Beam parameter processing method and device, electronic equipment, storage medium and product

Abstract

The invention discloses a beam parameter processing method and device, electronic equipment, a storage medium and a product. The beam parameter processing method comprises the following steps: inputting an expected beam state value into a parameter prediction model to obtain a predicted parameter value of at least one first component in the ion implanter, the predicted parameter value comprising a parameter value required to reach a numerical value of the first component corresponding to the beam state; for each first component, inputting the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into a time correlation model corresponding to the first component to obtain a group of climbing data of the first component; and for each first component, setting the corresponding group of climbing data to the first component in sequence until the first component reaches the corresponding prediction parameter value. The service life of the first component is prevented from being shortened or even damaged due to the fact that the current parameter value is set to the predicted parameter value. And the setting safety of the parameter value of the first component is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a beam current parameter processing method, apparatus, electronic device, storage medium, and product. Background Art

[0002] An ion implanter is a widely used device, which is widely applied in semiconductor manufacturing, material property optimization, and manufacturing optical devices. During the ion implantation process of the ion implanter, specific ions are accelerated to an energy of several hundred kilo-electron volts to form a beam current.

[0003] The generation and regulation of the beam current is a complex process with numerous parameters, and it is necessary to adjust the parameters of each component in the ion implanter to obtain a stable and uniform beam current. However, since each part of the ion implantation consists of precision components, if the predicted parameter values are directly set to each component, it is very likely to cause a reduction or even damage to the lifespan of the components. Summary of the Invention

[0004] The present invention provides a beam current parameter processing method, apparatus, electronic device, storage medium, and product to avoid reducing or even damaging the lifespan of the components in the ion implanter.

[0005] According to one aspect of the present invention, there is provided a beam current parameter processing method, including:

[0006] Inputting the desired beam current state value into a parameter prediction model to obtain the predicted parameter values of at least one first component in the ion implanter, where the predicted parameter values include the parameter values required for the first component corresponding to achieving the beam current state value to reach the corresponding numerical value;

[0007] For each first component, inputting the predicted parameter value corresponding to the first component and the current parameter value in the current state of the first component into the time correlation model corresponding to the first component to obtain a set of ramp data for the first component;

[0008] For each of the first components, sequentially setting the corresponding set of ramp data to the first component until the first component reaches the corresponding predicted parameter value.

[0009] According to another aspect of the present invention, there is provided a beam current parameter processing apparatus, including:

[0010] A first input module, configured to input the desired beam current state value into a parameter prediction model to obtain the predicted parameter values of at least one first component in the ion implanter, where the predicted parameter values include the parameter values required for the first component corresponding to achieving the beam current state value to reach the corresponding numerical value;

[0011] A second input module, configured to input, for each first component, the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component, so as to obtain a set of ramp data of the first component;

[0012] A setting module, configured to sequentially set a corresponding set of ramp data to each of the first components until the first component reaches the corresponding predicted parameter value.

[0013] According to another aspect of the present invention, there is provided an electronic device, including:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for enabling a processor to implement the method according to any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, there is provided a computer program product including a computer program which, when executed by a processor, implements the method according to any embodiment of the present invention.

[0019] The technical solution of the embodiment of the present invention, to achieve the desired beam state, determines the predicted parameter value for each first component in the ion implanter through a parameter prediction model, improving the accuracy of determining the parameter value that the first component needs to reach. After determining the predicted parameter value of each first component, for each first component, use the time correlation model corresponding to the first component to determine the ramp data required for the first component to reach the predicted parameter value from the current parameter value, so that the first component sequentially experiences the ramp data and changes the current parameter value to the predicted parameter value, avoiding directly setting the current parameter value to the predicted parameter value, which may reduce the lifespan of the first component or even cause damage. The safety of setting the parameter value of the first component is improved.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Brief Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 is a flowchart of a beam parameter processing method provided in Embodiment 1 of the present invention;

[0023] Figure 2 is a flowchart of another beam parameter processing method provided in Embodiment 2 of the present invention;

[0024] Figure 3 is a schematic structural diagram of an ion implanter provided according to the beam parameter processing method of the embodiments of the present invention;

[0025] Figure 4 is a schematic structural diagram of a beam parameter processing device provided in Embodiment 3 of the present invention;

[0026] Figure 5 is a schematic structural diagram of an electronic device for implementing the beam parameter processing method of the embodiments of the present invention. Detailed Description of the Embodiments

[0027] To enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0029] The ion implanter ionizes dopant atoms through an ion source to form ions. These ions are extracted and accelerated under the action of an electric field to form an ion beam with a certain energy and direction.

[0030] The ion beam is composed of charged particle beams of atoms or molecules that have lost or gained at least one electron, and thus will react to electric and magnetic fields. The stability and uniformity (also known as homogeneity) of the ion beam are one of the crucial decisive factors for the quality of the finished product.

[0031] The ion implanter uses electromagnetic fields generated by a series of components to screen and guide the ion beam. Therefore, the generation and regulation of the beam current are a complex process with numerous parameters. Traditional tuning methods require operators to have rich experience and knowledge, knowing the impact of changes in the parameters of each component in the ion implanter on the ion beam. However, manually adjusting the parameters of each component is cumbersome and difficult to obtain a stable and uniform beam current. Even an experienced operator can hardly quickly and accurately adjust all variable parameters to obtain a stable and uniform beam current.

