Imaging configuration determination method and device for wafer measurement and electronic equipment
By automatically determining the combination of imaging configuration parameters for wafers through an imaging configuration recommendation model, the problems of low efficiency and insufficient accuracy in existing technologies are solved, and rapid and accurate optimization of imaging configuration parameters is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SUZHOU MEGAROBO TECH CO LTD
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-21
AI Technical Summary
In the wafer lithography process, selecting the appropriate combination of imaging configuration parameters is inefficient and greatly affected by human experience, making it difficult to guarantee accuracy.
By acquiring the imaging attribute information of the target wafer, the imaging configuration recommendation model is used to automatically determine the combination of target imaging configuration parameters. The model is trained by training the imaging attribute information of the wafer, and optimizes the imaging configuration parameters by combining process parameters, measurement point positions and basic imaging information.
It enables the rapid and accurate determination of imaging configuration parameter combinations, applicable to different materials and processes, improving efficiency and reducing time consumption.
Smart Images
Figure CN121900107A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of semiconductor technology. More specifically, it relates to an imaging configuration determination method for wafer metrology, an imaging configuration determination apparatus for wafer metrology, an electronic device, a storage medium, and a computer program product. Background Technology
[0002] In the wafer lithography process, overlay marks need to be imaged to measure overlay error. To accurately measure this error, a suitable combination of imaging configuration parameters must be selected. This combination includes a series of parameters such as wavelength, bandwidth, numerical aperture, polarization, and focal plane. The number of possible imaging configuration parameter combinations is extremely large, reaching hundreds of thousands. Selecting a suitable combination from this vast pool is an extremely inefficient and time-consuming process.
[0003] In related technologies, it is necessary to rely on human experience to select suitable imaging configuration parameter combinations from a massive number of imaging configuration parameter combinations. This is not only greatly affected by human subjectivity and makes it difficult to guarantee the accuracy of the selected imaging configuration, but also takes a long time and is inefficient. Summary of the Invention
[0004] The present invention was proposed in view of the above-mentioned problems.
[0005] According to one aspect of the present invention, an imaging configuration determination method for wafer metrology is provided. The imaging configuration determination method for wafer metrology includes:
[0006] The target imaging attribute information of the target wafer is obtained. The target imaging attribute information includes: target process parameter information, target measurement point location information and target basic imaging information. The target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement point.
[0007] The target imaging attribute information is input into the imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement point. The imaging configuration recommendation model is obtained by training the model using the training imaging attribute information of the training wafer.
[0008] For example, the training imaging attribute information includes training process parameter information, and the training operation includes: acquiring the training imaging attribute information of the training wafer; determining multiple optional imaging configuration parameter combinations based on the training process parameter information; acquiring multiple sets of training overlay error measurement data corresponding one-to-one with the multiple optional imaging configuration parameter combinations, wherein the multiple sets of training overlay error measurement data include data obtained after measuring training measurement points based on different optional imaging configuration parameter combinations; determining the training imaging configuration parameter combination corresponding to the training imaging attribute information from the multiple optional imaging configuration parameter combinations based on the multiple sets of training overlay error measurement data; and adjusting the parameters of the imaging configuration recommendation model based on the training imaging configuration parameter combination.
[0009] For example, the training imaging configuration parameter combination includes m first value combinations of the first parameter group; based on multiple sets of training overlay error measurement data, determining the training imaging configuration parameter combination corresponding to the training imaging attribute information from multiple optional imaging configuration parameter combinations includes: performing a first value combination selection operation, wherein the first value combination selection operation includes: for each of the m first value combinations, calculating the first standard deviation of the training overlay error measurement data corresponding to the first value combination, so as to obtain m first standard deviations that correspond one-to-one with the m first value combinations; comparing the m first standard deviations, so as to determine the first value combination corresponding to the smallest first standard deviation among the m first standard deviations as the target first value combination in the training imaging configuration parameter combination.
[0010] For example, the optional imaging configuration parameter combination further includes n second value combinations of the second parameter group; based on multiple sets of training overlay error measurement data, determining the training imaging configuration parameter combination corresponding to the training imaging attribute information among multiple optional imaging configuration parameter combinations further includes: for each of the n second value combinations, calculating the second standard deviation of the training overlay error measurement data of the second value combination under m first value combinations, to obtain n second standard deviations that correspond one-to-one with the n second value combinations; comparing the n second standard deviations to determine the second value combination corresponding to the smallest second standard deviation among the n second standard deviations as the target second value combination in the training imaging configuration parameter combination; wherein, the first value combination selection operation is performed under the target second value combination.
[0011] For example, the first parameter set includes: imaging wavelength and imaging focal plane, and the second parameter set includes: numerical aperture and / or polarization parameters.
[0012] For example, the training wafers are the same type of wafer.
[0013] For example, the method further includes: acquiring target overlay error measurement data, wherein the target overlay error measurement data includes data obtained after measuring the target measurement points based on the target imaging configuration parameter combination; and retraining the imaging configuration recommendation model using the target imaging attribute information and the target overlay error measurement data.
[0014] For example, the method further includes: determining whether the target overlay error measurement data meets the preset quality requirements of the target wafer to obtain a determination result; if the determination result indicates that it does not meet the preset quality requirements, inputting the target imaging attribute information into the imaging configuration recommendation model that has been retrained to redetermine the target imaging configuration parameter combination.
[0015] For example, the method further includes: providing a human-computer interaction interface for the user; and adjusting the target imaging configuration parameter combination in response to the user's adjustment operation using the human-computer interaction interface.
