CN118096942B - Method and device for optimizing wafer inspection configuration parameters - Google Patents

Abstract

The present application belongs to the field of semiconductor manufacturing technology, and specifically discloses a method and device for optimizing wafer detection configuration parameters, the method comprising: rasterizing the graphics of the illumination pupil and the collection pupil respectively to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix; determining the light source point information of the illumination pupil based on the polarized illumination method and the first two-dimensional distribution matrix; establishing a defect-free structure model and a defect structure model of the target defect type, and solving the defect-free near-field distribution set and the defect near-field distribution set of the illumination pupil in combination with the light source point information; initializing a bright field microscopic imaging model, calculating a first spatial image of a defect-free wafer and a second spatial image of a defective wafer; determining an evaluation index and an evaluation function based on the first spatial image and the second spatial image; executing an iterative optimization process based on the selected optimization object and optimization method, and updating the variable matrix of the optimization object; and outputting the optimized optimization object when the updated evaluation index meets the iteration condition.

Description

Method and device for optimizing wafer inspection configuration parameters

Technical Field

The present application belongs to the field of semiconductor manufacturing technology, and more specifically, relates to a method and device for optimizing wafer detection configuration parameters.

Background Art

For semiconductor manufacturing companies, yield is the key to determining whether the production line can proceed smoothly according to the production cycle and generate good profits. In the lithography process, defect detection of wafers is crucial to improving yield. Although the critical dimension (Critical Dimension, CD) based on scanning electron microscopy (SEM) has a good detection accuracy in wafer defect detection, in order to meet the needs of fast and non-destructive detection in the semiconductor integrated circuit production and manufacturing process, the main force of wafer lithography process defect detection is still based on optical bright field detection equipment. With the reduction of the critical size of integrated circuit (IC) devices, the size of defects has also decreased. For nodes below 45nm, the size of critical defects is already less than 20nm, which is a challenge for current wafer defect detection equipment.

In principle, wafer defect detection is a process of generating and collecting scattered signals from defects. A lens system with a high numerical aperture (NA) can collect more signals from defects. The performance of bright field inspection equipment depends on the complex optical conditions of the illumination and collection systems. The parameters of the illumination system include wavelength, illumination polarization, illumination pupil, etc., and the parameters of the collection system include collection pupil, defocus, polarization filtering, etc.

For these complex detection parameters, a detection recipe will be formed to cooperate with the use of the detection equipment. The wafer bright field detection equipment uses a wide-band light source system and channels of different modes of collecting signals to cooperate with each other to meet the wafer defect detection for different process layers and process nodes in the integrated circuit manufacturing plant (Fab). For defect detection in the lithography process, engineers need to screen out the detection recipe for detecting key defects through a large number of experiments. For some specific defect types, the detection of these defects will be optimized in a targeted manner, that is, the contrast (Contrast) or signal-to-noise ratio (SNR) of the defect will be enhanced to produce an optimized detection recipe. At the same time, due to the variety of wafer defect types, it is difficult to use a single detection recipe to complete the defect detection work in the entire lithography process, and the defect detection results of different detection recipes are also quite different. Quickly and efficiently formulating detection recipes for key or specific defect types is crucial to ensure the smooth progress of the wafer manufacturing cycle and achieve economic benefits.

Regarding the research on relevant parameters in the detection recipe (wavelength, illumination polarization, illumination pupil, collection pupil, defocus, polarization filtering, etc.), the relevant literature (Fujii T, Konno Y, Okada N, Yoshino K and Yamazaki Y2009 Development of optical simulation tool for defect inspection Proc. SPIE7272 72721A) uses a modeling method to study the influence of wavelength on the SNR of different types of defect detection; the experiments in the relevant literature (Astudy of the defect detection technology using the optic simulation for these microconductor device) verify that polarization and the traditional regular illumination pupil and collection pupil patterns can improve the SNR of defect signals.

The traditional regular illumination pupil and collection pupil are determined by regular shapes and sizes, and the freedom of selection and optimization of the illumination pupil and the collection pupil is greatly limited, and the advantage of changing the illumination pupil and the collection pupil to enhance the defect detection signal cannot be fully utilized.

Summary of the invention

In view of the defects of the related art, the purpose of the present application is to provide a method and device for optimizing wafer inspection configuration parameters, aiming to solve the problem that the selection and optimization freedom of the traditional regular illumination pupil and collection pupil are limited.

