Ion beam polishing multi-working-condition removal function obtaining method

Abstract

The invention provides an ion beam polishing multi-working-condition removal function obtaining method which comprises the steps that according to a to-be-machined optical surface shape and a planned movement track of an ion source relative surface shape, the change range of an ion beam incident angle and the change range of a machining distance in a polishing machining task are determined; carrying out limited times of single-point etching experiments, and detecting the surface shape of the workpiece after the single-point etching experiments; calculating a processing amount distribution, and calculating a removal function considering the incident angle of the ion beam and the processing distance based on the processing amount distribution data and the processing time; training an artificial neural network by taking the removal function as training data; and on the basis of the trained neural network, obtaining a removal function fitting parameter pair according to the actual processing task requirement, and performing inverse processing on the removal function fitting parameter pair to obtain a removal function of the actual processing point. According to the removal function obtained through the method, the influence under multiple working conditions is considered, and the current high-precision machining requirement for the high surface slope or various non-planar optical surface shapes can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of optical high-precision processing, and particularly relates to a method for obtaining a removal function under multiple working conditions of ion beam polishing. Background Art

[0002] The principle of ion beam polishing technology is to use high-energy ions to bombard the surface atoms of materials, generating a sputtering effect, giving the material atoms sufficient kinetic energy to break away from the material surface, so as to precisely perform subtractive processing on the material. It is a non-contact processing method with relatively stable removal ability under the material removal points and given working conditions.

[0003] For ion beam polishing, the removal ability per unit time, that is, the accuracy of the removal function, will directly determine the accuracy of the final single processing or the accuracy convergence ability of multiple processes. For the method of obtaining the removal function, the patent with the publication number CN114564688A in the prior art records a method for determining the ion beam polishing removal function based on a BP neural network. Specifically, a database is established through a finite number of Faraday scans and line scan experiments to determine the relationship between the ion beam current density and the distribution coefficient of the removal function. According to the Faraday scan results, the peak removal rate of the removal function is calculated through a linear formula, and the distribution coefficient of the removal function is predicted through a BP neural network. This patent states that this method is applicable to all materials that can be used for ion beam processing and is not restricted by material characteristics.

[0004] However, the above technical solution only considers the influence of the processed material on the removal function, and does not simultaneously consider the technical problem that the accuracy of the removal function is affected by specific working conditions such as different ion beam incident angles and processing distance differences during the ion beam polishing process. Summary of the Invention

[0005] Aiming at the deficiencies existing in the prior art, the present invention proposes a method for obtaining the ion beam polishing removal function to solve the deficiencies existing in the prior art. The technical solution adopted by the present invention is as follows:

[0006] A method for obtaining a removal function under multiple working conditions of ion beam polishing is provided, including the following steps:

[0007] According to the to-be-processed optical surface shape and the planned movement trajectory of the ion source relative to the surface shape, determine the variation ranges of the ion beam incident angle and the processing distance in the polishing processing task;

[0008] Conduct a finite number of single-point etching experiments according to the variation ranges of the ion beam incident angle and the processing distance, and detect the surface shape of the workpiece after the single-point etching experiment;

[0009] Calculate the material removal distribution based on the initial surface shape of the workpiece and the surface shape after single-point etching experiment, and calculate the removal function considering the ion beam incident angle and machining distance based on the material removal distribution data and machining time;

[0010] Use the removal function as training data to train an artificial neural network;

[0011] Based on the trained neural network, obtain the removal function fitting parameter pair according to the actual machining task requirements, and perform inverse processing on the removal function fitting parameter pair to obtain the removal function of the actual machining point.

[0012] Further, determine the variation ranges of the ion beam incident angle and machining distance in the polishing machining task, including:

[0013] According to the planned machining points and machining path, determine the actual movement path of the ion source; then, by measuring the maximum and minimum values of the incident angle of the ion beam relative to the machining surface and the actual distance of the ion source relative to the machining point at each machining point on this path, determine the variation ranges of the ion beam incident angle and machining distance.

[0014] Further, detect the initial surface shape of the workpiece and the surface shape after single-point etching experiment by a laser interferometer, and keep the workpiece in the same clamping method in the detection system when detecting the two surface shapes.

[0015] Further, subtract the surface shape after single-point etching experiment from the initial surface shape of the workpiece to obtain the material removal distribution.

