Silicon dioxide wafer laser processing dynamic temperature control method, system, equipment and medium

Abstract

The invention discloses a silicon dioxide wafer laser processing dynamic temperature control method, system, equipment and medium, and relates to the wafer laser processing related technical field, and the method comprises the following steps: obtaining a wafer surface temperature distribution image sequence; obtaining a temperature change rate distribution image sequence; obtaining a temperature analysis sample point distribution topology network; determining a sample point temperature change rate topology network sequence; determining a target associated sample point temperature change rate topology network; determining a temperature change rate concentrated value of the target associated sample point; determining a target temperature control parameter; and performing temperature control dynamic optimization on the target wafer in the laser processing process according to the target temperature control parameter. The technical problems that in the prior art, due to uneven temperature distribution and delayed response of temperature control means, the machining control precision is insufficient, the wafer is damaged, and the dynamic change of the wafer machining temperature cannot be responded in real time are solved, and the technical effect of improving the control precision and the machining controllability and reliability is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to wafer laser processing, and specifically to a dynamic temperature control method, system, equipment and medium for silicon dioxide wafer laser processing. Background Art

[0002] Silicon dioxide wafers are widely used in optoelectronic devices, integrated circuits and other fields, and their processing quality directly affects the performance and reliability of the devices. Laser processing technology has become the main method for wafer micro-processing due to its high precision and high efficiency. 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 of the wafer, which may cause microcracks, warping or other damage. Traditional temperature control methods mostly use fixed temperature control parameters or simple temperature monitoring methods, which are difficult to reflect the dynamic temperature changes in real time, resulting in temperature control lag or insufficient regulation. Especially in high-precision laser processing scenarios, the temperature distribution of silicon dioxide wafers is often highly dynamic and non-uniform. The existing method cannot fully perceive the temperature change law of the wafer surface, and it is difficult to achieve dynamic and accurate control of the processing temperature. It not only limits the further improvement of processing accuracy, but also may cause irreversible thermal damage to the wafer, seriously affecting the product qualification rate and production efficiency.

[0003] Therefore, in the current silicon dioxide wafer laser processing related technologies, there are technical problems such as insufficient processing control accuracy, wafer damage and inability to respond to dynamic changes in wafer processing temperature in real time due to uneven temperature distribution and delayed response of temperature control measures. Summary of the invention

[0004] The present application solves the technical problems existing in the prior art such as insufficient processing control accuracy, chip damage and inability to respond to dynamic changes in chip processing temperature in real time due to uneven temperature distribution and delayed response of temperature control means by providing a dynamic temperature control method, system, equipment and medium for silicon dioxide chip laser processing, thereby achieving the technical effect of improving control accuracy, processing controllability and reliability.

[0005] The present application provides a method for dynamic temperature control of silicon dioxide wafer laser processing, the method comprising: using a high frame rate thermal imaging device to collect images of the wafer surface temperature distribution of a target wafer within a preset laser processing window to obtain 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 size of the target wafer to obtain a temperature analysis sample point distribution topological network, wherein each temperature analysis sample point includes a sample point position; taking the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topological network as an index, performing temperature change rate distribution image sequence extraction on the temperature analysis sample point distribution topological network to obtain a temperature change rate distribution image sequence; performing temperature analysis sample point distribution topological network ...; performing temperature analysis sample point distribution topological network extraction on the temperature analysis The method comprises the following steps: performing a row search to determine a topological network sequence of sample point temperature change rates; performing an iterative mapping association analysis on the sample point temperature change rate topological network sequence in a time-ordered order to determine a target-associated sample point temperature change rate topological network; performing a target-associated sample point temperature change rate centralized analysis on the target-associated sample point temperature change rate topological network to determine a target-associated sample point temperature change rate centralized value; 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 target-associated sample point temperature change rate centralized value to determine a target temperature control parameter; and performing a temperature control dynamic optimization on the target wafer during laser processing according to the target temperature control parameter.

