A Method, Device, Storage Medium and Equipment for Monitoring the Energy of a Laser Annealing Probe

Abstract

The present invention provides a method, device, storage medium and equipment for monitoring the energy of a laser annealing probe, including: obtaining a target image, where 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 during laser annealing of the target area; where the target network model is a neural network model in which the sample wafers for training are matched with the target wafers. During the subsequent laser annealing process of IGBT wafers, the depth of the special texture marks on the back of the wafers can be directly monitored, and then the energy used during the laser annealing process can be obtained. Timely online monitoring of the energy during laser annealing facilitates directly capturing data when troubleshooting abnormal chip test parameters, saving the time for extended sheet resistance testing in failure analysis.

Description

Technical Field

[0001] The present invention relates to the field of semiconductors, and more particularly, to a method, device, storage medium, and equipment for monitoring the energy of a laser annealing probe. 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 maintained a high boom; the current demand for a single IGBT chip in different usage scenarios ranges from a few amperes to several hundred amperes, and the corresponding IGBT chip area also ranges from a few square millimeters to several hundred square millimeters. With the continuous iteration and upgrading of products and the cost reduction and efficiency improvement of manufacturers, the products of the small pitch (specifically referring to the distance between the centers of two adjacent units in the semiconductor industry) generation are gradually launched, which also brings great challenges to the IGBT chip manufacturing process. Especially in deep trench etching, polysilicon filling and backside annealing processes, more precise processes are required to manufacture micro areas.

[0003] In the backside annealing process, the laser annealing process has gradually replaced the traditional furnace annealing process, playing a highly precise role in doping activation and defect repair of the device structure. However, in the current existing process manufacturing, the means of monitoring the laser annealing process is only the off-line test of the sheet resistance of the control wafer (i.e., Dummy wafer), and then the laser annealing energy is obtained.

[0004] The specific operation process is as follows: The engineer puts the Dummy wafer that has been ion implanted into the laser annealing equipment, adjusts the energy of the laser probe at the top of the equipment, anneals the Dummy wafer, takes out the Dummy wafer after executing according to the predetermined menu training recipe for sheet resistance testing (SRP), obtains the resistance of different thicknesses, calculates the activation efficiency through calculus, and then obtains the energy usage value of the laser annealing probe.

[0005] Obviously, the above-mentioned monitoring means has low efficiency and serious lag, and cannot monitor the probe energy in the manufacturing process in time. For the increasingly competitive wafer foundry, the first element is high efficiency and high quality. The online monitoring of the laser annealing process is imminent and imperative. Summary of the Invention

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

[0007] To achieve the above purpose, the technical solutions adopted in the embodiments of the present invention are as follows:

[0008] In a first aspect, an embodiment of the present invention provides a method for monitoring the energy of a laser annealing probe, the method including:

[0009] Obtain a target image, where 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;

[0010] Input the target image into a target network model, and the target network model outputs the probe energy value when laser annealing is performed on the target area;

[0011] where the target network model is a neural network model in which the sample wafers used for training match the target wafer.

[0012] In a second aspect, an embodiment of the present invention provides a device for monitoring the energy of a laser annealing probe, the device including:

[0013] A first processing unit for obtaining a target image, where 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;

[0014] A second processing unit for inputting the target image into a target network model, and the target network model outputs the probe energy value when laser annealing is performed on the target area;

[0015] where the target network model is a neural network model in which the sample wafers used for training match the target wafer.

[0016] In a third aspect, an embodiment of the present invention provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented.

[0017] In a fourth aspect, an embodiment of the present invention provides an electronic device, the electronic device including: a processor and a memory, the memory being used for storing one or more programs; when the one or more programs are executed by the processor, the above method is implemented.

[0018] Compared with the prior art, a method, device, storage medium and equipment for monitoring the energy of a laser annealing probe provided by an embodiment of the present invention include: obtaining a target image, where 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 laser annealing is performed on the target area; wherein the target network model is a neural network model in which a sample wafer for training is matched with the target wafer. During the subsequent laser annealing process of IGBT wafers, the depth of the special texture marks on the back of the wafers can be directly monitored, and then the energy used during the laser annealing process can be obtained. Timely online monitoring of the energy during laser annealing facilitates directly capturing data when troubleshooting CP Issues (abnormal chip test parameters), saving the time for extended sheet resistance testing in failure analysis.

[0019] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0022] Figure 2 It is one of the schematic flowcharts of the method for monitoring the energy of a laser annealing probe provided by an embodiment of the present invention.

[0023] Figure 3 It is another schematic flowchart of the method for monitoring the energy of a laser annealing probe provided by an embodiment of the present invention.

[0024] Figure 4 It is a schematic diagram of a fan-shaped area provided by an embodiment of the present invention.

[0025] Figure 5 It is a schematic diagram of a sample image provided by an embodiment of the present invention.