[0016] Further, calculate the removal function considering the ion beam incident angle and machining distance based on the material removal distribution data and machining time, including:

[0017] Flip the material removal distribution data in the distribution area of the machining point in the opposite direction according to the actually assigned ion beam incident angle of the single-point etching machining point to obtain the actual material removal distribution of this machining point;

[0018] Divide the actual material removal distribution by the given machining time to obtain the removal function considering the ion beam incident angle and machining distance.

[0019] Further, use the removal function as training data to train an artificial neural network, including:

[0020] Preprocess the removal function to obtain the ion beam incident angle - machining distance and removal function fitting parameter pair;

[0021] Use the ion beam incident angle - machining distance as the input and the removal function fitting parameter pair as the output to train the artificial neural network.

[0022] Further, preprocess the removal function, including: performing two-dimensional Gaussian fitting and linear normalization on the removal function in sequence.

[0023] Further, the artificial neural network is an RBF neural network.

[0024] Further, perform inverse processing on the pair of fitting parameters of the removal function, including: performing inverse normalization processing and inverse two-dimensional Gaussian fitting on the pair of fitting parameters of the removal function.

[0025] Further, the single-point etching experiment includes: placing the sample in the instrument, and respectively adjusting the placement angle of the sample in the instrument, as well as the height and horizontal position of the ion source in the processing instrument according to the arranged incident angle - processing distance combination and the area on the sample surface where the processing point corresponding to this combination is allocated. Under the conditions of a given time and given other process parameters, perform stable etching processing on this single point; the other process parameters include: ion beam voltage, acceleration voltage, radio frequency power, rare gas type and flow rate, and neutralization current.

[0026] As can be seen from the above technical solutions, the beneficial technical effects of the present invention are as follows:

[0027] The present invention fully considers the influence of different ion beam incident angles and ion source processing distances on the removal function, determines the complex variation law of the removal function under different working conditions through an RBF neural network, makes full use of the advantage of the neural network in fitting non-linear models, and the obtained removal function can better meet the current high-precision processing requirements for high surface slopes or various non-planar optical surface shapes. Description of the Drawings

[0028] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0029] Figure 1 It is a flowchart of the method for obtaining the removal function according to the embodiment of the present invention;

[0030] Figure 2 It is a schematic structural diagram of the workpiece to be processed according to the embodiment of the present invention;

[0031] Figure 3 It is a schematic diagram of the distribution of processing points in the single-point etching processing experiment according to the embodiment of the present invention;

[0032] Figure 4 It is a schematic diagram of the distribution of the processing amount in the single-point etching processing experiment according to the embodiment of the present invention;

[0033] Figure 5 Schematic diagram of effective actual removal data of a certain processing point separated after single-point etching processing in an embodiment of the present invention. Specific implementation manners

[0034] Hereinafter, embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present invention more clearly, and thus are only examples and cannot be used to limit the protection scope of the present invention.

[0035] It should be noted that unless otherwise specified, the technical terms or scientific terms used in this application should have the ordinary meanings understood by those skilled in the art to which the present invention belongs.

[0036] Embodiment

[0037] The inventors of the present application have found through research that ion beam polishing processing can calculate the removal function under ideal conditions according to the assumed given working conditions. However, in the actual processing process, even when carried out in a sealed vacuum chamber, various changes will still occur in the actual working conditions:

[0038] First of all, different incident angles of the ion beam will lead to different ratios of the number of incident ions to the number of bombarded atoms, that is, different sputtering yields, during the microscopic sputtering process. As a result, when the same ion beam performs single-point etching on material samples with different placement angles macroscopically, different removal functions are obtained. At the same time, when the distance between the ion source and the surface of the processed material changes, since the processing point may not be at the focus of the ion beam electric field focusing system at this time, it will also cause a difference between the actual ion concentration on the material surface and the theoretical expectation, thereby affecting the stability of the removal function.

[0039] Regarding these two influencing factors of the ion beam incident angle and the processing distance, in view of the fact that most of the existing ion beam polishing equipment currently only has the functions of X, Y, and Z-axis movement, and by default, during the processing, the control parameters mainly based on the processing time distribution are automatically planned under the condition that the removal function is absolutely stable; using such a removal function for processing obviously cannot meet the high-precision processing requirements of large-slope optical surfaces and the gradually emerging aspherical surfaces, especially free-form optical surfaces.

