Dark field scattering based wafer defect detection method and system

Abstract

The application discloses a wafer defect detection method and system based on dark field scattering, and relates to the field of semiconductor wafer defect detection.The method comprises the following steps: respectively establishing simulation models of particle type and pit type defects; performing scattering field simulation calculation on the simulation models under different illumination parameters by using the finite difference time domain method to obtain simulated dark field scattering images; determining optimal illumination parameters suitable for both types of defects according to the principle of minimum spatial entropy and maximum root mean square contrast; generating polarized illumination light according to the optimal illumination parameters, and sequentially irradiating and synchronously collecting dark field scattering images of each point on the surface of a wafer to be detected; determining that there is no defect if no scattering light spot is identified, and determining that there is a defect if a scattering light spot is identified.The application realizes automatic determination of optimal illumination parameters, can realize high-sensitivity detection under optimal conditions, and improves detection accuracy and reliability.

Description

Technical Field

[0001] This application relates to the field of semiconductor wafer defect detection technology, and in particular to a wafer defect detection method and system based on dark field scattering. Background Technology

[0002] Currently, in optical inspection methods, wafer defect detection is mainly divided into two types: bright-field inspection and dark-field inspection. Bright-field inspection uses a vertical or near-vertical incident light source and relies on reflected light imaging. Although it has high resolution, it has low sensitivity for wafers without patterns, and the strong background light can easily mask the scattering signals of tiny defects. At the same time, the inspection speed is slow and the cost is high, making it difficult to meet the needs of rapid online screening on production lines.

[0003] Dark-field scattering (DFS) technology, due to its unique detection principle and technological advantages, achieves effective separation of defect scattering signals from background signals through cleverly designed illumination and imaging optical paths, making it an important technological direction for the detection of micro-defects in wafers. However, existing DFS detection methods still have significant shortcomings: First, due to the complexity of the interaction between surface defects and light, the scattering characteristics of defects are affected by various physical conditions such as light source wavelength, incident angle, defect size, shape, and material; second, the analysis of defect scattering fields largely relies on qualitative observation and analysis, distinguishing defects solely by visually judging the brightness and distribution of the scattering field.

[0004] Therefore, there is an urgent need for a wafer defect detection method based on dark field scattering to solve the technical problems of traditional dark field scattering detection methods, such as relying on qualitative observation and analysis for the analysis of defect scattering fields and low sensitivity, so as to achieve accurate detection of tiny defects. Summary of the Invention

[0005] The purpose of this application is to provide a wafer defect detection method and system based on dark field scattering, which can achieve accurate detection of tiny defects.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a wafer defect detection method based on dark field scattering, comprising: Simulation models of wafer surface defects of different defect types were established; the defect types include particle defects and pit defects. The scattering field of wafer surface defects of various types under different illumination parameters was simulated and calculated by the finite-difference time-domain method, and the simulated dark field scattering images of each defect type under different illumination parameters were obtained. Calculate the spatial entropy and root mean square contrast of the simulated dark field scattering images corresponding to each defect type under different lighting parameters; Based on the principle of minimizing spatial entropy and maximizing root-mean-square contrast, the optimal lighting parameters are determined to be applicable to all defect types. The optimal lighting parameters include the optimal light source wavelength, the optimal polarization state, and the optimal incident angle. Polarized illumination light is generated based on the optimal illumination parameters; Each point on the surface of the wafer to be inspected is sequentially illuminated by polarized light, while a dark-field scattering image of the wafer to be inspected is acquired simultaneously. If no scattered light spot is detected in the dark field scattering image of the wafer to be inspected, the defect detection result of the wafer to be inspected is determined to be defect-free; If a scattered light spot is detected in the dark field scattering image of the wafer to be inspected, the defect detection result of the wafer to be inspected is determined to be that a defect exists.

