A wafer film defect detection system and method

Abstract

The present invention discloses a wafer film pasting defect detection system and method, which relates to the technical field of wafer film pasting defect detection, and includes the following steps: during the wafer film pasting defect detection process, a laser scanner emits a laser beam according to a preset beam coverage rate to cover the surface of the wafer for preliminary scanning; during the scanning of the laser beam, the intensity and consistency of the laser reflection signal are analyzed in real time. The present invention conducts preliminary scanning through a preset beam coverage rate, utilizes a convolutional neural network to evaluate the saliency of potential defect areas, realizes accurate classification of obvious defect areas and non-obvious defect areas, and for non-obvious defect areas, dynamically adjusts the beam coverage rate for high-density secondary scanning to capture more subtle surface information, effectively reducing the risk of missed detection and false detection while improving the detection efficiency, ensuring the accurate detection of minute defects, significantly enhancing the quality control of wafer film pasting, and avoiding production losses.

Description

Technical Field

[0001] The present invention relates to the technical field of wafer film pasting defect detection, and particularly relates to a wafer film pasting defect detection system and method. Background Art

[0002] Wafer film pasting defect detection refers to the process of conducting quality inspection during the process of pasting a protective film or a functional film on the surface of a wafer to identify possible defects or flaws. In semiconductor manufacturing, a wafer is a crucial basic material, and the film pasting step is used to prevent the wafer surface from being contaminated or damaged during subsequent processing. However, defects such as bubbles, wrinkles, scratches, and particles may occur during the film pasting process, and these defects will seriously affect the overall quality of the wafer and the performance of the final product. To ensure the surface integrity of the wafer and the film pasting effect, defect detection usually uses high-precision optical detection equipment or machine vision systems to scan and analyze the film on the wafer surface through image processing technology, and timely discover any subtle problems generated during the film pasting process. This detection process is crucial for improving the yield and performance of semiconductor products, ensuring that no greater production losses and product rejections occur due to film pasting defects in subsequent manufacturing processes.

[0003] In the prior art, a laser scanner is usually used to detect wafer film pasting defects. The laser scanner has extremely high detection accuracy and resolution, and can accurately identify minute defects such as bubbles, scratches, and uneven film layers. Laser scanning, as a non-contact detection method, avoids physical damage to the wafer surface, and is particularly suitable for sensitive semiconductor manufacturing processes. In addition, the laser scanner can also provide three-dimensional topography data, accurately measure the depth and shape of the defects, and has the ability of automated detection, thereby improving production efficiency and the stability of product quality.

[0004] To improve detection efficiency and reduce costs, the laser scanners in the prior art usually do not set the beam coverage too high, because too high a coverage will significantly increase the detection time and cost. Wafer film pasting defect detection mainly targets specific types of defects such as bubbles, scratches, and uneven film layers, and these defects often concentrate in local areas. Therefore, by reasonably adjusting the beam coverage, it is possible to optimize the detection efficiency and reduce unnecessary time consumption while ensuring sufficient detection accuracy. However, a lower coverage may not be able to detect all defects. Especially in the case where minute defects are difficult to predict, some areas may be overlooked, resulting in missed detections. The undetected defects may cause more serious quality problems in subsequent production, and even lead to the scrapping of the entire wafer, thereby increasing production costs and reducing the yield and reliability of the product.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a wafer film defect detection system and method. Through preliminary scanning with a preset beam coverage rate, the significance of potential defect areas is evaluated using a convolutional neural network to achieve accurate classification of obvious defect areas and non-obvious defect areas. For non-obvious defect areas, the beam coverage rate is dynamically adjusted for high-density secondary scanning to capture more subtle surface information, and combined with three-dimensional topography data to accurately locate the position and shape of the defects, which can effectively reduce the risk of missed detection and false detection while improving the detection efficiency, ensure the accurate detection of micro defects, significantly improve the quality control of wafer films, and avoid production losses, so as to solve the problems in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: A wafer film defect detection method, comprising the following steps:

[0008] During the wafer film defect detection process, a laser scanner emits laser beams according to a preset beam coverage rate to cover the surface of the wafer for preliminary scanning;

[0009] During the scanning of the laser beams, by analyzing the intensity and consistency of the laser reflection signals in real time, the reflection difference areas existing between the surface of the wafer and the film are identified, and the reflection difference areas are marked as suspected defect areas;

[0010] Feature extraction is performed on the reflection signals of the suspected defect areas, and in-depth analysis is performed on the extracted feature data. Based on the features after in-depth analysis, the obviousness of potential defects in the wafer film is evaluated through a pre-trained convolutional neural network, and the suspected defect areas are further divided into obvious defect areas and non-obvious defect areas;

[0011] For the obvious defect areas, the wafer film is directly determined to be of unqualified quality. For the non-obvious defect areas, the beam coverage rate is dynamically expanded according to the prediction results of the convolutional neural network to increase the laser scanning density. At this time, the laser scanning range no longer covers the entire wafer, but is concentrated on the non-obvious defect areas;

[0012] Higher-density laser beams are emitted in the marked suspected defect areas through the adjusted beam coverage rate for secondary scanning. The high-density laser beams are used to capture more subtle surface information, and combined with three-dimensional topography data to obtain the three-dimensional features of the defects, accurately locate and identify the film defects.

[0013] Preferably, the intensity and consistency of the laser reflection signal are analyzed in real time to identify the reflection difference area between the wafer surface and the film. The specific steps are as follows:

[0014] The laser scanner emits laser beams according to a preset beam coverage rate, scans the entire wafer surface, and collects the reflected optical signals in real time. The reflected light intensity value I(x, y) of each scan point is recorded as a two-dimensional coordinate matrix, where I(x, y) represents the reflected light intensity value at (x, y), and x and y represent the spatial coordinates of the scan. The optical intensity information of the entire wafer surface is stored through the two-dimensional coordinate matrix;

[0015] Perform local consistency calculation on the light intensity value I(x, y) of each point (x, y). Specifically: compare the light intensity of this point with the light intensities of other points in its neighborhood, and calculate the local consistency deviation D(x, y). The calculation formula is: In the formula, (x l , y l ) is the neighborhood point of the point (x, y), l represents the index of the neighborhood point, and N is the number of points in the neighborhood;

[0016] After calculating the local consistency deviation D(x, y) of each point, compare D(x, y) with a preset threshold T. If the local consistency deviation D(x, y) of this point > T, then mark this point as a suspected defect point;

[0017] Apply a clustering algorithm to process the marked suspected defect points, aggregate the defect points according to the spatial distribution and density, and form a continuous defect area. This continuous defect area is the reflection difference area between the wafer surface and the film.