[0040] In view of the above research results, this embodiment provides a method for constructing a multi-condition removal function model for ion beam polishing, including the following steps:

[0041] S1. Determine the change ranges of the ion beam incident angle and the processing distance in the polishing processing task according to the optical surface shape to be processed and the planned movement trajectory of the ion source relative to the surface shape

[0042] First, determine the actual movement path of the ion source according to the processing points and processing paths planned for the optical surface to be processed. Then, determine the variation ranges of the ion beam incident angle and the processing distance by measuring the incident angle of the ion beam relative to the processing surface and the maximum and minimum values of the actual distance between the ion source and the processing points along this path.

[0043] The following is the specific process of obtaining the removal function. Taking a workpiece with a large-slope optical surface as an example, specific numerical examples are given for various parameters in the processing process:

[0044] In a specific implementation, the workpiece to be processed is as Figure 2 shown. Ion beam polishing is performed on a symmetric unpolished quartz glass cylindrical surface with a length and width of 10 cm and a maximum thickness of 3 cm, using a number of scanning paths with uniform intervals.

[0045] Assume that the distance between the ion source and the highest point of the cylindrical surface is 7 cm. Then the minimum value L min of the processing distance is 7 cm, and the maximum value L max is 7 + 3 = 10 cm. Since the cylindrical surface has a symmetric shape, when the ion source moves to directly above the geometric center of the cylindrical surface, the ion beam is perpendicularly incident, and at this time, the minimum incident angle is 0°, that is, θ min = 0°. When the ion source moves to directly above the left and right sides of the cylindrical surface, the incident angle of the ion beam is the largest, which is 30° in this example, that is, θ max = 30°.

[0046] In some embodiments, before performing each group of single-point etching experiments, in order to calculate the removal function subsequently, it is necessary to first detect the initial surface shape of each sample; the method of detecting the initial surface shape is not limited. For example, a laser interferometer can be used for detection; the clamping method of the sample needs to be kept fixed during the detection process. After the detection is completed, record the surface shape data h i0 (x, y), where x and y are the abscissa and ordinate of each detection point on the sample respectively; and record the fixing method of each sample during the detection process.

[0047] S2. Conduct a limited number of single-point etching experiments according to the variation ranges of the ion beam incident angle and the processing distance, and detect the surface shape of the workpiece after the single-point etching experiment.

[0048] Within the range of variation of the ion beam incident angle and the processing distance obtained in step S1, 5 to 8 discrete incident angles and processing distance values are selected respectively. The obtained incident angle - processing distance combinations are at least 5×5 = 25 kinds in total. Then, 25 or more single-point etching experiments that meet the conditions of each incident angle - processing distance combination can be arranged according to these 25 or more combinations. In order to quickly obtain the results, 3 to 5 different single-point etching experiments can be arranged on different regions of the same material sample to be processed. By changing the clamping angle of the material sample during each single-point etching to meet different ion beam incident angle conditions, and by adjusting the height of the ion source in the processing instrument during each single-point etching to meet different processing distance conditions. In order to avoid potential mutual interference between different etched single points on the same material sample, the distance between each etched point is ensured to be more than 40 mm. In this embodiment, 7 samples are selected for single-point etching experiments.

[0049] In a specific implementation manner, taking the selection of 5 discrete incident angles and processing distance values respectively as an example:

[0050] The discrete values of the incident angle are taken as 5°, 10°, 15°, 20°, 25° respectively, and the discrete values of the processing distance are taken as 75 mm, 80 mm, 85 mm, 90 mm, 95 mm respectively. The obtained incident angle - processing distance combinations are 5×5 = 25 kinds in total, written in the form of number pairs as (θ1, L1) to (θ 25 , L 25 ), and the specific values are as follows: (5,75), (5,80), (5,85), (5,90), (5,95), (10,75), (10,8), (10,85), (10,90), (10,95), (15,75), (15,80), (15,85), (15,90), (15,95), (20,75), (20,80), (20,85), (20,90), (20,95), (25,75), (25,80), (25,85), (25,90), (25,95). In this way, 25 or more single-point etching experiments that meet the conditions of each incident angle - processing distance combination can be carried out according to these 25 or more combinations. When conducting the experiment, a quartz wafer with a diameter of 100 mm and a thickness of 10 mm of the same material is selected, and 4 single-point processing experiment points are evenly selected on two mutually perpendicular diameters of it. The distribution of the experiment points is as Figure 3 shown. In order to achieve the effect of oblique incidence of the ion beam during the etching process, the quartz wafer can be fixed obliquely on the fixture. In the experiment of this embodiment, a total of 7 wafers are selected as the workpieces to be experimented.