[0016] According to another aspect of the present invention, an imaging configuration determination apparatus for wafer metrology is also provided. The apparatus includes a first acquisition module and a first determination module. The first acquisition module is used to acquire target imaging attribute information of a target wafer, wherein the target imaging attribute information includes target process parameter information, position information of a target measurement point, and target basic imaging information, wherein the target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement point; the first determination module is used to input the target imaging attribute information into an imaging configuration recommendation model to determine a combination of target imaging configuration parameters for the target measurement point, wherein the imaging configuration recommendation model is obtained by training on training imaging attribute information of a training wafer.
[0017] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, are used to perform the imaging configuration determination method for wafer metrology as described above.
[0018] According to another aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which, when executed, are used to perform the imaging configuration determination method for wafer metrology as described above.
[0019] According to another aspect of the present invention, a computer program product is also provided, comprising computer program instructions which, when executed, are used to perform the imaging configuration determination method for wafer metrology as described above.
[0020] In the above technical solution, target imaging attribute information of the target wafer is obtained. This target imaging attribute information includes target process parameter information, target measurement point location information, and target basic imaging information. The target imaging attribute information is then input into an imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement points. Therefore, based on the target imaging attribute information of the target wafer, the target imaging configuration parameter combination can be determined automatically, intelligently, and more accurately, with high speed, high efficiency, and time savings. Furthermore, the imaging configuration recommendation model can integrate process parameter information, target measurement point location information, and target basic imaging information to adaptively determine the optimal imaging configuration parameter combination for measurement points of different materials, different processing techniques, and different locations, making it applicable to new processes and designs.
[0021] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description
[0022] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.
[0023] Figure 1 A schematic flowchart of an imaging configuration determination method for wafer metrology according to an embodiment of the present invention is shown;
[0024] Figure 2 A schematic flowchart illustrating the training operation of an imaging configuration recommendation model according to an embodiment of the present invention is shown.
[0025] Figure 3 A visual schematic diagram of m combinations of first values according to an embodiment of the present invention is shown;
[0026] Figure 4 A schematic block diagram of an imaging configuration determination apparatus for wafer metrology according to an embodiment of the present invention is shown;
[0027] Figure 5 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a subset of embodiments of the present invention.
[0029] To at least partially address the aforementioned technical problems, this invention provides an imaging configuration determination method for wafer metrology. This method determines the target imaging configuration parameter combination for the target measurement point by inputting the target imaging attribute information of the target wafer into an imaging configuration recommendation model. Based on the target imaging attribute information of the target wafer, the target imaging configuration parameter combination can be determined automatically and intelligently. This imaging configuration determination method for wafer metrology can be applied to any electronic device, i.e., executed by any electronic device. Specifically, this imaging configuration determination method for wafer metrology can be executed by the processor of any electronic device.
[0030] For example, Figure 1 A schematic flowchart of an imaging configuration determination method for wafer metrology according to an embodiment of the present invention is shown. Figure 1 As shown, the imaging configuration determination method for wafer measurement includes steps S1100 and S1200.
[0031] In step S1100, target imaging attribute information of the target wafer is acquired. This target imaging attribute information includes: target process parameter information, target measurement point location information, and target basic imaging information. The target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement points.
[0032] Target process parameter information may include the material parameters of the target wafer. Material parameters may include the material type, reflectivity, transmittance, and roughness of the target wafer. The material type can indicate whether the target wafer is made of silicon, silicon carbide, gallium nitride, or gallium arsenide, etc.
[0033] The target process parameter information may also include the process technology of the target wafer. It may also include process error parameters of the target wafer. The location information of the target measurement point indicates its position within the target wafer. After determining the target wafer, the metrology equipment can perform automatic exposure and automatic focusing operations to obtain the target basic imaging information. The target basic imaging information may include the exposure parameters and focus parameters obtained after automatic exposure and automatic focusing. These parameters may include signal-to-noise ratio (SNR), exposure, contrast, and resolution, among others.
[0034] In some embodiments, after determining the target wafer, the target process parameters and the location information of the target measurement points can be automatically matched, and the target measurement points can be automatically exposed and focused to determine the basic imaging information of the target wafer, thereby determining the target imaging attribute information of the target wafer. For example, the target wafer may have multiple target measurement points, and automatic exposure and focusing can be performed on each target measurement point separately to determine the basic imaging information corresponding to each target measurement point, thereby obtaining multiple target imaging attribute information corresponding one-to-one with the multiple target measurement points. Optionally, the user can select some target measurement points from the multiple target measurement points, and can determine only the target imaging attribute information of some target measurement points.
[0035] In other embodiments, the user can input target process parameter information and the location information of the target measurement point. Then, the target measurement point can be automatically exposed and automatically focused to determine the target basic imaging information and thus determine the target imaging attribute information.
[0036] In step S1200, the target imaging attribute information is input into the imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement point. The imaging configuration recommendation model is obtained by training the model using the training imaging attribute information from the training wafer.
[0037] Imaging configuration recommendation models can be constructed using machine learning models such as Multi-Layer Perceptron (MLP) and Graph Neural Network (GNN). Alternatively, traditional mathematical models can also be used.
[0038] It can have multiple training wafers, each corresponding to multiple training measurement points at different locations. The training imaging attribute information of the training wafers can belong to the same category as the target imaging attribute information mentioned above, but the values corresponding to each category can be the same or different. Based on the training imaging attribute information and the corresponding training imaging configuration parameter combination, the imaging configuration recommendation model can be trained so that it can determine the target imaging configuration parameter combination of the target measurement points based on the target imaging attribute information.