[0007] Secondly, this application provides a wafer defect detection system based on dark field scattering, comprising: Lighting module, motion module, imaging module, and defect detection module; The illumination module and the imaging module are respectively disposed above the motion module, and the output optical axis of the illumination module and the collection optical axis of the imaging module intersect at the bearing surface of the motion module; the imaging module and the defect detection module are connected. The lighting module is used to generate polarized lighting light according to optimal lighting parameters; the optimal lighting parameters include optimal light source wavelength, optimal polarization state, and optimal incident angle. The motion module is used to carry the wafer to be inspected and perform scanning motion, so that each point on the surface of the wafer to be inspected passes through the intersection of the output optical axis of the illumination module and the collection optical axis of the imaging module in sequence, until all points on the surface of the wafer to be inspected are scanned. The imaging module is used to acquire a dark field scattering image of the wafer to be inspected while the motion module is performing scanning motion, and send the dark field scattering image to the defect detection module. The defect detection module is used to determine that the defect detection result of the wafer to be inspected is no defect if no scattered light spot is detected in the dark field scattering image of the wafer to be inspected; and to determine that the defect detection result of the wafer to be inspected is defective if a scattered light spot is detected in the dark field scattering image of the wafer to be inspected.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a wafer defect detection method and system based on dark field scattering. Simulation models of granular and pitted defects are established separately, and the scattering field of these models under different illumination parameters is simulated using the finite-difference time-domain method to obtain simulated dark field scattering images corresponding to each defect type. Based on this, the spatial entropy and root-mean-square contrast of each simulated image are calculated. By minimizing spatial entropy and maximizing root-mean-square contrast, the technical problem of relying on empirical trial-and-error for illumination parameter selection and difficulty in simultaneously adapting to multiple defect types is solved. The system automatically determines the optimal illumination parameters applicable to both granular and pitted defects. Furthermore, polarized illumination light is generated based on the optimal illumination parameters, allowing each point on the wafer surface to be inspected to pass through the light sequentially and simultaneously acquire dark field scattering images. By identifying the presence or absence of scattered light spots, the technical problem of low signal-to-noise ratio of defect signals in actual detection is solved. This achieves high-sensitivity detection of wafer surface defects under optimal illumination conditions, effectively improving the accuracy and reliability of wafer defect detection. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of a wafer defect detection system based on dark field scattering provided in an embodiment of this application; Figure 2 This is an overall flowchart of a wafer defect detection method and system design based on dark field scattering provided in the embodiments of this application; Figure 3 The scattered light spot images of a wafer defect detection system based on dark field scattering provided in this application embodiment are before and after installing a collimating lens; wherein, Figure 3 (a) is a scattering spot diagram of the wafer defect detection system based on dark field scattering before the collimating lens is installed; Figure 3 (b) is a scattering spot diagram of the wafer defect detection system based on dark field scattering after installing a collimating lens; Figure 4 This is a schematic diagram of a wafer dark-field scattering image provided in an embodiment of this application; wherein, Figure 4 (a) is a dark-field scattering image of a defect-free wafer; Figure 4 (b) is a dark-field scattering image of a defective wafer; Figure 5 This is a graph showing the relationship between the peak intensity of the particle scattering field and the wavelength under the P-polarization state and the S-polarization state, as provided in the embodiments of this application. Figure 6 The spatial entropy and contrast provided in the embodiments of this application are quantized and normalized results of particle scattering fields at different incident angles; Figure 7 This is a graph showing the relationship between the peak intensity of the pit scattering field and the wavelength under the P-polarization state and the S-polarization state, as provided in the embodiments of this application. Figure 8 The spatial entropy and contrast provided in the embodiments of this application are quantized and normalized results of the pit scattering field under different incident angles; Figure 9 This is a graph showing the relationship between the peak intensity of the scattered field and the pit diameter before and after using the equivalent method provided in the embodiments of this application; Figure 10 The graph shows the relationship between the peak intensity of the scattered field and the depth of the pit before and after using the equivalent method provided in the embodiments of this application.

[0011] Reference numerals: 1-Light source generator; 2-Collimating lens; 3-Polarizing beam splitter; 4-Reflecting mirror; 5-Converging lens; 6-Scattering and collecting objective lens; 7-First adjustable aperture; 8-Scattering focusing lens; 9-Second adjustable aperture; 10-Image sensor; 11-Wafer to be inspected; 12-Motion module. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] In an exemplary embodiment, a wafer defect detection method based on dark field scattering is provided, including the following steps 101-108: Step 101: Establish wafer surface defect simulation models for different defect types; the defect types include granular defects and pitted defects.

[0015] Step 102: The scattering field of the wafer surface defect simulation model for each defect type under different illumination parameters is simulated and calculated using the finite-difference time-domain method to obtain the simulated dark field scattering image corresponding to each defect type under different illumination parameters.

[0016] Step 103: Calculate the spatial entropy and root mean square contrast of the simulated dark field scattering images corresponding to each defect type under different lighting parameters.

[0017] Step 104: Based on the principle of minimizing spatial entropy and maximizing root mean square contrast, determine the optimal lighting parameters that are applicable to all defect types simultaneously; the optimal lighting parameters include the optimal light source wavelength, the optimal polarization state, and the optimal incident angle.

[0018] Step 105: Generate polarized illumination light according to the optimal illumination parameters.

[0019] Step 106: Each point on the surface of the wafer to be tested is sequentially illuminated by polarized light, and a dark-field scattering image of the wafer to be tested is acquired at the same time.

[0020] Step 107: If no scattered light spot is detected in the dark field scattering image of the wafer to be inspected, the defect detection result of the wafer to be inspected is determined to be defect-free.

[0021] Step 108: If a scattered light spot is identified in the dark-field scattering image of the wafer to be inspected, then the defect detection result of the wafer to be inspected is determined to be defective. Figure 4 As shown, a schematic diagram of a wafer dark-field scattering image is provided; wherein, Figure 4 (a) is a dark-field scattering image of a defect-free wafer; Figure 4 (b) is a dark field scattering image of a defective wafer.

