temperature value of the i-th pixel in the i-th image of the wafer surface temperature distribution image sequence at time, and i is an integer greater than or equal to 1; By performing frame-by-frame analysis on the continuously acquired temperature distribution images through the temperature difference calculation formula, calculate the temperature change rate of each position on the wafer surface at consecutive time points, and generate a new set of image sequences to visually reflect the dynamic process of the temperature change on the wafer surface.

[0037] Step S300, perform balanced extraction of temperature analysis sample points according to the wafer area size of the target wafer to obtain a temperature analysis sample point distribution topology network, where each temperature analysis sample point includes a sample point position.

[0038] Preferably, according to the physical area and temperature distribution characteristics of the wafer, a set of representative temperature analysis points are selected, and a spatial distribution network (topological network) of these points is constructed according to certain rules. Specifically, the wafer area size refers to the physical size of the target wafer, such as diameter, length, and width. Larger wafers may require more sample points, while smaller wafers can reduce the number of sample points. According to the area size of the wafer and combined with the required analysis accuracy, the number of sample points to be extracted is dynamically determined. For example, larger wafers require more sample points to cover the entire surface area. The balanced extraction of temperature analysis sample points means that the distribution of sample points should cover the key areas of the wafer (such as the center, edge, heat-sensitive areas, etc.) to ensure the representativeness of the data. Specifically, the wafer surface is divided into several regular grids, and sample points are selected according to the center positions of the grids. The density of the grids is dynamically adjusted according to the area of the wafer and the requirements of temperature control accuracy. The sample point density is increased in areas with rapid temperature changes (such as laser processing areas) and decreased in areas with slow temperature changes (such as the wafer edge). A certain degree of randomness is introduced under the premise of meeting uniformity to improve the comprehensiveness of the sample point distribution, and areas with significant temperature characteristics such as laser incident points and heat source diffusion boundaries are preferentially selected as sample points. A temperature analysis sample point distribution topological network is obtained, and each sample point contains sample point position and associated information. The position is defined as a coordinate point on the wafer surface, such as (x, y). The sample points are connected to form a network through a certain topological relationship (such as distance, connection strength). For example, through the adjacency relationship method, that is, according to the geometric distance between sample points, a connection weight (such as Euclidean distance) is generated to form a weighted topological graph, that is, the temperature analysis sample point distribution topological network.

[0039] Step S400: Using the sample point positions of each temperature analysis sample point in the temperature analysis sample point distribution topological network as indexes, retrieve the temperature change rate distribution image sequence to determine the sample point temperature change rate topological network sequence.

[0040] Preferably, using the sample point positions (i.e., the coordinates of each sample point) in the extracted temperature analysis sample point distribution topology network, the temperature change rate values at the corresponding positions are extracted from the temperature change rate distribution image sequence, and the topology network data in time series is generated according to the structure of the topology network. Specifically, taking the sample point positions of the temperature analysis sample points as indexes, the data of the corresponding pixel points are directly extracted from the temperature change rate distribution image sequence, avoiding full-image traversal. Specifically, taking the sample point positions in the sample point distribution topology network and the temperature change rate distribution image sequence as inputs, for each sample point, according to its coordinates, the corresponding pixel point is found in each frame of the image sequence. In each frame of the image, the temperature change rate value is read from the corresponding pixel point. For each sample point, the temperature change rate values of it in all frames of the image are arranged in chronological order to form the time series data of the sample point; then the temperature change rate time series of each sample point is assigned to the corresponding node, and edges are established according to the connection relationship of the original sample point distribution topology network to form a complete topology network structure, obtaining the sample point temperature change rate topology network sequence. Among them, the node is the sample point, the node value is the corresponding temperature change rate time series, and the edge is the connection relationship between the sample points (such as geometric adjacency relationship), representing the potential path of heat transfer. Each sample point contains a time-series temperature change rate data.

[0041] Step S500, perform iterative mapping correlation analysis on the sample point temperature change rate topology network sequence in chronological order from front to back to determine the target correlation sample point temperature change rate topology network.

