Wafer pre-alignment method for transmission process

Abstract

The invention relates to the technical field of wafer pre-alignment, and discloses a wafer pre-alignment method for a transmission process, and the method comprises the steps: scanning a wafer edge on a rotating platform through a control line laser sensor, obtaining a continuous data set of two-dimensional coordinate points of the wafer edge, carrying out the filtering and abnormal point elimination of the continuous data set, and generating a discrete data set. Performing circle fitting on the discrete data set based on a least square method, and accurately calculating the circle center coordinate of the wafer; meanwhile, Fourier transform is utilized to extract trimming features, and the trimming angle is determined through inverse Fourier transform. In this way, the pre-alignment speed and accuracy are remarkably improved. The method is particularly suitable for a high-precision requirement scene in semiconductor manufacturing, and a reliable positioning basis is provided for subsequent processing steps.

Description

Technical Field

[0001] The present application relates to the technical field of wafer pre-alignment, and more specifically, to a wafer pre-alignment method for a transmission process. Background Art

[0002] During the wafer transfer process, the wafer often needs to be pre-aligned to provide a higher position for subsequent processing. Pre-alignment is the core issue that needs to be solved during the wafer transfer process.

[0003] The pre-alignment process involves the problem of finding the center of the circle and the cutting angle. To solve these problems, CCD, point laser or line laser sensors are generally used to detect the edge of the wafer, and then the center of the circle and the cutting angle are obtained through algorithms. Disadvantages of existing technologies: 1. Point laser measurement requires multiple tests, which takes a long time and has been gradually eliminated. 2. CCD and line laser sensors are the industry's first choice due to their high precision. However, even if the selected hardware system is the same, its accuracy and efficiency may not be the same. The software algorithm determines the accuracy and speed of pre-alignment. At present, many machines on the market either need to rotate multiple times for pre-alignment, which takes too long; or the accuracy is not enough.

[0004] Based on this, the present application provides a wafer pre-alignment method for a transmission process. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a wafer pre-alignment method for a transmission process, which significantly improves the speed and accuracy of pre-alignment. The method is particularly suitable for high-precision requirements in semiconductor manufacturing and provides a reliable positioning basis for subsequent processing steps.

[0006] According to one aspect of the present application, a wafer pre-alignment method for a transmission process is provided, comprising: controlling a line laser sensor to scan a wafer edge of a wafer on a rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge; preprocessing the continuous data set of two-dimensional coordinate points of the wafer edge to obtain a discrete data set of two-dimensional coordinate points of the wafer edge; performing circle fitting on the discrete data set of two-dimensional coordinate points of the wafer edge based on the least squares method to obtain the coordinates of the center of the wafer; and processing the discrete data set of two-dimensional coordinate points of the wafer edge based on Fourier transform and inverse Fourier transform to obtain a cutting angle.

[0007] In the above-mentioned wafer pre-alignment method for the transmission process, a line laser sensor is controlled to scan the wafer edge of the wafer on a rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge, including: placing the wafer on the rotating platform; driving the rotating platform to drive the wafer, and in the process of rotating the wafer, the line laser sensor collects a continuous data set of two-dimensional coordinate points of the wafer edge.

[0008] In the above-mentioned wafer pre-alignment method for the transmission process, the continuous data set of the two-dimensional coordinate points of the wafer edge is preprocessed to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge, including: data filtering the continuous data set of the two-dimensional coordinate points of the wafer edge to obtain a filtered data set of the two-dimensional coordinate points of the wafer edge; and abnormal points are eliminated from the filtered data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge.

[0009] In the wafer pre-alignment method for the transmission process, the continuous data set of the two-dimensional coordinate points of the wafer edge is filtered to obtain a filtered data set of the two-dimensional coordinate points of the wafer edge, including: determining a neighborhood window of each data point in the continuous data set of the two-dimensional coordinate points of the wafer edge; based on the sample distribution of all data points in the neighborhood window of each data point, updating the two-dimensional coordinates of the wafer edge of each data point, expressed as:

[0010]

[0011]

[0012] in, is the radius of the neighborhood window, is the two-dimensional coordinate point of the original wafer edge of the neighborhood window, is an index variable used to traverse all data points in the neighborhood window. is the index of the current data point, is the updated two-dimensional coordinate point of the wafer edge.