[0022] By implementing steps 101 to 108 above, this application solves the technical problem of low signal-to-noise ratio of defect signals in actual detection, achieves high sensitivity to wafer surface defects under optimal illumination conditions, and effectively improves the accuracy and reliability of wafer defect detection.

[0023] In another exemplary embodiment of this application, step 103 specifically includes: Using any one of the simulated dark-field scattering images corresponding to each defect type under different illumination parameters as the current simulated dark-field scattering image, the spatial entropy and root mean square contrast of the current simulated dark-field scattering image are calculated through the following steps: The current simulated dark field scattering image is converted into a current polar coordinate pseudo-color image, and the current polar coordinate pseudo-color image is subjected to grayscale transformation to obtain the current grayscale image; Calculate the grayscale histogram of the current grayscale image to obtain the probability of each grayscale level occurring; The spatial entropy corresponding to the current simulated dark field scattering image is calculated using the following formula: ; in, This represents the spatial entropy corresponding to the current simulated dark field scattering image; N represents the total number of gray levels. This represents the probability of the i-th gray level appearing; The root mean square contrast of the current simulated dark field scattering image is calculated using the following formula: ; ; in, This represents the root mean square contrast of the current simulated dark field scattering image; This indicates the total number of rows of pixels in the current simulated dark field scattering image; This represents the total number of columns of pixels in the current simulated dark field scattering image; This represents the grayscale value of the pixel in the m-th row and n-th column of the current simulated dark field scattering image; This represents the average gray level of the current simulated dark field scattering image.

[0024] In another exemplary embodiment of this application, the wafer defect detection method based on dark field scattering further includes: The effective scattering volume of the wafer surface defect simulation model with pit-type defects is calculated using the following formula: ; in, Indicates the effective scattering volume; This indicates the ratio of the sidewall scattering efficiency to the pit bottom scattering efficiency; Represents pi; Indicates the diameter of the pit; Indicates the depth of the pit; The effective scattering volume of the wafer surface defect simulation model for pit-type defects is equivalent to the volume of the wafer surface defect simulation model for granular defects of the same volume. The equivalent scattering diameter of the wafer surface defect simulation model for pit-type defects is calculated using the following formula: ; in, The equivalent scattering diameter of the simulation model of a wafer surface defect representing a pit-type defect; Based on the theoretical scattering peak intensity-equivalent scattering diameter calibration curve, the theoretical scattering peak intensity corresponding to the equivalent scattering diameter of the simulation model of the pit-type wafer surface defect is obtained. Based on the theoretical scattering peak intensity, the scattering characteristic difference information between granular defects and pitted defects is determined; the scattering characteristic difference information between granular defects and pitted defects includes that the peak intensity of the scattered light spot of the wafer surface defect simulation model of pitted defects is less than the theoretical scattering peak intensity; and the peak intensity of the scattered light spot of the wafer surface defect simulation model of granular defects is greater than or equal to the theoretical scattering peak intensity.

[0025] In one exemplary embodiment, a wafer defect detection system based on dark field scattering is provided, comprising: The module includes an illumination module, a motion module 12, an imaging module, and a defect detection module. The illumination module and the imaging module are respectively disposed above the motion module, and the output optical axis of the illumination module and the collection optical axis of the imaging module intersect at the bearing surface of the motion module; the imaging module and the defect detection module are connected. The lighting module is used to generate polarized lighting light according to optimal lighting parameters; the optimal lighting parameters include optimal light source wavelength, optimal polarization state, and optimal incident angle. The motion module 12 is used to carry the wafer 11 to be inspected and to perform scanning motion, so that each point on the surface of the wafer to be inspected passes through the intersection of the output optical axis of the illumination module and the collection optical axis of the imaging module in sequence until all points on the surface of the wafer 11 to be inspected are scanned. The imaging module is used to acquire dark field scattering images of each point of the wafer 11 to be inspected while the motion module is performing scanning motion, and send the dark field scattering images to the defect detection module. The defect detection module is used to determine that the defect detection result of the wafer to be inspected is no defect if no scattered light spot is detected in the dark field scattering image of the wafer to be inspected; and to determine that the defect detection result of the wafer to be inspected is defective if a scattered light spot is detected in the dark field scattering image of the wafer to be inspected.

[0026] As an optional implementation method, such as Figure 1 As shown, the lighting module specifically includes: a light source generator 1, a collimating lens 2, a polarizing beam splitter 3, a reflector 4, and a converging lens 5 arranged sequentially along the output optical axis of the lighting module; The light source generator 1 is used to generate a light source with a wavelength of the optimal light source wavelength; The collimating lens 2 is used to collimate the light source; The polarization beam splitter 3 is used to separate the polarized light with the optimal polarization state from the collimated light source and use the polarized light with the optimal polarization state as polarized illumination light. The reflector 4 is used to reflect the polarized illumination light to the converging lens at an optimal incident angle; The converging lens 5 is used to converge the polarized illumination light onto the bearing surface of the motion module 12.

