A semiconductor detection equipment calibration system

Abstract

The present invention relates to the technical field of equipment calibration, and particularly relates to a calibration system for semiconductor detection equipment, including the following modules: Initialization module: Initialize the parameters of the semiconductor detection equipment; Warpage prediction module: Establish an edge warpage prediction model to predict the edge warpage conditions of wafers with different diameters; Diameter compensation module: Establish an edge compensation model, and calculate the diameter compensation value according to the wafer edge warpage degree predicted by the edge warpage prediction model; Feedback adjustment module: Automatically adjust the image generation parameters of the semiconductor detection equipment according to the diameter compensation value; Verification module: Compare and verify the compensated wafer image with the actual wafer diameter based on experimental analysis; Application module: Automatically match and perform diameter compensation. In the present invention, by introducing the edge warpage prediction model and the diameter compensation model, it is possible to effectively predict and compensate for the diameter measurement error caused by wafer warpage, and has achieved remarkable results in the pre-calibration of semiconductor detection equipment.

Description

[0001] This application is a divisional application of the application filed on September 18, 2024, with the application number 202411298032.4 and the invention title "A Calibration System for Semiconductor Detection Equipment". Technical Field

[0002] The present invention relates to the technical field of equipment calibration, and particularly to a calibration system for semiconductor detection equipment. Background Art

[0003] Semiconductor detection equipment is used for wafer detection, including whether the diameter of the wafer is qualified. In the semiconductor manufacturing industry, the accuracy of the detection equipment is crucial for ensuring the quality of the final product. As the basic material for semiconductor manufacturing, the dimensional accuracy of the wafer, especially the accurate measurement of the diameter, is one of the key indicators for evaluating its quality. However, wafers often exhibit edge warping due to their physical properties and different diameters, and this warping directly affects the measurement accuracy of the wafer diameter.

[0004] The reasons for warping are diverse, including but not limited to internal stress of the material, temperature gradient, mechanical stress during the processing, and stress mismatch between materials during the thin film deposition process. Different wafer diameters also exacerbate this problem. Larger diameter wafers are more prone to edge warping, which not only affects the diameter measurement but may also affect the microcircuit patterns on the wafer, thereby affecting the performance of semiconductor devices.

[0005] Although significant progress has been made in the design and manufacturing technology of semiconductor detection equipment, in the case of wafer warping problems, the prior art often fails to provide effective compensation solutions. Traditional detection equipment often does not consider the impact of wafer warping on the measurement results during calibration, which limits the improvement of detection accuracy and increases the uncertainty in the manufacturing process. Therefore, there is an urgent need to develop a new calibration system for semiconductor detection equipment. Summary of the Invention

[0006] Based on the above purposes, the present invention provides a calibration system for semiconductor detection equipment.

[0007] A calibration system for semiconductor detection equipment includes the following modules:

[0008] Initialization module: Initialize the parameters of the semiconductor detection equipment, including image processing parameters;

[0009] Warping prediction module: Based on the different diameters and material properties of the wafers, establish an edge warping prediction model to predict the edge warping conditions of wafers with different diameters;

[0010] Diameter compensation module: Establish an edge compensation model, and calculate the diameter compensation value according to the warpage degree of the wafer edge predicted by the edge warpage prediction model;

[0011] Feedback adjustment module: Automatically adjust the image generation parameters of the semiconductor detection equipment according to the diameter compensation value, and then obtain the compensated wafer image through the imaging of the semiconductor detection equipment to compensate for the influence of warpage on the detection result;

[0012] Verification module: Compare and verify the compensated wafer image with the actual wafer diameter according to experimental analysis;

[0013] Application module: Divide the wafer diameter into multiple diameter range segments, establish a range segment database, after actually detecting and obtaining the wafer image, import the diameter of the wafer image before compensation into the range segment database, and automatically match and perform diameter compensation according to the compensation values of multiple range segments.

[0014] Furthermore, the image processing parameters include pixel distance. When performing diameter compensation, first compensate the pixel distance in the image to compensate for the diameter after the pixel distance is converted into the actual physical distance.

[0015] Furthermore, the material properties include elastic modulus, coefficient of thermal expansion, and material stress.