[0006] In a possible implementation, the sample point temperature change rate topological network sequence is iteratively mapped and associated with each other in order from front to back in time to determine the target associated sample point temperature change rate topological network, and the following processing is also performed: a first sample point temperature change rate topological network and a second sample point temperature change rate topological network are extracted from the sample point temperature change rate topological network sequence, and mapping and association analysis are performed according to the sample point positions to determine the first associated sample point temperature change rate topological network; a third sample point temperature change rate topological network is extracted from the sample point temperature change rate topological network sequence again, and mapping and association analysis is performed on the third sample point temperature change rate topological network with the first associated sample point temperature change rate topological network to determine the second associated sample point temperature change rate topological network; and the sample point temperature change rate topological network sequence is iteratively mapped and associated with each other in combination with the second associated sample point temperature change rate topological network 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.

[0007] In a possible implementation, a first sample point temperature change rate topological network and a second sample point temperature change rate topological network are extracted from the sample point temperature change rate topological network sequence, a mapping association analysis is performed according to the sample point positions, and a first associated sample point temperature change rate topological network is determined. The following processing is also 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 topological network and the second sample point temperature change rate topological network to obtain a first sample point temperature similarity topological network; the first sample point temperature similarity topological network is traversed 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.

[0008] In a possible implementation, the mapping association network layer is called and the following processing is also performed: multiple historical similarity association matrices, multiple historical sample point temperature change rate topological networks and corresponding multiple historical associated sample point temperature change rate topological networks are obtained as training data; the training data is used to perform supervised training on a network framework built based on a graph convolutional network, and during the training process, 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.

[0009] In a possible implementation, a centralized analysis of the temperature change rates of the target-associated sample points is performed 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, and the following processing is also performed: extracting multiple target-associated sample point temperature change rates of multiple temperature analysis sample points in the target-associated sample point temperature change rate topological network; performing mean processing on the multiple target-associated sample point temperature change rates to determine the mean value of the target-associated sample point temperature change rates; taking the mean value of the target-associated sample point temperature change rate as the starting point of centralized analysis, iteratively analyzing the multiple target-associated sample point temperature change rates according to a preset centralized bandwidth to determine the centralized value of the target-associated sample point temperature change rate.

[0010] In a possible implementation, the multi-step control logic is configured for parallel control, and the following processing is also performed: taking the mean temperature change rate of the target-associated sample point as the starting point of centralized analysis, iterating in the multiple temperature change rates of the target-associated sample points according to a preset centralized bandwidth to obtain an iterative temperature change rate of the target-associated sample point; constructing a mean neighborhood of the mean 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 based on the preset centralized bandwidth respectively; 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 rate of the target-associated sample point as the starting point of centralized analysis, continuing to iterate in the multiple temperature change rates of the target-associated sample points according to the preset centralized bandwidth until the preset number of iterations is met, and taking the iterative temperature change rate of the target-associated sample point obtained in the last iteration as the centralized value of the temperature change rate of the target-associated sample point.

[0011] In a possible implementation, adjacent temperature difference calculation is performed on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence, and the following processing is also performed: obtaining a temperature difference calculation formula, wherein 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; the temperature difference calculation formula is used to perform adjacent temperature difference calculation on the chip surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0012] The present application also provides a dynamic temperature control system for laser processing of silicon dioxide wafers, including: an image acquisition module, which is used to use a high frame rate thermal imaging device to acquire images of the wafer surface temperature distribution of the target wafer within a preset laser processing window, and obtain a wafer surface temperature distribution image sequence; a temperature difference calculation module, which is used to perform adjacent temperature difference calculations on the wafer surface temperature distribution image sequence, and obtain a temperature change rate distribution image sequence; a balanced extraction module, which is used to perform balanced extraction of temperature analysis sample points according to the wafer area size of the target wafer, and obtain a temperature analysis sample point distribution topological network, wherein each temperature analysis sample point includes a sample point position; an image sequence retrieval module, which is used to retrieve the temperature change rate distribution image sequence using the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topological network as an index , determine the sample point temperature change rate topological network sequence; a mapping association analysis module, used to iteratively map and associate the sample point temperature change rate topological network sequence in order from front to back, and determine the target associated sample point temperature change rate topological network; a temperature change analysis module, used to perform a target associated sample point temperature change rate centralized analysis on the target associated sample point temperature change rate topological network, and determine the target associated sample point temperature change rate centralized value; a temperature control parameter determination module, used to obtain the temperature control parameter set of the target chip 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 the target temperature control parameter; a temperature control dynamic optimization module, used to perform temperature control dynamic optimization of the target chip during laser processing according to the target temperature control parameter.