[0042] Preferably, according to the sample point temperature change rate topology network sequence in the time series, analyze the dynamic correlation characteristics between the sample points at the same position in the topology network in chronological order, and construct the target correlation sample point temperature change rate topology network reflecting the actual heat transfer or dynamic correlation between the sample points through iterative calculation and mapping rules. Specifically, the iterative mapping correlation analysis from front to back in time is used to find the sample points whose temperature change rates are correlated with each other in the time series, and determine the heat transfer path or dynamic correlation relationship between the correlation sample points. The iterative mapping steps are as follows. At time t0, based on the existing topology network structure and sample point temperature change rate values, calculate the dynamic correlation degree between the initial nodes. For each time t i , according to the temperature change rate data at the current moment and the correlation analysis results at the previous moment, update the weights and connection relationships of the edges in the topology network. The update rules may include the Fourier heat conduction formula to calculate the heat diffusion trend between sample points, or calculate the correlation coefficient between temperature change rates using a sliding window. According to the update results, generate the topology network at time t i and for time t i+1The data is combined with the results from t0 to t1 before for multi-step prediction or correlation verification to further optimize the connection weights between sample points or delete meaningless edges; finally, the target correlation sample point temperature change rate topology network is obtained, with the nodes being the same as the initial sample points, the value of each node being the final state of the temperature change rate, and the edges being the correlation analysis results after iterative mapping, indicating the connections with strong correlation or significant heat transfer between sample points.

[0043] Further, step S500 further includes step S510 of extracting the first sample point temperature change rate topology network and the second sample point temperature change rate topology network from the sample point temperature change rate topology network sequence, performing mapping correlation analysis based on the sample point positions to determine the first correlation sample point temperature change rate topology network; step S520 of extracting the third sample point temperature change rate topology network from the sample point temperature change rate topology network sequence again, performing mapping correlation analysis with the first correlation sample point temperature change rate topology network to determine the second correlation sample point temperature change rate topology network; step S530 of performing iterative mapping correlation analysis on the sample point temperature change rate topology network sequence in combination with the second correlation sample point temperature change rate topology network until reaching the last sample point temperature change rate topology network in the sample point temperature change rate topology network sequence to obtain the target correlation sample point temperature change rate topology network.

[0044] Preferably, the first sample point temperature change rate topology network at the first position is extracted from the sample point temperature change rate topology network sequence. Similarly, the second sample point temperature change rate topology network is extracted from the sample point temperature change rate topology network sequence, representing the temperature change rate distribution and its correlation at the second position in the sequence. Based on the mapping relationship of the sample point positions between the first and second sample point topology networks, the dynamic correlation between them is analyzed to determine a new topology network (the first correlation sample point temperature change rate topology network), whose structure reflects the correlation characteristics between the two (such as heat transfer paths, dynamic change trends, etc.); then the third sample point temperature change rate topology network is extracted from the sample point temperature change rate topology network sequence. Based on the same principle of obtaining the first correlation sample point temperature change rate topology network, the third sample point topology network is subjected to mapping correlation analysis with the first correlation sample point temperature change rate topology network to analyze their dynamic correlation characteristics and obtain the second correlation sample point temperature change rate topology network; new sample point topology networks (the fourth, fifth,...) are continuously extracted from the topology network sequence, and the new topology networks are sequentially subjected to mapping correlation analysis with the previous correlation results (such as the second correlation sample point temperature change rate topology network). This process is repeated until all sample point topology networks participate in the mapping correlation analysis to obtain the target correlation sample point temperature change rate topology network, reflecting the heat transfer paths and dynamic change characteristics in the entire processing process.

[0045] Further, step S510 further includes step S511, calculating the temperature inner product of the temperature analysis sample points located at the same sample point position in the first sample point temperature change rate topology network and the second sample point temperature change rate topology network to obtain the first sample point temperature similarity topology network; step S512, traversing the first sample point temperature similarity topology network for similarity normalization processing to obtain the first similarity correlation matrix; step S513, calling the mapping correlation network layer to perform convolution calculation on the first similarity correlation matrix and the second sample point temperature change rate topology network to determine the first associated sample point temperature change rate topology network.

