Laser annealing probe energy monitoring method and device, storage medium and equipment

Abstract

The invention provides a laser annealing probe energy monitoring method and device, a storage medium and equipment, and the method comprises the steps: obtaining a target image which is an image corresponding to a target region of the back surface of a target wafer after laser annealing in a production process; the target image is input into a target network model, and the target network model outputs a probe energy value when the target area is subjected to laser annealing; wherein the target network model is a neural network model used for training sample wafers and target wafers. And when the subsequent IGBT wafer passes through the laser annealing process, the depth of the special line mark on the back surface of the wafer can be directly monitored, so that the energy used in the laser annealing process can be obtained. Energy in laser annealing is monitored online in time, data can be captured directly when chip test parameter abnormity is checked, and the time for expanding square resistance test in failure analysis is saved.

Description

Technical Field

[0001] The present invention relates to the field of semiconductors, and in particular to a laser annealing probe energy monitoring method, device, storage medium and equipment. Background Art

[0002] In recent years, with the rapid development of new energy vehicles, photovoltaics, energy storage, rail transit and other fields, the IGBT industry has continued to maintain a high level of prosperity; the current demand for a single IGBT chip in different usage scenarios ranges from a few amperes to hundreds of amperes, and the corresponding IGBT chip area also ranges from a few square millimeters to hundreds of square millimeters. With the continuous iteration and upgrading of products and the cost reduction and efficiency improvement of manufacturers, small pitch (the distance between the centers of two adjacent units in the semiconductor industry) generation products are gradually launched, which also brings great challenges to the IGBT chip manufacturing process. Especially in deep trench etching, polysilicon filling and back annealing processes, more precise processes are needed to manufacture tiny areas.

[0003] In the back annealing process, the laser annealing process has gradually replaced the traditional furnace annealing process, which plays a highly accurate role in doping activation and defect repair for the device structure. However, in the current process manufacturing, the only means of laser annealing process monitoring is to test the block resistance of the control wafer (i.e., dummy wafer) offline, and then derive the laser annealing energy.

[0004] The specific operation process is as follows: the engineer places the dummy wafer that has been ion-implanted into the laser annealing equipment, adjusts the energy of the laser probe on the top of the equipment, anneals the dummy wafer, and takes out the dummy wafer for a square resistance test (SRP) after executing the predetermined menu training recipe to obtain the resistance of different thicknesses. The activation efficiency is calculated through calculus, and then the probe energy usage value of the laser annealing is obtained.

[0005] Obviously, the above-mentioned monitoring methods are inefficient and have serious lags, and cannot monitor the probe energy in the manufacturing process in a timely manner. For wafer foundries with increasingly fierce competition, the first factor is high efficiency and high quality, and online monitoring of the laser annealing process is imminent and imperative. Summary of the invention

[0006] The object of the present invention is to provide a laser annealing probe energy monitoring method, device, storage medium and equipment to improve the above-mentioned problems.

[0007] In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows: In a first aspect, an embodiment of the present invention provides a laser annealing probe energy monitoring method, the method comprising: Acquire a target image, wherein the target image is an image corresponding to a target area on the back side of a target wafer after laser annealing during a production process; Inputting the target image into a target network model, the target network model outputting a probe energy value when the target area is subjected to laser annealing; Among them, the target network model is a neural network model that matches the sample wafer used for training with the target wafer.

[0008] In a second aspect, an embodiment of the present invention provides a laser annealing probe energy monitoring device, the device comprising: A first processing unit is used to obtain a target image, wherein the target image is an image corresponding to a target area on the back side of a target wafer after laser annealing during a production process; A second processing unit, used for inputting the target image into a target network model, and the target network model outputs a probe energy value when the target area is subjected to laser annealing; Among them, the target network model is a neural network model that matches the sample wafer used for training with the target wafer.

[0009] In a third aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, and the computer program implements the above method when executed by a processor.