[0032] To address the above problems, the present invention proposes an automatic tuning method based on deep learning to improve the quality and efficiency of generating ion beams.

[0033] Embodiment 1

[0034] Figure 1 is a flowchart of a beam current parameter processing method provided according to Embodiment 1 of the present invention. This embodiment is applicable to the situation of controlling the formation of a beam current by an ion implanter. The ion implanter is a key device in semiconductor manufacturing, and forms a beam current by accelerating specific ions to an energy of several hundred keV to dope materials or perform surface modification. The beam current refers to the flow formed by a group of particles (such as electrons, ions, or atoms) moving in space in a certain direction and at a certain speed.

[0035] The beam current parameter processing method can be executed by a beam current parameter processing device. The beam current parameter processing device can be implemented in the form of hardware and / or software, and the beam current parameter processing device can be configured in an electronic device. The electronic device can be a device such as a mobile phone, a computer, or a server.

[0036] As Figure 1 shown, the method includes:

[0037] S110. Input the desired beam current state value into the parameter prediction model to obtain the predicted parameter values of at least one first component in the ion implanter.

[0038] The beam current state value includes the value characterizing the state of the beam formed by the ion implanter. The beam current state value can be used to describe the characteristics of the beam. For example, in an ion implanter, the beam current state value may involve indicators such as the intensity and energy of the beam.

[0039] In one embodiment, the beam current state value includes one or more of the following:

[0040] The percentage of beam impurities, the beam current value, the beam uniformity measurement information, and the beam stability measurement information.

[0041] Among them, the percentage of beam impurities includes the proportion of impurities in the beam. The percentage of beam impurities can be calculated by measuring the number or energy of impurity particles and non-impurity particles in the beam. For example, the percentage of the ratio of the number of impurity particles to the total number of particles is determined as the percentage of beam impurities.

[0042] The beam current value includes the amount of charge passing through a certain cross-section per unit time in the beam. The beam current value can reflect the relationship between the amount of charge carried by the particles in the beam and time. The beam charge value includes the ratio of the total charge to time.

[0043] The beam uniformity measurement information includes the information for measuring the beam uniformity. The beam uniformity measurement information includes the index for measuring whether the beam is uniform in spatial distribution. The beam uniformity includes the degree of uniformity of the particle distribution of the beam in the cross-section, and can be evaluated by measuring the beam density or intensity distribution. The measurement of the beam uniformity includes measuring the current distribution or intensity distribution of the beam in space, and then evaluating its uniformity. For example, the proportion of the positions where the beam current is uniform is determined as the beam uniformity. Another example is to measure the beam uniformity by parameters such as the variance of the current at each measurement point in the beam.

[0044] The beam stability measurement information includes the information for measuring the beam stability. The beam stability includes the index for measuring whether the particle beam remains stable in time or space. The beam stability can refer to the degree of change of parameters such as the intensity, energy, or position of the beam in time or space. The beam stability includes the ability to reflect that the key parameters (such as intensity, energy, position, etc.) of the beam remain stable in the time dimension. The beam stability measurement information includes the proportion of stability determined by measuring the set parameters of the beam multiple times.

[0045] The desired beam current state includes the beam current state values that the ion implanter needs to achieve, that is, the state values that the beam current formed by the prefetch ion implanter reaches. The parameter prediction model includes a model for predicting the parameter values of components in the ion implanter. Based on the input beam current state value, the parameter prediction model can determine the predicted parameter values that the components in the ion implanter need to reach in order to achieve this beam current state value. The predicted parameter values include the parameter values that the value of the first component corresponding to the predicted parameter value needs to reach in order to achieve the beam current state value. That is, after the value of the first component in the ion implanter is set to the predicted parameter value, the ion implanter can form a beam current that satisfies the beam current state value.

[0046] In this operation, the input desired beam current state value can be obtained. The input method is not limited. It can be input through the human-machine interaction interface, or through voice input, or through an input device. Here, it is not limited. After obtaining the desired beam current state value, the beam current state value can be input into the parameter prediction model, and after being processed by the parameter prediction model, the predicted parameter values of at least one first component in the ion implanter are output. Different beam current state values can correspond to the same or different first components. The first component includes the components that the ion implanter needs to set in order to achieve the desired beam current state value.

[0047] The parameter prediction model can output the predicted parameter values of at least one first component in order to achieve the beam current state value. One first component corresponds to one predicted parameter value. The predicted parameter value is used to set the corresponding first component so that the first component operates at the predicted parameter value, and further enables the ion implanter to form a beam current that satisfies the beam current state value.

[0048] S120. For each first component, input the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component to obtain a set of ramp data of the first component.

[0049] Each first component has a current state. The current state includes the state where the first component is currently located. The current parameter value includes the parameter value where the first component is located in the current state. The current parameter value includes the initial parameter value when the first component changes to the predicted parameter value. The first component will change from the current parameter value to the predicted parameter value.

[0050] The time correlation model includes a time-related model. The respective stable numerical states and the acceptable numerical fluctuation ranges of components in different stages of the life cycle are different. The time correlation model can capture periodic data changes, so as to set the ramp ranges for each component in different stages respectively to achieve the best working effect of the components.