[0013] The present application also provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing a dynamic temperature control method for laser processing of silicon dioxide wafers when executing the executable instructions stored in the memory.

[0014] The present application also provides a computer-readable storage medium, including: a computer program stored thereon, which implements a dynamic temperature control method for laser processing of silicon dioxide wafers when executed by a processor.

[0015] Through the method, system, equipment and medium for dynamic temperature control of silicon dioxide wafer laser processing proposed in this application, it is intended to obtain a wafer surface temperature distribution image sequence; obtain a temperature change rate distribution image sequence; obtain a temperature analysis sample point distribution topological network; determine a sample point temperature change rate topological network sequence; determine a target associated sample point temperature change rate topological network; determine a target associated sample point temperature change rate concentration value; determine a target temperature control parameter; and dynamically optimize the temperature control of the target wafer during laser processing according to the target temperature control parameter. The technical problems existing in the prior art, such as insufficient processing control accuracy, wafer damage and inability to respond to dynamic changes in wafer processing temperature in real time due to uneven temperature distribution and delayed reaction of temperature control means, are solved, and the technical effect of improving control accuracy, processing controllability and reliability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. 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 operations can be removed from these processes.

[0017] Figure 1 A schematic diagram of a process flow of a dynamic temperature control method for laser processing of silicon dioxide wafers provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a dynamic temperature control system for laser processing of silicon dioxide wafers provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0018] Explanation of the accompanying drawings: image acquisition module 10, temperature difference calculation module 20, equilibrium 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 DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] like Figure 1 As shown, the embodiment of the present application provides a method for dynamic temperature control of silicon dioxide wafer laser processing, comprising: Step S100, using a high frame rate thermal imaging device to collect images of the wafer surface temperature distribution of the target wafer within a preset laser processing window to obtain a wafer surface temperature distribution image sequence.

[0023] 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., tens to hundreds of frames per second) to record the temperature distribution in the processing area in real time. Specifically, a 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 temperature distribution images, which is suitable for capturing the rapidly changing dynamic characteristics of 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 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, each pixel represents the temperature value at a certain position on the wafer surface, and the image reflects the spatial distribution of temperature on the wafer surface at different time points, forming a temperature field. The image acquisition process is performed at a high frame rate, and the multiple frames of temperature distribution images acquired continuously are arranged in time to form a time series, that is, a wafer surface temperature distribution image sequence, which records the dynamic changes of the wafer surface temperature during the processing.

[0024] Step S200, performing adjacent temperature difference calculation on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0025] Preferably, during the laser processing, the temperature distribution images collected continuously are mathematically processed to calculate the rate of change of temperature between adjacent time points, thereby generating a new image sequence to reflect the dynamic process of the temperature change on the chip surface. Specifically, differential calculation refers to the difference calculation of the temperature value of each pixel point in two consecutive frames in 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 at a certain position on the chip surface (i.e., the temperature change amount per unit time). This image sequence can intuitively reflect the speed of temperature change on the chip surface during the processing process, as well as 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 process can be more comprehensively understood, and the rapid heating or cooling of the local heating area can be revealed, providing richer information than the simple temperature distribution.

[0026] Step S200 further includes step S210, obtaining a temperature difference calculation formula, wherein 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; step S220, using the temperature difference calculation formula to perform adjacent temperature difference calculation on the chip surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0027] Preferably, 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 moment, i is an integer greater than or equal to 1; the continuously collected temperature distribution images are analyzed frame by frame through the temperature difference calculation formula, the temperature change rate of each position on the chip surface at continuous time points is calculated, and a new set of image sequences is generated to intuitively reflect the dynamic process of chip surface temperature change.

[0028] Step S300 , 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.