[0013] In the above-mentioned wafer pre-alignment method for the transmission process, it also includes: when updating the two-dimensional coordinate points of the wafer edge of each data point based on the sample distribution of all data points in the neighborhood window of each data point, a neighborhood-based circle tangent fitting mechanism is introduced to optimize the updated two-dimensional coordinate points of the wafer edge.

[0014] In the wafer pre-alignment method for the transmission process, a neighborhood-based circle tangent fitting mechanism is introduced to optimize the updated wafer edge two-dimensional coordinate points, including:

[0015] For each pair of coordinates weighted by the neighborhood window ( )and( ), first let:

[0016]

[0017] To obtain the relationship between each coordinate ( ) The corresponding tangent circle fitting parameter pair ( );

[0018] Based on the tangent circle fitting parameter pair ( ), through the data distribution in the neighborhood window to ( ) for optimization:

[0019]

[0020]

[0021] in, and is the scaling factor used to optimize fine tuning, is the optimized two-dimensional coordinate point of the wafer edge.

[0022] In the above-mentioned wafer pre-alignment method for the transmission process, abnormal points are eliminated from the filtered data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge, including: determining a preliminary fitting center; calculating the radial distance between each data point in the filtered data set of the two-dimensional coordinate points of the wafer edge and the preliminary fitting center to obtain a set of radial distances; calculating the mean and standard deviation of the set of radial distances; based on the mean and standard deviation, calculating the standard score of each radial distance in the set of radial distances; based on the comparison between the standard score and a preset condition, determining whether to eliminate the two-dimensional coordinate point of the wafer edge corresponding to the radial distance.

[0023] In the wafer pre-alignment method for the transfer process, based on the mean and the standard deviation, calculating the standard score of each radial distance in the set of radial distances includes: calculating the standard score of each radial distance using the following formula, expressed as:

[0024]

[0025] in, is the radial distance, is the mean, is the standard deviation, Represents the standard fraction of radial distance.

[0026] In the above-mentioned wafer pre-alignment method for the transfer process, the preset condition is that the absolute value of the standard score is greater than 3.

[0027] In the above-mentioned wafer pre-alignment method for the transmission process, the discrete data set of the two-dimensional coordinate points of the wafer edge is processed based on Fourier transform and inverse Fourier transform to obtain the trimming angle, including: arranging the discrete data set of the two-dimensional coordinate points of the wafer edge in the order of the wafer edge to obtain a wafer edge contour signal; performing Fourier transform on the wafer edge contour signal to obtain a complex spectrum; extracting trimming features from the complex spectrum; mapping the slicing features back to the spatial domain through inverse Fourier transform to obtain the coordinates of the boundary points of the trimming; and determining the trimming angle based on the coordinates of the boundary points of the trimming.

[0028] Compared with the prior art, the wafer pre-alignment method for the transmission process provided by the present application controls the line laser sensor to scan the edge of the wafer on the rotating platform, obtains a continuous two-dimensional coordinate point data set, and filters and removes abnormal points to generate a discrete data set. The discrete data set is circle-fitted based on the least squares method to accurately calculate the coordinates of the center of the wafer; at the same time, the trimming features are extracted using Fourier transform, and the trimming angle is determined by inverse Fourier transform. In this way, the speed and accuracy of pre-alignment are significantly improved. This method is particularly suitable for high-precision scenarios in semiconductor manufacturing, and provides a reliable positioning basis for subsequent processing steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0030] Figure 1 It is a schematic flow chart of a wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0031] Figure 2 4 is a schematic flowchart of S1 in a wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0032] Figure 3 It is a schematic diagram of the structural part according to an embodiment of the present application.

[0033] Figure 4 4 is a schematic flowchart of S2 in the wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0034] Figure 5 4 is a schematic flowchart of S21 in the wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0035] Figure 6 4 is a schematic flowchart of S22 in the wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0036] Figure 7 It is a graphical diagram of data obtained through preprocessing according to an embodiment of the present application.

[0037] Figure 8 Schematic diagram of least squares circle fitting according to an embodiment of the present application.

[0038] Fig. 9 4 is a schematic flowchart of S4 in the wafer pre-alignment method for a transfer process according to an embodiment of the present application.