[0051] To conduct a single-point etching experiment, place the sample in the instrument. According to the arranged incident angle-processing distance combination and the area on the sample surface where the processing point corresponding to the combination is allocated, adjust the sample placement angle in the instrument and the height and horizontal position of the ion source in the processing instrument. i Under the given other process parameters, the single point is stably etched. Other process parameters include: ion beam voltage, acceleration voltage, RF power, rare gas type and flow rate, and neutralization current. After the single point is processed, continue to follow the single point etching experiment operation steps recorded above to perform the single point etching experiment on the next processing point corresponding to the incident angle-processing distance on the same sample until all single point etching experiments on the sample are completed.

[0052] After all single-point etching processes on a sample are completed, the processed sample is fixed in the detection system according to the clamping method used to detect the initial surface shape of the sample before processing, and the new surface shape data h is obtained by re-detection. i1 (x,y).

[0053] S3. Calculate the processing volume distribution according to the initial surface shape of the workpiece and the surface shape after the single-point etching experiment. Calculate the removal function taking into account the ion beam incident angle and processing distance based on the processing volume distribution data and processing time.

[0054] According to the initial surface shape of the workpiece and the surface shape after the single-point etching experiment, the processing volume distribution brought by the single-point etching test is calculated as follows:

[0055] g i (x,y)= h i0 (x,y)-h i1 (x,y) (1)

[0056] In order to obtain the processing volume distribution g i (x, y), the processing amount distribution data g in the area of the processing point is allocated according to the ion beam incident angle actually allocated to each single-point etching processing point. i (x, y) is flipped in the opposite direction to return to the actual processing situation during the single-point etching experiment to obtain the actual processing amount distribution g' of the point i (x,y). Example: Processing volume distribution g i (x,y) Figure 4 As shown, Figure 4 The processing range of the four processing points in the processing volume distribution is allocated to the area. For example, the jth processing point (which is just allocated on the i-th sample) is allocated according to the ion beam incident angle-processing distance combination (θ j , L j ), the spatial area of effective material removal is delineated, such as Figure 5As shown. After the distribution area is allocated, it can reduce the interference caused by other adjacent processing points when the processing amount distribution data is flipped in the reverse direction in subsequent steps.

[0057] Then, according to the ion beam incident angle, the processing amount distribution data g i (x, y) within the distribution area is flipped in the reverse direction to obtain the actual processing amount distribution g' i (x, y); then divide g' i (x, y) by the given processing time t i , and the removal function r j (x, y) of this processing point considering the ion beam incident angle - processing distance combination can be obtained:

[0058] r j (x, y) = g' j (x, y) ÷ t i (2)

[0059] There are multiple removal functions obtained in this step, including the ion beam incident angle - processing distance combinations (θ1, L1) to (θ 25 , L 25 ) and their corresponding removal functions r1(x, y) to r 25 (x, y).

[0060] S4. Use the removal function as training data to train the artificial neural network

[0061] S41. Preprocess the removal function to obtain the coordinate pairs of the ion beam incident angle - processing distance and the fitting parameters of the removal function

[0062] In order to prevent the data input for neural network training from being too high - dimensional or huge, preprocess multiple groups of removal functions first. From the sputtering theory and the principle of electron lens focusing, it can be known that the removal function of ion beam processing has good rotational symmetry. Through two - dimensional Gaussian fitting, using the peak value R and variance σ of the Gaussian function as the parameters to be solved, fit the existing removal function data. After obtaining the Gaussian fitting peak values R and variances σ of all removal functions, perform linear normalization. Similarly, perform linear normalization on the incident angle - processing distance combinations corresponding to these removal functions. This step can greatly reduce the dimension and size of the data to be used for subsequent neural network training.

[0063] In a specific implementation, from step S3, (θ1, L1) to (θ 25 , L 25 ) and their corresponding removal functions r1(x, y) to r 25 (x, y) have been obtained. First, perform two - dimensional Gaussian fitting on r1(x, y) to r 25 (x, y), with (θj , L j ) corresponding r j Taking (x, y) as an example, the fitting calculation method is as follows:

[0064]

[0065] In the above formula, the fitting parameter R j is the peak value, σ j is the variance.