[0046] Preferably, the temperature inner product calculation refers to comparing the sample points at the same position in the first sample point temperature change rate topology network and the second sample point temperature change rate topology network, calculating the similarity between their temperature change rates, for example, calculating using the cosine similarity formula to obtain the first sample point temperature similarity topology network, and the connection weight of each node is defined by the temperature inner product value of adjacent sample points, reflecting the similarity between sample points; then traversing all nodes and their connections in the first sample point temperature similarity topology network, extracting the similarity weight values, and performing similarity normalization processing, that is, adjusting the similarity values to the range of [0, 1] to ensure the consistency of the proportional relationship of the weights, obtaining the first similarity correlation matrix; the mapping correlation network layer performs joint analysis on the first similarity correlation matrix and the second sample point temperature change rate topology network, extracts the deep dynamic correlation characteristics between sample points, similar to the convolution operation in the graph neural network, calculates the weighted aggregation characteristics between nodes. Specifically, taking the first similarity correlation matrix (normalized similarity matrix) and the second sample point temperature change rate topology network as inputs, performing convolution calculation to obtain the first associated sample point temperature change rate topology network, providing accurate theoretical and data support for the temperature control optimization in the laser processing process.

[0047] Further, step S513 further includes step S5131, obtaining multiple historical similarity correlation matrices, multiple historical sample point temperature change rate topology networks, and corresponding multiple historical associated sample point temperature change rate topology networks as training data; step S5132, using the training data to perform supervised training on the network framework constructed based on the graph convolutional network, updating the network parameters according to the training results during the training process until the training converges, obtaining the trained mapping correlation network layer.

[0048] Preferably, through supervised training of historical data, the network model based on the Graph Convolutional Network (GCN) is optimized to obtain a mapping correlation network layer, enabling it to perform more accurate dynamic correlation analysis and topological optimization of the temperature change rate of sample points. Specifically, multiple historical similarity correlation matrices, multiple historical sample point temperature change rate topology networks, and corresponding multiple historical correlated sample point temperature change rate topology networks are obtained. The historical similarity correlation matrix is calculated from the temperature change rate data of multiple historical processing tasks (e.g., using the temperature inner product formula), reflecting the similarity of the temperature change rates between sample points during the historical processing; each historical topology network in the historical sample point temperature change rate topology network represents the temperature change rate distribution at different time nodes in a processing task and the initial correlation between its sample points; the historical correlated sample point temperature change rate topology network refers to the correlation topology network generated through actual processing results, containing the dynamic correlation results between sample points during the processing.

[0049] Preferably, then, using the historical similarity correlation matrix and the sample point temperature change rate topology network as input data, and the historical correlated sample point temperature change rate topology network as output data (label), the network framework constructed based on the graph convolutional network is subjected to supervised training. Among them, the graph convolutional network aggregates features of the input graph structure data (such as topology network and similarity correlation matrix), capturing the relationship between nodes and neighbor nodes, including an input layer, a hidden layer (multiple graph convolutional layers, gradually extracting the dynamic correlation features between sample points), and an output layer. Specifically, randomly initialize the weight matrix of the graph convolutional network, input the similarity correlation matrix and the sample point topology network, calculate the prediction result, and then calculate the loss between the prediction result and the true correlation topology network. In each training iteration, adjust the network weights according to the gradient of the loss function, gradually improving the model's prediction ability for sample point correlation. When the loss function drops to a certain threshold, or the loss change tends to be stable for several consecutive iterations, the training ends, and the trained mapping correlation network layer is obtained, which can efficiently predict a new correlation topology network based on the new similarity correlation matrix and sample point topology network.

[0050] Step S600, perform centralized analysis on the target correlated sample point temperature change rate topology network to determine the target correlated sample point temperature change rate central value.

[0051] Preferably, based on the target correlated sample point temperature change rate topology network, through statistical analysis methods, centralize the temperature change rates of all sample points in the topology network to obtain a central value that can comprehensively reflect the temperature change characteristics of the target area. Specifically, the core of the centralized analysis is to inductively statistically analyze the temperature change rates of the sample points in the topology network, extracting the overall trend and characteristic values. Among them, the target correlated sample point temperature change rate central value reflects the general situation of the temperature change rate of the target wafer.

[0052] Further, step S600 further includes step S610 of extracting the temperature change rates of multiple target associated sample points in the temperature change rate topology network of the target associated sample points; step S620 of performing a mean process on the temperature change rates of the multiple target associated sample points to determine the mean value of the temperature change rates of the target associated sample points; and step S630 of performing iterative analysis on the temperature change rates of the multiple target associated sample points with the mean value of the temperature change rates of the target associated sample points as the central analysis starting point according to a preset central bandwidth to determine the central value of the temperature change rates of the target associated sample points.