[0039] Fig.10 This is a schematic flow chart of a specific embodiment of the present application. DETAILED DESCRIPTION

[0040] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0041] Based on this, the present application provides a wafer pre-alignment method for a transmission process. Figure 1 FIG. 1 is a schematic flow chart of a wafer pre-alignment method for a transmission process according to an embodiment of the present application. Figure 1 As shown, the wafer pre-alignment method for the transmission process includes: S1, controlling the line laser sensor to scan the wafer edge of the wafer on the rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge; S2, preprocessing the continuous data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge; S3, performing circle fitting on the discrete data set of the two-dimensional coordinate points of the wafer edge based on the least squares method to obtain the coordinates of the center of the wafer; S4, processing the discrete data set of the two-dimensional coordinate points of the wafer edge based on Fourier transform and inverse Fourier transform to obtain the cutting angle.

[0042] In one embodiment, Figure 2 As shown, in step S1, the line laser sensor is controlled to scan the wafer edge of the wafer on the rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge, including: S11, placing the wafer on the rotating platform; S12, driving the rotating platform to drive the wafer, and in the process of rotating the wafer, the line laser sensor collects a continuous data set of two-dimensional coordinate points of the wafer edge. Here, it can be seen that the structural part of the wafer pre-alignment method for the transmission process of the present application adopts a line laser plus a rotating platform, such as Figure 3shown.

[0043] In step S11, first, the wafer is correctly placed on the rotating platform. In this application, a robotic arm or manual operation is used to carefully place the wafer at the specified position, and ensure that the contact surface between the wafer and the rotating platform is flat and has no deviation. This is because any deviation in the initial position will directly affect the subsequent data acquisition accuracy.

[0044] In step S12, once the wafer is correctly placed, the next step is to start the rotating platform. At this time, the rotation speed and stability of the platform are crucial. Ideally, the rotation speed should be kept constant to ensure that the line laser sensor can collect data points at the edge of the wafer at uniform time intervals. This stability and consistency are the basis for ensuring data quality. The line laser sensor is one of the core components of the entire system. Its function is to capture the information of the edge of the wafer in real time and convert it into two-dimensional coordinate points. When the rotating platform starts to rotate, the line laser sensor will emit a narrow light along the radial direction of the wafer, which will intersect with the edge of the wafer to form a series of reflected light spots. The position information of these reflected light spots is the two-dimensional coordinate point of the edge of the wafer. It is worth noting that the resolution and response time of the line laser sensor directly affect the accuracy and efficiency of data acquisition. Therefore, when selecting a sensor, a sensor that meets the requirements of high precision and high speed can be selected according to the actual production situation. As the rotating platform continues to rotate, the line laser sensor continuously collects data points at the edge of the wafer, forming a continuous data stream, that is, a continuous data set of the two-dimensional coordinate points of the edge of the wafer.

[0045] In one embodiment, Figure 4 As shown, in step S2, the continuous data set of the two-dimensional coordinate points of the wafer edge is preprocessed to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge, including: S21, data filtering the continuous data set of the two-dimensional coordinate points of the wafer edge to obtain a filtered data set of the two-dimensional coordinate points of the wafer edge; S22, abnormal points are eliminated from the filtered data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge.

[0046] In step S21, after obtaining a continuous data set of two-dimensional coordinate points of the wafer edge, the first thing to do is data filtering. Since the data collected by the sensor will inevitably be affected by environmental noise, the raw data must be filtered to reduce noise interference and improve data quality. Specifically, a low-pass filter or other types of filtering algorithms, such as Kalman filtering or median filtering, can be used. These filtering algorithms can effectively smooth the data curve, remove high-frequency noise components, and make the data more stable and reliable.

[0047] In a specific embodiment, Figure 5As shown, S21, data filtering is performed on the continuous data set of the wafer edge two-dimensional coordinate points to obtain a filtered data set of the wafer edge two-dimensional coordinate points, including: S211, determining the neighborhood window of each data point in the continuous data set of the wafer edge two-dimensional coordinate points; S212, based on the sample distribution of all data points in the neighborhood window of each data point, the wafer edge two-dimensional coordinates of each data point are updated, which is expressed as:

[0048]

[0049]