[0027] As an optional implementation method, such as Figure 1 As shown, the imaging module specifically includes: a scattering and collecting objective lens 6, a first adjustable aperture 7, a scattering and focusing lens 8, a second adjustable aperture 9, and an image sensor 10 arranged sequentially along the collecting optical axis of the imaging module. The scattering and collecting objective lens 6 is used to collect the light signal generated by polarized illumination light at the current point on the surface of the wafer 11 to be inspected. The first adjustable aperture 7 is used to perform the first stray light suppression on the optical signal; The scattering focusing mirror 8 is used to focus the optical signal after the first stray light suppression; The second adjustable aperture 9 is used to suppress stray light a second time on the focused optical signal; The image sensor 10 is used to convert the light signal after the second stray light suppression into a dark field scattering image, and send the dark field scattering image to the defect detection module.

[0028] As an optional implementation, the optimal light source wavelength is 405 nm; the optimal polarization state is P polarization state; and the optimal incident angle is 60°.

[0029] The following example illustrates this application using a specific wafer defect detection process based on dark field scattering.

[0030] In one exemplary embodiment, such as Figure 2 As shown, an overall flowchart of a wafer defect detection method and system design based on dark field scattering is presented.

[0031] One of the wafer defect detection systems based on dark field scattering specifically includes the following components: The illumination module includes a light source generator and a collimating lens, a polarizing beam splitter, a reflector, and a converging lens arranged along the optical propagation path. The collimating lens is used to filter stray light and optimize the half-divergence angle of the laser beam provided by the illumination source to reduce the propagation difference of the beam in different directions. The polarizing beam splitter is used to generate P-polarized light to ensure polarization purity. The reflector and the converging lens adjust the angle between the incident beam and the wafer surface and converge the beam to the wafer surface.

[0032] The motion module, including a displacement stage and a displacement stage control assembly, is located below the wafer and is used to control the movement of the wafer to achieve continuous scanning of the wafer.

[0033] An imaging module is used to capture scattered light signals generated by wafer defects and convert them into dark-field scattering images.

[0034] As an optional implementation, the light source generator is configured as a semiconductor laser with a wavelength of 405 nm.

[0035] As an optional implementation, the divergence angle performance of the fast and slow axes is significantly improved after the light source passes through the collimated optical path. The collimated light is used to generate P-polarized light using a polarization beam splitter. Through optical path design, only P-polarized light participates in the illumination process to ensure polarization purity. At the same time, the combination of a reflector and a converging lens achieves high-precision control of 60° oblique incidence, ensuring that the angle adjustment accuracy of the reflector is better than 0.1° and the absolute error of the incident angle is less than 0.5°, thereby meeting the requirements of the detection for angle accuracy. Finally, the light is focused onto the wafer surface.

[0036] As an optional implementation, the imaging module includes a large nano-aspheric condenser lens positioned above the wafer as a scattering and collecting objective to maximize the collection of large-angle scattered light from defects. An adjustable aperture is also coaxially arranged along the light transmission direction. The aperture size is mechanically adjusted to further block stray light from non-target angles. The image sensor (CMOS camera) is placed directly behind the aperture, with its optical axis strictly aligned to avoid signal loss. The CMOS camera selected in the system is a mature area-array industrial digital camera with dimensions of only 29mm × 29mm × 29mm, making it compact, lightweight, robust, and durable.

[0037] A semiconductor laser (wavelength 405nm) is used as the light source. After the laser output is collimated by a collimating lens, the light then passes through a polarization beam splitter to obtain the P-polarized component. The P-polarized light passes through a converging lens and a mirror, and is incident on the wafer surface at 60°. The wafer is placed on a displacement stage. The scattered light excited by defects is collected by two converging lenses and then passes through two adjustable apertures to suppress stray light before reaching the CMOS camera for imaging. This inspection system connects the CMOS camera to computer imaging software. When the scattered light enters the photosensitive area of ​​the CMOS camera, a bright spot corresponding to the size and shape of the defect is formed in the image of the computer imaging software. Different parameters such as the size of the defect will result in differences in the brightness and outline sharpness of the bright spot.

[0038] As an optional implementation method, before wafer defect detection, the light source needs to be preheated for 30 minutes to ensure power stability. A power meter is used to measure the energy output of the light source and the energy before and after passing through the beam splitter and reflector. The energy loss rate is calculated to evaluate the energy transmission performance of the beam splitter and reflector. The test results show that the laser output energy is 6mW, and the energy in the horizontal and vertical directions after passing through the beam splitter is 3mW, with a splitting ratio of 1:1. The device is highly symmetrical in power distribution. The energy after passing through the reflector is 3mW with no energy loss. This verifies that the transmission performance of the optical devices such as the beam splitter and reflector meets the experimental requirements and can ensure the energy stability of subsequent detection.