[0053] Preferably, the temperature change rate characteristics are extracted from the topology network of the target associated sample points and central analysis is performed to determine the final central value. Specifically, the temperature change rates of multiple target associated sample points are extracted from the temperature change rate topology network of the target associated sample points, and the mean value of the temperature change rates of the target associated sample points is calculated as the initial reference value for analysis. Then, the distribution characteristics of the temperature change rates of the sample points are judged according to the mean value, and iterative analysis is performed according to the preset central bandwidth. The preset central bandwidth refers to the preset analysis range (bandwidth) preset by those skilled in the art to limit the iterative analysis range of the central value of the temperature change rate, and then the central range (bandwidth) is dynamically adjusted to gradually narrow the distribution range of the temperature change rates of the target sample points to determine the final central value. Specifically, an initial range is set, the target sample points meeting the conditions are screened within the current bandwidth range, and the mean value of the remaining sample points is recalculated. When the central value iteration converges, the final mean value is the central value of the temperature change rates of the target associated sample points, representing the overall characteristic value of the temperature change rates of the sample points in the target area and comprehensively reflecting the dynamic heat transfer characteristics of this area during the processing.

[0054] Further, step S630 further includes step S631 of performing iteration on the temperature change rates of the multiple target associated sample points with the mean value of the temperature change rates of the target associated sample points as the central analysis starting point according to the preset central bandwidth to obtain the iterative temperature change rates of the target associated sample points; step S632 of respectively constructing a mean neighborhood of the mean value of the temperature change rates of the target associated sample points and an iterative neighborhood of the iterative temperature change rates of the target associated sample points based on the preset central bandwidth; and step S633 of judging whether the data volume of the mean neighborhood is less than or equal to the data volume of the iterative neighborhood. If so, updating the iterative temperature change rates of the target associated sample points to the central analysis starting point and continuing to perform iteration on the temperature change rates of the multiple target associated sample points according to the preset central bandwidth until the preset iteration times are met, and taking the iterative temperature change rates of the target associated sample points obtained in the last iteration as the central value of the temperature change rates of the target associated sample points.

[0055] Preferably, the mean value of the temperature change rate of the target-related sample points is used as the starting point for analysis, and iteration is performed among the temperature change rates of multiple target-related sample points according to the preset centralized bandwidth to obtain the iterative target-related sample point temperature change rate. Based on the preset centralized bandwidth, the mean neighborhood of the mean value of the target-related sample point temperature change rate and the iterative neighborhood of the iterative target-related sample point temperature change rate are constructed respectively. The mean neighborhood is a region constructed with the mean value of the target-related sample point temperature change rate as the center and the preset centralized bandwidth as the radius; the iterative neighborhood refers to the region where the target-related sample points are further screened within the centralized bandwidth range. It is judged whether the data volume of the mean neighborhood is less than or equal to that of the iterative neighborhood. If it is satisfied, the central value of the iterative neighborhood is updated to a new centralized analysis starting point and enters the next iteration; if not, the iteration is stopped and the current centralized analysis starting point is used as the final centralized value; when the preset number of iterations is reached, or the neighborhood change is no longer significant during the iteration process (such as the neighborhood data volume tends to be stable), the final centralized value of the target-related sample point temperature change rate is determined, which reflects the overall characteristics of the sample point temperature change rate and is an important reference index for temperature control optimization.

[0056] Step S700, obtain the temperature control parameter set of the target wafer within the preset laser processing window, and optimize the temperature control parameter set based on the centralized value of the target-related sample point temperature change rate to determine the target temperature control parameter.