[0050] in, is the radius of the neighborhood window, is the two-dimensional coordinate point of the original wafer edge of the neighborhood window, is an index variable used to traverse all data points in the neighborhood window. is the index of the current data point, is the updated two-dimensional coordinate point of the wafer edge. Here, considering that the original collected data often contains random noise caused by factors such as sensor accuracy limitation and environmental interference, these noises will directly affect the subsequent data analysis and fitting results if they are not processed. By averaging each data point with its neighboring data points, the randomly distributed noise components can be effectively "offset", thereby obtaining a more stable and reliable data set. Although this method is simple, it can significantly reduce local fluctuations without changing the overall trend of the data, laying a good foundation for the next processing steps. Furthermore, such smoothing not only helps to reduce noise, but also improves the consistency and continuity of the data. In actual operation, due to measurement errors or other unforeseen factors, some data points may deviate from the overall trend. By integrating the information of neighboring data points, these "abrupt" data points can be effectively corrected to make them more in line with the overall trend.

[0051] In another specific embodiment, data filtering is performed on the continuous data set of the two-dimensional coordinate points of the wafer edge to obtain a filtered data set of the two-dimensional coordinate points of the wafer edge, and also includes: when the two-dimensional coordinate points of the wafer edge of each data point are updated based on the sample distribution of all data points within a neighborhood window of each data point, a neighborhood-based circle tangent fitting mechanism is introduced to optimize the updated two-dimensional coordinate points of the wafer edge.

[0052] Here, when outliers are removed from the filtered data set of the two-dimensional coordinate points of the wafer edge by using the standard score based on the radial distance, considering that when the two-dimensional coordinates of the wafer edge of each data point are updated based on the sample distribution of all data points in the neighborhood window of each data point, the neighborhood window of the data point substantially includes both radial and tangential neighborhood directions. Therefore, in order to further improve the accuracy of outlier removal, a neighborhood-based circular tangent fitting mechanism can be introduced, that is, a neighborhood-based circular tangent fitting mechanism is introduced to optimize the updated two-dimensional coordinate points of the wafer edge, including: for each pair of coordinates ( )and( ), first let:

[0053]

[0054] To obtain the relationship between each coordinate ( ) The corresponding tangent circle fitting parameter pair ( ).

[0055] In this way, the tangent circle fitting parameter pair ( ) is based on the minimum enclosing circle and the data distribution in the neighborhood window is used to ( ) for optimization:

[0056]

[0057]

[0058] and is the scaling factor used to optimize fine tuning, is the optimized two-dimensional coordinate point of the wafer edge.

[0059] That is, by making the geometric properties of the fitting parameters of the circle's tangent direction consistent with the data distribution trend in the local neighborhood, when the filtered data set of the two-dimensional coordinate points of the wafer edge obtained based on the neighborhood calculation is subjected to outlier elimination based on the radial distance of the fitting circle, the natural connection between adjacent neighborhoods of the outlier points can be achieved by smoothing based on the radial fitting accuracy of the circle fitting, so as to avoid overfitting of the outlier point elimination affecting the accuracy of the cutting angle calculation.

[0060] In step S22, although the filtered data set is relatively clean, it may still have some abnormal points. These abnormal points may be caused by sensor failure, measurement error or other unforeseen factors. If not processed, these abnormal points will seriously affect the subsequent fitting and calculation results. Therefore, the present application then removes abnormal points from the filtered data set.

[0061] In a specific embodiment, Figure 6 As shown, S22, removing abnormal points from the filtered data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge, including: S221, determining a preliminary fitting center; S222, calculating the radial distance between each data point in the filtered data set of the two-dimensional coordinate points of the wafer edge and the preliminary fitting center to obtain a set of radial distances; S223, calculating the mean and standard deviation of the set of radial distances; S224, calculating the standard score of each radial distance in the set of radial distances based on the mean and standard deviation; S225, determining whether to remove the two-dimensional coordinate points of the wafer edge corresponding to the radial distance based on a comparison between the standard score and a preset condition.

[0062] Specifically, in the process of outlier removal, a preliminary fitting center is first determined. This center can be estimated by simple geometric methods or the least square method based on existing data points. Then, the radial distance from each data point to the preliminary fitting center is calculated to form a set of radial distances. Next, the mean and standard deviation of the set are calculated, and these statistics are used to evaluate the rationality of each data point.