[0029] Preferably, a group of representative temperature analysis points are selected according to the physical area and temperature distribution characteristics of the wafer, 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, etc. Large-sized wafers may require more sample points, while small-sized wafers can reduce the number of sample points. According to the area size of the wafer, combined with the required analysis accuracy, the number of extracted sample points is dynamically determined. For example, larger wafers require more sample points to cover the entire surface area. Balanced extraction of temperature analysis sample points means that the distribution of sample points should cover key areas of the wafer (such as the center, edge, heat-sensitive area, 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 position of the grid. The density of the grid is determined according to the wafer area. The area and temperature control accuracy requirements are dynamically adjusted, the sample point density is increased for areas with faster temperature changes (such as laser processing areas), and the sample point density is reduced for areas with slower temperature changes (such as wafer edges). A certain degree of randomness is introduced under the premise of satisfying uniformity to improve the comprehensiveness of 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, each sample point contains the sample point position and associated information, the position is defined as a coordinate point on the wafer surface, such as (x, y), and a network is constructed between sample points through certain topological relationships (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, a temperature analysis sample point distribution topological network.

[0030] Step S400 , 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.

[0031] Preferably, the sample point positions (i.e., the coordinates of each sample point) in the extracted temperature analysis sample point distribution topological network are used to extract the temperature change rate value of the corresponding position from the temperature change rate distribution image sequence, and the time-series topological network data is generated according to the structure of the topological network. Specifically, the sample point positions of the temperature analysis sample points are used as indexes to directly extract the data of the corresponding pixel points from the temperature change rate distribution image sequence to avoid traversing the entire image. Specifically, the sample point positions in the sample point distribution topological network and the temperature change rate distribution image sequence are used as inputs, and for each sample point, the corresponding pixel point is found in each frame of the image sequence according to its coordinates. In each frame of the image, the pixel point corresponding to the sample point is obtained. The temperature change rate value is read from the corresponding pixel point. For each sample point, its temperature change rate values ​​in all frame images 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 the edge is established according to the connection relationship of the original sample point distribution topological network to form a complete topological network structure, and the sample point temperature change rate topological network sequence is obtained, where 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), indicating the potential path of heat transfer. Each sample point contains a time-series temperature change rate data.

[0032] Step S500 , iteratively mapping and associating analysis is performed on the sample point temperature change rate topological network sequence in order from front to back in time, to determine a target associated sample point temperature change rate topological network.

[0033] Preferably, according to the topological network sequence of the temperature change rate of the sample points in the time series, the dynamic correlation characteristics between the sample points at the same position in the topological network are analyzed in time order, and a target associated sample point temperature change rate topological network reflecting the actual heat transfer or dynamic correlation between the sample points is constructed through iterative calculation and mapping rules. Specifically, an iterative mapping association analysis is performed from front to back in time 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 associated sample points. The iterative mapping steps are as follows: at time t0, based on the existing topological network structure and the sample point temperature change rate value, the dynamic correlation degree between the initial nodes is calculated. For each time t i , based on 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 topological network. The update rules may include the Fourier heat conduction formula to calculate the heat diffusion trend between sample points, or the sliding window to calculate the correlation coefficient between the temperature change rates. Based on the update results, generate the time t i The topological network of time t i+1The data is combined with the results from t0 to t1 to perform multi-step prediction or association verification, and the connection weights between sample points are further optimized or meaningless edges are deleted. Finally, the temperature change rate topological network of the target associated sample points is obtained. The nodes are the same as the initial sample points, and the value of each node is the final state of the temperature change rate. The edges are based on the association analysis results after iterative mapping, indicating connections with strong correlation or significant heat transfer between sample points.

[0034] Further, step S500 also includes step S510, 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 position, and determining a first associated sample point temperature change rate topological network; step S520, again extracting a third sample point temperature change rate topological network from the sample point temperature change rate topological network sequence, performing mapping association analysis on it and the first associated sample point temperature change rate topological network, and determining a second associated sample point temperature change rate topological network; step S530, performing iterative mapping association analysis on the sample point temperature change rate topological network sequence in combination with the second associated sample point temperature change rate topological network until reaching the last sample point temperature change rate topological network of the sample point temperature change rate topological network sequence, and obtaining the target associated sample point temperature change rate topological network.