[0018] Preferably, feature extraction is performed on the reflection signal of the suspected defect area. The extracted features include the asymmetry of the signal scattering after the laser is reflected on the wafer surface and the depth change in the three-dimensional topography. After in-depth analysis of the asymmetry of the signal scattering after the laser is reflected on the wafer surface and the depth change in the three-dimensional topography, a scattering asymmetry reference value and a depth change reference value are generated respectively. The degree of non-uniformity of the signal scattering after the laser reflection is quantified by the scattering asymmetry reference value, and the height difference of the three-dimensional topography of the wafer surface is quantified by the depth change reference value. The scattering asymmetry reference value and the depth change reference value obtained after in-depth analysis are used to predict the defect obviousness coefficient by a pre-trained convolutional neural network, and the obviousness of potential defects in the wafer film is evaluated by the defect obviousness coefficient.

[0019] Preferably, compare and analyze the defect obviousness coefficient generated by predicting potential defects in the suspected defect area through a pre-trained convolutional neural network with a pre-set reference threshold of the defect obviousness coefficient, and further divide the suspected defect area into a defect obvious area and a defect non-obvious area. The specific division steps are as follows:

[0020] If the defect obviousness coefficient is greater than or equal to the reference threshold of the defect obviousness coefficient, divide the suspected defect area into a defect obvious area; if the defect obviousness coefficient is less than the reference threshold of the defect obviousness coefficient, divide the suspected defect area into a defect non-obvious area.

[0021] Preferably, the specific steps for generating the scattering asymmetry reference value by deeply analyzing the asymmetry of the signal scattering after the laser is reflected on the wafer surface are as follows:

[0022] In the suspected defect area, collect the laser reflection signals in multiple directions. The collected signals are expressed as the reflection intensities in different directions, and the laser reflection signal is expressed as a vector field S(x, y, z). Among them, the signal of each point is decomposed into vector components in different directions, and the decomposition expression is: Where S x 、S y 、S z respectively represent the components of the laser signal in the three-dimensional directions, is the corresponding unit direction vector;

[0023] Evaluate the scattering non-uniformity of the signal by comparing the vector differences of adjacent signal points. Define the signal difference between each point and its neighborhood points as ΔS ij , then: ΔS ij =‖S(x i , y i , z i ) - S(x j , y j , z j )‖, where ΔS ij is the vector difference between the i-th point and the j-th point, and ‖·‖ represents the Euclidean distance of the vector; then construct a signal difference matrix D, where each element D ij represents the difference degree between the i-th point and the j-th point, and the construction expression of the signal difference matrix is: D = [D ij = [ΔS ij ;

[0024] Based on the signal difference matrix, calculate the scattering asymmetry reference value. The generation of the scattering asymmetry reference value is achieved by extracting the high-order change information in the signal difference matrix. Define the scattering asymmetry reference value as the harmonic increment cumulant of the signal difference matrix. Then the calculation expression of the scattering asymmetry reference value is:

[0025]

[0026] Among them, γ is the weight parameter for controlling spatial attenuation, and r ij represents the Euclidean distance between point i and point j, and S asym is the scattering asymmetry reference value.

[0027] Preferably, the specific steps for generating the depth change reference value after performing depth analysis on the depth change of the three-dimensional topography are as follows:

[0028] For the suspected defect area, obtain the three-dimensional topography data of the wafer surface in this suspected defect area. Let the dot matrix data scanned by the laser be (x u , y u , z u ), where (x u , y u ) is the two-dimensional coordinate of the wafer surface, and z u is the corresponding height information. The spatial distribution of the wafer surface height is represented by a three-dimensional height function. Among them, the expression of the three-dimensional height function is: Z(x u , y u ) = f(x u , y u ), where Z(x u , y u ) is the three-dimensional height data of the wafer surface, reflecting the topography of the surface, and f(x u , y u ) is the height distribution function collected from the two-dimensional coordinates of the wafer surface;

[0029] Perform calculations on the three-dimensional height gradient and three-dimensional height curvature of the collected three-dimensional topography data to quantify the severity of the surface change in the suspected defect area. The three-dimensional height gradient is defined as the directional change of the three-dimensional surface height, and the three-dimensional height curvature represents the degree of curvature of the wafer surface. The calculation expressions are:

[0030] ,

[0031] In the formula, represents the three-dimensional height gradient at the two-dimensional coordinates (x u , y u ) of the wafer surface, represents the change in the three-dimensional height gradient of the three-dimensional height function along the x-axis, represents the change in the three-dimensional height gradient of the three-dimensional height function along the y-axis, and k(x u , y u ) represents the three-dimensional height curvature at the two-dimensional coordinates (x u , y u ) of the wafer surface;

[0032] By means of the three-dimensional height gradient u at the two-dimensional coordinates (x u ) and the three-dimensional height curvature κ(x at the two-dimensional coordinates (x u ,y u ) on the wafer surface, calculate the severity S(x u ,y u ) of the depth change at the two-dimensional coordinates (x u ,y u ) on the wafer surface. The calculation expression is:

[0033] According to the severity S(x u ,y u ) of the depth change at the two-dimensional coordinates (x u ,y u ) on the wafer surface, calculate the reference value of the depth change, which is used to quantify the height difference of the three-dimensional topography of the wafer surface. The calculation expression of the reference value of the depth change is: Depth var =∫ A S(x u ,y u )dxdy, where Depth var represents the reference value of the depth change. The reference value of the depth change is defined as the integral of the severity of the depth change over the entire suspected defect area, that is, the comprehensive measure of the surface change of all points in the suspected defect area.