[0010] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store one or more programs; when the one or more programs are executed by the processor, the above method is implemented.

[0011] Compared with the prior art, the embodiments of the present invention provide a laser annealing probe energy monitoring method, device, storage medium and equipment, including: obtaining a target image, wherein the target image is an image corresponding to a target area on the back of a target wafer after laser annealing during the production process; inputting the target image into a target network model, and the target network model outputs the probe energy value when the target area is laser annealed; wherein the target network model is a neural network model for matching the sample wafer used for training with the target wafer. In the subsequent IGBT wafer transit laser annealing process, the depth of the special texture imprint on the back of the wafer can be directly monitored to obtain the energy used in the laser annealing process. Timely online monitoring of the energy in laser annealing facilitates direct data capture when troubleshooting CP Issue (abnormal chip test parameters), saving time for extended block resistance testing in failure analysis.

[0012] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0015] Figure 2 One of the flow charts of the laser annealing probe energy monitoring method provided in an embodiment of the present invention.

[0016] Figure 3 The second flowchart of the laser annealing probe energy monitoring method provided in the embodiment of the present invention.

[0017] Figure 4 A schematic diagram of a sector area provided by an embodiment of the present invention.

[0018] Figure 5 A schematic diagram of a sample image provided by an embodiment of the present invention.

[0019] Figure 6 A unit schematic diagram of a laser annealing probe energy monitoring device provided in an embodiment of the present invention.

[0020] In the figure: 10 - processor; 11 - memory; 12 - bus; 13 - communication interface; 401 - first processing unit; 402 - second processing unit. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0024] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0025] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0026] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "disposed" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0028] An embodiment of the present invention provides an electronic device, which may be a laser annealing machine, a mobile phone device, a computer device or a server device. Figure 1, a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12, and the processor 10 is used to execute an executable module stored in the memory 11, such as a computer program.

[0029] The processor 10 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the laser annealing probe energy monitoring method can be completed by the hardware integrated logic circuit in the processor 10 or the instructions in the form of software. The above-mentioned processor 10 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0030] The memory 11 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0031] The bus 12 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Figure 1 Although only one bidirectional arrow is used in the figure, it does not mean that there is only one bus 12 or only one type of bus 12 .

[0032] The memory 11 is used to store programs, such as programs corresponding to the laser annealing probe energy monitoring device. The laser annealing probe energy monitoring device includes at least one software function module that can be stored in the memory 11 in the form of software or firmware or solidified in the operating system (OS) of the electronic device. After receiving the execution instruction, the processor 10 executes the program to implement the laser annealing probe energy monitoring method.

[0033] Possibly, the electronic device provided by the embodiment of the present invention further includes a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0034] It should be understood that Figure 1 The structure shown is only a schematic diagram of a portion of the electronic device. The electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0035] A laser annealing probe energy monitoring method provided by an embodiment of the present invention can be applied to, but not limited to, Figure 1 For detailed procedures, please refer to the electronic equipment shown in Figure 2 , the laser annealing probe energy monitoring method includes: S301 and S302, which are specifically described as follows.

[0036] S301, acquiring a target image.

[0037] The target image is an image corresponding to a target area on the back side of a target wafer after laser annealing during the production process.

[0038] S302, inputting the target image into the target network model, and the target network model outputs the probe energy value when the target area is subjected to laser annealing.

[0039] Among them, the target network model is a neural network model used for matching the sample wafer and the target wafer for training.

[0040] Through the laser annealing probe energy monitoring method provided by the embodiment of the present invention, when the IGBT wafer is subsequently subjected to the laser annealing process, the depth of the special texture mark on the back of the wafer can be directly monitored, thereby obtaining the energy used in the laser annealing process. Timely online monitoring of the energy in laser annealing facilitates direct data capture when troubleshooting CP Issue (chip test parameter abnormality), saving time for extended block resistance testing in failure analysis.