[0066] Through two-dimensional Gaussian fitting, (θ1, L1) ~ (θ 25 , L 25 ) corresponding fitting parameter pairs (R1, σ1) ~ (R 25 , σ 25 ) can be obtained. In order to further reduce the data complexity, linear normalization processing is now performed on all four types of data, that is, taking the kth pair of incident angle - machining distance combination (θ k , L k ) corresponding removal function fitting parameter pair (R k , σ k ) as an example, it is transformed into ((θ k - θ min ) / (θ max - θ min ), (L k - L min ) / (L max - L min )) corresponding to ((R k - R min ) / (R max - R min ), (σ k - σ min ) / (σ max - σ min )) and briefly recorded as (θ knorm , L knorm ) and (R knorm , σ knorm ). The above relationship is briefly recorded as:

[0067] (θ knorm , L knorm ) = ( (θ k - θ min ) / ( θ max - θ min ) , (L k - L min ) / ( L max - L min ) ) (4)

[0068] (R knorm , σknorm ) = ((R k -R min ) / (R max -R min ) , (σ k -σ min ) / (σ max -σ min )) (5)

[0069] Thus, 25 sets of one-to-one corresponding coordinate pairs can be obtained:

[0070] [(θ 1norm , L 1norm ), (θ 2norm , L 2norm ), (θ 3norm , L3 norm ), …, (θ mnorm , L mnorm )] corresponds to [(R 1norm , σ 1norm ), (R 2norm , σ 2norm ), (R 3norm , σ 3norm ), …, (R mnorm , σ mnorm )].

[0071] S42. Train the artificial neural network with the ion beam incident angle - machining distance as the input and the removal function fitting parameter pair as the output

[0072] When training the artificial neural network, [(θ 1norm , L 1norm ), (θ 2norm , L 2norm ), (θ 3norm , L3 norm ), …, (θ mnorm , L mnorm )] will be used as the input of the neural network, and [(R 1norm , σ 1norm ), (R 2norm , σ 2norm ), (R 3norm , σ 3norm ), …, (R mnorm , σ mnorm )] as the output of the neural network.

[0073] For the artificial neural network, the RBF neural network is selected in this embodiment. The RBF neural network has outstanding advantages in training efficiency, approximation accuracy, and robustness, and is especially suitable for real-time processing systems. When training the RBF neural network, it is necessary to determine the center C of the basis function, the variance S, and the weight vector W from the hidden layer to the output layer. Among them, the determination processes of C and S are unsupervised learning processes; the solution of W is a supervised learning process. In a specific implementation manner, K-means clustering is first used to determine C and S. All initial values C j (j = 1, 2, 3,..., h) are the first h sample data. Let the sample data be X, and all sample data are classified according to the principle of being closest to the initial clustering center. The sample mean of each class is obtained by the following formula, and this mean will be used as the new clustering center value, that is, C j :

[0074] C j =(ΣX) / N j (6)

[0075] In the above formula, N j is the number of samples in the jth class.

[0076] Each obtained C j can be used to determine a corresponding variance S by the following formula j :

[0077] S j =Σ[(X - C j ) T (X - C j )] / N j (7)

[0078] This operation is continuously repeated until the value of C j no longer changes. At this time, its value is the final required center value of the basis function. When all C and S parameters are determined, the transfer function of the neurons in the hidden layer is determined, that is

[0079]

[0080] Suppose constitutes the transfer function vector Φ. Then the output vector Y of the neural network is as follows

[0081] Y = WΦ (9)

[0082] Suppose the expected input vector of the neural network is U. Then the performance index function J can be introduced at this time

[0083] J = [(U - Y) T (U - Y)] / 2 (10)

[0084] Adjust W continuously until the value of J reaches the minimum, which indicates that the RBF neural network has completed training.

[0085] S5. Based on the trained neural network, obtain the fitting parameter pairs of the removal function according to the actual processing task requirements, and perform inverse processing on the fitting parameter pairs of the removal function to obtain the removal function of the actual processing point.

[0086] After the RBF neural network is trained, when any combination of linearly normalized parameters within the original incident angle - machining distance change range is input, the linearly normalized Gaussian fitting parameters of the removal function corresponding to this working condition combination can be obtained, including the peak value and variance. For example:

[0087] For the actual processing task, according to the processing requirements, as the ion source moves, different processing points will correspond to multiple different ion beam incident angle - machining distance combinations. Suppose the ion beam incident angle - machining distance combination condition of the processing point a in a certain processing task is (θ a , L a ). In order to be able to use the neural network trained in step S4 to predict the (R a , σ a ) corresponding to (θ a , L a ), first normalize (θ a , L a ) according to formula (4) to obtain (θ anorm , L anorm ), and then input it into the trained RBF neural network to obtain the fitting parameter pairs of the removal function (R anorm , σ anorm ) at the processing point a in the actual processing task.