[0035] Preferably, the first sample point temperature change rate topological network located at the first position is extracted from the sample point temperature change rate topological network sequence. Similarly, the second sample point temperature change rate topological network is extracted from the sample point temperature change rate topological network sequence to represent the temperature change rate distribution and its association located at the second position in the sequence. Based on the mapping relationship between the sample point positions between the first and second sample point topological networks, the dynamic association between them is analyzed to determine a new topological network (the first associated sample point temperature change rate topological network), the structure of which reflects the association characteristics between the two (such as heat transfer path, dynamic change trend, etc.); then, the third sample point temperature change rate topological network is extracted from the sample point temperature change rate topological network sequence, based on The same principle as that of obtaining the first associated sample point temperature change rate topological network is used to map and associate the third sample point topological network with the first associated sample point temperature change rate topological network, analyze the dynamic association characteristics between them, and obtain the second associated sample point temperature change rate topological network; continue to extract new sample point topological networks (the fourth, fifth, etc.) from the topological network sequence, and map and associate the new topological networks with the association results of the previous step (such as the second associated sample point temperature change rate topological network) in turn, and repeat this process until all sample point topological networks are involved in the mapping association analysis, and obtain the target associated sample point temperature change rate topological network, which reflects the heat transfer path and dynamic change characteristics of the entire processing process.

[0036] Furthermore, step S510 also includes step S511, 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 the first sample point temperature similarity topological network; step S512, traversing the first sample point temperature similarity topological network to perform similarity normalization processing to obtain a first similarity association matrix; step S513, calling the mapping association network layer 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.

[0037] Preferably, the temperature inner product calculation refers to comparing the sample points at the same position in the first sample point temperature change rate topological network and the second sample point temperature change rate topological network, and calculating the similarity between their temperature change rates, for example, using the cosine similarity formula to calculate, and obtaining the first sample point temperature similarity topological network, the connection weight of each node is defined by the temperature inner product value of the adjacent sample points, reflecting the similarity between the sample points; then traverse all nodes and their connections in the first sample point temperature similarity topological network, extract similarity weight values, and perform similarity normalization processing, that is, adjust the similarity values ​​to the range of [0,1] to ensure that the proportional relationship of the weights is consistent, and obtain the first similarity association matrix; the mapping association network layer jointly analyzes the first similarity association matrix and the second sample point temperature change rate topological network, extracts the deep dynamic association characteristics between the sample points, similar to the convolution operation in the graph neural network, and calculates the weighted aggregation characteristics between the nodes. Specifically, the first similarity association matrix (normalized similarity matrix) and the second sample point temperature change rate topological network are used as inputs, and convolution calculation is performed to obtain the first associated sample point temperature change rate topological network, providing accurate theoretical and data support for temperature control optimization in the laser processing process.

[0038] Furthermore, step S513 also includes step S5131, obtaining multiple historical similarity association matrices, multiple historical sample point temperature change rate topological networks and corresponding multiple historical associated sample point temperature change rate topological networks as training data; step S5132, using the training data to supervise 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, and obtaining the mapping association network layer that has completed the training.

[0039] Preferably, through supervised training of historical data, a network model based on a graph convolutional network (GCN) is optimized to obtain a mapping association network layer, so that it can more accurately perform dynamic association analysis and topology optimization of the temperature change rate of sample points. Specifically, multiple historical similarity association matrices, multiple historical sample point temperature change rate topological networks and corresponding multiple historical associated sample point temperature change rate topological networks are obtained. The historical similarity association matrix is ​​calculated from the temperature change rate data of multiple historical processing tasks (for example, using the temperature inner product formula), reflecting the similarity of the temperature change rate between sample points in the historical processing process; each historical topological network in the historical sample point temperature change rate topological network represents the temperature change rate distribution at different time nodes in a processing task and the initial association between its sample points; the historical associated sample point temperature change rate topological network refers to an association topological network generated by actual processing results, including dynamic association results between sample points in the processing process.

[0040] Preferably, the historical similarity association matrix and the sample point temperature change rate topological network are used as input data, and the historical associated sample point temperature change rate topological network is used as output data (label) to supervise the training of the network framework built based on the graph convolution network, wherein the graph convolution network performs feature aggregation on the input graph structure data (such as the topological network and the similarity association matrix) to capture the relationship between the node and the neighboring node, including the input layer, the hidden layer (multi-layer graph convolution layer, gradually extracting the dynamic association features between the sample points) and the output layer. Specifically, the weight matrix of the graph convolution network is randomly initialized, the similarity association matrix and the sample point topological network are input, the prediction results are calculated, and then the loss of the prediction results and the real associated topological network is calculated. In each training iteration, the network weights are adjusted according to the gradient of the loss function to gradually improve the model's prediction ability for the association of the sample points. When the loss function drops to a certain threshold, or the loss change tends to be stable after several consecutive iterations, the training ends, and the trained mapping association network layer is obtained, which can efficiently predict the new associated topological network based on the new similarity association matrix and the sample point topological network.