[0051] In the present invention, ramping up includes gradually adjusting the parameter value to gradually increase (or decrease) the value of the first component to the predicted parameter value. For example, ramping up the data includes gradually adjusting the first component from the current parameter value to the predicted parameter value.

[0052] Ramping data includes the data required to be experienced during the process of gradually adjusting the parameter value of the first component.

[0053] A set of ramping data may include the current parameter value and the predicted parameter value, and may also include the intermediate parameter values that need to be set during the process of ramping up from the current parameter value to the predicted parameter value. The intermediate parameter values include the parameter values whose values are between the current parameter value and the predicted parameter value. The number of intermediate parameter values can be at least one. When setting the parameter value of the first component, it can be set from the current parameter value to the intermediate parameter values in sequence, and finally to the predicted parameter value, to achieve a gradient change in the parameter value and avoid damaging the first component by directly setting it to the predicted parameter value.

[0054] In the present invention, a set of ramping data may or may not include the current parameter value. In the case of retaining the current parameter value, since the first component is currently at the current parameter value, the first component can be set starting from the parameter value subsequent to the current parameter value.

[0055] In this operation, for each predicted parameter value output by the parameter prediction model, determine the first component corresponding to the predicted parameter value, and then determine the time correlation model corresponding to the first component. For each first component, the following operations are performed: input the predicted parameter value and the current parameter value of the first component into the time correlation model, so as to output a set of ramping data for the first component through the time correlation model, facilitating the setting of the parameter value of the first component.

[0056] When the time correlation model outputs ramping data, it can combine the historical ramping data of the first component to determine the ramping data in the current state.

[0057] In one example, the number of parameter prediction models is one, and the number of time correlation models is multiple. The number of time correlation models is associated with the number of components in the ion implanter. For example, the number of time correlation models is equal to the number of components in the ion implanter.

[0058] The predicted parameter values output by the parameter prediction model are sequentially input into the corresponding time correlation models to determine the ramping data. The time correlation model corresponding to the predicted parameter value includes the time correlation model corresponding to the first component corresponding to the predicted parameter value.

[0059] S130. For each of the first components, a corresponding set of ramp data is sequentially set to the first component until the first component reaches the corresponding predicted parameter value.

[0060] For each first component for which the ramp data has been determined, after determining the ramp data of the first component, this operation can traverse each data in a set of ramp data. The traversed data is sequentially set to the first component until the first component is set to the predicted parameter value, avoiding damage to the first component.

[0061] Exemplarily, a set of ramp data corresponding to the first component is 0, 100, and 200. Among them, 0 corresponds to the current parameter value, 200 corresponds to the predicted parameter value, and 100 corresponds to the intermediate parameter value. In addition, the current parameter value may not be included in a set of ramp data. For example, a set of ramp data can be 100 and 200.

[0062] This operation can set 100 to the first component, so that the parameter value of the first component changes from 0 to 100. After the parameter value of the first component changes to 100, continue to set 200 to the first component.

[0063] When this operation sequentially sets the ramp data to the first component, for each set of ramp data set, after the first component reaches this set of ramp data, continue to set the next set of ramp data until the predicted parameter value is reached.

[0064] The beam parameter processing method provided by the present invention, in order to achieve the desired beam state, determines the predicted parameter value for each first component in the ion implanter through the parameter prediction model, improving the accuracy of determining the parameter value required for the first component. After determining the predicted parameter value of each first component, for each first component, use the time correlation model corresponding to the first component to determine the ramp data required for the first component to reach the predicted parameter value from the current parameter value, so that the first component sequentially experiences the ramp data and changes the current parameter value to the predicted parameter value, avoiding directly setting from the current parameter value to the predicted parameter value, resulting in a reduction in the lifespan of the first component or even damage. It improves the safety of setting the parameter value of the first component.

[0065] On the basis of the above embodiments, a variant embodiment of the above embodiments is proposed. Here, it should be noted that in order to make the description brief, only the differences from the above embodiments are described in the variant embodiments.

[0066] In one embodiment, the set of ramp data includes the predicted parameter value; for each of the first components, sequentially setting the corresponding set of ramp data to the first component until the first component reaches the corresponding predicted parameter value includes:

[0067] For each of the first components, select, from a set of ramp-up data corresponding to the first component, the data with the largest difference from the predicted parameter value corresponding to the first component as the target data;

[0068] Set the target data to the first component;

[0069] After the data of the first component reaches the target data, return to continue selecting the next target data from the remaining data of the set of ramp-up data corresponding to the first component, and perform the setting operation until all the data in the set of ramp-up data corresponding to the first component is traversed.

[0070] In this embodiment, a set of ramp-up data includes at least predicted parameter values. During the process of setting the first component, the setting method for each first component is the same. Here, one first component is taken as an example for description.

[0071] The target data includes the data with the largest difference from the predicted parameter value in the ramp-up data. When a set of ramp-up data is sorted in ascending or descending order, the target data includes the data sequentially selected from the ramp-up data, and this data includes the first data in the set of ramp-up data. The predicted parameter value can be the last data in the set of ramp-up data.