[0016] Furthermore, the edge warpage prediction model specifically includes:

[0017] Parameter definition: D is the wafer diameter, E is the elastic modulus of the material, α is the coefficient of thermal expansion of the material, ΔT is the temperature change, σ is the material stress, and δ is the warpage degree;

[0018] Use a physical model to describe the relationship between the warpage degree and the parameters: σ = EαΔT, where σ represents the internal stress of the material caused by the temperature change ΔT;

[0019] The warpage degree δ is estimated based on the stress σ and the wafer diameter D. Regarding the warpage as the bending of a circular plate, its warpage degree is expressed as: where f(D) is a function related to the wafer diameter, used to describe the geometric factor of the influence of the diameter on the warpage;

[0020] Elastic modulus E: Reflects the ability of the material to resist deformation. The larger E is, the harder the material is, and the smaller the warpage degree may be.

[0021] Coefficient of thermal expansion α: Describes the degree of change in the unit length of the material due to temperature change. The larger α and ΔT are, the greater the internal stress caused by the temperature change, resulting in greater warpage.

[0022] Function f(D): It describes how the wafer diameter affects the warpage degree. The larger the wafer diameter, the greater the possibility of uneven force on the edge, and the warpage degree may increase.

[0023] Furthermore, the specific functional form of the f(D) is:

[0024] f(D) = aD b + c, where D is the diameter of the wafer, and a, b, and c are model parameters determined by collecting warpage data of wafers with different diameters and performing data fitting. aD b represents the main trend of the warpage degree changing with the diameter, b represents the non - linear degree of the relationship, and c represents the baseline warpage, that is, the warpage degree that exists even at a very small diameter.

[0025] Furthermore, the data fitting adopts the non - linear NLS method to minimize the difference between the model prediction value and the actual observed value, which specifically includes:

[0026] Collect wafers with a series of different diameters and corresponding edge warpage measurement values as the input in the fitting process. Let D i be the diameter of the i - th wafer sample, and W i be the corresponding actual edge warpage measurement value, where i = 1, 2,..., N, and N is the total number of samples;

[0027] Define the objective function (also known as the loss function or cost function) to quantify the difference between the model prediction value and the actual measurement value, that is, the sum of the squares of the differences of all samples:

[0028] Use numerical optimization methods to minimize the objective function S(a, b, c), so as to find the optimal parameters a, b, and c, which is completed through the gradient - descent iterative algorithm;

[0029] Evaluate the quality and prediction ability of the fitting model by calculating the coefficient of determination R 2 index. R 2 close to 1 indicates a good fitting effect.

[0030] Furthermore, the edge compensation model calculates the compensation value C of the diameter based on the warpage degree δ to correct the diameter measurement error caused by warpage. The edge compensation model is expressed as: C = γδ+ζ, where C is the compensation value that needs to be applied to the measured diameter, γ is the compensation coefficient, representing the influence degree of the warpage degree δ on the compensation value C, and ζ is the baseline compensation value, which is the compensation required when there is no warpage, that is, δ = 0.

[0031] Furthermore, the compensation coefficient γ and the baseline compensation value ζ are determined by using the non - linear NLS method, expressed as: where C i and δ iThey are the actual compensation value of the i-th wafer sample and the warpage obtained through the edge warpage prediction model, respectively.

[0032] Once the model parameters γ and ζ are determined, the compensation model can be applied to any new wafer sample. First, calculate the warpage of the wafer through the edge warpage prediction model, and then calculate the corresponding diameter compensation value C using the compensation model.

[0033] Furthermore, the feedback adjustment module specifically includes:

[0034] Determine the pixel scale: The pixel scale is defined as the actual physical length represented by one pixel in the image captured by the industrial camera, expressed as: For example, if a standard-sized object occupies 100 pixels in the image and the actual length of the object is 1 millimeter, then the pixel scale is 0.01 millimeter / pixel;

[0035] Calculate the pixel compensation value: After determining the pixel scale, convert the compensation value C into the pixel distance in the image, and the pixel compensation value C 像素 is calculated by the following formula: C is the diameter compensation value calculated by the edge compensation model, and C 像素 is the pixel compensation value corresponding to the diameter compensation value;

[0036] Apply the pixel compensation value: The calculated pixel compensation value C 像素 is directly used for adjusting the image processing parameters.

[0037] The application module is as follows:

[0038] 1. Define the diameter range segments:

[0039] According to the size distribution of the wafers and the detection requirements, divide the wafer diameter into several range segments. These range segments cover all possible wafer diameters, and the width of each range segment should be determined according to the accuracy requirements of the actual application. For example, if the wafer diameter ranges from 100 millimeters to 300 millimeters, it can be divided into several 20-millimeter-wide range segments.