[0041] Step S600 , 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.

[0042] Preferably, based on the temperature change rate topological network of target-associated sample points, the temperature change rates of all sample points in the topological network are centrally processed through statistical analysis methods to obtain a centralized value that can comprehensively reflect the temperature change characteristics of the target area. Specifically, the core of the centralized analysis is to summarize and statistically analyze the temperature change rates of the sample points in the topological network to extract overall trends and characteristic values, wherein the centralized value of the temperature change rate of the target-associated sample points reflects the general temperature change rate of the target chip.

[0043] Furthermore, step S600 also includes step S610, 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; step S620, performing mean processing on the temperature change rates of the multiple target-associated sample points to determine the mean of the temperature change rates of the target-associated sample points; step S630, taking the mean of the temperature change rate of the target-associated sample points as the starting point of centralized analysis, iteratively analyzing the temperature change rates of the multiple target-associated sample points according to a preset centralized bandwidth, and determining the centralized value of the temperature change rate of the target-associated sample points.

[0044] Preferably, the temperature change rate characteristics are extracted from the target-associated sample point topological network and concentrated analysis is performed to determine the final concentrated value. Specifically, the temperature change rates of multiple target-associated sample points are extracted from the target-associated sample point temperature change rate topological network, and the mean of the temperature change rate of the target-associated sample points is calculated as the initial reference value for the analysis. Then, the distribution characteristics of the temperature change rate of the sample points are judged according to the mean, and iterative analysis is performed according to the preset concentrated bandwidth, wherein the preset concentrated bandwidth refers to a preset analysis range (bandwidth) pre-set by a person skilled in the art to limit the iterative analysis range of the temperature change rate concentrated value, and then the concentrated range (bandwidth) is dynamically adjusted to gradually narrow the distribution range of the temperature change rate of the target sample points to determine the final concentrated value. Specifically, the initial range is set, and the target sample points that meet the conditions are screened within the current bandwidth range, and the mean of the remaining sample points is recalculated. When the concentrated value iteration converges, the final mean is the concentrated value of the temperature change rate of the target-associated sample points, which represents the overall characteristic value of the temperature change rate of the sample points in the target area, and comprehensively reflects the dynamic heat transfer characteristics of the area during the processing process.

[0045] Further, step S630 also includes step S631, taking the mean temperature change rate of the target-associated sample point as the starting point of centralized analysis, iterating in the multiple temperature change rates of the target-associated sample points according to the preset centralized bandwidth, and obtaining the iterative temperature change rate of the target-associated sample point; step S632, constructing the mean neighborhood of the mean temperature change rate of the target-associated sample point and the iterative neighborhood of the iterative temperature change rate of the target-associated sample point based on the preset centralized bandwidth respectively; step S633 determines whether the data volume of the mean neighborhood is less than or equal to the data volume of the iterative neighborhood, and if so, updates the iterative temperature change rate of the target-associated sample point as the starting point of centralized analysis, and continues to iterate in the multiple temperature change rates of the target-associated sample points according to the preset centralized bandwidth until the preset number of iterations is met, and the iterative temperature change rate of the target-associated sample point obtained in the last iteration is used as the centralized value of the temperature change rate of the target-associated sample point.

[0046] Preferably, the mean of the temperature change rate of the target-associated sample points is used as the starting point of the analysis, and the temperature change rates of multiple target-associated sample points are iterated according to the preset concentrated bandwidth to obtain the iterative target-associated sample point temperature change rate. Based on the preset concentrated bandwidth, the mean neighborhood of the temperature change rate of the target-associated sample points and the iterative neighborhood of the temperature change rate of the iterative target-associated sample points are constructed respectively, wherein the mean neighborhood is an area constructed with the mean of the temperature change rate of the target-associated sample points as the center and the preset concentrated bandwidth as the radius; the iterative neighborhood refers to an area for further screening the target-associated sample points within the concentrated bandwidth, and it is determined whether the data volume of the mean neighborhood is less than or equal to the iterative neighborhood. If it is satisfied, the center value of the iterative neighborhood is updated as a new concentrated analysis starting point, and the next iteration is entered; if it is not satisfied, the iteration is stopped, and the current concentrated analysis starting point is used as the final concentrated value; when the preset number of iterations is reached, or the neighborhood changes are no longer significant during the iteration process (such as the neighborhood data volume tends to be stable), the final target-associated sample point temperature change rate concentrated value is determined, which reflects the overall characteristics of the sample point temperature change rate and is an important reference indicator for temperature control optimization.