[0072] In an example, the target data is sequentially selected from the high positions in a set of ramp-up data until the predicted parameter value is selected, or the target data is sequentially selected from the high positions in a set of ramp-up data until the predicted parameter value is selected.

[0073] After selecting the target data, the target data can be set to the first component. After the first component reaches the target data, continue to select the next target data from the remaining data of a set of ramp-up data, and continue to perform the operation of setting the target data to the first component until all the data in a set of ramp-up data is traversed. The predicted data in a set of ramp-up data is the last data to be traversed. After traversing all the data in a set of ramp-up data, the parameter value of the first component is set to the predicted parameter value. Among them, the remaining data can be considered as the data in a set of ramp-up data that has not been selected.

[0074] After a set of ramp-up data selects the target data once, the target data can be removed from the ramp-up data to facilitate selecting the remaining target data from the remaining ramp-up data. Or after a set of ramp-up data selects the target data once, the next target data can be selected from the remaining unselected data.

[0075] In this embodiment, each target data in a set of ramp-up data is traversed, gradually traversing to the predicted parameter value. Each time of traversing, the traversed data is set to the first component, so that the first component gradually reaches the predicted parameter value, improving the safety of the first component.

[0076] In one embodiment, for each first component, the predicted parameter value corresponding to the first component and the current parameter value in the current state of the first component are input into the time correlation model corresponding to the first component, and a set of ramp data for the first component is obtained, including:

[0077] For each first component, the predicted parameter value corresponding to the first component and the current parameter value in the current state of the first component are input into the time correlation model corresponding to the first component;

[0078] Through the time correlation model and combining the data of each ramp in the life cycle of the first component, the ramp data from the current parameter value to the predicted parameter value is determined and output.

[0079] This embodiment details the operation of outputting a set of ramp data for each first component through the time correlation model.

[0080] In this embodiment, when the time correlation model determines the ramp data, it combines the data of each historical ramp in the life cycle of the first component to determine a set of ramp data that the first component needs to experience to change from the current parameter value to the predicted parameter value in the current state.

[0081] Since each part of the ion implantation is composed of precision components, if the predicted parameter value is directly set to each component, it is very likely to cause damage to the instrument. At the same time, if the numerical ramp is simply set to linearly increase within the same time interval, it is also not suitable. For example, it may only take 1 second to increase from 0 to 100, while it may take 5 seconds to safely and stably increase from 100 to 200. Therefore, setting the parameters of the components should be a non-linear change process.

[0082] In order to extend the service life of the ion implanter, the present invention uses a time correlation model, such as a long short-term memory model, to perform safe and stable numerical setting on the components until the numerical value of the components reaches the predicted parameter value of the model.

[0083] In a general neural network model, each node in each layer is interconnected. When data with time and period correlation passes through this model, the correlation before and after this data will be lost. The time correlation model introduces memory units, which can maintain the flow of information in a long sequence, can generate the value of the next time point based on the data of multiple past time nodes, and at the same time, the fluctuations between these values are relatively gentle.

[0084] Embodiment 2

[0085] Figure 2It is a flowchart of another beam parameter processing method provided according to Embodiment 2 of the present invention. This embodiment provides training operations for a parameter prediction model and a time correlation model. As Figure 2 shown, the method includes:

[0086] S210. Obtain a first training set.

[0087] The first training set includes the parameter values of the second component and the corresponding beam state values when generating and adjusting the ion beam.

[0088] The second component includes the components included in the ion implanter. The first component and the second component can be components of the same type or model. The ion implanter in the parameter prediction model training stage and the ion implanter in the application stage of the present invention can be ion implanters of the same type or model.

[0089] The parameter values of the second component include the parameter values that need to be set to make the beam reach the state value. The state value of the beam can correspond to the parameter values of multiple second components. The parameter values of the second component correspond to the predicted parameter values. The parameter values of the second component include the parameter values in the parameter prediction model stage. The predicted parameter values include the parameter values output in the parameter prediction model application stage. The desired beam state value corresponds to the beam state value in the first training set. The desired beam state value includes the value input in the parameter prediction model application stage. The beam state value in the first training set includes the value input in the parameter prediction model training stage.

[0090] The data in the first training set includes the data in the process of generating and adjusting the ion beam. The parameter values of the second component and the corresponding beam state values can be used as sample pairs to train the model to obtain a trained parameter prediction model.

[0091] In one example, a series of parameter values of components (as the parameter values of the second component) and various state values of the final beam when generating and adjusting the ion beam each time are collected as the training data of the model.

[0092] S220. Train a first initial model based on the first training set to obtain a parameter prediction model.

[0093] The first initial model includes a basic model for determining predicted parameter values that has not been trained. A trained parameter prediction model can be obtained by training the first initial model with the first training set.

[0094] When creating the first initial model, randomly generated data is used. If these random values are not adjusted through training, the predicted values obtained will be very different from the expected values.

[0095] During the process of training the first initial model, an optimization algorithm is used to train the model. A large amount of training data in the first training set can be used to slightly adjust the initially generated random data, making the final output value of the model closer to the optimal value required in practice.