[0063] In a specific embodiment, a set of filtered wafer edge data points have been obtained, and a preliminary fitting center is determined. Next, the radial distance from each data point to the center is calculated, and the mean and standard deviation of these distances are obtained. Then, according to the standard score formula (i.e., Z-score), the standard score of each radial distance is calculated. In the embodiment of the present application, the preset condition is that the absolute value of the standard score is greater than 3. If the absolute value of the standard score of a data point is greater than 3, the data point is considered to be an outlier and should be removed. This method of outlier removal is based on statistical principles and can effectively identify and remove data points that deviate from the normal range. Doing so can not only improve the overall consistency of the data, but also avoid fitting errors caused by individual outliers. In addition, setting the standard score threshold to 3 is also an empirical choice, because under the normal distribution, data points exceeding 3 standard deviations are considered to be extremely low probability events, usually outliers.

[0064] In a specific embodiment, calculating the standard score of each radial distance in the set of radial distances based on the mean and the standard deviation includes: calculating the standard score of each radial distance using the following formula, expressed as:

[0065]

[0066] in, is the radial distance, is the mean, is the standard deviation, Represents the standard fraction of radial distance.

[0067] After completing data filtering and outlier removal, the present application obtains a relatively clean discrete data set of two-dimensional coordinate points of the wafer edge. However, this does not mean that the work of this application has ended. In order to further improve the fitting accuracy, the preliminary fitting results can also be optimized. Specifically, an iterative method can be used to continuously adjust the fitting parameters until the best fitting effect is achieved. The advantage of this method is that it not only takes into account the results of the initial fitting, but also further improves the fitting accuracy through iterative optimization. Especially when dealing with complex wafer edge data, this method can better deal with various possible sources of error and ensure the reliability of the final result. In a specific embodiment, the following is obtained through preprocessing: Figure 7 Data graphic shown.

[0068] Specifically, in step S3, a circle is fitted to the discrete data set of the two-dimensional coordinate points of the wafer edge based on the least squares method to obtain the coordinates of the center of the wafer. The least squares method is a mathematical method widely used in curve fitting. Its core idea is to find the best fitting parameters by minimizing the sum of squared errors. In the wafer pre-alignment process, the least squares method is used to fit the data points of the wafer edge to determine the coordinates and radius of the center of the wafer. The least squares method is used to fit the circle. Figure 8 As shown. The reason for using the least squares method for circle fitting is that it has good mathematical properties and wide applicability. The least squares method finds the best fitting parameters by minimizing the sum of squared errors. This method is not only simple and easy, but also performs well when processing noisy data. Since wafer edge data usually contains certain noise and measurement errors, the least squares method can effectively smooth out these noises and provide more accurate fitting results.

[0069] Specifically, there is a set of 2D coordinate points at the edge of the wafer ,in The goal is to find the equation of a circle , so that the circle can best fit these data points. Here, and represent the horizontal and vertical coordinates of the center of the circle, respectively, and Indicates the radius of the circle.

[0070] The core of the least squares method is to minimize the following objective function:

[0071]

[0072] In order to solve this optimization problem, we can , and Find the partial derivatives and set them equal to zero, thus obtaining a set of nonlinear equations. However, in practical applications, it may be complicated to directly solve this set of nonlinear equations. Therefore, iterative optimization algorithms, such as gradient descent or Levenberg-Marquardt algorithm, are usually used to gradually approach the optimal solution.

[0073] Specifically, first, choose the initial estimate , , These initial estimates can be obtained by simple geometric methods, such as calculating the average coordinates of all data points as the initial estimate of the center of the circle, and then estimating the initial radius based on the distance from these points to the center of the circle. Next, the Levenberg-Marquardt algorithm is applied for iterative optimization. This algorithm gradually adjusts the fitting parameters , and , so that the residual sum of squares is minimized. Specifically, each iteration calculates the residual vector under the current parameters and updates the fitting parameters based on the residual vector. After multiple iterations, it finally converges to the optimal solution.

[0074] In one embodiment, in step S4, it can be seen from the collected data that the cut edge is more prominent than other points, but it may be at any position of the wafer and there are some interference signals around it, so it is difficult to process in the time and space domain. Through Fourier transform, it is converted into frequency domain for processing, and then the cut edge is extracted by screening, and then the spatial coordinates of the cut edge are obtained by inverse Fourier transform.