[0039] It is understandable that the basic imaging information of a wafer can affect image quality. For example, exposure time can affect image brightness and sharpness. Contrast ratio can reflect the difference between a measurement point on the wafer and its surrounding area. Signal-to-noise ratio determines whether the image is sharp. Similarly, the location information of a measurement point can reflect its spatial distribution within the wafer. For example, the farther the measurement point is from the wafer center, the more difficult the measurement is generally; the higher the density of measurement points around the measurement point, the more difficult the measurement is generally. The optical properties of the wafer material (reflectivity, transmittance, roughness) in the wafer's process parameters also have a significant impact on the combination of imaging configuration parameters. Process error parameters in the process parameters can represent the impact of uncertainties on the combination of imaging configuration parameters. The process technology in the process parameters can represent the impact of different process technologies on the combination of imaging configuration parameters. The imaging configuration recommendation model is trained based on the training imaging attribute information of the training wafer, in which the correlation between imaging attribute information and the combination of imaging configuration parameters has been established. The target imaging attribute information of the target wafer can be input into the imaging configuration recommendation model, which can then provide a more suitable combination of target imaging configuration parameters for the target measurement points. This combination of target imaging configuration parameters represents the most suitable combination for imaging measurement of the target measurement points based on the target imaging attribute information.
[0040] In the above technical solution, target imaging attribute information of the target wafer is obtained. This target imaging attribute information includes target process parameter information, target measurement point location information, and target basic imaging information. The target imaging attribute information is then input into an imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement points. Therefore, based on the target imaging attribute information of the target wafer, the target imaging configuration parameter combination can be determined automatically, intelligently, and more accurately, with high speed, high efficiency, and time savings. Furthermore, the imaging configuration recommendation model can integrate process parameter information, target measurement point location information, and target basic imaging information to adaptively determine the optimal imaging configuration parameter combination for measurement points of different materials, different processing techniques, and different locations, making it applicable to new processes and designs.
[0041] For example, the training imaging attribute information includes training process parameter information. The training imaging attribute information may also include the location information of the training measurement points and the basic training imaging information. The basic training imaging information includes the exposure parameters and focus parameters obtained after automatic exposure and automatic focusing for the training measurement points. The basic training imaging information for each training measurement point on each training wafer can be obtained by performing automatic exposure and automatic focusing on each training measurement point separately. The training imaging configuration parameter combination corresponding to this training imaging attribute information can be labeled.
[0042] For example, with 10 training wafers, each containing 10 training measurement points, training imaging attribute information for 100 training measurement points can be obtained. Based on historical imaging configuration parameter combinations, the training imaging configuration parameter combinations for each training measurement point can be labeled. For example, the training imaging configuration parameter combinations may include the optimal imaging focal plane, imaging wavelength, numerical aperture, and polarization parameters for imaging measurements of the training measurement points. Therefore, based on the training imaging attribute information of the aforementioned training wafers and the labeled training imaging configuration parameter combinations, the imaging configuration recommendation model can be trained.
[0043] Figure 2 A schematic flowchart illustrating the training operation of an imaging configuration recommendation model according to an embodiment of the present invention is shown. Figure 2 As shown, the training operation includes steps S2100, S2200, S2300, S2400, and S2500.
[0044] In step S2100, the training imaging attribute information of the training wafer is obtained.
[0045] In some embodiments, training imaging attribute information can be automatically determined based on the type of training wafer. For example, the training process parameters and the location information of training measurement points on the training wafer can be determined based on the type of training wafer, and the training measurement points on the training wafer can be automatically exposed and automatically focused to obtain basic training imaging information. In other embodiments, after obtaining the training process parameters and the location information of training measurement points input by the user, the training measurement points on the training wafer can be automatically exposed and automatically focused to obtain basic training imaging information.
[0046] In step S2200, based on the training process parameter information, a number of optional imaging configuration parameter combinations are determined.
[0047] The training process parameters can include the manufacturing process of the training wafer. Different manufacturing processes can correspond to different combinations of optional imaging configuration parameters. For example, for manufacturing processes above 90nm, the optional imaging configuration parameter combinations can include numerical aperture and imaging focal plane. For manufacturing processes from 28nm to 90nm, the optional imaging configuration parameter combinations can include numerical aperture, imaging focal plane, and imaging wavelength.
[0048] For process technologies below 28nm, the optional imaging configuration parameters can include numerical aperture, imaging focal plane, imaging wavelength, and polarization parameters. Different process technologies may have the same parameters, but the number of selectable values for each parameter may differ.
[0049] Based on the fabrication process of the training wafer, multiple optional imaging configuration parameter combinations can be determined. For example, if the fabrication process of the training wafer is 15nm, the parameters of the optional imaging configuration parameter combinations can include numerical aperture, imaging focal plane, imaging wavelength, and polarization parameter. The numerical aperture can have 3 selectable values, the imaging focal plane can have 300 selectable values, the imaging wavelength can have 42 selectable values, and the polarization parameter can have 3 selectable values, totaling 113,400 optional imaging configuration parameter combinations. All optional imaging configuration parameter combinations can be used as the multiple optional imaging configuration parameter combinations determined in step S2200.
[0050] Optionally, the training imaging attribute information of the training wafer can be input into the imaging configuration recommendation model that was previously trained to obtain the output imaging configuration parameter combination. This may not be optimal. Multiple imaging configuration parameter combinations that are close to the output imaging configuration parameter combination can be determined as multiple optional imaging configuration parameter combinations for the training wafer.
[0051] In step S2300, multiple sets of training overlay error measurement data corresponding to multiple optional imaging configuration parameter combinations are obtained. The multiple sets of training overlay error measurement data include data obtained after measuring the training measurement points based on different optional imaging configuration parameter combinations.