[0041] It should be understood that during the wafer production process, it may go through different process flows, the thickness of the wafer may be different, and the ion implantation energy may be different. In order to avoid the above differences causing the probe energy value to be incorrectly identified. Figure 3 The laser annealing probe energy monitoring method also includes: S201, S202 and S203, which are specifically described as follows.

[0042] S201, acquiring process characteristic data of a target wafer.

[0043] The process characteristic data include the thickness data of the target wafer and the ion implantation energy data.

[0044] S202 : Determine a sample wafer that meets the first matching condition, the second matching condition, and the third matching condition as a sample wafer that matches the target wafer.

[0045] Among them, the first matching condition indicates that the process flow of the sample wafer before laser annealing is the same as the process flow of the target wafer before laser annealing, the second matching condition indicates that the thickness data of the sample wafer is the same as the thickness data of the target wafer, and the third matching condition indicates that the absolute value of the difference between the ion implantation energy data of the sample wafer and the ion implantation energy data of the target wafer is less than the error threshold.

[0046] It should be understood that when the ion implantation energy difference is large, it may affect the wafer structure. Therefore, setting the third matching condition can eliminate the impact caused by the ion implantation energy difference.

[0047] It should be noted that when training the neural network model, under the condition that the process flow and thickness data are the same, samples under a variety of different ion implantation energies can be obtained for training.

[0048] S203, using the neural network model trained based on the sample wafer matched with the target wafer as the target network model.

[0049] On the basis of the above, regarding the process of training the neural network model, the embodiment of the present invention further provides an optional implementation, please refer to the following: The process of training the neural network model includes: S101 to S110, which are specifically described as follows.

[0050] S101, obtaining M×N target sample wafers.

[0051] The target sample wafers are sample wafers with the same process flow, thickness data and ion implantation energy data before laser annealing.

[0052] S102 , controlling the laser annealing machine to perform laser annealing on the i th region of the m th target sample wafer in the n th group according to the (m−1)×4+i th level of energy.

[0053] Among them, 1≤n≤N, 1≤m≤M, 1≤i≤4.

[0054] Optionally, the back side of each target sample wafer is divided into four sector-shaped regions of equal area, and the wafer can be divided into four sector-shaped regions according to a cross; for each group of target sample wafers, the initial values ​​of i, n, and m are all 1.

[0055] Please refer to Figure 4 , Figure 4 A schematic diagram of a sector region provided by an embodiment of the present invention, wherein Qi represents the i-th region of the target sample wafer.

[0056] S103, after completing the laser annealing of the i-th region of the m-th target sample wafer in the n-th group, determine whether (m-1)×4+i is less than the maximum energy level. If (m-1)×4+i is less than the maximum energy level, execute S104; if (m-1)×4+i is equal to the maximum energy level, execute S107.

[0057] Among them, the maximum energy level is ≤M×4.

[0058] S104, determine whether i<4. If i<4, execute S105; if i=4, execute S106.

[0059] It should be noted that, because the execution result of S103 is (m-1)×4+i which is less than the maximum energy level, if i<4, it means that the mth target sample wafer in the nth group has not completed the laser annealing of all regions, so let i=i+1. If i=4, m must be less than M, and the m+1th target sample wafer needs to be laser annealed, so let m=m+1.

[0060] S105, let i=i+1.

[0061] and repeatedly executing S102 to control the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level of energy; S106, if i=4, then let i=1, and let m=m+1.

[0062] And repeatedly execute S102 to control the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level energy.

[0063] If (m-1)×4+i is equal to the maximum energy level, execute S107.

[0064] S107, determine whether n<N. If n<N, execute S108; if n=N, execute S109.

[0065] S108, let n=n+1, let m=1, let i=1.

[0066] And repeatedly execute S102 to control the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level energy.

[0067] S109, stopping laser annealing, acquiring a sample image corresponding to each area in each target sample wafer, and adding a corresponding label to the sample image.