[0075] In a specific embodiment, Fig. 9 As shown, S4, based on Fourier transform and inverse Fourier transform, the discrete data set of the two-dimensional coordinate points of the wafer edge is processed to obtain the trimming angle, including: S41, arranging the discrete data set of the two-dimensional coordinate points of the wafer edge in the order of the wafer edge to obtain a wafer edge contour signal; S42, performing Fourier transform on the wafer edge contour signal to obtain a complex spectrum; S43, extracting trimming features from the complex spectrum; S44, mapping the slicing features back to the spatial domain through inverse Fourier transform to obtain the coordinates of the boundary points of the trimming; S45, determining the trimming angle based on the coordinates of the boundary points of the trimming.

[0076] Specifically, first, the discrete data set of the two-dimensional coordinate points of the wafer edge is arranged in the order of the wafer edge to form a continuous wafer edge profile signal. This wafer edge profile signal is actually a one-dimensional time series data, in which each data point represents the coordinate value of a certain position on the edge of the wafer. The angle information of these points (for example, the angle relative to the center of the wafer) can be used as a time domain signal, or the index order of the points can be directly used as the "time" axis of the time domain. The key is to form a one-dimensional signal that represents the profile changes along the edge of the wafer.

[0077] Then, the wafer edge profile signal is Fourier transformed to obtain a complex spectrum, and the fast Fourier transform (FFT) algorithm can be used to efficiently calculate the discrete Fourier transform. Here, the Fourier transform is a powerful mathematical tool that can convert time domain signals into frequency domain signals. During the wafer pre-alignment process, the Fourier transform is used to analyze the frequency components in the wafer edge profile signal to extract the cutting edge features. The Fourier transform decomposes the time domain signal into a superposition of sine waves and cosine waves of different frequencies. The result is a complex spectrum that contains an amplitude spectrum and a phase spectrum, which describes the intensity and phase information of the signal at different frequencies. Specifically, the profile signal formed by arranging the two-dimensional coordinate points of the wafer edge in sequence is converted into a complex spectrum in the frequency domain by Fourier transform. Here, the Fourier transform formula is:

[0078]

[0079] in, Represents the result of Fourier transform, representing the function Representation in the frequency domain. It describes the distribution of the signal in different frequency components. Represents a raw signal or function, typically in the space or time domain. Represents a frequency variable, which is used to describe the frequency component of a signal. represents a complex exponential function, which is used to convert signals from the space or time domain to the frequency domain. Is an imaginary unit.

[0080] Next, the edge cutting features are extracted from the complex spectrum. Since the edge cutting features are usually manifested as mutations or discontinuities in the contour signal, they are often manifested as some significant frequency components in the frequency domain. By analyzing these frequency components, those parts corresponding to the edge cutting features can be identified. Specifically, those frequency components with larger amplitudes can be selected from the complex spectrum as potential edge cutting features. The phase information corresponding to these frequency components can help us determine the specific location of the edge cutting. In order to further improve the accuracy, some filtering techniques can also be applied to remove noise and other irrelevant frequency components. For example, a threshold can be set to retain only those frequency components whose amplitude exceeds the threshold. After the edge cutting features are extracted, the slicing features are mapped back to the spatial domain through the inverse Fourier transform to obtain the coordinates of the boundary points of the edge cutting. The inverse Fourier transform formula is:

[0081]

[0082] in, Represents the original signal or function, which is restored from the frequency domain to the space or time domain through the inverse Fourier transform. Represents the result of Fourier transform, which represents the representation of the signal in the frequency domain. represents the frequency variable, which is the same as the frequency variable in the Fourier transform. Represents the complex exponential function, which is used to convert a signal from the frequency domain back to the space or time domain. represents the normalization factor that ensures energy conservation between the Fourier transform and the inverse transform.

[0083] Finally, the trimming angle is determined based on the coordinates of the boundary points of the trimming. In the time domain signal obtained by the inverse transformation, the trimming position will appear as a significant change in the signal (for example, a peak or valley), and the position of this significant change in the time domain signal is found, which corresponds to the trimming position on the wafer edge contour. According to the index of the trimming in the data point sequence, the original wafer edge coordinate point can be inferred, and the center coordinates of the trimming or the boundary point coordinates of the trimming can be calculated, and finally the angle of the trimming relative to the wafer reference direction (for example, the horizontal axis or the vertical axis) can be determined. This can be done by calculating the angle between the center point of the trimming and the center of the wafer, or by analyzing the coordinates of the trimming boundary points to determine the direction of the trimming.