[0040] 2. Establish a range segment database:

[0041] Create a database for storing the relevant information of each diameter range segment, including the boundaries of the range segment and the corresponding compensation value. Each record in the database corresponds to a diameter range segment.

[0042] 3. Actual detection and diameter matching:

[0043] When obtaining the wafer image through the semiconductor detection equipment and measuring its diameter, automatic diameter compensation is performed.

[0044] 4. Automatic compensation process:

[0045] The process of automatic matching and diameter compensation can be achieved through automation.

[0046] Advantages of the present invention:

[0047] In the present invention, by introducing an edge warpage prediction model and a diameter compensation model, it is possible to effectively predict and compensate for the diameter measurement error caused by wafer warpage. This prediction and compensation mechanism takes into account the physical characteristics of the wafer (such as diameter and material properties) and its performance during the image acquisition process, thereby ensuring that the measurement results are closer to the actual physical size of the wafer. This is particularly important for quality control in the semiconductor manufacturing process because even a small size error may lead to a significant decline in chip performance. It has a significant effect on the pre-calibration of semiconductor detection equipment, making the diameter determination more accurate when actually acquiring wafer images.

[0048] In the present invention, by establishing a range segment database and implementing the process of automatic matching and diameter compensation, the automation level and adaptability of the detection process are improved. It can automatically identify the diameter range of the wafer, and thus select the appropriate compensation value for diameter correction. This automated process not only reduces the need for manual intervention but also improves the efficiency and consistency of the detection process, especially when dealing with a large number of wafers.

[0049] The present invention can update and expand the edge warpage prediction model and the diameter compensation model to adapt to changes in new wafer material characteristics or manufacturing processes. By updating the compensation values in the database or adjusting the model parameters, the calibrated detection equipment can quickly adapt to new detection requirements and maintain its measurement accuracy. Description of the drawings

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a schematic diagram of the system function modules of an embodiment of the present invention. Detailed implementation manners

[0052] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further elaborates on the present invention in combination with specific embodiments.

[0053] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0054] As Figure 1 shown, a calibration system for a semiconductor detection device includes the following modules:

[0055] Initialization module: Initialize the parameters of the semiconductor detection device, including image processing parameters;

[0056] Warpage prediction module: Based on the different diameters and material properties of the wafers, establish an edge warpage prediction model to predict the edge warpage conditions of wafers with different diameters;

[0057] Diameter compensation module: Establish an edge compensation model, and calculate the diameter compensation value according to the wafer edge warpage degree predicted by the edge warpage prediction model;

[0058] Feedback adjustment module: According to the diameter compensation value, automatically adjust the image generation parameters of the semiconductor detection device, and then obtain a compensated wafer image through the imaging of the semiconductor detection device to compensate for the influence of warpage on the detection result;

[0059] Verification module: According to experimental analysis, compare and verify the compensated wafer image with the actual wafer diameter;

[0060] Application module: Divide the wafer diameter into multiple diameter range segments, and establish a range segment database. After actually detecting and obtaining the wafer image, import the diameter of the uncompensated wafer image into the range segment database, and automatically match and perform diameter compensation according to the compensation values of multiple range segments.

[0061] The image processing parameters include pixel distance. When performing diameter compensation, first compensate the pixel distance in the image to compensate for the diameter after converting the pixel distance to the actual physical distance.

[0062] The material properties include elastic modulus, coefficient of thermal expansion, and material stress.

[0063] The edge warpage prediction model specifically includes:

[0064] Parameter definition: D is the wafer diameter, E is the elastic modulus of the material, α is the thermal expansion coefficient of the material, ΔT is the temperature change, σ is the stress of the material, and δ is the warpage;

[0065] Use a physical model to describe the relationship between the warpage and the parameters: σ = EαΔT, where σ represents the internal stress of the material caused by the temperature change ΔT;

[0066] The warpage δ is estimated based on the stress σ and the wafer diameter D. Regarding the warpage as the bending of a circular plate, its warpage is expressed as: where f(D) is a function related to the wafer diameter, which is a geometric factor used to describe the influence of the diameter on the warpage;

[0067] Elastic modulus E: Reflects the ability of the material to resist deformation. The larger E is, the harder the material is and the smaller the warpage degree.