[0068] The label of the sample image is the probe energy value when the corresponding area is laser annealed.

[0069] Optionally, the sample image includes a special texture mark formed in the corresponding area during laser annealing, and the depth of the mark is related to the energy value of the laser annealing.

[0070] Please refer to Figure 5 , Figure 5 A schematic diagram of a sample image provided by an embodiment of the present invention. Figure 5 In the figure, the energy difference is 0.05 mJ, the first level energy value corresponding to the target sample wafer is 1.9 mJ, and the Kth level energy value is 2.8 mJ.

[0071] S110, training the neural network model based on the sample images carrying the labels.

[0072] Optionally, the k+1th level energy-the kth level energy=a preset energy difference, 1≤k≤K-1, and K represents the maximum energy level.

[0073] When laser annealing is performed, the change in the depth of the laser annealing mark of the wafer caused by the energy difference is greater than or equal to the minimum visible difference.

[0074] Optionally, the energy difference is 0.05 mJ.

[0075] Optionally, when the thickness of the target sample wafer is less than the thickness threshold, the first level energy value corresponding to the target sample wafer is 1.9 mJ, and the Kth level energy value is 2.4 mJ; When the thickness of the target sample wafer is less than the thickness threshold, the first level energy value corresponding to the target sample wafer is 2.4 mJ, and the Kth level energy value is 2.8 mJ.

[0076] See also Figure 6 , Figure 6 An embodiment of the present invention provides a laser annealing probe energy monitoring device. Optionally, the laser annealing probe energy monitoring device is applied to the electronic device described above.

[0077] The laser annealing probe energy monitoring device includes: a first processing unit 401 and a second processing unit 402 .

[0078] The first processing unit 401 is used to obtain a target image, wherein the target image is an image corresponding to a target area on the back side of a target wafer after laser annealing in a production process; The second processing unit 402 is used to input the target image into the target network model, and the target network model outputs the probe energy value when the target area is subjected to laser annealing; Among them, the target network model is a neural network model used for matching the sample wafer and the target wafer for training.

[0079] Optionally, the second processing unit 402 may execute the above S302, and the first processing unit 401 may execute other steps in the above method embodiment.

[0080] It should be noted that the laser annealing probe energy monitoring device provided in this embodiment can execute the method flow shown in the above method flow embodiment to achieve the corresponding technical effect. For the sake of brief description, for parts not mentioned in this embodiment, please refer to the corresponding content in the above embodiment.

[0081] The embodiment of the present invention further provides a storage medium, which stores computer instructions and programs, which, when read and run, execute the laser annealing probe energy monitoring method of the above embodiment. The storage medium may include memory, flash memory, register, or a combination thereof.

[0082] The following provides an electronic device, which may be a laser annealing machine, a mobile phone device, a computer device or a server device. Figure 1 As shown, the above-mentioned laser annealing probe energy monitoring method can be implemented; specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 can be a CPU. The memory 11 is used to store one or more programs. When one or more programs are executed by the processor 10, the laser annealing probe energy monitoring method of the above-mentioned embodiment is executed.

[0083] In summary, the embodiments of the present invention provide a method, device, storage medium and equipment for monitoring the energy of a laser annealing probe, including: obtaining a target image, wherein the target image is an image corresponding to a target area on the back of a target wafer after laser annealing during the production process; inputting the target image into a target network model, and the target network model outputs the probe energy value when the target area is laser annealed; wherein the target network model is a neural network model for matching a sample wafer with a target wafer for training. In the subsequent laser annealing process of IGBT wafers, the depth of the special texture marks on the back of the wafer can be directly monitored to obtain the energy used in the laser annealing process. Timely online monitoring of the energy in laser annealing facilitates direct data capture when troubleshooting CP Issue (abnormal chip test parameters), saving time for extended block resistance testing in failure analysis.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0085] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A laser annealing probe energy monitoring method, characterized in that: The method comprises: Acquire a target image, wherein the target image is an image corresponding to a target area on the back side of a target wafer after laser annealing during a production process; Inputting the target image into a target network model, the target network model outputting a probe energy value when the target area is subjected to laser annealing; Among them, the target network model is a neural network model that matches the sample wafer used for training with the target wafer.