[0034] Preferably, for the area where the defect is not obvious, the beam coverage rate is dynamically expanded according to the prediction result of the convolutional neural network, and the laser scanning density is increased. The specific steps are as follows:

[0035] Calculate the relative measure of the defect obviousness coefficient in the area where the defect is not obvious with respect to the reference threshold of the defect obviousness coefficient. The calculation expression is:

[0036]

[0037] , where τ 显著 represents the relative significance measure, which is used to measure the ratio of the current defect obviousness coefficient Defect sign to the reference threshold of the defect obviousness coefficient Defect 阈值 ;

[0038] According to the relative significance measure τ 显著 calculate the coverage adjustment factor to determine the amount of beam coverage rate that needs to be increased. The calculation expression of the coverage adjustment factor is: where ΔC is the coverage adjustment factor, which is used to dynamically expand the beam coverage rate, and C p$C_0$ is the preset beam coverage rate, the coverage rate during the initial scan, and $\omega$ is the adjustment coefficient, which controls the sensitivity and non-linearity of the coverage rate adjustment factor;

[0039] Apply the calculated coverage rate adjustment factor $\Delta C$ to the preset beam coverage rate $C$ p , to obtain a new beam coverage rate for performing a second scan with a higher density on the suspected defect area. The calculation expression for the new beam coverage rate is: In the formula, $C$ new is the new beam coverage rate, which is applied to the second high-density scan.

[0040] A wafer film defect detection system includes a preliminary scan module, a signal analysis and marking module, a feature extraction and classification module, a dynamic extended scan module, and a fine scan and positioning module;

[0041] The preliminary scan module, during the wafer film defect detection process, emits laser beams according to the preset beam coverage rate through a laser scanner to cover the wafer surface for preliminary scanning;

[0042] The signal analysis and marking module, during the process of laser beam scanning, analyzes the intensity and consistency of the laser reflection signal in real time to identify the reflection difference area between the wafer surface and the film, and marks the reflection difference area as a suspected defect area;

[0043] The feature extraction and classification module extracts features from the reflection signals of the suspected defect areas, deeply analyzes the extracted feature data, evaluates the obviousness of potential defects in the wafer film based on the features after in-depth analysis through a pre-trained convolutional neural network, and further divides the suspected defect areas into obvious defect areas and non-obvious defect areas;

[0044] The dynamic extended scan module, for the obvious defect areas, directly determines that the wafer film is unqualified in quality. For the non-obvious defect areas, according to the prediction result of the convolutional neural network, it dynamically extends the beam coverage rate, increases the laser scan density. At this time, the laser scan range no longer covers the entire wafer, but focuses on the non-obvious defect areas;

[0045] The fine scan and positioning module emits a higher density of laser beams in the marked suspected defect areas through the adjusted beam coverage rate for a second scan, uses the high-density laser beams to capture more subtle surface information, and combines three-dimensional topography data to obtain the three-dimensional features of the defects, accurately locate and identify the film defects.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0047] The present invention conducts a preliminary scan by presetting the beam coverage rate, quickly identifies potential defect areas, and uses a convolutional neural network to evaluate the saliency of the potential defect areas, achieving precise classification of obvious defect areas and non-obvious defect areas. For non-obvious defect areas, the beam coverage rate is dynamically expanded according to the prediction results of the convolutional neural network, and a high-density secondary scan is performed again. This can capture more subtle surface information and accurately locate the specific position and shape of the defect in combination with three-dimensional topography data. It can effectively reduce the risks of missed detection and false detection while maintaining the detection efficiency, ensure the precise detection of micro-defects, significantly improve the quality control level of wafer film pasting, and avoid losses and quality problems caused by missed detection in subsequent production. Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of a method for detecting wafer film pasting defects according to the present invention.

[0050] Figure 2 It is a schematic diagram of the modules of a system for detecting wafer film pasting defects according to the present invention. Detailed Embodiments

[0051] Now, the exemplary embodiments will be described more comprehensively with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0052] The present invention provides a method for detecting wafer film pasting defects as shown in Figure 1 and includes the following steps:

[0053] During the process of detecting wafer film pasting defects, a laser scanner emits laser beams according to a preset beam coverage rate to cover the wafer surface for a preliminary scan;

[0054] During the process of detecting defects in wafer film pasting, a laser scanner emits laser beams according to a preset beam coverage rate to preliminarily scan the wafer surface, aiming to quickly identify potential abnormal areas. Compared with the existing technology, this preset beam coverage rate is slightly lower, mainly used to improve the detection efficiency and reduce costs. This lower coverage rate can preliminarily detect abnormal phenomena such as reflection differences in the wafer film pasting, but cannot accurately identify defects. In this way, the areas that may have defects can be quickly locked, without the need to perform high-density scanning on the entire wafer surface, thus avoiding unnecessary resource waste while ensuring the detection speed. The preset beam coverage rate is not limited to a fixed value or setting, but is flexibly adjusted according to the actual process requirements and specific product characteristics to adapt to the detection requirements in different scenarios. By reasonably setting this coverage rate, reference data can be provided for subsequent more accurate detection steps without sacrificing the preliminary detection efficiency.

[0055] During the process of laser beam scanning, by analyzing the intensity and consistency of the laser reflection signal in real time to identify the reflection difference areas existing between the wafer surface and the film, and marking the reflection difference areas as suspected defect areas;

[0056] To analyze the intensity and consistency of the laser reflection signal in real time to identify the reflection difference areas existing between the wafer surface and the film, the specific steps are as follows:

[0057] The laser scanner emits laser beams according to the preset beam coverage rate, covering the entire wafer surface for scanning, and collecting the reflected optical signals in real time. The reflected light intensity value I(x, y) of each scanning point is recorded as a two-dimensional coordinate matrix, where I(x, y) represents the reflected light intensity value at (x, y), and x and y represent the spatial coordinates of the scan. The optical intensity information of the entire wafer surface is stored through this two-dimensional coordinate matrix;

[0058] To identify the reflection difference areas between the wafer surface and the film, local consistency calculation is performed on the light intensity value I(x, y) of each point (x, y). Specifically: compare the light intensity of this point with the light intensities of other points in its neighborhood, and calculate the local consistency deviation D(x, y). The calculation formula is: In the formula, (x l , y l ) is the neighborhood point of the point (x, y), l represents the index of the neighborhood point, and N is the number of points in the neighborhood;

[0059] A higher D(x, y) value indicates that there is a significant reflection difference between this point and the surrounding area, which may be a defect area.