In a first aspect, an embodiment of the present application provides a method for optimizing wafer detection configuration parameters, including:

rasterizing the graphics of the illumination pupil and the collection pupil respectively to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initializing a first variable matrix corresponding to the illumination pupil based on the intensity distribution of the illumination pupil, and initializing a second variable matrix corresponding to the collection pupil based on whether the illumination pupil is within the graphics of the collection pupil;

Based on the polarized illumination method and the first two-dimensional distribution matrix, determining light source point information of the light source point corresponding to each grid point after rasterization;

Establish a defect-free structure model and a defect structure model of the target defect type, and solve the defect-free near-field distribution set and defect near-field distribution set corresponding to the illumination pupil in combination with the light source point information;

Initializing a bright field microscopic imaging model, and calculating a first aerial image of a non-defective wafer and a second aerial image of a defective wafer;

determining an evaluation index based on the first aerial image and the second aerial image, and determining an evaluation function based on the evaluation index;

An optimization object and an optimization method are selected, an iterative optimization process is performed based on the selected optimization object and optimization method, and a variable matrix of the optimization object and an evaluation index under the updated variable matrix are updated; the optimization object is an illumination pupil and/or a collection pupil;

When the updated evaluation index meets the preset threshold or the number of iterations reaches the maximum number, the optimized graph of the optimization object is determined by the variable matrix of the optimization object updated by the last iterative optimization process.

In a second aspect, an embodiment of the present application provides a device for optimizing wafer detection configuration parameters, including:

a rasterization unit, for rasterizing the graphics of the illumination pupil and the collection pupil respectively to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initializing a first variable matrix corresponding to the illumination pupil based on the intensity distribution of the illumination pupil, and initializing a second variable matrix corresponding to the collection pupil based on whether the illumination pupil is within the graphics of the collection pupil;

A light source point information determination unit, used to determine the light source point information of the light source point corresponding to each grid point after rasterization based on the polarized illumination mode and the first two-dimensional distribution matrix;

A near-field distribution determination unit is used to establish a defect-free structure model and a defect structure model of the target defect type, and solve a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil in combination with light source point information;

An aerial image determination unit, used to initialize a bright field microscopic imaging model and calculate a first aerial image of a non-defective wafer and a second aerial image of a defective wafer;

An evaluation index determination unit, configured to determine an evaluation index based on the first aerial image and the second aerial image, and determine an evaluation function based on the evaluation index;

An iterative optimization unit, used for selecting an optimization object and an optimization method, executing an iterative optimization process based on the selected optimization object and optimization method, updating a variable matrix of the optimization object and an evaluation index under the updated variable matrix; the optimization object is an illumination pupil and/or a collection pupil;

The optimization output unit is used to determine the optimized graph of the optimization object with the variable matrix of the optimization object updated by the last iterative optimization process when the updated evaluation index meets the preset threshold or the number of iterations reaches the maximum number.

In a third aspect, an embodiment of the present application provides an electronic device, comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory, wherein when the programs stored in the memory are executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation manner of the first aspect.

In general, the above technical solutions conceived by this application have the following beneficial effects compared with the related art:

(1) Compared with the traditional selection of illumination pupil and collection pupil, the embodiment of the present application utilizes the method of establishing an imaging model to rasterize the illumination pupil and the collection pupil, thereby improving the optimization freedom of the illumination pupil and the collection pupil. It also provides a variety of optimization process schemes for the illumination pupil and the collection pupil, which can adapt to the optimization of different scenes. These optimization process schemes can improve the contrast of defect signals to different degrees. By adopting different optimization processes for different defects, the illumination pupil and collection pupil shapes that are most suitable for a specified defect type can be selected to achieve the best detection contrast for the specified defect type.

(2) During the optimization process, the defect-free spatial image under the same illumination pupil and collection pupil conditions is also calculated as a reference to ensure the optimization direction of the optimization process, that is, the defect signal contrast in the defective image is always enhanced. It can be seen from the simulation results that the optimized parameter configuration can achieve defect detection without a reference image, avoiding the situation where the traditional die-to-die detection method may cause systematic defects to be undetectable.

(3) When using the imaging model for optimization, the near-field information only needs to be calculated once based on the light source information after initial discretization. During the optimization process, there is no need to repeatedly calculate the near-field of the wafer surface due to changes in lighting conditions. This greatly shortens the calculation time of the imaging model during optimization and improves optimization efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the present application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

FIG1 is a flow chart of a method for optimizing wafer inspection configuration parameters according to an embodiment of the present application;

FIG2 is a schematic diagram of the principle of imaging of a wafer inspection device provided in an embodiment of the present application;

FIG3 is a schematic diagram of a flow chart of an imaging model provided in an embodiment of the present application;

FIG4 is a second flow chart of a method for optimizing wafer inspection configuration parameters provided in an embodiment of the present application;

5 is a schematic diagram of an initial illumination pupil, a binary image of a defect-free wafer, an initial collection pupil and imaging results thereof provided in an embodiment of the present application;

6 is a schematic diagram of an initial illumination pupil, a defective wafer binary image, an initial collection pupil and imaging results thereof provided in an embodiment of the present application;

FIG7 is a schematic diagram of an imaging structure when only the illumination pupil is optimized according to an embodiment of the present application;

FIG8 is a schematic diagram of an imaging structure when only the collection pupil is optimized according to an embodiment of the present application;

FIG9 is one of the schematic diagrams of the imaging structure when optimizing the illumination pupil and the collection pupil provided in an embodiment of the present application;

FIG10 is a second schematic diagram of the imaging structure when optimizing the illumination pupil and the collection pupil provided in an embodiment of the present application;

11 is a schematic diagram of the structure of a device for optimizing wafer detection configuration parameters provided in an embodiment of the present application;

FIG. 12 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

The term "and/or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol "/" in this article indicates that the associated objects are in an or relationship, for example, A/B means A or B.