[0057] Preferably, according to the processing requirements of the target wafer and the actual situation of the processing window, a set of temperature control-related parameters is extracted. By analyzing the central value of the temperature change rate of the target associated sample points, these parameters are dynamically adjusted to determine the optimized temperature control parameters suitable for the current processing state, ensuring processing quality and efficiency. Specifically, according to the wafer material and processing requirements, an initial set of temperature control parameters is obtained. The set of temperature control parameters is the key parameters for controlling the wafer processing temperature, including but not limited to laser power (the output power of the laser), laser scanning speed (the speed at which the laser moves on the wafer surface), pulse frequency (the repetition frequency of the laser pulses), cooling system parameters (such as cooling flow rate, cooling intensity), and processing window time (the time when the laser acts on the preset processing window); then the set of temperature control parameters is optimized according to the central value of the temperature change rate of the target associated sample points. The objectives of optimizing the set of temperature control parameters according to the central value usually include controlling temperature stability, improving processing efficiency, and balancing heat distribution. Specifically, if the temperature change rate is too high (the central value exceeds the set threshold), the laser power is reduced to reduce heat input; for the hot spot area, the scanning speed is reduced to avoid overheating; for the area with large fluctuations in the central value of the rate, the cooling intensity is enhanced to balance the heat; combining the real-time temperature monitoring data and the analysis results of the central value, a feedback control method (such as PID control or fuzzy control) is used to dynamically optimize the parameters, or a model trained based on historical data (such as machine learning algorithms) is used to predict the optimal parameter combination, shortening the optimization time, and finally determining the target temperature control parameters to guide the laser processing process, ensuring higher temperature control accuracy in laser processing and more uniform heat distribution within the processing window, and further improving the reliability and efficiency of wafer processing.

[0058] Step S800, perform dynamic optimization of temperature control during the laser processing of the target wafer according to the target temperature control parameters.

[0059] Preferably, using the optimized set of temperature control parameters, the temperature of the target wafer is adjusted in real time and dynamically during the laser processing to ensure stable and uniform temperature distribution in the processing area, while avoiding phenomena such as local overheating or insufficient cooling, thereby improving processing quality and efficiency. Specifically, a high-frame-rate thermal imaging device is used to continuously collect the temperature distribution on the wafer surface, and the real-time feedback of the temperature change in the current processing area is obtained. According to the real-time monitored temperature change data, the temperature control parameters are adjusted immediately to adapt to the changes in the processing environment and process requirements, avoiding wafer thermal stress, cracks or warping caused by overheating or local temperature concentration, ensuring the integrity and processing accuracy of the wafer. While ensuring quality, through dynamic optimization, the laser processing speed and cooling efficiency are improved, ensuring the stability and high efficiency of the processing effect, and at the same time reducing the risk of processing defects.

[0060] In the above text, with reference to Figure 1A dynamic temperature control method for laser processing of silica wafers according to an embodiment of the present invention is described in detail. Next, a dynamic temperature control system for laser processing of silica wafers according to an embodiment of the present invention will be described with reference to Figure 2 A dynamic temperature control system for laser processing of silica wafers according to an embodiment of the present invention will be described.

[0061] The dynamic temperature control system for laser processing of silica wafers according to an embodiment of the present invention is used to solve the technical problems existing in the prior art, such as insufficient processing control accuracy, wafer damage, and inability to respond in real time to the dynamic changes of the wafer processing temperature due to uneven temperature distribution and lagging response of temperature control means, and achieves the technical effects of improving control accuracy, processing controllability, and reliability. The dynamic temperature control system for laser processing of silica wafers includes: an image acquisition module 10, a temperature difference calculation module 20, an equalization extraction module 30, an image sequence retrieval module 40, a mapping correlation analysis module 50, a temperature change analysis module 60, a temperature control parameter determination module 70, and a temperature control dynamic optimization module 80.

[0062] The image acquisition module 10 is used to acquire an image sequence of the surface temperature distribution of the target wafer within a preset laser processing window by using a high-frame-rate thermal imaging device; the temperature difference calculation module 20 is used to perform adjacent temperature difference calculation on the image sequence of the surface temperature distribution of the wafer to obtain an image sequence of the temperature change rate distribution; the equalization extraction module 30 is used to perform equalization extraction of temperature analysis sample points according to the wafer area size of the target wafer to obtain a temperature analysis sample point distribution topology network, where each temperature analysis sample point includes a sample point position; the image sequence retrieval module 40 is used to retrieve the image sequence of the temperature change rate distribution by using the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topology network to determine a sample point temperature change rate topology network sequence; the mapping correlation analysis module 50 is used to perform iterative mapping correlation analysis on the sample point temperature change rate topology network sequence in order from front to back in time to determine a target associated sample point temperature change rate topology network; the temperature change analysis module 60 is used to perform centralized analysis of the target associated sample point temperature change rate on the target associated sample point temperature change rate topology network to determine a target associated sample point temperature change rate centralized value; the temperature control parameter determination module 70 is used to obtain a temperature control parameter set of the target wafer within the preset laser processing window, optimize the temperature control parameter set based on the target associated sample point temperature change rate centralized value, and determine a target temperature control parameter; the temperature control dynamic optimization module 80 is used to perform dynamic temperature control optimization during the laser processing of the target wafer according to the target temperature control parameter.