[0052] For each of the multiple optional imaging configuration parameter combinations, a measurement device can be used to measure the overlay error of training measurement points based on that optional imaging configuration parameter combination. This yields the corresponding training overlay error measurement data, allowing for the acquisition of multiple sets of training overlay error measurement data that correspond one-to-one with each of the multiple optional imaging configuration parameter combinations. Any existing or future-developed measurement method can be used to perform overlay error measurement on training measurement points based on optional imaging configuration parameter combinations.
[0053] In step S2400, based on multiple sets of training overlay error measurement data, a training imaging configuration parameter combination corresponding to the training imaging attribute information is determined from multiple optional imaging configuration parameter combinations.
[0054] This training imaging configuration parameter combination is more suitable for the training imaging attribute information than other imaging configuration parameter combinations among multiple optional combinations. In other words, for a training wafer with this training imaging attribute information, overlaying based on this training imaging configuration parameter combination yields more ideal training overlay error measurement data and smaller overlay error compared to other imaging configuration parameter combinations.
[0055] In some embodiments, multiple sets of training overlay error measurement data can be input into the imaging configuration parameter evaluation model to determine the combination of training imaging configuration parameters corresponding to the training imaging attribute information. Any existing or future-developed imaging configuration parameter evaluation model can be used. The imaging configuration parameter evaluation model can screen multiple optional imaging configuration parameter combinations based on evaluation indicators such as overlay error repeatability, environmental error, and standard deviation to determine the optimal imaging configuration parameter combination as the training imaging configuration parameter combination corresponding to the training imaging attribute information.
[0056] In other embodiments, statistical analysis can be performed on multiple sets of training overlay error measurement data to determine the combination of training imaging configuration parameters corresponding to the training imaging attribute information. For example, correlation analysis can be performed on multiple sets of training overlay error measurement data and multiple optional imaging configuration parameter combinations to determine the training imaging configuration parameter combination.
[0057] In step S2500, the parameters of the imaging configuration recommendation model are adjusted based on the trained imaging configuration parameter combination. For example, the training imaging attribute information of the training wafer can be input into the imaging configuration recommendation model, and the trained imaging configuration parameter combination can be used as the training target of the imaging configuration recommendation model. The difference between the imaging configuration parameter combination output by the imaging configuration recommendation model and the trained imaging configuration parameter combination can be calculated using any loss function, and the parameters of the imaging configuration recommendation model are adjusted based on the difference. For example, the weight matrix and interaction coefficient matrix in the imaging configuration recommendation model can be adjusted. The adjusted imaging configuration recommendation model can further establish the correlation between imaging attribute information and imaging configuration parameter combination, thus enabling it to output an imaging configuration parameter combination that is more suitable for the input imaging attribute information.
[0058] The above training operation can be performed multiple times, and the training imaging attribute information of the training wafer can be different each time. This can improve the generalization ability of the model.
[0059] In the above technical solution, training imaging attribute information of the training wafer is obtained; based on training process parameter information, multiple optional imaging configuration parameter combinations are determined; based on multiple sets of training overlay error measurement data, the training imaging configuration parameter combination corresponding to the training imaging attribute information is determined from the multiple optional imaging configuration parameter combinations; based on the training imaging configuration parameter combination, the parameters of the imaging configuration recommendation model are adjusted. Thus, guided by actual measurement performance, the parameters of the imaging configuration recommendation model are adjusted to ensure that the imaging configuration parameter combinations recommended by the imaging configuration recommendation model meet the actual measurement needs.
[0060] For example, the training imaging configuration parameter combination includes m first value combinations of the first parameter group. m can be a positive integer greater than 1. Step S2400 determines the training imaging configuration parameter combination corresponding to the training imaging attribute information from multiple optional imaging configuration parameter combinations based on multiple sets of training overlay error measurement data, including step S2410. In step S2410, a first value combination selection operation is performed. The first value combination selection operation includes steps S2411 and S2412.
[0061] In step S2411, for each of the m first value combinations, the first standard deviation of the training overlay error measurement data corresponding to the first value combination is calculated to obtain m first standard deviations that correspond one-to-one with the m first value combinations.
[0062] In one example, multiple optional imaging configuration parameter combinations can include combinations of values for parameter item 1 and parameter item 2. Parameter item 1 can include 100 optional values, and parameter item 2 can include 200 optional values, resulting in 20,000 optional imaging configuration parameter combinations. The 100 optional values of parameter item 1 can be divided into 10 first sub-parameter groups, meaning each first sub-parameter group includes 10 optional values of parameter item 1. The 200 optional values of parameter item 2 can be divided into 10 second sub-parameter groups, meaning each second sub-parameter group includes 20 optional values of parameter item 2. The 10 second sub-parameter groups can be combined with the 10 first sub-parameter groups to obtain 100 first value combinations, i.e., m = 100. Each first value combination includes 10 optional values of parameter item 1 and 20 optional values of parameter item 2, meaning each first value combination includes 200 optional imaging configuration parameter combinations. It can be understood that in this example, the number of first and second sub-parameter groups is the same, but in other examples, they can differ as needed.
[0063] The training overlay error measurement data corresponding to each first value combination can be determined based on the optional imaging configuration parameter combinations included in that first value combination. It can be understood that the training overlay error measurement data corresponding to each first value combination may include 200 (10*20) sets of training overlay error measurement data. For example, the standard deviation or three times the standard deviation of the training overlay error measurement data corresponding to each first value combination can be calculated as the first standard deviation. Thus, the first standard deviation corresponding to each first value combination can be determined.
[0064] In step S2412, the m first standard deviations corresponding to the m first value combinations are compared one by one, so that the first value combination corresponding to the smallest first standard deviation among the m first standard deviations is determined as the target first value combination in the training imaging configuration parameter combination.