[0084] In a specific example, an array of length 32 is used to simulate the wafer edge profile signal. The array index represents the angle position (0 to 31), and the value represents a certain characteristic value of the edge. Assume that the cut edge is near index 8. The following is a signal example, which is only used for illustration: signal = [1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], index 0-7 is smoothed, the value at index 8-9 decreases to simulate the cut edge, and index 10-31 is smoothed again. First, Fourier transform is performed and FFT is performed on the signal. The frequency domain signal (complex array) is obtained. Then, the amplitude spectrum of the frequency domain signal is analyzed. It may be found that in addition to the low-frequency components, there are also some obvious amplitudes in the mid- and high-frequency regions. Reporter, frequency domain filtering is performed. In order to simplify the example, it is assumed that the frequency components with the largest amplitude (except the DC component) are directly found. Then, perform an inverse Fourier transform to retain only these main frequency components, and then perform an inverse FFT. Next, perform edge location. The inverse transformed signal may show a more prominent drop in value near indexes 8 and 9. By finding the minimum value of the signal or the position where the gradient changes the most, the approximate position of the edge can be located (index 8-9). Finally, perform an angle calculation. According to the position of indexes 8 and 9 in the entire data sequence, the angle range corresponding to the edge can be calculated. For example, if 32 indexes correspond to 360 degrees, then index 8 corresponds to (8 / 32)*360=90 degrees, and index 9 corresponds to (9 / 32)*360 =101.25 degrees. The angle range of the edge is approximately 90-101.25 degrees.

[0085] like Fig.10 As shown, in a specific embodiment, first, start, which is the starting point of the process. Then, the robot takes the wafer: the robot is used to take the wafer out of the storage position and place it on the pre-alignment platform. Then, the pre-alignment platform rotates 360 degrees and collects data: the pre-alignment platform rotates 360 degrees, and a line laser sensor or other detection equipment is used to scan the edge of the wafer to collect a continuous two-dimensional coordinate point data set. This step ensures the comprehensiveness and accuracy of the data. Then, calculate the center and angle: based on the collected data, the center coordinates of the wafer are calculated by mathematical algorithms (such as the least squares method), and the trimming features are extracted using Fourier transform to determine the trimming angle. This step is the core of the entire pre-alignment process and ensures the accuracy of wafer positioning. Then, the center of the circle is aligned: according to the calculated center coordinates, the position of the wafer is adjusted to align it with the preset processing position. Angle rotation: according to the calculated trimming angle, the rotation angle of the wafer is adjusted to ensure that the wafer is in the correct orientation during the subsequent processing. Finally, end: after all adjustments are completed, the process ends and the wafer is ready for the next step of processing or treatment.

[0086] In summary, the present application provides a wafer pre-alignment method for the transmission process, which controls the line laser sensor to scan the edge of the wafer on the rotating platform, obtains a continuous two-dimensional coordinate point data set, and filters and removes abnormal points to generate a discrete data set. Based on the least squares method, the discrete data set is circle-fitted to accurately calculate the coordinates of the center of the wafer; at the same time, the trimming features are extracted by Fourier transform, and the trimming angle is determined by inverse Fourier transform. In this way, the speed and accuracy of pre-alignment are significantly improved. This method is particularly suitable for high-precision scenarios in semiconductor manufacturing, and provides a reliable positioning basis for subsequent processing steps.

[0087] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0088] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0089] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0090] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0091] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A wafer pre-alignment method for a transfer process, characterized in that: include: Controlling a line laser sensor to scan a wafer edge of a wafer on a rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge; Preprocessing the continuous data set of the wafer edge two-dimensional coordinate points to obtain a discrete data set of the wafer edge two-dimensional coordinate points; Performing circle fitting on the discrete data set of the two-dimensional coordinate points of the wafer edge based on the least squares method to obtain the coordinates of the center of the wafer; The discrete data set of the two-dimensional coordinate points of the wafer edge is processed based on Fourier transform and inverse Fourier transform to obtain the trimming angle.

2. The wafer pre-alignment method for a transfer process according to claim 1, characterized in that: The line laser sensor is controlled to scan the wafer edge of the wafer on the rotating platform to obtain a continuous data set of two-dimensional coordinate points of the wafer edge, including: placing the wafer on the rotating platform; The rotating platform is driven to move the wafer. During the process of rotating the wafer, the line laser sensor collects a continuous data set of two-dimensional coordinate points of the edge of the wafer.