[0039] To reduce the interference of stray light from non-target angles on the detection signal-to-noise ratio (SNR), the stray light suppression effect of the variable aperture was experimentally verified. Under the same experimental conditions, the morphology of the scattered light spot was recorded before and after installing the collimating lens, as shown below. Figure 3 As shown, the corresponding image signal-to-noise ratio was calculated. After calculation, the signal-to-noise ratio before installing the variable aperture was 10.56dB. By comparing the signal-to-noise ratio and spot sharpness in the two states, the effect of the variable aperture on suppressing stray light from non-target angles was verified. After installing the aperture, stray light interference was reduced and the spot edge was sharper, laying the foundation for high signal-to-noise ratio in defect detection.

[0040] To verify the applicability and detection performance of the constructed dark-field scattering-based wafer defect detection system in a real industrial setting, a commercially available 6-inch Si wafer (675 μm thick, n=5.57, k=0.387@405nm) was used as the experimental sample. The main defect types included nanoscale particle contamination, micro-scratches, crystal-originated particles (COP), and surface pitting, with defect sizes ranging from 60 nm to 2 μm. By setting a unified experimental environment and system parameters, the system's defect detection capability was characterized. n represents the real refractive index, and k represents the imaginary part, referring to the extinction coefficient.

[0041] First, background calibration is performed using a standard defect-free reference wafer. The incident angle, polarizer angle, and CMOS camera are adjusted to ensure that the substrate background signal is uniform and free from significant stray light interference. Then, the displacement stage is started for continuous scanning, and the CMOS camera simultaneously acquires dark field scattering images.

[0042] The wafer to be inspected is fixed on the displacement stage, and the displacement stage is adjusted so that the wafer to be inspected is in the inspection reference position. This ensures that the relative position of the wafer surface to be inspected with the incident beam and the imaging lens meets the inspection requirements, avoiding inspection errors caused by positional offset. Finally, the imaging result of the scattered light excited by the defects on the surface of the wafer to be inspected in the CMOS camera is obtained.

[0043] In one exemplary embodiment, the optimal lighting parameters are determined through simulation, specifically including: (1) Simulation parameter settings: Silicon (Si) (n=5.57, k=0.387) was selected as the substrate material, polystyrene microspheres (n=1.6, k=0) were set as particles, and the pits were set as cylinders. Then, the simulation area (3 μm long, 3 μm wide, 0.5 μm high) was defined and Yee mesh was divided to realize the spatial discretization of the computational domain. At the same time, in order to ensure that the incident wave does not generate reflected waves at the interface and decays rapidly, the boundary of the simulation area was set as a perfectly matched layer (PML), and the excitation source was set as a 405nm TFSF (Total-Field Scattered-Field) light source. This area was selected as a small area to contain only particles, providing the incident electromagnetic wave signal for the entire simulation. The 2D power monitor located above the light source will be used to calculate the far-field scattering. Only the light passing through this monitor will be included in the far-field projection.

[0044] (2) Confirmation of optimal lighting parameters: For two typical defects in wafer manufacturing, simulation analysis of the optimal parameters of the lighting module was carried out to clarify the scattering response characteristics of the two types of defects under different lighting conditions, providing a theoretical basis for the selection of lighting module parameters. Figure 5 As shown, a simulation modeling diagram is provided; in which, Figure 5 (a) is a schematic diagram of the simulation model of the scattering field of a particulate defect; Figure 5 (b) is a schematic diagram of the simulation model of the scattering field of the pit-type defect. In the simulation, the diameter of PSL (polystyrene microspheres) particles is set to 100 nm (equivalent to the average particle diameter), the pit diameter is 100 nm, and the depth is 50 nm. The changes in the scattering field intensity of the particles and pits are observed at different incident angles, different polarization states, and light source wavelengths of 405 nm, 450 nm, 532 nm, and 635 nm. The dark field scattering image quantization method combining spatial entropy and root mean square contrast is used to solve the problem of the difficulty in accurately optimizing parameters such as light source wavelength, polarization state, and incident angle of the illumination module, and to achieve the optimal configuration of the illumination module parameters of the optical system. Here, the image processing is simulated image processing. The image is divided into regions and calculated using MATLAB programming. In the actual detection process, no regions are divided. Only during the scanning process, the image sensor is connected to the computer, and the appearance of scattered light spots on the computer proves the existence of defects.