[0060] After calculating the local consistency deviation D(x, y) of each point, compare D(x, y) with a pre-set threshold T. If the local consistency deviation D(x, y) of this point > T, then mark this point as a suspected defective point;

[0061] The threshold T can be adjusted according to the specific production process and material characteristics, and no specific limitation is made here to ensure appropriate detection sensitivity.

[0062] Apply a clustering algorithm (such as K-Means or DBSCAN) to process the marked suspected defective points, aggregate the defective points according to spatial distribution and density, and form a continuous defective area. This continuous defective area is the reflection difference area between the wafer surface and the film;

[0063] During the detection process, all detected suspected defective points are grouped by the clustering algorithm according to their distribution positions in space and the density of surrounding points. When some defective points are close in space and have similar distribution characteristics, these points will be grouped into the same group, thus forming an overall defective area. Through such processing, large-area defects or continuously distributed defect areas can be more clearly identified, facilitating subsequent analysis and further processing. The aggregation step helps to integrate discrete points into a meaningful area, reflecting a more obvious defect pattern.

[0064] These differences may be caused by defects such as bubbles, scratches, and uneven film layers. When the reflection signal of the laser beam has a significant deviation from the preset normal range, this suspected defective area will be automatically marked as a suspected defective area. The optimization of this step can improve the sensitivity of difference detection through high-precision sensors and signal analysis algorithms, ensuring that subtle reflection changes can be detected, especially for the early identification of minor defects.

[0065] Extract the features of the reflection signal of the suspected defective area, and conduct in-depth analysis on the extracted feature data. Based on the features after in-depth analysis, evaluate the obviousness of potential defects in the wafer film through a pre-trained convolutional neural network, and further divide the suspected defective area into an obvious defect area and an unobvious defect area;

[0066] Extract the features of the reflected signals in the suspected defect areas. The extracted features include the asymmetry of the signal scattering after the laser is reflected from the wafer surface and the depth changes in the three-dimensional topography. After in-depth analysis of the asymmetry of the signal scattering after the laser is reflected from the wafer surface and the depth changes in the three-dimensional topography, a scattering asymmetry reference value and a depth change reference value are generated respectively. The degree of non-uniformity of the signal scattering after the laser reflection is quantified by the scattering asymmetry reference value. The larger the value, the more significant the non-uniformity of the wafer surface, indicating that there may be obvious defects. The height difference of the three-dimensional topography of the wafer surface is quantified by the depth change reference value. The larger the value, the greater the degree of unevenness of the surface, indicating that there may be serious defects. The scattering asymmetry reference value and the depth change reference value obtained after the in-depth analysis are used to predict and generate a defect obviousness coefficient by a pre-trained convolutional neural network. The defect obviousness coefficient is used to evaluate the obviousness of potential defects in the wafer film pasting.

[0067] A pre-trained convolutional neural network is a deep learning model specialized for processing data with a grid structure. The convolutional neural network extracts the features of the data through a series of convolutional layers, pooling layers, and fully connected layers, and can automatically identify and learn feature representations at different levels. In the detection of wafer film pasting defects, the convolutional layer is responsible for extracting the local features of the original data (such as the scattering asymmetry reference value and the depth change reference value), while the pooling layer is used to reduce the feature dimension, retain important information, and prevent overfitting. This structure makes the convolutional neural network efficient and has strong feature learning ability when processing high-dimensional data, especially suitable for image and signal data.

[0068] A pre-trained convolutional neural network is usually trained on a large-scale dataset to learn effective feature representations. When applied to a specific task (such as wafer film pasting defect detection), the pre-learned weights and features can be directly used for the new task through transfer learning. This method not only saves training time but also improves the accuracy and generalization ability of the model. In this application, the trained convolutional neural network can combine the scattering asymmetry reference value and the depth change reference value to generate a defect obviousness coefficient. The defect obviousness coefficient is used to quantify and evaluate the severity of potential defects, thereby helping to identify and repair defects in a timely manner in the production process, improving the yield and reliability of products.

[0069] When the signal scattering asymmetry after the laser is reflected on the wafer surface is relatively high, it usually indicates that there are obvious defects in the suspected defect area. The scattering asymmetry reflects the difference in the reflection intensity of the laser signal in different directions, which is usually caused by the non-uniformity of the wafer surface or the film, such as air bubbles, scratches, uneven film layers, etc. These defects will cause the optical path of the laser reflection to deviate, increasing the difference in the scattering intensity in different directions, thus showing a higher asymmetry. On the contrary, if the scattering asymmetry is low, it means that the laser is not significantly disturbed during surface reflection, the surface is relatively smooth and uniform, so the defects in the suspected area are not obvious or do not exist. By analyzing the scattering asymmetry, the severity of the suspected defect area can be quickly evaluated. A higher asymmetry means that the reflected signal is disturbed by the surface irregular structure, which can be used as a reliable indicator to mark the defect area.

[0070] The specific steps to generate the scattering asymmetry reference value after in-depth analysis of the asymmetry of the signal scattering after the laser is reflected on the wafer surface are as follows:

[0071] In the suspected defect area, collect the laser reflection signals in multiple directions. The collected signals can be expressed as the reflection intensities in different directions. Represent the laser reflection signal as a vector field S(x, y, z). Among them, the signal at each point is decomposed into vector components in different directions, and the decomposition expression is: Where S x 、S y 、S z represent the components of the laser signal in the three-dimensional directions respectively, is the corresponding unit direction vector;

[0072] The purpose of this step is to extract the reflection intensity components at each point in different directions, laying a foundation for subsequent non-uniformity analysis.

[0073] Evaluate the scattering non-uniformity of the signal by comparing the vector differences between adjacent signal points. Define the signal difference between each point and its neighborhood points as ΔS ij , then: ΔS ij =‖S(x i , y i , z i ) - S(x j , y j , z j )‖, where ΔS ij is the vector difference between the i-th point and the j-th point, and ‖·‖ represents the Euclidean distance of the vector; Subsequently, construct a signal difference matrix D, where each element D ij represents the difference degree between the i-th point and the j-th point. The construction expression of the signal difference matrix is: D = [D ij = [ΔSij , the larger the element in the difference matrix, the greater the signal difference between the point and its neighboring points, reflecting the non-uniformity of the scattered signal;

[0074] This step quantifies the spatial variation pattern of the signal into a difference matrix, capturing the changes in signal scattering in different directions.