3. The wafer pre-alignment method for a transfer process according to claim 1, characterized in that: Preprocessing the continuous data set of the wafer edge two-dimensional coordinate points to obtain a discrete data set of the wafer edge two-dimensional coordinate points includes: Performing data filtering on the continuous data set of the two-dimensional coordinate points of the wafer edge to obtain a filtered data set of the two-dimensional coordinate points of the wafer edge; Outliers are eliminated from the filtered data set of the two-dimensional coordinate points of the wafer edge to obtain a discrete data set of the two-dimensional coordinate points of the wafer edge.

4. The wafer pre-alignment method for a transfer process according to claim 3, characterized in that: Performing data filtering on the continuous data set of the wafer edge two-dimensional coordinate points to obtain a filtered data set of the wafer edge two-dimensional coordinate points, including: Determine a neighborhood window of each data point in the continuous data set of the two-dimensional coordinate points of the wafer edge; Based on the sample distribution of all data points within the neighborhood window of each data point, the two-dimensional coordinates of the wafer edge of each data point are updated, which is expressed as: in, is the radius of the neighborhood window, is the two-dimensional coordinate point of the original wafer edge of the neighborhood window, is an index variable used to traverse all data points in the neighborhood window. is the index of the current data point, is the updated two-dimensional coordinate point of the wafer edge.

5. The wafer pre-alignment method for a transfer process according to claim 4, characterized in that: Also includes: When updating the wafer edge two-dimensional coordinate points of each data point based on the sample distribution of all data points in the neighborhood window of each data point, a neighborhood-based circle tangent fitting mechanism is introduced to optimize the updated wafer edge two-dimensional coordinate points.

6. The wafer pre-alignment method for a transfer process according to claim 5, characterized in that: A neighborhood-based circle tangent fitting mechanism is introduced to optimize the updated wafer edge two-dimensional coordinate points, including: For each pair of coordinates weighted by the neighborhood window ( )and( ), first let: To obtain the relationship between each coordinate ( ) The corresponding tangent circle fitting parameters are ( ); Based on the tangent circle fitting parameter pair ( ), through the data distribution in the neighborhood window to ( ) for optimization: in, and is the scaling factor used to optimize fine tuning, is the optimized two-dimensional coordinate point of the wafer edge.

7. The wafer pre-alignment method for a transfer process according to claim 3, characterized in that: The method of removing abnormal points from the filtered data set of the wafer edge two-dimensional coordinate points to obtain a discrete data set of the wafer edge two-dimensional coordinate points includes: Determine the initial fitting center; Calculating the radial distance between each data point in the filtered data set of the wafer edge two-dimensional coordinate point and the center of the preliminary fitting circle to obtain a set of radial distances; Calculating the mean and standard deviation of the set of radial distances; Calculating a standard score for each radial distance in the set of radial distances based on the mean and the standard deviation; Based on the comparison between the standard score and the preset condition, it is determined whether to eliminate the two-dimensional coordinate point of the wafer edge corresponding to the radial distance.

8. The wafer pre-alignment method for a transfer process according to claim 7, characterized in that: Calculating the standard score of each radial distance in the set of radial distances based on the mean and the standard deviation includes: calculating the standard score of each radial distance using the following formula, expressed as: in, is the radial distance, is the mean, is the standard deviation, Represents the standard fraction of radial distance.

9. The wafer pre-alignment method for a transfer process according to claim 7, characterized in that: The preset condition is that the absolute value of the standard score is greater than 3.

10. The wafer pre-alignment method for a transfer process according to claim 1, characterized in that: The discrete data set of the two-dimensional coordinate points of the wafer edge is processed based on Fourier transform and inverse Fourier transform to obtain the trimming angle, including: Arranging the discrete data sets of the wafer edge two-dimensional coordinate points in the order of the wafer edge to obtain a wafer edge contour signal; Performing Fourier transform on the wafer edge profile signal to obtain a complex frequency spectrum; extracting a cut edge feature from the complex spectrum; Mapping the slice features back to the spatial domain through inverse Fourier transform to obtain the boundary point coordinates of the cut edge; The cutting angle is determined based on the coordinates of the boundary points of the cutting edge.

Images