2. The laser annealing probe energy monitoring method according to claim 1, characterized in that: The method further comprises: Acquiring process characteristic data of the target wafer, wherein the process characteristic data includes thickness data and ion implantation energy data of the target wafer; Determine a sample wafer that meets the first matching condition, the second matching condition, and the third matching condition as a sample wafer that matches the target wafer; The first matching condition indicates that the process flow of the sample wafer before laser annealing is the same as the process flow of the target wafer before laser annealing, the second matching condition indicates that the thickness data of the sample wafer is the same as the thickness data of the target wafer, and the third matching condition indicates that the absolute value of the difference between the ion implantation energy data of the sample wafer and the ion implantation energy data of the target wafer is less than an error threshold; The neural network model trained based on the sample wafer matched with the target wafer is used as the target network model.

3. The laser annealing probe energy monitoring method according to claim 1, characterized in that: The process of training the neural network model includes: Acquire M×N target sample wafers, wherein the target sample wafers are a type of sample wafers having the same process flow, the same thickness data, and the same ion implantation energy data before laser annealing; Controlling the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level energy, wherein 1≤n≤N, 1≤m≤M, 1≤i≤4; After completing the laser annealing of the i-th region of the m-th target sample wafer in the n-th group, determining whether (m-1)×4+i is less than the maximum energy level; If (m-1)×4+i is less than the maximum energy level, determine whether i<4 holds; If i is less than 4, then i=i+1 is set, and the laser annealing machine is repeatedly controlled to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level of energy; If i=4, then set i=1, set m=m+1, and repeatedly control the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level of energy; If (m-1)×4+i equals the maximum energy level, determine whether n<N holds; If n<N, then set n=n+1, set m=1, set i=1, and repeatedly control the laser annealing machine to perform laser annealing on the i-th region of the m-th target sample wafer in the n-th group according to the (m-1)×4+i-th level of energy; If n=N, stop laser annealing, obtain a sample image corresponding to each area in each target sample wafer, and add a corresponding label to the sample image, wherein the label of the sample image is the probe energy value when the corresponding area is subjected to laser annealing; The neural network model is trained based on the sample images carrying the labels.

4. The laser annealing probe energy monitoring method according to claim 3, characterized in that: The k+1th level energy - the kth level energy = the preset energy difference, 1≤k≤K-1, K represents the maximum energy level.

5. The laser annealing probe energy monitoring method according to claim 4, characterized in that: When laser annealing is performed, the change in the depth pattern of the laser annealing mark of the wafer caused by the energy difference is greater than or equal to the minimum visible difference.

6. The laser annealing probe energy monitoring method according to claim 4, characterized in that: The energy difference is set to 0.05 mJ.

7. The laser annealing probe energy monitoring method according to claim 4, characterized in that: When the thickness of the target sample wafer is less than the thickness threshold, the first level energy value corresponding to the target sample wafer is 1.9 mJ, and the Kth level energy value is 2.4 mJ; When the thickness of the target sample wafer is less than the thickness threshold, the first level energy value corresponding to the target sample wafer is 2.4 mJ, and the Kth level energy value is 2.8 mJ.

8. A laser annealing probe energy monitoring device, characterized in that: The device comprises: A first processing unit is used to acquire a target image, wherein the target image is an image corresponding to a target area on the back side of a target wafer after laser annealing during a production process; A second processing unit, used for inputting the target image into a target network model, and the target network model outputs a probe energy value when the target area is subjected to laser annealing; Among them, the target network model is a neural network model that matches the sample wafer used for training with the target wafer.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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