[0075] Based on the signal difference matrix, calculate the scattering asymmetry reference value. The generation of the scattering asymmetry reference value is achieved by extracting the high-order variation information in the signal difference matrix. Define the scattering asymmetry reference value as the harmonic increment cumulant of the signal difference matrix. Then the calculation expression of the scattering asymmetry reference value is:

[0076] ,

[0077] where γ is the weight parameter controlling the spatial attenuation, r ij represents the Euclidean distance between point i and point j, and S asym is the scattering asymmetry reference value;

[0078] The weight parameter controlling the spatial attenuation refers to a regulating factor used to adjust the degree of mutual influence between adjacent signal points when analyzing the signal. It reduces the influence of signal points at a greater distance on the overall signal analysis by assigning smaller weights to them, thereby highlighting the relationship between nearby signals. The setting of this parameter needs to be adjusted according to the specific application scenario: when focusing on local signal changes, the weight parameter γ should be set larger to enhance the influence of nearby signals; while when observing the signal trend over a larger range, γ should be set smaller so that distant signals also have a certain influence. Generally, the value of γ is optimized through experiments or based on data characteristics to obtain the best signal attenuation effect.

[0079] This formula highlights the asymmetry characteristics of the reflected signal in different directions by performing harmonic weighting on the difference values in the signal difference matrix. The larger the scattering asymmetry reference value, the higher the degree of signal non-uniformity, and thus the more obvious the wafer surface defects are reflected.

[0080] From the scattering asymmetry reference value, it can be seen that the larger the performance value of the scattering asymmetry reference value generated after in-depth analysis of the asymmetry of the signal scattering after the laser is reflected on the wafer surface, the stronger the signal scattering non-uniformity after the laser is reflected on the wafer surface. This usually means that there are obvious defects in the suspected defect area. A larger scattering asymmetry reference value reflects a significant difference in the intensity of the reflected signal in multiple directions, indicating that there may be large irregularities or defects on the surface; conversely, when the scattering asymmetry reference value is small, it indicates that the signal scattering is relatively uniform, the surface is relatively smooth or uniform, and the possibility of defects is small or not obvious. Therefore, the larger the scattering asymmetry reference value, the more obvious the defect, and the smaller the scattering asymmetry reference value, the less obvious the defect.

[0081] The depth change in the three-dimensional topography can be used as an important feature for judging the suspected defect area. When the depth change in the three-dimensional topography is large, it usually means that there are significant unevenness or irregular structures on the surface, which may be caused by serious defects, such as bubbles, scratches or separation of the film layer, etc. These obvious depth differences often indicate that the defects in the suspected defect area not only affect the surface flatness, but may also have a negative impact on subsequent processing or the function of the wafer. Therefore, the larger the depth change, the higher the severity of the defect. When the depth change in the three-dimensional topography is small or close to zero, it indicates that the surface of the suspected defect area is relatively flat, the defect may be relatively small or insignificant, and it is less likely to have a significant impact on the overall quality.

[0082] The specific steps for generating the depth change reference value after in-depth analysis of the depth change in the three-dimensional topography are as follows:

[0083] For the suspected defect area, obtain the three-dimensional topography data of the wafer surface in this suspected defect area. Let the dot matrix data scanned by the laser be (x u , y u , z u ), where (x u , y u ) is the two-dimensional coordinate of the wafer surface, and z u is the corresponding height information. The spatial distribution of the wafer surface height is represented by a three-dimensional height function. Among them, the expression of the three-dimensional height function is: Z(x u , y u ) = f(x u , y u ), where Z(x u , y u ) is the three-dimensional height data of the wafer surface, reflecting the topography of the surface, and f(x u , y u ) is the height distribution function collected from the two-dimensional coordinates of the wafer surface;

[0084] Height data can be directly collected by a laser scanner, representing the surface height changes.

[0085] Perform three-dimensional height gradient and three-dimensional height curvature calculations on the collected three-dimensional topography data to quantify the severity of surface changes in the suspected defect area. By calculating the three-dimensional height gradient and three-dimensional height curvature, the height differences and surface slope changes can be more accurately reflected, thereby identifying areas with large depth changes. The three-dimensional height gradient is defined as the directional change of the three-dimensional surface height, and the three-dimensional height curvature represents the degree of curvature of the wafer surface. The calculation expressions are as follows:

[0086]

[0087] In the formula, represents the three-dimensional height gradient at the two-dimensional coordinates (x u , y u ) on the wafer surface. represents the change in the three-dimensional height gradient of the three-dimensional height function along the x-axis. represents the change in the three-dimensional height gradient of the three-dimensional height function along the y-axis. κ(x u , y u ) represents the three-dimensional height curvature at the two-dimensional coordinates (x u , y u ) on the wafer surface;

[0088] Calculate the severity of depth change S(x u , y u ) at the two-dimensional coordinates (x , y u , y u ) on the wafer surface through the three-dimensional height gradient u , y u ) and the three-dimensional height curvature κ(x u , y u ). The calculation expression is as follows:

[0089] The severity of depth change S(x u , y u ) on the wafer surface is represented by the product of the three-dimensional height gradient and the three-dimensional height curvature κ(x u , y u ) to comprehensively measure the severity of three-dimensional height changes on the wafer surface. The larger the three-dimensional height gradient, the faster the surface changes; the larger the three-dimensional height curvature, the more severe the surface bending. The combination of the two can quantify complex surface changes.

[0090] According to the two-dimensional coordinates (xu ,y u ) is the depth variation S(x u ,y u ) calculates the depth variation reference value, which is used to quantify the height difference of the three-dimensional topography of the wafer surface. The calculation expression of the depth variation reference value is: var =∫ A S(x u ,y u )dxdy,Depth var It represents the depth change reference value. The depth change reference value is defined as the integral of the depth change severity in the entire suspected defect area, that is, the comprehensive measure of the surface change of all points in the suspected defect area. The larger the depth change reference value, the more severe the surface change of the suspected defect area is and the more obvious the defect is.