[0047] Step S700, obtaining a set of temperature control parameters of the target wafer within the preset laser processing window, optimizing the set of temperature control parameters based on a concentrated value of the temperature change rate of the target associated sample points, and determining target temperature control parameters.

[0048] Preferably, according to the processing requirements of the target wafer and the actual situation of the processing window, a set of parameters related to temperature control is extracted, and by analyzing the concentrated 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 to ensure the processing quality and efficiency. Specifically, according to the wafer material and processing requirements, an initial set of temperature control parameters is obtained. The temperature control parameter set is a key parameter for controlling the wafer processing temperature, including but not limited to laser power (output power of the laser), laser scanning speed (speed at which the laser moves on the wafer surface), pulse frequency (repetition frequency of laser pulses), cooling system parameters (such as cooling flow, cooling intensity) and processing window time (the time the laser acts on the preset processing window); then the temperature control parameter set is adjusted according to the concentrated value of the temperature change rate of the target-associated sample points. Optimization is performed. The goals of optimizing the temperature control parameter set based on the concentrated value usually include controlling temperature stability, improving processing efficiency and balancing heat distribution. Specifically, if the temperature change rate is too high (the concentrated value exceeds the set threshold), reduce the laser power to reduce heat input; for hot spots, reduce the scanning speed to avoid overheating; for areas with large fluctuations in the rate concentrated value, increase the cooling intensity to balance the heat; combine real-time temperature monitoring data and concentrated value analysis results, use feedback control methods (such as PID control or fuzzy control) to dynamically optimize parameters, or use models trained based on historical data (such as machine learning algorithms) to predict the optimal parameter combination, shorten the optimization time, and finally determine the target temperature control parameters to guide the laser processing process, ensure higher laser processing temperature control accuracy, more uniform heat distribution within the processing window, and further improve the reliability and efficiency of chip processing.

[0049] Step S800, dynamically optimizing the temperature control of the target wafer during laser processing according to the target temperature control parameters.

[0050] Preferably, the temperature of the target chip is dynamically adjusted in real time during the laser processing using an optimized set of temperature control parameters to ensure that the temperature distribution in the processing area is stable and uniform, while avoiding 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 chip surface, and real-time feedback is provided on the temperature changes in the current processing area. The temperature control parameters are adjusted immediately based on the real-time monitored temperature change data to adapt to changes in the processing environment and process requirements, avoid overheating or local temperature concentration that may cause thermal stress, cracks or warping of the chip, ensure the integrity and processing accuracy of the chip, and while ensuring quality, improve the laser processing speed and cooling efficiency through dynamic optimization to ensure the stability and efficiency of the processing effect while reducing the risk of processing defects.

[0051] In the above, refer to Figure 1The dynamic temperature control method for laser processing of silicon dioxide wafers according to an embodiment of the present invention is described in detail. Figure 2 A dynamic temperature control system for laser processing of silicon dioxide wafers according to an embodiment of the present invention is described.

[0052] The dynamic temperature control system for laser processing of silicon dioxide wafers according to the 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 to the dynamic changes of wafer processing temperature in real time due to uneven temperature distribution and delayed reaction of temperature control means, thereby achieving the technical effect of improving control accuracy and processing controllability and reliability. The dynamic temperature control system for laser processing of silicon dioxide wafers includes: an image acquisition module 10, a temperature difference calculation module 20, a balance extraction module 30, an image sequence retrieval module 40, a mapping association analysis module 50, a temperature change analysis module 60, a temperature control parameter determination module 70, and a temperature control dynamic optimization module 80.