[0096] During the process of optimizing and training the model, gradient descent and hyperparameter adjustment can be performed. When the predicted value approaches the expected value, the magnitude of the change in the random value can be adjusted. Stochastic gradient descent and hyperparameter adjustment can change this magnitude of change. For example, when the difference between the predicted value and the expected value is large, a large magnitude of change is used; when the difference is small, a small magnitude of change is selected, thereby making the model better fit the training data.

[0097] S230. Obtain a second training set.

[0098] The second training set includes target parameter values, ramp-up data of the third component in the current state, and the current parameter value of the third component. The target parameter value is the parameter value that the third component ultimately needs to reach.

[0099] The third component includes the components included in an ion implanter. The second component and the third component include components of the same type or model. The second component and the third component also include the same components.

[0100] In this embodiment, the time correlation model and the parameter prediction model can be jointly trained or separately trained.

[0101] The target parameter value includes empirical values and also includes the parameter values output during the training process of the parameter prediction model.

[0102] The ramp-up data of the third component in the current state includes the ramp-up data that the third component needs to experience to reach the target parameter value.

[0103] The target parameter value corresponds to the predicted parameter value. The target parameter value includes the data input during the training process of the time correlation model. The predicted parameter value includes the data input during the application process of the time correlation model. The current parameter value of the third component can correspond to the current parameter value, and the current parameter value includes the data input during the application process of the time correlation model. The current parameter value of the third component includes the data input during the training process of the time correlation model. The ramp-up data of the third component in the current state corresponds to a set of ramp-up data of the first component. The former is the output during the training stage of the time correlation model, and the latter is the output during the application stage of the time correlation model.

[0104] The second training set includes empirical values and is also formed based on the data obtained by manually adjusting the component parameters historically to obtain the required beam current.

[0105] S240. Train a second initial model related to time based on the second training set to obtain a time correlation model.

[0106] The second initial model includes a basic model for determining ramp-up data that has not been trained. Training the second initial model with the second training set can obtain a trained time correlation model.

[0107] During the training process of the second initial model, gradient descent and hyperparameter adjustment can also be performed to improve the training effect, which will not be elaborated here.

[0108] In one embodiment, the target parameter value includes the parameter value output during the training process of the parameter prediction model.

[0109] In this embodiment, the parameter prediction model and the time correlation model can be jointly trained. During the training process, the parameter value output by the first initial model can be used as the target parameter value and input into the second initial model to process the ramp-up data under the current state of the third component input into the second initial model, output the ramp-up data of this round, and process the ramp-up data of this round with the ramp-up data of the third component in the current state to adjust the parameters of the time correlation model. That is, during the training process of the parameter prediction model and the time correlation model, the output of the parameter prediction model is input into the corresponding time correlation model to achieve joint training and realize the output of each round of the first initial model and input it into the second initial model trained in this round. Among them, the corresponding relationship can be determined based on the second component corresponding to the ramp-up data output by the parameter prediction model and the third component corresponding to the time correlation model.

[0110] S250. Input the desired beam current state value into the parameter prediction model to obtain the predicted parameter values of at least one first component in the ion implanter.

[0111] S260. For each first component, input the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component to obtain a set of ramp-up data for the first component.

[0112] S270. For each of the first components, sequentially set the corresponding set of ramp-up data to the first component until the first component reaches the corresponding predicted parameter value.

[0113] In one example, the ramp-up data is sequentially set to each of the first components in order, and it is judged whether to continue setting the next ramp-up data after reaching the set ramp-up data until the predicted parameter value is reached. After reaching the predicted parameter value, the stability and uniformity can be measured.

[0114] The beam parameter processing method provided in this embodiment trains a parameter prediction model through a first training set and trains a time correlation model through a second training set. Based on the parameter prediction model and the time correlation model, the predicted parameter values of the first component in the ion implanter are determined, so as to achieve the expected beam state value, improve the stability of the beam, and the safety of the first component.

[0115] Based on the above embodiment, a variant embodiment of the above embodiment is proposed. Here, it should be noted that for the sake of brief description, only the differences from the above embodiment are described in the variant embodiment.

[0116] In one embodiment, the predicted parameter values include one or more of the following:

[0117] Gas type, gas flow rate, solid type, evaporator heating temperature, arc chamber voltage, arc chamber current, cathode voltage, cathode current, filament current, source magnet current, extraction electrode vertical tilt angle, lateral left and right position, gap position, extraction suppression voltage, extraction suppression current, analysis magnet current, high-voltage discharge rod voltage, high-voltage discharge rod current, extraction voltage, extraction current, accelerator voltage, accelerator current, quadrupole lens voltage, quadrupole lens current, steering electrode voltage, steering electrode current, scan amplifier voltage, parallel lens voltage, parallel lens current, scan suppression voltage, scan suppression current, angle energy focusing voltage, angle energy focusing current, energy slit width.

[0118] Gases can be used to generate the required ions in the ion source. These gases are ionized in the ion source of the ion implanter to form a charged ion beam, and then the required ions are separated and finally implanted into the material. The predicted parameters corresponding to the ion source can include gas type and gas flow rate.

[0119] Gas type includes the types of gases used in the ion implanter. Gas flow rate includes the flow rates of the gases used in the ion implanter.