[0065] Among the m first standard deviations determined in step S2411, the smallest first standard deviation can be identified. The first value combination corresponding to the smallest first standard deviation can then be determined as the target first value combination in the training imaging configuration parameter combination. It can be understood that the smaller the standard deviation corresponding to the first value combination, the stronger the consistency of its overlay error measurement data, indicating more stable imaging under that measurement environment. In other words, the smaller the influence of the imaging configuration parameters in the first parameter group on the overlay error measurement data, the smaller the value combination of the parameters in the first parameter group can be determined as the corresponding part in the training imaging configuration parameter combination. Taking the example described in step S2411 above, the 100 first standard deviations corresponding one-to-one with the 100 first value combinations can be compared to determine the smallest first standard deviation. The first value combination corresponding to this smallest first standard deviation can then be determined as the target first value combination in the training imaging configuration parameter combination. The first combination of target values includes 200 possible imaging configuration parameter combinations. All 200 possible imaging configuration parameter combinations included in the first combination of target values can be used as training imaging configuration parameter combinations. Alternatively, an average imaging configuration parameter combination can be determined from the 200 possible imaging configuration parameter combinations corresponding to the first combination of target values, and used as the training imaging configuration parameter combination.
[0066] It is understandable that in the example above, the values of parameter 1 and parameter 2 are discrete numerical values. In an alternative example, the values of the parameters can also be continuous ranges. In such an example, the continuous range of values can be divided into multiple continuous smaller ranges. Assume that the value of parameter 1 is a continuous value within a first range, and the value of parameter 2 is a continuous value within a second range. The first range of parameter 1 can be divided into 10 first sub-ranges. The second range of parameter 2 can be divided into 20 second sub-ranges. The 10 first sub-ranges and 20 second sub-ranges can be combined to obtain 200 combinations of first values, i.e., m = 200. Similarly, for each of these 200 combinations of first values, the first standard deviation of the training overlay error measurement data corresponding to that first value combination can be calculated to obtain 200 first standard deviations corresponding one-to-one with the 200 combinations of first values. These 200 first standard deviations can be compared to determine the first value combination corresponding to the smallest first standard deviation as the target first value combination in the training imaging configuration parameter combination.
[0067] Figure 3 A visual schematic diagram of m combinations of first values according to an embodiment of the present invention is shown. Figure 3As shown, each rectangular color block represents a first combination of values. The vertical direction of the rectangular color block can represent the first sub-range of parameter item 1, and the horizontal direction of the rectangular color block can represent the second sub-range of parameter item 2. The redder the color of the rectangular color block, the larger its corresponding first standard deviation; the bluer the color of the rectangular color block, the smaller its corresponding first standard deviation. Figure 3 Within the red circle, the rectangular color blocks are dark blue. The first value combination corresponding to the rectangular color block with the smallest first standard deviation is the target first value combination.
[0068] In the above technical solution, the training imaging configuration parameter combination includes m first value combinations of the first parameter group. For each of the m first value combinations, the first standard deviation of the training overlay error measurement data corresponding to that first value combination is calculated to obtain m first standard deviations that correspond one-to-one with the m first value combinations. The first value combination corresponding to the smallest first standard deviation among the m first standard deviations is determined as the target first value combination in the training imaging configuration parameter combination. Thus, by screening the first value combinations based on the standard deviation, it is ensured that the training imaging configuration parameter combination has low random error and stable measurement in actual measurement, thereby ensuring that the imaging configuration parameter combination determined by the training imaging configuration determination method is stable and reliable. Furthermore, dividing into m first value combinations and then screening the first value combinations can speed up the screening process, reduce training time, and improve training speed.
[0069] For example, the optional imaging configuration parameter combinations also include n second value combinations of the second parameter group. n can be a positive integer greater than 1. The parameter items included in the second parameter group can be different from the parameter items included in the first parameter group. For example, multiple optional imaging configuration parameter combinations can include combinations of values for parameter item 1, parameter item 2, parameter item 3, and parameter item 4. If the first parameter group can include parameter item 1 and parameter item 2, then the second parameter group can include parameter item 3 and parameter item 4. Parameter item 3 can include 2 optional values, and parameter item 4 can include 4 optional values. The different optional values of parameter item 3 and parameter item 4 can be combined to determine 2*4=8 second value combinations, i.e., n=8.
[0070] Step S2400, based on multiple sets of training overlay error measurement data, determines the training imaging configuration parameter combination corresponding to the training imaging attribute information from multiple optional imaging configuration parameter combinations, and may include steps S2420 and S2430.
[0071] In step S2420, for each of the n second value combinations, the second standard deviation of the training overlay error measurement data of the second value combination under the m first value combinations is calculated, so as to obtain n second standard deviations corresponding one-to-one with the n second value combinations.
[0072] For example, multiple optional imaging configuration parameter combinations can include parameter item 1, parameter item 2, parameter item 3, and parameter item 4. Parameter item 1 can include 100 optional values, parameter item 2 can include 200 optional values, parameter item 3 can include 2 optional values, and parameter item 4 can include 4 optional values, for a total of 160,000 optional imaging configuration parameter combinations. The second parameter group can include 8 second value combinations. The first value combination can correspond to 20,000 = 100 * 200 optional imaging configuration parameter combinations. For each of the 8 second value combinations, training overlay error measurement data under the 20,000 first value combinations of the first parameter group can be determined, that is, one second value combination corresponds to 20,000 sets of training overlay error measurement data. In other words, multiple sets of training overlay error measurement data are divided according to the combinations of optional values of the parameter items included in the second parameter group. In the example above, there are a total of 160,000 possible imaging configuration parameter combinations, corresponding to 160,000 sets of training overlay error measurement data. The second parameter group includes 8 possible values, which means the 160,000 sets of training overlay error measurement data can be divided into 8 groups, each containing 20,000 sets of training overlay error measurement data. The standard deviation or three times the standard deviation of the 20,000 sets of training overlay error measurement data corresponding to each second value combination can be calculated as the second standard deviation for that second value combination.