[0088] Then perform inverse normalization on (R anorm , σ anorm ) using equation (5): R k = R knorm × (R max - R min ) + R min , σ k = σ knorm × (σ max - σ min ) + σ min , and then obtain the removal function at the actual processing point a according to equation (3):

[0089]

[0090] By implementing the above technical solutions, the present invention fully considers the influence of different ion beam incident angles and ion source processing distances on the removal function, determines the complex variation law of the removal function under different working conditions through the RBF neural network, makes full use of the advantage of the neural network in fitting non-linear models, and the obtained removal function can better meet the current high-precision processing requirements for high surface slopes or various non-planar optical surface shapes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A method for obtaining an ion beam polishing multi-condition removal function, characterized in that It includes the following steps: According to the optical surface shape to be processed and the planned movement trajectory of the ion source relative to the surface shape, determine the variation ranges of the ion beam incident angle and the processing distance in the polishing processing task; Conduct a limited number of single-point etching experiments according to the variation ranges of the ion beam incident angle and the processing distance, and detect the surface shape of the workpiece after the single-point etching experiment; Calculate the processing amount distribution based on the initial surface shape of the workpiece and the surface shape after the single-point etching experiment, and calculate the removal function considering the ion beam incident angle and the processing distance based on the processing amount distribution data and the processing time; Use the removal function as training data to train an artificial neural network; Based on the trained neural network, obtain the removal function fitting parameter pair according to the actual processing task requirements, and perform inverse processing on the removal function fitting parameter pair to obtain the removal function of the actual processing point.

2. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, characterized in that Determine the variation ranges of the ion beam incident angle and the processing distance in the polishing processing task, including: According to the planned processing points and the processing path, determine the actual movement path of the ion source; then, by measuring the maximum and minimum values of the incident angle of the ion beam relative to the processing surface and the actual distance of the ion source relative to the processing point at each processing point on this path, determine the variation ranges of the ion beam incident angle and the processing distance.

3. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, wherein Detect the initial surface shape of the workpiece and the surface shape after the single-point etching experiment through a laser interferometer. The workpiece maintains the same clamping method in the detection system when detecting the two surface shapes.

4. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, wherein Subtract the surface shape after the single-point etching experiment from the initial surface shape of the workpiece to obtain the processing amount distribution.

5. The method for obtaining the ion beam polishing multi-condition removal function according to claim 4, characterized in that, Calculate the removal function considering the ion beam incident angle and the processing distance based on the processing amount distribution data and the processing time, including: According to the actually assigned ion beam incident angle of the single-point etching processing point, reverse the processing amount distribution data within the assigned area of this processing point to obtain the actual processing amount distribution of this processing point; Divide the actual processing amount distribution by the given processing time to obtain the removal function considering the ion beam incident angle and the processing distance.

6. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, wherein Use the removal function as training data to train an artificial neural network, including: Perform preprocessing on the removal function to obtain the ion beam incident angle - processing distance and the removal function fitting parameter pair; Use the ion beam incident angle - processing distance as the input and the removal function fitting parameter pair as the output to train the artificial neural network.

7. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, wherein Perform preprocessing on the removal function, including: successively performing two-dimensional Gaussian fitting and linear normalization processing on the removal function.

8. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, wherein The artificial neural network is an RBF neural network.

9. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, characterized in that, Perform inverse processing on the removal function fitting parameter pair, including: performing inverse normalization processing and inverse two-dimensional Gaussian fitting on the removal function fitting parameter pair.

10. The method for obtaining the ion beam polishing multi-condition removal function according to claim 1, characterized in that, The single-point etching experiment includes: placing the sample in the instrument, respectively adjusting the placement angle of the sample in the instrument, as well as the height and horizontal position of the ion source in the processing instrument according to the arranged incident angle - processing distance combination and the area where the corresponding processing point is assigned on the sample surface, and performing stable etching processing on this single point under the given time and other given process parameters; the other process parameters include: ion beam voltage, acceleration voltage, radio frequency power, rare gas type and flow rate, and neutralization current.

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