[0120] Solids in the ion implanter can be converted into a gaseous state by heating in the evaporator before use. Solid type includes the types of solids used in the ion implanter. The predicted parameters corresponding to the evaporator include solid type. The predicted parameters corresponding to the evaporator can also include the evaporator heating temperature, that is, the temperature to which the evaporator is heated.

[0121] Figure 3 It is a schematic structural diagram of an ion implanter provided according to the beam parameter processing method of the embodiments of the present invention. Refer to Figure 3 , the ion source 1 includes an arc chamber, a cathode, a filament, and a source magnet. Among them, the arc chamber includes the cavity of the ion source for accommodating the working gas. The cathode can be used to emit electrons. The filament includes the component for heating the cathode. The source magnet includes the component for generating a magnetic field inside the arc chamber.

[0122] The filament is located inside the arc chamber, the filament is located below the arc chamber, and the source magnet is located around the arc chamber. In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0123] The arc chamber voltage includes the voltage of the arc chamber inside the ion source 1. The arc chamber current includes the current of the arc chamber inside the ion source 1. The cathode voltage includes the voltage of the cathode in the ion source 1. The cathode current includes the current of the cathode in the ion source 1. The filament current includes the current of the filament in the ion source 1. The source magnet current includes the current of the source magnet in the ion source 1.

[0124] The extraction electrode 2 in the ion implanter is also called the Extraction Electrode, and the extraction electrode 2 includes an electrode assembly for extraction. The corresponding prediction parameters include the vertical tilt angle of the extraction electrode, the lateral left and right positions, and the gap position. In addition, the extraction voltage and the extraction current can also be included.

[0125] Among them, the vertical tilt angle of the extraction electrode includes the included angle between the extraction electrode 2 and the vertical direction. The lateral left and right positions include the position adjustment of the extraction electrode 2 in the horizontal direction. The gap position includes the distance between the extraction electrode 2 and the adjacent components. The extraction voltage includes the voltage of the extraction electrode 2. The extraction current includes the current of the extraction electrode 2.

[0126] The extraction suppression voltage includes the voltage of the electrode for suppression. The extraction suppression current includes the current of the electrode for suppression.

[0127] The predicted parameter value of the analysis magnet 3 in the ion implanter can include the analysis magnet current, that is, the current of the analysis magnet 3. In addition, the voltage of the analysis magnet can also be included. The analysis magnet 3 can be used for separating and screening ions.

[0128] The components included in the ion implanter further include a high-voltage discharge rod, a quadrupole lens, and an accelerator. Correspondingly, the corresponding expected parameter values include: the high-voltage discharge rod voltage, the high-voltage discharge rod current, the accelerator voltage, the accelerator current, the quadrupole lens voltage, and the quadrupole lens current.

[0129] Among them, the high-voltage discharge rod can be used to release residual high-voltage charges, where the high voltage includes a voltage higher than a certain voltage level, such as a voltage above 1 kV. The quadrupole lens can be used to adjust the shape and focusing effect of the ion beam. The accelerator can accelerate ions.

[0130] The high-voltage discharge rod voltage includes the voltage of the high-voltage discharge rod in the ion implanter. The high-voltage discharge rod current includes the current of the high-voltage discharge rod in the ion implanter. The accelerator voltage includes the voltage of the accelerator in the ion implanter. The accelerator current includes the current of the accelerator in the ion implanter. The quadrupole lens voltage includes the voltage of the quadrupole lens in the ion implanter, and the quadrupole lens current includes the current of the quadrupole lens in the ion implanter.

[0131] The components included in the ion implanter further include a scan amplifier and a parallel lens. The scan amplifier includes a device for scanning and amplifying an ion beam. The parallel lens includes a device for keeping each ion beam parallel. The predicted parameters corresponding to the scan amplifier include: scan amplifier voltage, scan suppression voltage, and / or scan suppression current. Scan amplifier current may also be included. The predicted parameters corresponding to the parallel lens include parallel lens voltage and / or parallel lens current.

[0132] Among them, the scan amplifier voltage includes a voltage signal for controlling the deflection of the ion beam. The scan amplifier current includes a current signal for controlling the deflection of the ion beam. The scan suppression voltage includes a voltage for restricting the scanning range of the ion beam to prevent the ion beam from exceeding a predetermined range during scanning. The scan suppression current is a parameter used in conjunction with the scan suppression voltage to control the current intensity during scanning and prevent uneven deflection of the ion beam caused by current fluctuations during scanning.

[0133] The parallel lens voltage includes the voltage applied to the parallel lens electrodes. The parallel lens current includes the current flowing through the electromagnetic coils of the parallel lens.

[0134] The steering electrode 4 in the ion implanter includes components for controlling the direction of the ion beam. The corresponding predicted parameter values include steering electrode voltage and steering electrode current. The steering electrode voltage is used to generate an electric field to deflect the ion beam in a specific direction. The steering electrode current acts together with the voltage to affect the intensity and stability of the electric field.

[0135] The angle energy filter 5 in the ion implanter includes adjusting the angle of the ion beam so that the ion beam can be implanted onto the wafer. The predicted parameters corresponding to the angle energy filter 5 include: angle energy focusing voltage, angle energy focusing current, and / or energy slit width. By adjusting the predicted parameters, the angle energy filter 5 is used to focus the energy and form an energy slit.