[0068] Thermal expansion coefficient α: Describes the degree of change in the unit length of the material due to temperature change. The larger α and ΔT are, the greater the internal stress caused by the temperature change, resulting in a larger warpage.

[0069] Function f(D): Describes how the wafer diameter affects the warpage degree. The larger the wafer diameter, the greater the possibility of uneven force on the edge, and the warpage degree may increase.

[0070] Considering the physical relationship between the wafer warpage and the diameter, the wafer warpage is usually caused by internal stress, which stems from material properties, heat treatment during the processing, and stress mismatch factors in thin film deposition. For the relationship between the diameter and the warpage, the warpage degree of the wafer increases with the increase of the diameter. The specific functional form of f(D) is:

[0071] f(D) = aD b + c, where D is the diameter of the wafer, and a, b, and c are model parameters determined by collecting warpage data of wafers with different diameters and performing data fitting. aD b represents the main trend of the warpage degree changing with the diameter, b represents the degree of nonlinearity of the relationship, and c represents the baseline warpage, that is, the warpage degree that exists even at a small diameter.

[0072] The data fitting uses the nonlinear NLS method to minimize the difference between the model prediction value and the actual observed value, specifically including:

[0073] Collect wafers with a series of different diameters and the corresponding edge warpage measurement values as the input in the fitting process. Let D i be the diameter of the i-th wafer sample, and W iis the corresponding actual edge warpage measurement value, where i = 1, 2,..., N, and N is the total number of samples;

[0074] Define the objective function (also known as the loss function or cost function) to quantify the difference between the model prediction value and the actual measurement value, that is, the sum of the squares of all sample differences:

[0075] Use numerical optimization methods to minimize the objective function S(a, b, c), so as to find the optimal parameters a, b, and c, which is completed through the gradient descent iteration algorithm;

[0076] By calculating the coefficient of determination R 2 index to evaluate the quality and prediction ability of the fitting model. R 2 Close to 1 indicates a good fitting effect.

[0077] The edge compensation model calculates the compensation value C of the diameter based on the warpage δ to correct the diameter measurement error caused by warpage. The edge compensation model is expressed as: C = γδ + ζ, where C is the compensation value that needs to be applied to the measured diameter, γ is the compensation coefficient, indicating the influence degree of the warpage δ on the compensation value C, and ζ is the baseline compensation value, which is the compensation required in the case of no warpage, that is, δ = 0.

[0078] The compensation coefficient γ and the baseline compensation value ζ are determined by applying the nonlinear NLS method, which is expressed as: where C i and δ i are the actual compensation value of the i-th wafer sample and the warpage obtained through the edge warpage prediction model respectively.

[0079] Once the model parameters γ and ζ are determined, the compensation model can be applied to any new wafer sample. First, calculate the warpage of the wafer through the edge warpage prediction model, and then calculate the corresponding diameter compensation value C using the compensation model.

[0080] The feedback adjustment module specifically includes:

[0081] Determine the pixel scale: The pixel scale is defined as the actual physical length represented by one pixel in the image captured by the industrial camera, which is expressed as: For example, if it is known that an object of a standard size occupies 100 pixels in the image and the actual length of the object is 1 millimeter, then the pixel scale is 0.01 millimeter / pixel;

[0082] Calculate the pixel compensation value: After determining the pixel scale, convert the compensation value C into the pixel distance in the image. The pixel compensation value C 像素 is calculated by the following formula: C is the diameter compensation value calculated by the edge compensation model, C 像素is the pixel compensation value corresponding to the diameter compensation value;

[0083] Apply the pixel compensation value: The calculated pixel compensation value C 像素 is directly used for adjusting image processing parameters.

[0084] The application module is as follows:

[0085] 1. Define the diameter range segments:

[0086] According to the size distribution of the wafers and the detection requirements, divide the wafer diameter into several range segments. These range segments cover all possible wafer diameters, and the width of each range segment should be determined according to the accuracy requirements of the actual application. For example, if the wafer diameter ranges from 100 mm to 300 mm, it can be divided into several range segments 20 mm wide:

[0087] Range segment 1: 100 - 119 mm;

[0088] Range segment 2: 120 - 139 mm;

[0089] Range segment 3: 140 - 159 mm; ...

[0091] The last range segment: 280 - 300 mm; In actual applications, the segmentation can also be further refined to reduce the numerical span of the range segments and improve the accuracy.