[0063] The specific configuration of the mapping correlation analysis module 50 includes extracting the first sample point temperature change rate topology network and the second sample point temperature change rate topology network from the sample point temperature change rate topology network sequence, performing mapping correlation analysis according to the sample point positions to determine the first correlated sample point temperature change rate topology network; extracting the third sample point temperature change rate topology network from the sample point temperature change rate topology network sequence again, performing mapping correlation analysis on it and the first correlated sample point temperature change rate topology network to determine the second correlated sample point temperature change rate topology network; performing iterative mapping correlation analysis on the sample point temperature change rate topology network sequence in combination with the second correlated sample point temperature change rate topology network until reaching the last sample point temperature change rate topology network in the sample point temperature change rate topology network sequence to obtain the target correlated sample point temperature change rate topology network.

[0064] The specific configuration of the mapping correlation analysis module 50 further includes calculating the temperature inner product of the temperature analysis sample points at the same sample point positions in the first sample point temperature change rate topology network and the second sample point temperature change rate topology network to obtain the first sample point temperature similarity topology network; traversing the first sample point temperature similarity topology network for similarity normalization processing to obtain the first similarity correlation matrix; calling the mapping correlation network layer to perform convolution calculation on the first similarity correlation matrix and the second sample point temperature change rate topology network to determine the first correlated sample point temperature change rate topology network.

[0065] The specific configuration of the mapping correlation analysis module 50 further includes obtaining multiple historical similarity correlation matrices, multiple historical sample point temperature change rate topology networks, and corresponding multiple historical correlated sample point temperature change rate topology networks as training data; using the training data to perform supervised training on the network framework constructed based on the graph convolutional network, and updating the network parameters according to the training results during the training process until the training converges to obtain the trained mapping correlation network layer.

[0066] The specific configuration of the temperature change analysis module 60 includes extracting multiple target correlated sample point temperature change rates of multiple temperature analysis sample points in the target correlated sample point temperature change rate topology network; performing mean processing on the multiple target correlated sample point temperature change rates to determine the target correlated sample point temperature change rate mean; using the target correlated sample point temperature change rate mean as the centralized analysis starting point, and performing iterative analysis on the multiple target correlated sample point temperature change rates according to the preset centralized bandwidth to determine the target correlated sample point temperature change rate centralized value.

[0067] The specific configuration of the temperature change analysis module 60 further includes taking the average value of the temperature change rates of the target-associated sample points as the starting point for centralized analysis, iterating among the temperature change rates of the multiple target-associated sample points according to a preset centralized bandwidth to obtain the iterated target-associated sample point temperature change rates; respectively constructing a mean neighborhood of the average value of the target-associated sample point temperature change rates and an iterative neighborhood of the iterated target-associated sample point temperature change rates based on the preset centralized bandwidth; determining whether the data volume of the mean neighborhood is less than or equal to the data volume of the iterative neighborhood, and if so, updating the iterated target-associated sample point temperature change rate as the starting point for centralized analysis, and continuing to iterate among the temperature change rates of the multiple target-associated sample points according to the preset centralized bandwidth until the preset number of iterations is satisfied, and taking the iterated target-associated sample point temperature change rate obtained in the last iteration as the centralized value of the target-associated sample point temperature change rate.

[0068] The specific configuration of the temperature difference calculation module 20 includes obtaining a temperature difference calculation formula, where the temperature difference calculation formula is;

[0069] ;

[0070] where is the temperature change rate of the i-th pixel point in any wafer surface temperature distribution image in the wafer surface temperature distribution image sequence, is the temperature value of the i-th pixel point in the wafer surface temperature distribution image sequence at time, is the temperature value of the i-th pixel point in the wafer surface temperature distribution image sequence at time, and i is an integer greater than or equal to 1; using the temperature difference calculation formula to perform adjacent temperature difference calculations on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0071] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device and a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor of the electronic device, it can implement the method described in any previous embodiment.