[0073] In step S2430, n second standard deviation values are compared, and the second value combination corresponding to the smallest second standard deviation value among the n second standard deviation values is determined as the target second value combination in the training imaging configuration parameter combination. The first value combination selection operation is performed under the target second value combination.
[0074] In step S2420, n second standard deviations are determined, each corresponding to one of the n combinations of second values. Similar to step S2412, the smallest second standard deviation can be determined from the n second standard deviations. The combination of second values corresponding to the smallest second standard deviation can then be determined as the target second value combination in the training imaging configuration parameter combination. It can be understood that the smaller the standard deviation corresponding to the second value combination, the stronger the consistency of its overlay error measurement data, indicating more stable imaging under that measurement environment. In other words, the smaller the influence of the imaging configuration parameters in the second parameter group on the overlay error measurement data, the smaller the value combination of the parameters in the second parameter group at this time can be determined as the corresponding part in the training imaging configuration parameter combination.
[0075] Taking the example described in step S2420 above, the eight second standard deviations corresponding one-to-one with the eight second value combinations can be compared to determine the smallest second standard deviation. The second value combination corresponding to this smallest second standard deviation can be determined as the target second value combination in the training imaging configuration parameter combination. Specifically, the second parameter group may include two optional values for parameter item 3 and four optional values for parameter item 4. The target second value combination may include one optional value for parameter item 3 and one optional value for parameter item 4.
[0076] Compared to the first parameter group, the second parameter group has fewer selectable values for its parameter items. Therefore, the number of second value combinations is also less than the number of first value combinations. Taking the aforementioned multiple selectable imaging configuration parameter combinations including parameter item 1, parameter item 2, parameter item 3, and parameter item 4 as an example, parameter item 1 can include 100 selectable values, parameter item 2 can include 200 selectable values, parameter item 3 can include 2 selectable values, and parameter item 4 can include 4 selectable values, totaling 160,000 selectable imaging configuration parameter combinations. After determining the target second value combination, the number of selectable imaging configuration parameter combinations can be reduced from 160,000 to 20,000. The aforementioned first value combination selection operation can filter the target first value combination from the 20,000 selectable imaging configuration parameter combinations corresponding to parameter items 1 and 2. This significantly speeds up the filtering process. For processes with low precision requirements, the second value combination may only include one case, which can be directly determined as the target second value combination, followed by the aforementioned first value combination selection operation.
[0077] In the above technical solution, the optional imaging configuration parameter combination also includes n second value combinations of the second parameter group. For each of the n second value combinations, the second standard deviation of the training overlay error measurement data under m first value combinations is calculated to obtain the corresponding n second standard deviations. The second value combination corresponding to the smallest second standard deviation is determined as the target second value combination. The first value combination selection operation is performed under the target second value combination. Thus, by first determining the target second value combination, then determining the target first value combination, and then determining the training imaging configuration parameter combination, the determination speed is accelerated and the determination efficiency is improved. Furthermore, the step-by-step determination can avoid local optima and ensure that the globally optimal optional imaging configuration parameter combination is determined as the training imaging configuration parameter combination.
[0078] For example, the first parameter set includes: imaging wavelength and imaging focal plane. The second parameter set includes: numerical aperture and / or polarization parameters.
[0079] It is understandable that the imaging wavelength can be a value within a certain wavelength range, and its corresponding selectable values are usually numerous. Similarly, the imaging focal plane can represent the depth of focus during imaging, and its corresponding selectable values are usually numerous. Numerical aperture can represent the ability of an imaging optical system to collect incident light, and its selectable values are usually few, such as 0.85, 0.90, 0.95, etc. Similarly, polarization parameters can represent the polarization state of the illumination light, and the selectable values for polarization parameters are usually few. First, the second set of parameters is filtered; the filtering speed is relatively fast, and the target second value combination can be quickly determined, thereby reducing the computational load of the first value combination selection operation of the first set of parameters and speeding up the determination of the training imaging configuration parameter combination. For process technologies with lower precision requirements, the imaging configuration parameters may not include polarization parameters.
[0080] In the above technical solution, the first parameter set includes the imaging wavelength and the imaging focal plane, and the second parameter set includes the numerical aperture and / or polarization parameters. This accelerates the determination of the training imaging configuration parameter combination and improves model training efficiency.
[0081] For example, the training wafers are of the same type. The training wafers can be wafers of the same material. Using wafers of the same material to train the imaging configuration recommendation model allows the trained model to provide better combinations of imaging configuration parameters for wafers of that material. The training wafers can also be wafers with the same process parameters, i.e., wafers processed using the same process. The trained imaging configuration recommendation model can provide better combinations of imaging configuration parameters for that type of process parameter. It is understood that different targeted training can be performed on the imaging configuration recommendation model to obtain multiple imaging configuration recommendation models for different types of wafers. Optionally, a targeted training imaging configuration recommendation model can be provided for a specific user. The training data can be stored locally on the user's device. The training data can be updated as needed, and the imaging configuration recommendation model can be retrained to iterate on the model. Storing the training data locally satisfies confidentiality requirements.
[0082] In the above technical solution, the training wafers are all of the same type. Therefore, the imaging configuration recommendation model is trained specifically to improve its ability to recommend imaging configurations for specific types of wafers. This also allows for the fulfillment of users' personalized needs for the model, enhancing the user experience.
[0083] For example, the imaging configuration determination method for wafer metrology further includes steps S1300 and S1400.