[0053] The image acquisition module 10 is used to use a high frame rate thermal imaging device to acquire images of the surface temperature distribution of the target wafer within a preset laser processing window to obtain a wafer surface temperature distribution image sequence; the temperature difference calculation module 20 is used to perform adjacent temperature difference calculations on the wafer surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence; the balanced extraction module 30 is used to 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 topological network, wherein each temperature analysis sample point includes a sample point position; the image sequence retrieval module 40 is used to retrieve the temperature change rate distribution image sequence using the sample point position of each temperature analysis sample point in the temperature analysis sample point distribution topological network as an index to determine the sample point temperature change rate topological network. a mapping association analysis module 50, which 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, and determine the target-associated sample point temperature change rate topological network; a temperature change analysis module 60, which is used to perform a target-associated sample point temperature change rate centralized analysis on the target-associated sample point temperature change rate topological network, and determine the target-associated sample point temperature change rate centralized value; a temperature control parameter determination module 70, which is used to obtain the temperature control parameter set of the target chip 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 the target temperature control parameter; a temperature control dynamic optimization module 80, which is used to perform temperature control dynamic optimization on the target chip during laser processing according to the target temperature control parameter.

[0054] The specific configuration of the mapping association analysis module 50 includes 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 position, 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, performing mapping association analysis on the third sample point temperature change rate topological network and the first associated sample point temperature change rate topological network, and determining a second associated sample point temperature change rate topological network; performing iterative mapping association analysis on the sample point temperature change rate topological network sequence in combination with the second associated sample point temperature change rate topological network until reaching the last sample point temperature change rate topological network of the sample point temperature change rate topological network sequence, and obtaining the target associated sample point temperature change rate topological network.

[0055] The specific configuration of the mapping association analysis module 50 also includes: 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 the 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; calling the mapping association network layer 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.

[0056] The specific configuration of the mapping association analysis module 50 also includes obtaining multiple historical similarity association matrices, multiple historical sample point temperature change rate topological networks and corresponding multiple historical associated sample point temperature change rate topological networks as training data; using the training data to supervise 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, thereby obtaining the mapping association network layer that has completed the training.

[0057] The specific configuration of the temperature change analysis module 60 includes: extracting the 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 multiple target-associated sample points to determine the mean of the temperature change rates of the target-associated sample points; taking the mean of the temperature change rate of the target-associated sample points as the starting point of centralized analysis, performing iterative analysis on the temperature change rates of the multiple target-associated sample points according to a preset centralized bandwidth to determine the centralized value of the temperature change rate of the target-associated sample points.

[0058] The specific configuration of the temperature change analysis module 60 also includes: taking the mean temperature change rate of the target-associated sample point as the starting point of centralized analysis, iterating in the multiple temperature change rates of the target-associated sample points according to a preset centralized bandwidth to obtain an iterative temperature change rate of the target-associated sample point; constructing a mean neighborhood of the mean 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 based on the preset centralized bandwidth respectively; 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 rate of the target-associated sample point as the starting point of centralized analysis, continuing to iterate in the multiple temperature change rates of the target-associated sample points according to the preset centralized bandwidth until the preset number of iterations is met, and taking the iterative temperature change rate of the target-associated sample point obtained in the last iteration as the centralized value of the temperature change rate of the target-associated sample point.

[0059] The specific configuration of the temperature difference calculation module 20 includes obtaining a temperature difference calculation formula, wherein 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; the temperature difference calculation formula is used to perform adjacent temperature difference calculation on the chip surface temperature distribution image sequence to obtain a temperature change rate distribution image sequence.

[0060] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of the electronic device, it can implement the method described in any of the foregoing embodiments.

[0061] Figure 3 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an implementation of the present invention. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. The electronic device is 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 may be one or more; the memory 403 may include a computer-readable medium and at least one program product, and the program product has a set (at least one) of program modules, which are configured to perform the functions of each embodiment of the present application.

[0062] The memory 403 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or device, 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 embodiment 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, thereby realizing the above-mentioned dynamic temperature control method for laser processing of silicon dioxide wafers.

[0063] Although the present application makes various references to certain modules in the embodiments of the present application, any number of different modules may be used and run on a user terminal and / or server, and 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 distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0064] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this 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 target-associated sample point temperature change rates as the starting point of centralized analysis, iterating among the plurality of target-associated sample point temperature change rates according to a preset centralized bandwidth to obtain an iterated target-associated sample point temperature change rate; 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; 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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