[0136] Among them, the angle energy focusing voltage includes the voltage applied to the angle energy filter 5. The angle energy focusing current includes the current applied to the angle energy filter 5. The energy slit width includes the width of the energy slit generated by the angle energy filter 5.

[0137] The beam parameter processing method provided by the present invention includes an ion beam automatic tuning method based on a deep learning model. In order to reproduce such a stable and uniform beam under different conditions, the present invention proposes an automatic tuning method based on a deep learning model.

[0138] The parameter prediction model receives the desired beam state value and can generate a series of predicted parameter values of components according to the received beam state value. After setting these predicted parameter values of the components on each component, it can be determined whether the beam reaches the desired setting and whether the stability and uniformity of the beam meet the standards. If so, it ends. At the same time, the grouped input-output parameters are used as the subsequent training data of the parameter prediction model to continuously adjust the model for subsequent prediction analysis.

[0139] In the case of non-compliance, the model can be continuously trained, or the desired beam state value can be continuously input to obtain the predicted parameter value, and then the ramp data is determined through the time correlation model and set to the first component to determine whether it meets the standard.

[0140] In the application stage of the model, each group of input-output data can be recorded and incorporated into the first training set and the second data set for subsequent training.

[0141] Compared with the manual tuning method, the present invention can efficiently adjust the variable parameters of all components in the beam generation and adjustment process to obtain a stable and uniform beam. Through the deep learning model, it becomes easy to reproduce the beam under different conditions. The cost of generating the beam is reduced, the efficiency is improved, the parameter adjustment ability is enhanced, and it has high adaptability.

[0142] Embodiment III

[0143] Figure 4 It is a schematic structural diagram of a beam parameter processing device according to Embodiment III of the present invention. As Figure 4 shown, the device includes:

[0144] A first input module 310, configured to input the desired beam state value into the parameter prediction model to obtain the predicted parameter values of at least one first component in the ion implanter, where the predicted parameter values include the parameter values required for the first component to reach the numerical value corresponding to the beam state value;

[0145] A second input module 320, configured to input, for each first component, the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component to obtain a set of ramp data of the first component;

[0146] A setting module 330 is configured to sequentially set a corresponding set of ramp-up data to each of the first components until the first component reaches the corresponding predicted parameter value.

[0147] In the beam parameter processing method provided in this embodiment, to achieve a desired beam state, the first input module determines the predicted parameter values for each first component in the ion implanter through a parameter prediction model, improving the accuracy of determining the parameter values required for the first component. After determining the predicted parameter values for each first component, the second input module uses the time correlation model corresponding to each first component to determine the ramp-up data required for the first component to reach the predicted parameter value from the current parameter value, so that the first component sequentially experiences the ramp-up data. The setting module changes the current parameter value to the predicted parameter value, avoiding a reduction in lifespan or even damage caused by directly setting from the current parameter value to the predicted parameter value. The safety of setting the parameter values of the first component is improved.

[0148] In one embodiment, a set of ramp-up data includes the predicted parameter value; the setting module 330 is specifically configured to:

[0149] For each of the first components, select the data with the largest difference from the predicted parameter value corresponding to the first component from a set of ramp-up data corresponding to the first component as the target data;

[0150] Set the target data to the first component;

[0151] After the data of the first component reaches the target data, return to continue selecting the next target data from the remaining data of the set of ramp-up data corresponding to the first component and perform the setting operation until all the data in the set of ramp-up data corresponding to the first component is traversed.

[0152] In one embodiment, the beam parameter processing module includes a first training module, and the training operation of the parameter prediction model executed by the first training model includes:

[0153] Obtain a first training set, where the first training set includes the parameter values of the second component and the corresponding beam state values when generating and adjusting the ion beam;

[0154] Train a first initial model based on the first training set to obtain a parameter prediction model.

[0155] In one embodiment, the beam parameter processing module includes a second training module, and the training operation of the time correlation model executed by the second training model includes:

[0156] Obtain a second training set, where the second training set includes target parameter values, ramp-up data of a third component in the current state, and current parameter values of the third component, and the target parameter values are the parameter values that the third component ultimately needs to reach;

[0157] Train a second initial model related to time based on the second training set to obtain a time correlation model.

[0158] In one embodiment, the target parameter values include the parameter values output during the training process of the parameter prediction model.

[0159] In one embodiment, the second input module 320 is specifically configured to:

[0160] For each first component, input the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component;

[0161] Through the time correlation model and combining the ramp-up data of each time within the life cycle of the first component, determine and output the ramp-up data from the current parameter value to the predicted parameter value.

[0162] In one embodiment, the beam current state values include one or more of the following:

[0163] Beam impurity percentage, beam current value, beam uniformity measurement information, and beam stability measurement information.

[0164] In one embodiment, the predicted parameter values include one or more of the following:

[0165] Gas type, gas flow rate, solid type, evaporator heating temperature, arc chamber voltage, arc chamber current, cathode voltage, cathode current, filament current, source magnet current, extraction electrode vertical tilt angle, lateral left and right position, gap position, extraction suppression voltage, extraction suppression current, analysis magnet current, high-voltage discharge rod voltage, high-voltage discharge rod current, extraction voltage, extraction current, accelerator voltage, accelerator current, quadrupole lens voltage, quadrupole lens current, steering electrode voltage, steering electrode current, scan amplifier voltage, parallel lens voltage, parallel lens current, scan suppression voltage, scan suppression current, angular energy focusing voltage, angular energy focusing current, energy slit width.