[0084] In step S1300, target overlay error measurement data is acquired. This target overlay error measurement data includes data obtained after measuring the target measurement points based on a combination of target imaging configuration parameters. For example, imaging can be performed based on the target imaging configuration parameter combination determined in step S1200 above to actually measure the target measurement points and obtain the target overlay error measurement data.
[0085] In step S1400, the imaging configuration recommendation model is trained again using the target imaging attribute information and the target overlay error measurement data. In some embodiments, labels can be set for the target imaging attribute information based on the target overlay error measurement data, and the imaging configuration recommendation model can be trained again using the labeled target imaging attribute information. For example, it can be determined whether the target imaging attribute information meets preset quality requirements based on the target overlay error measurement data. If it meets the requirements, a qualified label is set for the target imaging attribute information; otherwise, an unqualified label is set for the target imaging attribute information. In other embodiments, the target overlay error measurement data can be used as auxiliary training data and input together with the target imaging attribute information into the imaging configuration recommendation model for training again. For example, a feature fusion method can be used to fuse the target overlay error measurement data and the target imaging attribute information to train the imaging configuration recommendation model.
[0086] In the above technical solution, target overlay error measurement data is obtained, and the imaging configuration recommendation model is retrained using target imaging attribute information and target overlay error measurement data. Therefore, after measurement using the target imaging configuration parameter combination, the imaging configuration recommendation model can be iteratively upgraded, continuously incorporating feedback from the real production line, ensuring that the imaging configuration recommendation model remains synchronized with actual production.
[0087] For example, the imaging configuration determination method for wafer metrology further includes steps S1500 and S1600.
[0088] In step S1500, it is determined whether the target overlay error measurement data meets the preset quality requirements of the target wafer to obtain a judgment result. The target overlay error measurement data may include data from multiple measurements of the target measurement points. The preset quality requirements may include dynamic repeatability accuracy requirements, environmental error requirements, standard deviation requirements, etc. Corresponding calculations can be performed based on the target overlay error measurement data to determine whether the target overlay error measurement data meets the preset quality requirements of the target wafer.
[0089] In step S1600, if the judgment result indicates that it does not meet the preset quality requirements, the target imaging attribute information is input into the imaging configuration recommendation model that has been retrained to redetermine the target imaging configuration parameter combination.
[0090] The target overlay error measurement data can reflect whether the target imaging configuration parameter combination is qualified. If the target overlay error measurement data does not meet the preset quality requirements, it indicates that the target imaging configuration parameter combination is unqualified, that is, appropriate measurement cannot be performed under this target imaging configuration parameter combination. In the case that the target wafer does not meet the preset quality requirements, the target imaging attribute information can be input into the retrained imaging configuration recommendation model in step S1400 above to determine a new target imaging configuration parameter combination.
[0091] In the above technical solution, if the judgment result indicates that the target imaging attribute information does not meet the preset quality requirements, the target imaging attribute information is input into the retrained imaging configuration recommendation model to redetermine the target imaging configuration parameter combination. Therefore, the target overlay error measurement data can accurately reflect the actual measurement effect of the target imaging configuration parameter combination. If it does not meet the requirements, it can be redefined, thus ensuring that the final determined target imaging configuration parameter combination is more accurate.
[0092] For example, the imaging configuration determination method for wafer metrology further includes steps S1700 and S1800. In step S1700, a human-computer interaction interface is provided to the user. The human-computer interaction interface can be an interface of an input / output device that establishes a connection and exchanges information with a computer system. Input / output devices may include a monitor, mouse, keyboard, trackball, touchpad, touch screen, etc.
[0093] In step S1800, in response to an adjustment operation by the user using a human-computer interaction interface, the target imaging configuration parameter combination is adjusted. For example, a human-computer interaction interface may be displayed on the screen. The human-computer interaction interface may display a defined target imaging configuration parameter combination. The user can adjust the target imaging configuration parameter combination using an input device. For example, the user can input new values for some or all of the parameter items in the target imaging configuration parameter combination to adjust the target imaging configuration parameter combination.
[0094] The above technical solution provides a human-computer interaction interface for users; in response to user adjustments made through this interface, the target imaging configuration parameter combination is adjusted. This allows users to flexibly adjust the target imaging configuration parameter combination, improving the user experience.
[0095] By way of example, according to another aspect of the present invention, an imaging configuration determination apparatus for wafer measurement is also provided. Figure 4 A schematic block diagram of an imaging configuration determination apparatus 400 for wafer metrology according to an embodiment of the present invention is shown. The imaging configuration determination apparatus 400 for wafer metrology includes a first acquisition module 410 and a first determination module 420.
[0096] The first acquisition module 410 is used to acquire target imaging attribute information of the target wafer. This target imaging attribute information includes target process parameter information, target measurement point location information, and target basic imaging information. The target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement point. The first determination module 420 is used to input the target imaging attribute information into an imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement point. This imaging configuration recommendation model is obtained through training operations using the training imaging attribute information of a training wafer.
[0097] By way of example, according to another aspect of the present invention, an electronic device is also provided. Figure 5 A schematic block diagram of an electronic device 500 according to an embodiment of the present invention is shown. The electronic device 500 includes a processor 510 and a memory 520. The memory 520 stores computer program instructions, which, when executed by the processor 510, are used to perform the imaging configuration determination method for wafer metrology as described above.
[0098] By way of example, according to another aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which, when executed, are used to perform the imaging configuration determination method for wafer metrology as described above. The storage medium may, for example, include an erasable programmable read-only memory (EPROM), a portable read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The storage medium may be any combination of one or more computer-readable storage media.