[0092] 2. Establish a range segment database:

[0093] Create a database to store the relevant information of each diameter range segment, including the boundaries of the range segment and the corresponding compensation value. Each record in the database corresponds to a diameter range segment, and the record structure is as follows:

[0094] Range segment ID;

[0095] Minimum diameter;

[0096] Maximum diameter;

[0097] Compensation value;

[0098] The compensation value needs to be pre-calculated based on experimental data and prediction models and stored in the database.

[0099] 3. Actual detection and diameter matching:

[0100] When obtaining the wafer image through a semiconductor detection device and measuring its diameter, perform the following steps for automatic diameter compensation:

[0101] Measuring the wafer diameter: First, use image processing technology to measure the diameter of the wafer image (in pixels), and then convert it to the actual diameter (according to the pixel scale).

[0102] Range segment matching: Compare the measured wafer diameter with the range segments in the database to find the range segment that contains this diameter.

[0103] Applying the compensation value: According to the compensation value in the record of the matched range segment, compensate the measured wafer diameter.

[0104] 4. Automatic compensation process:

[0105] The process of automatic matching and diameter compensation can be realized through automation, achieving the following functions:

[0106] Read the wafer image and measure the diameter.

[0107] Convert the measured diameter to the actual size.

[0108] Query the corresponding diameter range segment in the database.

[0109] Read the compensation value of this range segment and apply it to the measured diameter.

[0110] To further explain the specific application of the solution of the present invention, the following example is given:

[0111] Calibrating a semiconductor inspection device for measuring the wafer diameter and compensating for measurement errors caused by wafer edge warping. The following is a complete example of the solution of the present invention:

[0112] The target wafer diameter range is from 200 mm to 300 mm.

[0113] The wafer material is single crystal silicon. During the production process, the wafer undergoes edge warping due to temperature changes and chemical processing; it is desired that the inspection device can automatically identify the wafer diameter and provide appropriate warping compensation for each diameter range to improve measurement accuracy.

[0114] Device calibration and testing:

[0115] Initialization and calibration: The device is calibrated before leaving the factory, and the basic parameters for image generation are set; a standard-sized calibration wafer is used to conduct a preliminary test on the device to determine the pixel scale (for example, 1 pixel = 0.001 mm).

[0116] Establishing an edge warping prediction model: Collect wafer samples with different diameters (from 200 mm to 300 mm) and measure their edge warping degrees; use the collected data to establish a warping prediction model, for example, determine the model parameters through linear regression analysis.

[0117] Diameter compensation model establishment: For each diameter range (200 - 210 mm, 210 - 220 mm), measure the actual diameter of the warped wafer and calculate the required compensation value; establish a compensation model to convert the predicted warpage into a diameter compensation value.

[0118] Range segment database creation: Create database records for each diameter range, including the minimum diameter, maximum diameter, and the corresponding compensation value.

[0119] Actual detection process:

[0120] Wafer diameter measurement: When a wafer to be measured is placed on the detection platform, the device first automatically acquires the wafer image and measures its diameter, and the measurement result is 250 mm.

[0121] Warpage prediction and compensation value query: The device uses the edge warpage prediction model to predict the warpage of the wafer; according to the measured diameter (250 mm), the device queries the compensation value of the corresponding range segment in the database.

[0122] Diameter correction: According to the queried compensation value, the device automatically corrects the measured diameter to obtain the accurate compensated diameter.

[0123] Result output: The device displays the compensated wafer diameter and records the data in the detection report for subsequent quality control and analysis.

[0124] Example summary: Through the above process, the semiconductor detection device of the present invention can automatically compensate for the measurement error caused by the edge warpage of the wafer, improving the accuracy and reliability of the wafer diameter measurement. This automated compensation mechanism is particularly suitable for high-precision semiconductor manufacturing processes, helping to improve product quality and production efficiency.

[0125] Those of ordinary skill in the art should understand that the discussion of any embodiment above is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above, which are not provided in detail for the sake of brevity.