[0026] Figure 6 It is a schematic diagram of the units of the device for monitoring the energy of a laser annealing probe provided by an embodiment of the present invention.

[0027] In the figure: 10 - processor; 11 - memory; 12 - bus; 13 - communication interface; 401 - first processing unit; 402 - second processing unit. Detailed implementation mode

[0028] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0029] 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 claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0031] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0032] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is customarily placed during use. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention.

[0033] In the description of the present invention, it should also be noted that, unless otherwise clearly specified and defined, the terms "arrangement" and "connection" 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 directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0034] The following will describe in detail some embodiments of the present invention with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0035] The embodiments of the present invention provide an electronic device, which can be a laser annealing machine tool, a mobile phone device, a computer device or a server device. Please refer to Figure 1 , the structural schematic diagram of the 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 through the bus 12, and the processor 10 is used to execute the executable module stored in the memory 11, such as a computer program.

[0036] 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 integrated logic circuit in the hardware of 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 for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0037] The memory 11 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory.

[0038] 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 .

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] S301, acquiring a target image.

[0044] 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.

[0045] 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.

[0046] Among them, the target network model is a neural network model used for matching the sample wafer and the target wafer for training.

[0047] Through the laser annealing probe energy monitoring method provided by the embodiments of the present invention, during the subsequent laser annealing process of IGBT wafers, the depth of the special texture marks on the back of the wafers can be directly monitored, and then the energy used in the laser annealing process can be obtained. Timely online monitoring of the energy in laser annealing facilitates directly capturing data when troubleshooting CP Issues (abnormal chip test parameters), saving the time for extended sheet resistance testing in failure analysis.

[0048] 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 also be different. To avoid incorrect identification of the probe energy value caused by the above differences. The embodiments of the present invention provide an alternative implementation manner. Please refer to Figure 3 , the laser annealing probe energy monitoring method further includes: S201, S202, and S203, which are specifically described as follows.

[0049] S201, obtain the process characteristic data according to the target wafer.

[0050] Among them, the process characteristic data includes the thickness data and ion implantation energy data of the target wafer.

[0051] S202, determine the sample wafers that meet the first matching condition, the second matching condition, and the third matching condition as the sample wafers matching the target wafer.

[0052] Among them, the first matching condition means that the process flow of the sample wafer before laser annealing is the same as that of the target wafer before laser annealing, the second matching condition means that the thickness data of the sample wafer is the same as the thickness data of the target wafer, and the third matching condition means 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.

[0053] 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 exclude the influence brought by the ion implantation energy difference.

[0054] It should be noted that when training the neural network model, under the condition of the same process flow and thickness data, samples with various different ion implantation energies can be obtained for training.

[0055] S203, use the neural network model trained based on the sample wafers matching the target wafer as the target network model.

[0056] On the basis of the foregoing, regarding the process of training the neural network model, the embodiments of the present invention also provide an alternative implementation manner. Please refer to the following text. The process of training the neural network model includes: S101 to S110, which are specifically described as follows.

[0057] S101, Obtain M×N target sample wafers.

[0058] Among them, the target sample wafers are a type of sample wafers with the same process flow before laser annealing, the same thickness data, and the same ion implantation energy data.

[0059] S102, 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 energy level of (m - 1)×4 + i.

[0060] Among them, 1≤n≤N, 1≤m≤M, 1≤i≤4.

[0061] Optionally, the back of each target sample wafer is divided into 4 fan-shaped regions with equal areas, and the wafer can be divided into 4 fan-shaped regions according to a cross; for each group of target sample wafers, the initial values of i, n, and m are all 1.

[0062] Please refer to Figure 4 , Figure 4 , which is the schematic diagram of the fan-shaped region provided by the embodiment of the present invention. Among them, Qi represents the i-th region of the target sample wafer.

[0063] 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.

[0064] Among them, the maximum energy level ≤ M×4.

[0065] S104, Determine whether i < 4 holds. If i < 4, execute S105; if i = 4, execute S106.

[0066] It should be noted that because the execution result of S103 is that (m - 1)×4 + i is less than the maximum energy level, if i < 4, it means that the m-th target sample wafer in the n-th 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 + 1)-th target sample wafer still needs to be subjected to laser annealing, so let m = m + 1.

[0067] S105, Let i = i + 1.

[0068] And repeat to execute S102, 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 energy level of (m - 1)×4 + i;

[0069] S106, if i = 4, then set i = 1 and set m = m + 1.

[0070] 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 energy level of (m - 1)×4 + i.

[0071] If (m - 1)×4 + i is equal to the maximum energy level, execute S107.

[0072] S107, determine whether n < N holds. If n < N, then execute S108; if n = N, then execute S109.

[0073] S108, set n = n + 1, set m = 1, and set i = 1.

[0074] 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 energy level of (m - 1)×4 + i.