[0072] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3The electronic device shown is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. The electronic device is presented in the form of a general computing device, and its components may include, but are not limited to, an input device 401, a processor 402, a memory 403, and an output device 404. Among them, the processor 402 can be one or more; the memory 403 may include a computer-readable medium and at least one program product. This program product has a set (at least one) of program modules, and these program modules are configured to execute the functions of the various embodiments of the present application.

[0073] The memory 403 shown in the embodiments of the present invention can adopt any combination of one or more computer-readable media; the computer-readable storage medium can be, but is not limited to, infrared rays, semiconductor systems, devices, or components, or any combination of the above, for storing software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the dynamic temperature control method for laser processing of silicon dioxide wafers in the embodiments of the present invention. The processor 402 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 403, that is, implementing the above-mentioned dynamic temperature control method for laser processing of silicon dioxide wafers.

[0074] Although the present application makes various references to certain modules in the embodiments according to the present application, however, any number of different modules can be used and run on the user terminal and / or server. 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 the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0075] The above specific implementation manners do not constitute a limitation on the protection scope of the present 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 principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for dynamic temperature control of silicon dioxide wafer laser processing, characterized in that: The method comprises: Using a high frame rate thermal imaging device to collect images of the target wafer surface temperature distribution within a preset laser processing window, and obtaining a wafer surface temperature distribution image sequence; Performing adjacent temperature difference calculation on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence; Performing balanced extraction of temperature analysis sample points according to the wafer area of ​​the target wafer to obtain a temperature analysis sample point distribution topology network, wherein each temperature analysis sample point includes a sample point position; Using the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topological network as an index, searching the temperature change rate distribution image sequence to determine the sample point temperature change rate topological network sequence; Iterative mapping association analysis is performed on the sample point temperature change rate topological network sequence in order from front to back in time to determine the target associated sample point temperature change rate topological network; Performing a centralized analysis of the target-associated sample point temperature change rate on the target-associated sample point temperature change rate topological network to determine a centralized value of the target-associated sample point temperature change rate; Acquire a set of temperature control parameters of the target wafer within the preset laser processing window, optimize the set of temperature control parameters based on a concentrated value of the temperature change rate of the target associated sample points, and determine a target temperature control parameter; The temperature control of the target wafer during laser processing is dynamically optimized according to the target temperature control parameters.

2. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 1, characterized in that: Iterative mapping association analysis is performed on the sample point temperature change rate topological network sequence in order from front to back in time to determine the target associated sample point temperature change rate topological network, including: Extracting a first sample point temperature change rate topological network and a second sample point temperature change rate topological network from the sample point temperature change rate topological network sequence, performing mapping association analysis according to the sample point positions, and determining a first associated sample point temperature change rate topological network; extracting a third sample point temperature change rate topological network from the sample point temperature change rate topological network sequence again, and performing mapping association analysis on the third sample point temperature change rate topological network with the first associated sample point temperature change rate topological network to determine a second associated sample point temperature change rate topological network; In combination with the second associated sample point temperature change rate topological network, an iterative mapping association analysis is performed on the sample point temperature change rate topological network sequence until the last sample point temperature change rate topological network of the sample point temperature change rate topological network sequence is reached to obtain the target associated sample point temperature change rate topological network.

3. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 2, characterized in that: Extracting a first sample point temperature change rate topological network and a second sample point temperature change rate topological network from the sample point temperature change rate topological network sequence, performing mapping association analysis according to the sample point positions, and determining a first associated sample point temperature change rate topological network, including: Performing temperature inner product calculation on the temperature analysis sample points located at the same sample point position in the first sample point temperature change rate topological network and the second sample point temperature change rate topological network to obtain a first sample point temperature similarity topological network; Traversing the first sample point temperature similarity topological network to perform similarity normalization processing to obtain a first similarity association matrix; The mapping association network layer is called to perform convolution calculation on the first similarity association matrix and the second sample point temperature change rate topological network to determine the first associated sample point temperature change rate topological network.

4. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 3, characterized in that: include: Acquire multiple historical similarity association matrices, multiple historical sample point temperature change rate topological networks, and multiple corresponding historical associated sample point temperature change rate topological networks as training data; The training data is used to perform supervised training on a network framework built based on a graph convolutional network. During the training process, the network parameters are updated according to the training results until the training converges, thereby obtaining the mapping association network layer that has completed the training.

5. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 1, characterized in that: Performing a centralized analysis of the target-associated sample point temperature change rate topological network to determine a centralized value of the target-associated sample point temperature change rate includes: Extracting temperature change rates of multiple target-associated sample points of multiple temperature analysis sample points in the target-associated sample point temperature change rate topological network; Performing mean processing on the temperature change rates of the plurality of target-associated sample points to determine a mean value of the temperature change rates of the target-associated sample points; Taking the mean value of the temperature change rate of the target-associated sample points as the starting point of centralized analysis, the temperature change rates of the multiple target-associated sample points are iteratively analyzed according to a preset centralized bandwidth to determine a centralized value of the temperature change rate of the target-associated sample points.

6. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 5, characterized in that: include: Taking the mean of the temperature change rates of the target-associated sample points as the starting point of centralized analysis, iterating among the temperature change rates of the plurality of target-associated sample points according to a preset centralized bandwidth to obtain an iterated temperature change rate of the target-associated sample points; Based on the preset concentrated bandwidth, respectively, constructing a mean neighborhood of the mean of the temperature change rate of the target associated sample point and an iterative neighborhood of the iterative temperature change rate of the target associated sample point; Determine whether the data volume of the mean neighborhood is less than or equal to the data volume of the iteration neighborhood. If so, update the iterative target-associated sample point temperature change rate to the centralized analysis starting point, and continue to iterate in the multiple target-associated sample point temperature change rates according to the preset centralized bandwidth until the preset number of iterations is met, and use the iterative target-associated sample point temperature change rate obtained in the last iteration as the target-associated sample point temperature change rate centralized value.

7. The method for dynamic temperature control of silicon dioxide wafer laser processing according to claim 1, characterized in that: Performing adjacent temperature difference calculation on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence, including: The temperature difference calculation formula is: ； in, is the temperature change rate of the i-th pixel in any wafer surface temperature distribution image in the wafer surface temperature distribution image sequence, is the i-th pixel point in the chip surface temperature distribution image sequence The temperature value at the moment, is the i-th pixel point in the chip surface temperature distribution image sequence The temperature value at the time, i is an integer greater than or equal to 1, yes Moment and The time difference between moments; Adjacent temperature difference calculations are performed on the wafer surface temperature distribution image sequence using the temperature difference calculation formula to obtain a temperature change rate distribution image sequence.

8. Dynamic temperature control system for laser processing of silicon dioxide wafers, characterized in that: The system is used to implement the dynamic temperature control method for laser processing of silicon dioxide wafers according to any one of claims 1 to 7, and the system comprises: An image acquisition module is used to acquire images of the surface temperature distribution of a target wafer within a preset laser processing window using a high frame rate thermal imaging device to obtain a sequence of wafer surface temperature distribution images; A temperature difference calculation module is used to perform adjacent temperature difference calculation on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence; A balanced extraction module, used for performing balanced extraction of temperature analysis sample points according to the wafer area of ​​the target wafer, and obtaining a temperature analysis sample point distribution topology network, wherein each temperature analysis sample point includes a sample point position; An image sequence retrieval module is used to retrieve the temperature change rate distribution image sequence by taking the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topological network as an index, and determine the sample point temperature change rate topological network sequence; A mapping association analysis module is used to perform iterative mapping association analysis on the sample point temperature change rate topological network sequence in order from front to back in time to determine the target associated sample point temperature change rate topological network; A temperature change analysis module, used for performing a centralized analysis of the temperature change rates of target-associated sample points on the target-associated sample point temperature change rate topological network to determine a centralized value of the temperature change rates of the target-associated sample points; A temperature control parameter determination module, used to obtain a set of temperature control parameters of the target wafer within the preset laser processing window, optimize the set of temperature control parameters based on a concentrated value of the temperature change rate of the target associated sample points, and determine a target temperature control parameter; The temperature control dynamic optimization module is used to dynamically optimize the temperature control of the target wafer during laser processing according to the target temperature control parameters.

9. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the dynamic temperature control method for laser processing of silicon dioxide wafers as described in any one of claims 1 to 7 when executing the executable instructions stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the dynamic temperature control method for laser processing of silicon dioxide wafers as described in any one of claims 1 to 7 is implemented.

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