[0126] The present invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A semiconductor testing equipment calibration system, characterized in that: Includes the following modules: Initialization module: Initialize the parameters of semiconductor inspection equipment, including image processing parameters; Warpage prediction module: Based on the different diameters and material properties of wafers, an edge warpage prediction model is established to predict the edge warpage of wafers with different diameters; Diameter compensation module: establish an edge compensation model, calculate the diameter compensation value according to the wafer edge warpage predicted by the edge warpage prediction model, and the edge warpage prediction model specifically includes: Parameter definition: is the wafer diameter, is the elastic modulus of the material, is the thermal expansion coefficient of the material, is the temperature change, is the stress of the material, is the warpage; A physical model is used to describe the relationship between the warpage degree and the parameters: ,in, Indicated by temperature change Internal stress of the material caused; Warpage Stress-based and wafer diameter To estimate, the warpage is regarded as the bending of a circular plate, and its warpage is expressed as: ,in, It is a function related to the wafer diameter and is used to describe the geometric factor of the effect of diameter on warpage; The physical relationship between wafer warpage and diameter. Wafer warpage is caused by internal stresses, which are derived from material properties, heat treatment during processing, and stress mismatch factors of thin film deposition. As for the relationship between diameter and warpage, the degree of wafer warpage increases with the increase of diameter. Said The specific function form is: ,in, is the diameter of the wafer, , and is the model parameter, which is determined by collecting the warpage data of wafers with different diameters and fitting the data. Indicates the main trend of warpage variation with diameter, Indicates the degree of nonlinearity of the relationship. represents the baseline warpage, the warpage that exists even at tiny diameters; The data fitting adopts the nonlinear NLS method to minimize the difference between the model prediction value and the actual observation value, specifically including: Collect a series of wafers with different diameters and the corresponding edge warpage measurements as input to the fitting process. For the The diameter of the wafer sample, is the corresponding actual edge warpage measurement, where is the total number of samples; The objective function is defined to quantify the difference between the model predictions and the actual measurements, i.e. the sum of the squares of all sample differences: ; Use numerical optimization methods to minimize the objective function , thus finding the optimal parameters , and , completed through the gradient descent iterative algorithm; By calculating the coefficient of determination Indicators to evaluate the quality and predictive ability of the fitted model, A value close to 1 indicates a good fit; Feedback adjustment module: automatically adjusts the image generation parameters of the semiconductor inspection equipment according to the diameter compensation value, and then obtains the compensated wafer image through the semiconductor inspection equipment imaging to compensate for the impact of warpage on the inspection results; Verification module: Based on experimental analysis, the compensated wafer image is compared and verified with the actual wafer diameter.

2. A semiconductor testing equipment calibration system according to claim 1, characterized in that: It also includes an application module: dividing the wafer diameter into multiple diameter range segments, and establishing a range segment database. After actually detecting and acquiring the wafer image, the diameter of the wafer image before compensation is imported into the range segment database, and the diameter compensation is automatically matched and performed based on the compensation values ​​of multiple range segments.

3. A semiconductor testing equipment calibration system according to claim 1, characterized in that: The image processing parameters include pixel distance. When compensating the diameter, the pixel distance in the image is first compensated to compensate for the diameter after the pixel distance is converted into the actual physical distance.

4. A semiconductor testing equipment calibration system according to claim 2, characterized in that: The material properties include elastic modulus, thermal expansion coefficient, and material stress.

5. A semiconductor testing equipment calibration system according to claim 1, characterized in that: The edge compensation model is based on the warpage Calculate the compensation value of the diameter , in order to correct the diameter measurement error caused by warpage, the edge compensation model is expressed as: ,in, is the compensation value that needs to be applied to the measured diameter, is the compensation coefficient, indicating the warpage Compensation value The degree of influence, is the baseline compensation value, when there is no warping, that is compensation required in case of 6. A semiconductor testing equipment calibration system according to claim 5, characterized in that: The compensation factor , baseline compensation value The nonlinear NLS method is used to determine it, which is expressed as: ,in, and They are The actual compensation value of a wafer sample and the warpage obtained by the edge warpage prediction model.

7. A semiconductor testing equipment calibration system according to claim 6, characterized in that: The feedback adjustment module specifically includes: Determine pixel scale: Pixel scale is defined as the actual physical length represented by one pixel in the image captured by the industrial camera, expressed as: ; Calculate pixel compensation value: After determining the pixel scale, the compensation value Convert to pixel distance in image, pixel compensation value Calculated by the following formula: , is the diameter compensation value calculated by the edge compensation model, is the pixel compensation value corresponding to the diameter compensation value; Apply pixel compensation value: Calculated pixel compensation value Directly used for image processing parameter adjustment.

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