[0075] S109, stop laser annealing, obtain the sample images corresponding to each region in each target sample wafer, and add corresponding labels to the sample images.

[0076] Wherein, the label of the sample image is the probe energy value during laser annealing of the corresponding region.

[0077] Optionally, the sample image includes special texture imprints formed in the corresponding region during laser annealing. The depth of the imprints is related to the energy value of laser annealing.

[0078] Please refer to Figure 5 , Figure 5 which is the schematic diagram of the sample image provided by the embodiment of the present invention. In Figure 5 , the value of the energy difference is 0.05 mJ, the value of the first-level energy corresponding to the target sample wafer is 1.9 mJ, and the value of the K-th level energy is 2.8 mJ.

[0079] S110, train the neural network model based on the sample images with labels.

[0080] Optionally, the (k + 1)-th level energy - the k-th level energy = the preset energy difference, 1 ≤ k ≤ K - 1, and K represents the maximum energy level.

[0081] During laser annealing, the change amount of the laser annealing imprint depth pattern of the wafer caused by the energy difference is greater than or equal to the minimum visual difference.

[0082] Optionally, the value of the energy difference is 0.05 mJ.

[0083] Optionally, when the thickness of the target sample wafer is less than the thickness threshold, the value of the first-level energy corresponding to the target sample wafer is 1.9 mJ, and the value of the K-level energy is 2.4 mJ;

[0084] When the thickness of the target sample wafer is less than the thickness threshold, the value of the first-level energy corresponding to the target sample wafer is 2.4 mJ, and the value of the K-level energy is 2.8 mJ.

[0085] Please refer to Figure 6 , Figure 6 A laser annealing probe energy monitoring device provided by an embodiment of the present invention. Optionally, the laser annealing probe energy monitoring device is applied to the electronic device described above.

[0086] The laser annealing probe energy monitoring device includes: a first processing unit 401 and a second processing unit 402.

[0087] The first processing unit 401 is configured to obtain a target image, where the target image is an image corresponding to a target area on the back of the target wafer after laser annealing during the production process;

[0088] The second processing unit 402 is configured to input the target image into the target network model, and the target network model outputs the probe energy value when laser annealing is performed on the target area;

[0089] Wherein, the target network model is a neural network model in which the sample wafer for training matches the target wafer.

[0090] 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.

[0091] 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 effects. For a brief description, for the parts not mentioned in this embodiment, reference may be made to the corresponding content in the above embodiment.

[0092] An embodiment of the present invention also provides a storage medium, which stores computer instructions and programs. When the computer instructions and programs are read and run, they execute the laser annealing probe energy monitoring method of the above embodiment. The storage medium may include memory, flash memory, registers or a combination thereof, etc.

[0093] The following provides an electronic device, which may be a laser annealing machine, a mobile phone device, a computer device or a server device. The electronic device is as Figure 1As 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 the one or more programs are executed by the processor 10, the laser annealing probe energy monitoring method of the above embodiment is executed.

[0094] In summary, a laser annealing probe energy monitoring method, device, storage medium, and equipment provided by an embodiment of the present invention include: obtaining a target image, where 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 subjected to laser annealing; where the target network model is a neural network model in which a sample wafer for training matches the target wafer. During the subsequent IGBT wafer transfer laser annealing process, the depth of the special texture marks on the back of the wafer can be directly monitored, and then the energy used during the laser annealing process can be obtained. Timely online monitoring of the energy during laser annealing facilitates directly capturing data when troubleshooting CP Issues (abnormal chip test parameters), saving the time for extended sheet resistance testing in failure analysis.

[0095] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0096] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to include all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A laser annealing probe energy monitoring method, characterized in that: The method comprises: Acquiring process characteristic data of a 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; Using a neural network model trained based on a sample wafer matched with the target wafer as a target network model; 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. The target network model monitors the depth of special texture marks on the back of the target wafer, and then obtains the probe energy value when the target area is laser annealed.

2. 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.

3. The laser annealing probe energy monitoring method according to claim 2, 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.

4. The laser annealing probe energy monitoring method according to claim 3, 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.

5. The laser annealing probe energy monitoring method according to claim 3, characterized in that: The energy difference is set to 0.05 mJ.

6. 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; The target network model is a neural network model that matches the sample wafer used for training with the target wafer, and the target network model monitors the depth of the special texture mark on the back of the target wafer, thereby obtaining the probe energy value when the target area is laser annealed; The first processing unit is also used to obtain process feature data based on the target wafer, wherein the process feature data includes thickness data and ion implantation energy data of the target wafer; determine a sample wafer that meets a first matching condition, a second matching condition, and a third matching condition as a sample wafer that matches the target wafer; wherein the first matching condition indicates that a process flow of the sample wafer before laser annealing is the same as a 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 an absolute value of a 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; and use a neural network model trained based on the sample wafer that matches the target wafer as the target network model.

7. 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 5 is implemented.

8. 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 5 is implemented.

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