[0091] The depth variation reference value is used to quantify the height difference in the three-dimensional morphology. The size of the depth variation reference value directly reflects the obviousness of the defect. Specifically, the depth variation reference value is generated by analyzing the height change of the wafer surface. When the depth variation reference value is large, it indicates that the surface height of the suspected defect area changes dramatically, there are large unevenness or irregularities, and the defect is obvious. On the contrary, when the depth variation reference value is small, it means that the height difference of the suspected defect area is small, the surface is relatively flat, and the defect is not obvious. Therefore, the size of the depth variation reference value can be effectively evaluated.

[0092] The convolutional neural network is not specifically limited here, and can realize the scattering asymmetric reference value S asym And depth change reference value Depth var Perform comprehensive analysis to generate defect apparent coefficient Defect sign In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Defect obvious coefficient Defect sign The generated calculation formula is: Defect sign =α*S asym +β*Depth var , where α and β are the scattering asymmetric reference values ​​S asym And depth change reference value Depth var The preset proportional coefficient, and α and β are both greater than 0.

[0093] From the defect obviousness coefficient, it can be seen that the larger the performance value of the scattering asymmetry reference value generated by deeply analyzing the asymmetry of the signal scattering after the laser is reflected on the wafer surface, and the larger the performance value of the depth change reference value generated by deeply analyzing the depth change on the three-dimensional topography. That is, the larger the performance value of the defect obviousness coefficient generated by predicting potential defects in the suspected defect area through a pre-trained convolutional neural network, the more obvious the defect in the suspected defect area. On the contrary, it indicates that the defect in the suspected defect area is less obvious.

[0094] Compare and analyze the defect obviousness coefficient generated by predicting potential defects in the suspected defect area through a pre-trained convolutional neural network with the pre-set defect obviousness coefficient reference threshold, and further divide the suspected defect area into a defect obvious area and a defect non-obvious area. The specific division steps are as follows:

[0095] If the defect obviousness coefficient is greater than or equal to the defect obviousness coefficient reference threshold, then divide the suspected defect area into a defect obvious area. If the defect obviousness coefficient is less than the defect obviousness coefficient reference threshold, then divide the suspected defect area into a defect non-obvious area.

[0096] For the defect obvious area, directly determine that the wafer film is unqualified in quality. For the defect non-obvious area, dynamically expand the beam coverage rate according to the prediction result of the convolutional neural network and increase the laser scanning density. At this time, the laser scanning range no longer covers the entire wafer, but is concentrated in the defect non-obvious area;

[0097] For the defect non-obvious area, dynamically expand the beam coverage rate according to the prediction result of the convolutional neural network and increase the laser scanning density. The specific steps are as follows:

[0098] Calculate the relative measure of the defect obviousness coefficient in the defect non-obvious area relative to the defect obviousness coefficient reference threshold. The calculation expression is:

[0099]

[0100] In the formula, τ 显著 represents the relative significance measure, which is used to measure the current defect obviousness coefficient Defect sign relative to the defect obviousness coefficient reference threshold Defect 阈值 ratio;

[0101] The defect obviousness coefficient Defect sign , obtained by predicting through the convolutional neural network, is used to quantify the obviousness of the defect.

[0102] According to the relative significance measure τ 显著Calculate the coverage adjustment factor to determine the amount of beam coverage that needs to be increased. This adjustment factor is based on a non-linear function to make more significant adjustments when the relative significance measure τ 显著 is close to the reference threshold of the defect visibility coefficient. The calculation expression of the coverage adjustment factor is: In the formula, ΔC is the coverage adjustment factor used to dynamically expand the beam coverage, C p is the preset beam coverage, the coverage at the initial scan, and ω is the adjustment coefficient that controls the sensitivity and non-linearity of the coverage adjustment factor. It is usually determined through experiments, such as ω = 2;

[0103] Apply the calculated coverage adjustment factor ΔC to the preset beam coverage C p to obtain the new beam coverage for performing a second scan with a higher density on the suspected defect area. The calculation expression of the new beam coverage is: In the formula, C new is the new beam coverage applied to the second high-density scan.

[0104] Emit a higher density laser beam for a second scan in the marked suspected defect area through the adjusted beam coverage. Use the high-density laser beam to capture more subtle surface information, and combine the three-dimensional topography data to obtain the three-dimensional characteristics of the defect, accurately locate and identify the film defects;

[0105] Compared with the preliminary scan, the second scan uses a higher density laser beam, which can capture more subtle information on the wafer surface, especially those subtle defects that cannot be accurately detected in the preliminary scan. The high-density laser beam can not only record the optical reflection characteristics of the surface, but also combine the three-dimensional topography data, that is, obtain the subtle height, concavity, depth and other spatial information on the wafer surface through the strength and change of the laser beam reflection. Through this process, the system can present the appearance, location and severity of the defect three-dimensionally. This detection method that combines optical and geometric features helps to more accurately locate the specific position of the defect and identify the specific type of the defect, such as bubbles, scratches or film layer non-uniformity, etc. Finally, through the second scan with a high-density laser beam, the system can further verify the results of the preliminary detection and make a more accurate defect judgment to ensure that effective treatment measures can be taken for these defects in the subsequent process.

[0106] The present invention conducts a preliminary scan by presetting the beam coverage rate, quickly identifies potential defect areas, and evaluates the saliency of the potential defect areas using a convolutional neural network, achieving precise classification of obvious defect areas and non-obvious defect areas. For non-obvious defect areas, the beam coverage rate is dynamically expanded according to the prediction results of the convolutional neural network, and a high-density secondary scan is performed again. This can capture more subtle surface information and accurately locate the specific position and shape of the defect in combination with three-dimensional topography data. It can effectively reduce the risks of missed detection and false detection while maintaining the detection efficiency, ensure the precise detection of micro-defects, significantly improve the quality control level of wafer film pasting, and avoid losses and quality problems caused by missed detection in subsequent production.