[0099] By way of example, according to another aspect of the present invention, a computer program product is also provided, including computer program instructions that, when executed, are used to perform the imaging configuration determination method for wafer metrology as described above.
[0100] Those skilled in the art can understand the specific implementation schemes and beneficial effects of the above-described imaging configuration determination method for wafer measurement by reading the relevant descriptions above. For the sake of brevity, these will not be elaborated further here.
[0101] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.
[0102] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.
[0104] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.
[0105] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.
[0106] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.
[0107] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.
[0108] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the imaging configuration determination apparatus for wafer metrology according to embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0109] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.
[0110] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.
Claims
1. A method for determining imaging configuration for wafer metrology, characterized in that, include: Acquire target imaging attribute information of the target wafer, wherein the target imaging attribute information includes: target process parameter information, target measurement point location information and target basic imaging information, wherein the target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement point; The target imaging attribute information is input into the imaging configuration recommendation model to determine the target imaging configuration parameter combination for the target measurement point, wherein the imaging configuration recommendation model is obtained by training the model using the training imaging attribute information of the training wafer.
2. The imaging configuration determination method for wafer metrology according to claim 1, characterized in that, The training imaging attribute information includes training process parameter information, and the training operation includes: Obtain the training imaging attribute information of the training wafer; Based on the training process parameter information, multiple optional imaging configuration parameter combinations are determined; Acquire multiple sets of training overlay error measurement data corresponding one-to-one with the multiple optional imaging configuration parameter combinations, wherein the multiple sets of training overlay error measurement data include data obtained after measuring the training measurement points based on different optional imaging configuration parameter combinations; Based on the multiple sets of training overlay error measurement data, a training imaging configuration parameter combination corresponding to the training imaging attribute information is determined from the multiple optional imaging configuration parameter combinations. Based on the training imaging configuration parameter combination, the parameters of the imaging configuration recommendation model are adjusted.
3. The imaging configuration determination method for wafer measurement according to claim 2, characterized in that, The training imaging configuration parameter combination includes m first value combinations of the first parameter group; The step of determining the training imaging configuration parameter combination corresponding to the training imaging attribute information from the multiple sets of training overlay error measurement data includes: performing a first value combination selection operation. The first value combination selection operation includes: For each of the m first value combinations, calculate the first standard deviation of the training overlay error measurement data corresponding to that first value combination, so as to obtain m first standard deviations that correspond one-to-one with the m first value combinations. Compare the m first standard deviations, and determine the first value combination corresponding to the smallest first standard deviation among the m first standard deviations as the target first value combination in the training imaging configuration parameter combination.
4. The imaging configuration determination method for wafer measurement according to claim 3, characterized in that, The optional imaging configuration parameter combination also includes n second value combinations of the second parameter group; The step of determining the training imaging configuration parameter combination corresponding to the training imaging attribute information from the multiple sets of training overlay error measurement data further includes: For each of the n second value combinations, calculate the second standard deviation of the training overlay error measurement data of that second value combination under the m first value combinations, so as to obtain n second standard deviations that correspond one-to-one with the n second value combinations; Compare the n second standard deviation values, and determine the second value combination corresponding to the smallest second standard deviation value among the n second standard deviation values as the target second value combination in the training imaging configuration parameter combination; The first value combination selection operation is performed under the target second value combination.
5. The imaging configuration determination method for wafer measurement according to claim 4, characterized in that, The first parameter set includes: imaging wavelength and imaging focal plane, and the second parameter set includes: numerical aperture and / or polarization parameters.
6. The imaging configuration determination method for wafer metrology according to claim 2, characterized in that, The training wafers are of the same type.
7. The imaging configuration determination method for wafer metrology according to claim 1, characterized in that, The method further includes: Acquire target overlay error measurement data, wherein the target overlay error measurement data includes data obtained after measuring the target measurement points based on the target imaging configuration parameter combination; The imaging configuration recommendation model is trained again using the target imaging attribute information and the target overlay error measurement data.
8. The imaging configuration determination method for wafer metrology according to claim 7, characterized in that, The method further includes: Determine whether the target overlay error measurement data meets the preset quality requirements of the target wafer to obtain a determination result; If the judgment result indicates that the preset quality requirements are not met, the target imaging attribute information is input into the imaging configuration recommendation model that has been retrained to redetermine the target imaging configuration parameter combination.
9. The imaging configuration determination method for wafer measurement according to claim 1, characterized in that, The method further includes: Provide users with a human-computer interaction interface; In response to the user's adjustment operation using the human-computer interaction interface, the combination of target imaging configuration parameters is adjusted.
10. An imaging configuration determination device for wafer measurement, characterized in that, include: The first acquisition module is used to acquire target imaging attribute information of the target wafer, wherein the target imaging attribute information includes: target process parameter information, target measurement point location information and target basic imaging information, wherein the target basic imaging information includes exposure parameters and focus parameters obtained after automatic exposure and automatic focusing of the target measurement point; The first determining module is used to input the target imaging attribute information into the imaging configuration recommendation model to determine the target imaging configuration parameter combination of the target measurement point, wherein the imaging configuration recommendation model is obtained by training the training imaging attribute information of the training wafer.
11. An electronic device, comprising: Processor and memory, characterized in that, The memory stores computer program instructions, which, when executed by the processor, are used to perform the imaging configuration determination method for wafer metrology as described in any one of claims 1 to 9.
12. A storage medium on which program instructions are stored, characterized in that, The program instructions, when executed, are used to perform the imaging configuration determination method for wafer metrology as described in any one of claims 1 to 9.
13. A computer program product comprising computer program instructions, characterized in that, The computer program instructions, when executed, are used to perform the imaging configuration determination method for wafer metrology as described in any one of claims 1 to 9.