[0166] The beam parameter processing device provided by the embodiments of the present invention can execute the beam parameter processing method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0167] Embodiment 4

[0168] Figure 5It is a schematic structural diagram of an electronic device for implementing the beam parameter processing method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0169] As Figure 5 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor 11, and the computer program is executed by the at least one processor 11 so that the at least one processor 11 can execute the method provided by the present invention.

[0170] The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0171] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0172] The processor 11 includes various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the beam parameter processing method.

[0173] In some embodiments, the beam parameter processing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the beam parameter processing method described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute the beam parameter processing method by any other suitable means (e.g., by means of firmware).

[0174] The various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), system on chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor including a special or general programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0175] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0176] In the context of the present invention, a computer-readable storage medium stores computer instructions for causing a processor to implement the beam parameter processing method provided by the present invention when executed.

[0177] The present invention also provides a computer program product, which includes a computer program that implements the method according to the embodiments of the present invention when executed by a processor.

[0178] A computer-readable storage medium includes tangible media that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium includes machine-readable signal media. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, feedback provided to the user includes any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0180] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0181] The computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server includes a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and virtual private server (VPS) services.

[0182] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0183] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A beam parameter processing method, characterized in that: include: Inputting the expected beam state value into the parameter prediction model to obtain a predicted parameter value of at least one first component in the ion implanter, wherein the predicted parameter value includes a parameter value required to achieve the value of the first component corresponding to the beam state value; For each first component, input the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component to obtain a set of climbing data of the first component; For each of the first components, a corresponding set of climbing data is sequentially set to the first component until the first component reaches the corresponding predicted parameter value.

2. The method according to claim 1, characterized in that: The set of ramp data includes the predicted parameter value; and for each of the first components, the corresponding set of ramp data is sequentially set to the first component until the first component reaches the corresponding predicted parameter value, including: For each of the first components, selecting data having the largest difference with the predicted parameter value corresponding to the first component from a group of ramp data corresponding to the first component as target data; Setting the target data to the first component; After the data of the first component reaches the target data, return to continue selecting the next target data from the remaining data of a set of climbing data corresponding to the first component, and perform a setting operation until the set of climbing data corresponding to the first component is traversed.

3. The method according to claim 1, characterized in that The training operation of the parameter prediction model includes: Acquire a first training set, wherein the first training set includes parameter values ​​of the second component and corresponding beam state values ​​when generating and adjusting the ion beam; A first initial model is trained based on the first training set to obtain a parameter prediction model.

4. The method according to claim 1, characterized in that: The training operation of the time association model includes: Acquire a second training set, where the second training set includes a target parameter value, climbing data of a third component in a current state, and a current parameter value of the third component, wherein the target parameter value is a parameter value that the third component needs to eventually reach; A second initial model related to time is trained based on the second training set to obtain a time-related model.

5. The method according to claim 4, characterized in that The target parameter value includes the parameter value output during the parameter prediction model training process.

6. The method according to claim 1, characterized in that For each first component, the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state are input into the time correlation model corresponding to the first component to obtain a set of climbing data of the first component, including: For each first component, inputting the predicted parameter value corresponding to the first component and the current parameter value of the first component in the current state into the time correlation model corresponding to the first component; The ramp data from the current parameter value to the predicted parameter value is determined and output by combining the time correlation model with the data of each ramp during the life cycle of the first component.

7. The method according to claim 1, characterized in that The beam state value includes one or more of the following: Beam impurity percentage, beam current value, beam uniformity metric information and beam stability metric information.

8. The method according to claim 1, characterized in that: The predicted parameter values ​​include one or more of the following: Gas type, gas flow, solid type, evaporator heating temperature, arc chamber voltage, arc chamber current, cathode voltage, cathode current, filament current, source magnet current, extraction electrode vertical tilt angle, lateral left and right position, gap position, extraction suppression voltage, extraction suppression current, analysis magnet current, high-voltage discharge rod voltage, high-voltage discharge rod current, extraction voltage, extraction current, accelerator voltage, accelerator current, quadruple lens voltage, quadruple lens current, steering electrode voltage, steering electrode current, scanning amplifier voltage, parallel lens voltage, parallel lens current, scanning suppression voltage, scanning suppression current, angle energy focusing voltage, angle energy focusing current, energy slit width.

9. A beam parameter processing device, characterized in that: include: A first input module, used for inputting a desired beam state value into a parameter prediction model to obtain a predicted parameter value of at least one first component in the ion implanter, wherein the predicted parameter value includes a parameter value required to achieve a value of the first component corresponding to the beam state value; A second input module is used to input, for each first component, a predicted parameter value corresponding to the first component and a current parameter value of the first component in a current state into a time correlation model corresponding to the first component to obtain a set of climbing data of the first component; The setting module is used to set a corresponding set of climbing data to each of the first components in sequence until the first component reaches the corresponding predicted parameter value.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method according to any one of claims 1 to 8 when executed.

12. A computer program product, characterized in that The computer program product comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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