[0107] The present invention provides a Figure 2 wafer film pasting defect detection system as shown, including a preliminary scan module, a signal analysis and marking module, a feature extraction and classification module, a dynamic expansion scan module, and a fine scan and positioning module;

[0108] The preliminary scan module, during the wafer film pasting defect detection process, emits laser beams according to the preset beam coverage rate through a laser scanner to cover the wafer surface for preliminary scanning;

[0109] The signal analysis and marking module, during the laser beam scanning process, analyzes the intensity and consistency of the laser reflection signal in real time to identify the reflection difference areas existing between the wafer surface and the film, and marks the reflection difference areas as suspected defect areas;

[0110] The feature extraction and classification module extracts features from the reflection signals of the suspected defect areas, deeply analyzes the extracted feature data, evaluates the obviousness of potential defects in the wafer film based on the features after in-depth analysis through a pre-trained convolutional neural network, and further divides the suspected defect areas into obvious defect areas and non-obvious defect areas;

[0111] The dynamic expansion scan module, for obvious defect areas, directly determines that the wafer film is unqualified in quality. For non-obvious defect areas, the beam coverage rate is dynamically expanded according to the prediction results of the convolutional neural network to increase the laser scan density. At this time, the laser scan range no longer covers the entire wafer but is concentrated on the non-obvious defect areas;

[0112] The fine scan and positioning module emits a higher density of laser beams in the marked suspected defect areas through the adjusted beam coverage rate for secondary scanning, uses the high-density laser beams to capture more subtle surface information, and combines three-dimensional topography data to obtain the three-dimensional features of the defect, accurately locate and identify the film pasting defect;

[0113] A method for detecting defects in wafer film pasting provided by an embodiment of the present invention is implemented through the above-mentioned system for detecting defects in wafer film pasting. For the specific method and process of the system for detecting defects in wafer film pasting, please refer to the embodiments of the above-mentioned method for detecting defects in wafer film pasting, which will not be elaborated here.

[0114] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0115] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0116] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A wafer film defect detection method, characterized in that: The following steps are involved: During the wafer film defect detection process, the laser scanner emits a laser beam according to the preset beam coverage rate to cover the wafer surface for preliminary scanning; During the laser beam scanning process, the intensity and consistency of the laser reflection signal are analyzed in real time to identify the reflection difference area between the wafer surface and the film, and mark the reflection difference area as a suspected defect area; Extract features from the reflection signals of suspected defective areas, and perform in-depth analysis on the extracted feature data. Based on the features after in-depth analysis, the pre-trained convolutional neural network is used to evaluate the degree of prominence of potential defects in the wafer film, and the suspected defective areas are further divided into obvious defect areas and inconspicuous defect areas. For areas with obvious defects, the wafer lamination is directly judged as unqualified. For areas with less obvious defects, the beam coverage is dynamically expanded according to the prediction results of the convolutional neural network to increase the laser scanning density. At this time, the laser scanning range no longer covers the entire wafer, but is concentrated in the area with less obvious defects. Through the adjusted beam coverage, a higher-density laser beam is emitted in the marked suspected defect area for secondary scanning. The high-density laser beam is used to capture more subtle surface information, and the three-dimensional features of the defect are obtained in combination with the three-dimensional morphology data, so as to accurately locate and identify the film defects. Extract features of the reflection signal of the suspected defective area, the extracted features include the asymmetry of the signal scattering after the laser is reflected on the wafer surface and the depth change in the three-dimensional morphology. After performing in-depth analysis on the asymmetry of the signal scattering after the laser is reflected on the wafer surface and the depth change in the three-dimensional morphology, generate a scattering asymmetric reference value and a depth change reference value respectively. The scattering asymmetric reference value is used to quantify the degree of unevenness of the signal scattering after the laser is reflected. The depth change reference value is used to quantify the height difference of the three-dimensional morphology of the wafer surface. The convolutional neural network pre-trained with the scattering asymmetric reference value and the depth change reference value obtained after the in-depth analysis is used to predict and generate a defect noticeability coefficient. The degree of noticeability of potential defects in the wafer film is evaluated by the defect noticeability coefficient. The defect visibility coefficient generated by predicting the potential defects in the suspected defect area through the pre-trained convolutional neural network is compared and analyzed with the pre-set defect visibility coefficient reference threshold, and the suspected defect area is further divided into an obvious defect area and an inconspicuous defect area. The specific division steps are as follows: If the defect obvious coefficient is greater than or equal to the defect obvious coefficient reference threshold, the suspected defect area is divided into an obvious defect area; if the defect obvious coefficient is less than the defect obvious coefficient reference threshold, the suspected defect area is divided into an inconspicuous defect area; For areas where defects are not obvious, the beam coverage is dynamically expanded based on the prediction results of the convolutional neural network to increase the laser scanning density. The specific steps are as follows: The relative measure of the defect obvious coefficient in the defect inconspicuous area relative to the reference threshold of the defect obvious coefficient is calculated. The calculation expression is: ， In the formula, τ 显著 Represents a relative significance measure, used to measure the current defect significance coefficient Defect sign Defect relative to the defect coefficient reference threshold 阈值 proportion; According to the relative significance measure τ 显著 Calculate the coverage adjustment factor to determine the amount of beam coverage that needs to be increased. The calculation expression of the coverage adjustment factor is: Where ΔC is the coverage adjustment factor, which is used to dynamically expand the beam coverage, C p is the preset beam coverage, the coverage during the initial scan, and ω is the adjustment factor, which controls the sensitivity and nonlinearity of the coverage adjustment factor; Apply the calculated coverage adjustment factor ΔC to the preset beam coverage C p , to obtain the new beam coverage, which is used to perform a higher-density secondary scan of the suspected defect area. The calculation expression of the new beam coverage is: In the formula, C new is the coverage of the new beam, which is applied to the secondary high-density scanning.