[0045] In this implementation, spatial entropy is used to quantify the degree of disorder in the spatial distribution of defect scattering field energy. This is achieved by dividing the scattering field into multiple spatial regions (calculated using MATLAB programming to divide the image into regions during simulation image processing; however, no region division is performed during actual detection), and then calculating the energy proportion of each region (i.e., based on...). For the first Light intensity in each spatial region Total light intensity The probability of energy distribution is calculated by determining the spatial entropy, which reflects the concentration of energy distribution. A smaller entropy value indicates a more concentrated energy distribution. In actual detection, the grayscale histogram of the grayscale image is statistically analyzed, and the formula for calculating the spatial entropy is as follows: ; in, This represents the spatial entropy corresponding to the current simulated dark field scattering image; N represents the total number of gray levels. This represents the probability of each gray level occurring at the i-th gray level, where the gray level can be used to characterize the brightness level of a pixel, i.e. Let i be the light intensity of the i-th spatial region. Total light intensity The probability of.

[0046] In this implementation method, the root mean square contrast ratio is a commonly used statistical indicator in image contrast quantification analysis. Based on the dispersion of pixel gray values, it objectively reflects the strength of the difference between the brightness and darkness of the image by describing the degree of deviation of all pixel gray values ​​in the image from the global gray value mean.

[0047] In a grayscale image, the brightness information of each pixel is represented by a grayscale value. If the pixel grayscale values ​​in a simulated dark-field scattering image are concentrated around the mean, it indicates a small difference between light and dark areas and low contrast; if a large number of pixel grayscale values ​​are far from the mean, it indicates high contrast. Compared with simple contrast, the root mean square contrast ratio has the advantage of taking into account the grayscale distribution characteristics of all pixels, rather than relying solely on extreme values. Therefore, it can more robustly avoid the interference of individual abnormal pixels on contrast evaluation and better aligns with the human eye's perception of the overall brightness difference in an image.

[0048] Suppose the simulated dark field scattering image is composed of Composed of pixels, with a brightness (grayscale) value of The mean value of the brightness (grayscale) of the simulated dark field scattering image is: ; in, This represents the average brightness (grayscale) value of the simulated dark-field scattering image; This represents the total number of rows of pixels in the simulated dark-field scattering image; This represents the total number of columns of pixels in the simulated dark-field scattering image; This represents the brightness (grayscale) value of the pixel in the m-th row and n-th column.

[0049] The mean square deviation is calculated by squaring the difference between the grayscale value of each pixel and the mean brightness (grayscale) of the dark field scattering image, and then taking the average of the squared deviations of all pixels. The formula is: ; The root mean square contrast is obtained by taking the square root of the mean square deviation. The final formula is: ; The spatial entropy and root mean square contrast of the obtained simulated dark field scattering images were calculated according to the above formulas. The scattering field analysis, combining the particle scattering field and the pit scattering field, was performed for different light source wavelengths, polarization states, and incident angles. Figures 5-8 As shown, graphs depicting the relationship between peak intensity and wavelength of the particle scattering field in the P-polarization and S-polarization states, and graphs depicting the relationship between peak intensity and wavelength of the pit scattering field in the P-polarization and S-polarization states are provided; according to Figures 5-8 It can be seen that the peak intensity of the scattered field decreases with increasing wavelength, the P-polarization state has stronger fault tolerance for small angles, the root mean square contrast is highest and the spatial entropy is lower around a 60° incident angle, resulting in strong image contrast, low texture complexity, and concentrated energy. Based on this, the optimal parameters for the illumination module in the wafer defect detection system based on dark field scattering are: a 405nm light source in the P-polarization state incident at a 60° incident angle onto the surface of the wafer to be inspected.

[0050] Based on the volume-scattering efficiency equivalence method, the FDTD simulation parameters were set using the controlled variable method, and the results were compared with those of pit-type defects under different D and h parameter settings. By differentiating the peak intensity of the scattered field of granular defects under different parameter settings, the differences between the scattered fields of the two can be found. By comparing the scattered field energy before and after the equivalent, granular defects and pitted defects can be effectively distinguished.

[0051] Pits are concave defects on the surface of a wafer. When incident light shines on a particle, some of the light enters the pit and undergoes multiple reflections, interferences, and destructive reactions on the sidewalls and bottom surface. A large amount of scattered energy is trapped inside the pit and cannot be radiated outward to the far field, resulting in an extremely low far-field scattering peak. In contrast, particles are three-dimensional entities completely exposed on the wafer surface. When incident light shines on a particle, the entire surface participates in scattering, with no internal energy loss. All scattered energy is radiated outward, and the three-dimensional symmetry of the sphere makes the scattered energy highly concentrated, naturally resulting in a far-field scattering peak that is much higher than that of a pit of the same volume.

[0052] The cylindrical pit is used as the research object. Its scattering contribution mainly comes from the interface reflection between the sidewall and the bottom of the pit. The equivalent scattering diameter is calculated using the volume-scattering efficiency equivalence method. The specific process is as follows: Since the sidewalls scatter incident light more strongly than the planar pit bottom, let's assume the sidewall scattering efficiency is equal to that of the pit bottom. If the effective scattering volume V of the cylindrical pit is multiplied by a factor of 1, then: ; V is equivalent to the volume of a spherical scatterer. : ; We can obtain: ; Where D is the diameter of the pit and h is the depth of the pit.