2. A wafer film defect detection method according to claim 1, characterized in that: The intensity and consistency of the laser reflection signal are analyzed in real time to identify the reflection difference area between the wafer surface and the film. The specific steps are as follows: The laser scanner emits a laser beam according to the preset beam coverage, covers the entire wafer surface for scanning, collects the reflected light signal in real time, and records the reflected light intensity value I(x, y) of each scanning point as a two-dimensional coordinate matrix, where I(x, y) represents the reflected light intensity value at (x, y), where x and y represent the spatial coordinates of the scan, and stores the light intensity information of the entire wafer surface through the two-dimensional coordinate matrix; The local consistency calculation is performed on the light intensity value I(x, y) of each point (x, y), specifically: the light intensity of the point is compared with the light intensity of other points in its neighborhood, and the local consistency deviation D(x, y) is calculated. The calculation expression is: In the formula, (x l ,y l ) is the neighborhood point of point (x, y), l represents the index of the neighborhood point, and N is the number of points in the neighborhood; After calculating the local consistency deviation D(x, y) of each point, D(x, y) is compared with the preset threshold T. If the local consistency deviation D(x, y) of the point is greater than T, the point is marked as a suspected defect point. A clustering algorithm is applied to process the marked suspected defect points, and the defect points are aggregated according to spatial distribution and density to form a continuous defect area. The continuous defect area is the reflection difference area between the wafer surface and the film.

3. The method for detecting wafer film defects according to claim 1, characterized in that: The specific steps for generating a scattering asymmetry reference value after in-depth analysis of the asymmetry of the signal scattering after the laser is reflected on the wafer surface are as follows: In the suspected defect area, the laser reflection signal is collected in multiple directions. The collected signals are expressed as reflection intensities in different directions. The laser reflection signal is expressed as a vector field S (x, y, z), where the signal at each point is decomposed into vector components in different directions. The decomposition expression is: Where S x , S y , Sx represent the components of the laser signal in three-dimensional directions, is the corresponding unit direction vector; The signal scattering inhomogeneity is evaluated by comparing the vector differences of adjacent signal points, and the signal difference between each point and its neighboring points is defined as ΔS ij , then: ΔS ij =||S(x i ,y i , z i )-S(x j ,y j , z j )||, where ΔS ij is the vector difference between the i-th point and the j-th point, ‖·‖ represents the Euclidean distance of the vector; then the signal difference matrix D is constructed, where each element D ij It represents the difference between the i-th point and the j-th point. The construction expression of the signal difference matrix is: D = [D ij ]=[ΔS ij ]; Based on the signal difference matrix, the scattering asymmetric reference value is calculated. The scattering asymmetric reference value is generated by extracting the high-order change information in the signal difference matrix and defining the scattering asymmetric reference value as the harmonic incremental accumulation of the signal difference matrix. The calculation expression of the scattering asymmetric reference value is: ， Among them, γ is the weight parameter that controls spatial attenuation, r ij represents the Euclidean distance between point i and point j, S asym is the reference value of scattering asymmetry.

4. The method for detecting wafer film defects according to claim 1, characterized in that: The specific steps of generating a depth change reference value after depth analysis of the depth change on the three-dimensional topography are as follows: For the suspected defect area, obtain the three-dimensional morphological data of the wafer surface in the suspected defect area, and assume that the dot matrix data of the laser scanning is (x u ,y u , z u ), where (x u ,y u ) is the two-dimensional coordinate of the wafer surface, z u The spatial distribution of the wafer surface height is represented by a three-dimensional height function, where the expression of the three-dimensional height function is: Z(x u ,y u )=f(x u ,y u ), where Z(x u ,y u ) The three-dimensional height data of the wafer surface reflects the surface morphology, f(x u ,y u ) is the height distribution function collected from the two-dimensional coordinates of the wafer surface; The 3D height gradient and 3D height curvature of the collected 3D topography data are calculated to quantify the severity of the surface change in the suspected defect area. The 3D height gradient is defined as the directional change of the 3D surface height, and the 3D height curvature indicates the degree of curvature of the wafer surface. The calculation expression is: ， In the formula, Represents the two-dimensional coordinates (x u ,y u ), Represents the three-dimensional height gradient change of the three-dimensional height function along the x-axis, represents the three-dimensional height gradient change of the three-dimensional height function along the y-axis, κ(x u ,y u ) represents the two-dimensional coordinate (x u ,y u ) at the three-dimensional height curvature; By calculating the two-dimensional coordinates (x u ,y u ) and the three-dimensional height curvature κ(x u ,y u ) Calculate the two-dimensional coordinates (x u ,y u ) is the depth variation S(x u ,y u ), the expression for calculation is: According to the two-dimensional coordinates (x u ,y u ) is the depth variation S(x u ,y u ) calculates the depth variation reference value, which is used to quantify the height difference of the three-dimensional topography of the wafer surface. The calculation expression of the depth variation reference value is: var =∫ A S(x u ,y u )dxdy,Depth var It represents the depth change reference value, which is defined as the integral of the depth change severity in the entire suspected defect area, that is, the comprehensive measure of the surface change of all points in the suspected defect area.

5. A wafer film defect detection system, used to implement the wafer film defect detection method according to any one of claims 1 to 4, characterized in that: It includes preliminary scanning module, signal analysis and marking module, feature extraction and classification module, dynamic expansion scanning module and fine scanning and positioning module; A preliminary scanning module, during the wafer film defect detection process, emits a laser beam according to a preset beam coverage rate through a laser scanner to cover the wafer surface for preliminary scanning; The signal analysis and marking module analyzes the intensity and consistency of the laser reflection signal in real time during the laser beam scanning process to identify the reflection difference area between the wafer surface and the film, and marks the reflection difference area as a suspected defect area; The feature extraction and classification module extracts features from the reflection signals of suspected defective areas and performs in-depth analysis on the extracted feature data. Based on the features after in-depth analysis, the module uses a pre-trained convolutional neural network to evaluate the degree of prominence of potential defects in the wafer film and further divides the suspected defective areas into areas with obvious defects and areas with inconspicuous defects. Dynamically expand the scanning module. For areas with obvious defects, the wafer lamination is directly judged as unqualified. For areas with less obvious defects, the beam coverage is dynamically expanded according to the prediction results of the convolutional neural network to increase the laser scanning density. At this time, the laser scanning range no longer covers the entire wafer, but is concentrated in the area with less obvious defects. The fine scanning and positioning module uses the adjusted beam coverage to emit a higher-density laser beam in the marked suspected defect area for a second scan. It uses a high-density laser beam to capture more subtle surface information and combines it with three-dimensional morphology data to obtain the three-dimensional features of the defect, accurately locate and identify film defects.

Images