[0053] Depend on Figures 9-10 It can be seen that for pits and particles of the same volume, the scattering peak intensity increases significantly with the increase of D and h. The scattering peak of the particle is always much higher than that of the pit defect, and the gap continues to widen with the increase of D and h. The peak difference between the two types of defects exceeds 80% in the entire size range, and the difference between the large size and deep pit range exceeds 200%. There is no misjudgment range, and the resolution is extremely strong.

[0054] In summary, this application has the following beneficial effects: First, reducing detection errors caused by optical path aberrations and uneven energy distribution effectively improves imaging clarity and overall system stability. The components selected in the illumination module achieve a comprehensive performance improvement in terms of efficiency, uniformity, and stability. The imaging module uses two converging lenses and two adjustable apertures to focus the scattered light excited by defects onto the CMOS camera, successfully suppressing interference from background stray light. Based on light scattering theory, high-contrast imaging results of scattered light excited by wafer surface defects on the CMOS detector were successfully obtained. The wafer defect scattering field was modeled, and the influence of parameters such as incident angle, polarization state, and light source wavelength on the intensity of the defect scattering field was investigated using the controlled variable method. This solved the problem of accurately optimizing parameters such as light source wavelength, polarization state, and incident angle in the illumination module, achieving optimal configuration of the illumination module parameters in the optical system.

[0055] Secondly, based on the optimal configuration of lighting module parameters, for pits and particle-type defects of the same volume, reasonable selection can be achieved. This value can effectively solve the problem of weak and difficult-to-detect original pit scattering signals. At the same time, it can extract the peak intensity of the defect scattering field, realize the distinction between granular and pit-type defects, and provide accurate defect type data for subsequent process optimization.

[0056] Then, by selecting appropriate illumination optical path parameters and high numerical aperture objectives, high-sensitivity identification of multiple types of defects was achieved; the component selection scheme was optimized, and suitable domestic components were used to complete the hardware construction, effectively controlling the system production cost. These multifaceted innovative achievements demonstrate the invention's significant contribution to the detection of wafer defects using dark-field scattering.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0058] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A wafer defect detection method based on dark field scattering, characterized in that, include: Simulation models of wafer surface defects of different defect types were established; the defect types include particle defects and pit defects. The scattering field of wafer surface defects of various types under different illumination parameters was simulated and calculated by the finite-difference time-domain method, and the simulated dark field scattering images of each defect type under different illumination parameters were obtained. Calculate the spatial entropy and root mean square contrast of the simulated dark field scattering images corresponding to each defect type under different lighting parameters; Based on the principle of minimizing spatial entropy and maximizing root-mean-square contrast, the optimal lighting parameters are determined to be applicable to all defect types. The optimal lighting parameters include the optimal light source wavelength, the optimal polarization state, and the optimal incident angle. Polarized illumination light is generated based on the optimal illumination parameters; Each point on the surface of the wafer to be inspected is sequentially illuminated by polarized light, while a dark-field scattering image of the wafer to be inspected is acquired simultaneously. If no scattered light spot is detected in the dark field scattering image of the wafer to be inspected, the defect detection result of the wafer to be inspected is determined to be defect-free; If a scattered light spot is detected in the dark field scattering image of the wafer to be inspected, the defect detection result of the wafer to be inspected is determined to be that a defect exists.

2. The wafer defect detection method based on dark field scattering according to claim 1, characterized in that, The spatial entropy and root mean square contrast of the simulated dark-field scattering images corresponding to each defect type under different illumination parameters were calculated, specifically including: Using any one of the simulated dark-field scattering images corresponding to each defect type under different illumination parameters as the current simulated dark-field scattering image, the spatial entropy and root mean square contrast of the current simulated dark-field scattering image are calculated through the following steps: The current simulated dark field scattering image is converted into a current polar coordinate pseudo-color image, and the current polar coordinate pseudo-color image is subjected to grayscale transformation to obtain the current grayscale image; Calculate the grayscale histogram of the current grayscale image to obtain the probability of each grayscale level occurring; The spatial entropy corresponding to the current simulated dark field scattering image is calculated using the following formula: ； in, This represents the spatial entropy corresponding to the current simulated dark field scattering image; N represents the total number of gray levels. This represents the probability of the i-th gray level appearing; The root mean square contrast of the current simulated dark field scattering image is calculated using the following formula: ； ； in, This represents the root mean square contrast of the current simulated dark field scattering image; This indicates the total number of rows of pixels in the current simulated dark field scattering image; This represents the total number of columns of pixels in the current simulated dark field scattering image; This represents the grayscale value of the pixel in the m-th row and n-th column of the current simulated dark field scattering image; This represents the average gray level of the current simulated dark field scattering image.

3. The wafer defect detection method based on dark field scattering according to claim 1, characterized in that, The wafer defect detection method based on dark field scattering also includes: The effective scattering volume of the wafer surface defect simulation model with pit-type defects is calculated using the following formula: ； in, Indicates the effective scattering volume; This indicates the ratio of the sidewall scattering efficiency to the pit bottom scattering efficiency; Represents pi; Indicates the diameter of the pit; Indicates the depth of the pit; The effective scattering volume of the wafer surface defect simulation model for pit-type defects is equivalent to the volume of the wafer surface defect simulation model for granular defects of the same volume. The equivalent scattering diameter of the wafer surface defect simulation model for pit-type defects is calculated using the following formula: ； in, The equivalent scattering diameter of the simulation model of a wafer surface defect representing a pit-type defect; Based on the theoretical scattering peak intensity-equivalent scattering diameter calibration curve, the theoretical scattering peak intensity corresponding to the equivalent scattering diameter of the simulation model of the pit-type wafer surface defect is obtained. Based on the theoretical scattering peak intensity, the scattering characteristic difference information between particulate defects and pit defects is determined; the scattering characteristic difference information between particulate defects and pit defects includes that the peak intensity of the scattered light spot of the wafer surface defect simulation model of pit defects is less than the theoretical scattering peak intensity; and the peak intensity of the scattered light spot of the wafer surface defect simulation model of particulate defects is greater than or equal to the theoretical scattering peak intensity.

4. A wafer defect detection system based on dark field scattering, characterized in that, The wafer defect detection system based on dark field scattering is used to implement the wafer defect detection method based on dark field scattering according to any one of claims 1-3. The wafer defect detection system based on dark field scattering includes: an illumination module, a motion module, an imaging module, and a defect detection module. The illumination module and the imaging module are respectively disposed above the motion module, and the output optical axis of the illumination module and the collection optical axis of the imaging module intersect at the bearing surface of the motion module; the imaging module and the defect detection module are connected. The lighting module is used to generate polarized lighting light according to optimal lighting parameters; the optimal lighting parameters include optimal light source wavelength, optimal polarization state, and optimal incident angle. The motion module is used to carry the wafer to be inspected and perform scanning motion, so that each point on the surface of the wafer to be inspected passes through the intersection of the output optical axis of the illumination module and the collection optical axis of the imaging module in sequence, until all points on the surface of the wafer to be inspected are scanned. The imaging module is used to acquire a dark field scattering image of the wafer to be inspected while the motion module is performing scanning motion, and send the dark field scattering image to the defect detection module. The defect detection module is used to determine that the defect detection result of the wafer to be inspected is no defect if no scattered light spot is detected in the dark field scattering image of the wafer to be inspected; and to determine that the defect detection result of the wafer to be inspected is defective if a scattered light spot is detected in the dark field scattering image of the wafer to be inspected.

5. The wafer defect detection system based on dark field scattering according to claim 4, characterized in that, The illumination module specifically includes: a light source generator, a collimating lens, a polarizing beam splitter, a reflector, and a converging lens arranged sequentially along the output optical axis of the illumination module; The light source generator is used to generate a light source with a wavelength of the optimal light source wavelength; The collimating lens is used to collimate the light source; The polarization beam splitter is used to separate the polarized light with the optimal polarization state from the collimated light source, and use the polarized light with the optimal polarization state as polarized illumination light. The reflector is used to reflect the polarized illumination light to the converging lens at an optimal incident angle; The converging lens is used to converge the polarized illumination light onto the support surface of the motion module.

6. The wafer defect detection system based on dark field scattering according to claim 4, characterized in that, The imaging module specifically includes: a scattering and collecting objective lens, a first adjustable aperture, a scattering and focusing lens, a second adjustable aperture, and an image sensor arranged sequentially along the collecting optical axis of the imaging module; The scattering and collecting objective lens is used to collect the light signal generated by polarized illumination light at the current point on the surface of the wafer to be inspected. The first adjustable aperture is used to perform the first stray light suppression on the optical signal; The scattering focusing mirror is used to focus the optical signal after the first stray light suppression; The second adjustable aperture is used to suppress stray light a second time on the focused optical signal; The image sensor is used to convert the light signal after the second stray light suppression into a dark field scattering image, and send the dark field scattering image to the defect detection module.

7. The wafer defect detection system based on dark field scattering according to claim 4, characterized in that, The optimal light source wavelength is 405 nm; the optimal polarization state is P polarization state; and the optimal incident angle is 60°.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the wafer defect detection method based on dark field scattering as described in any one of claims 1-3.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the wafer defect detection method based on dark field scattering as described in any one of claims 1-3.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the wafer defect detection method based on dark field scattering as described in any one of claims 1-3.

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