The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

Next, the technical solutions provided in the embodiments of the present application are introduced in conjunction with the accompanying drawings.

FIG. 1 is a flow chart of a method for optimizing wafer detection configuration parameters provided in an embodiment of the present application. As shown in FIG. 1 , the method includes at least the following steps:

S101. Rasterize the graphics of the illumination pupil and the collection pupil respectively to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initialize the first variable matrix corresponding to the illumination pupil based on the intensity distribution of the illumination pupil, and initialize the second variable matrix corresponding to the collection pupil based on whether it is within the graphics of the collection pupil.

Specifically, the image of the illumination pupil is rasterized to obtain a first two-dimensional distribution matrix Q of size _Ns × _Ns ; the image of the collection pupil is rasterized to obtain a second two-dimensional distribution matrix C of size _Np × _Np ; the first variable matrix of the illumination pupil of size _Ns × _Ns is initialized with the intensity distribution of the illumination pupil The value range is between [0,1]; whether to initialize the second variable matrix of size _Np × _Np collection pupil in the collection pupil graph When the point (i _p ,j _p ) is within the collection pupil pattern, When point (i _p ,j _p ) is outside the collection pupil pattern, Wherein _Ns and _Np are both integers.

S102: Based on the polarized illumination method and the first two-dimensional distribution matrix, determine the light source point information of the light source point corresponding to each grid point after rasterization.

Specifically, the polarized illumination mode can be TE, TM, X, Y, Natural and other different modes. After the pattern of the illumination pupil is rasterized, each grid point represents an illumination light source point. According to the polarized illumination mode and the first two-dimensional distribution matrix corresponding to the illumination pupil, the light source point information of the light source point corresponding to each grid point after rasterization is calculated. The light source point information can specifically include the incident angle (θ), azimuth angle, and the like of each light source point. Polarization angle (ρ) information, etc.

S103, establishing a defect-free structure model and a defect structure model of the target defect type, and solving a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil in combination with light source point information.

Specifically, for the target defect type, or the specified defect type, a no defect (No Defect, ND) structural model and a defect (With Defect, WD) structural model are established, and then the light source point information of each light source point obtained in S102 is used to solve the ND near-field distribution and WD near-field distribution of each light source point, thereby obtaining the ND near-field distribution set and WD near-field distribution set corresponding to the illumination pupil.

S104 , initializing a bright field microscopic imaging model, and calculating a first aerial image of a non-defective wafer and a second aerial image of a defective wafer.

Specifically, FIG2 is a schematic diagram of the imaging principle of the wafer inspection device provided in the embodiment of the present application. As shown in FIG2, the bright field microscopic imaging model can be simplified into four modules, namely, a light source module 201, a wafer module 202, a lens module 203, and an imaging module 204. The parameters and information obtained in S101 to S103 are used to initialize the parameters of the illumination module, the wafer module, and the lens module in the bright field microscopic imaging model, and calculate the first aerial image I _ND of the ND wafer and the second aerial image I _WD of the WD wafer under the conditions of the illumination pupil Q and the collection pupil C.

S105 . Determine an evaluation index based on the first aerial image and the second aerial image, and determine an evaluation function based on the evaluation index.

Specifically, after obtaining a first aerial image of a defect-free wafer and a second aerial image of a defective wafer, an evaluation index is established according to the difference or contrast between the two, and an evaluation function is determined, the evaluation function aims to maximize the evaluation index or minimize the evaluation index. The evaluation index and the evaluation function are used to characterize the defect signal contrast in the defective image.

S106, selecting an optimization object and an optimization method, executing an iterative optimization process based on the selected optimization object and optimization method, updating a variable matrix of the optimization object and evaluation indicators under the updated variable matrix; the optimization object includes an illumination pupil and/or a collection pupil.

Specifically, after determining the optimization target (OT) and the matching optimization method (OM), the iterative optimization process is performed according to the selected optimization target and optimization method, and the variable matrix of the optimization target and the evaluation index under the updated variable matrix are updated. The optimization target here refers to at least one of the illumination pupil and the collection pupil.

S107. When the updated evaluation index meets the preset threshold or the number of iterations reaches the maximum number, the optimized graph of the optimization object is determined by using the variable matrix of the optimization object updated by the last iterative optimization process.

Specifically, during the iterative optimization process, the variable matrix of the optimization object is continuously updated. When the updated evaluation index meets the preset threshold or the number of iterations reaches the maximum number, the optimized graph of the optimization object is determined by the variable matrix of the optimization object updated by the last iterative optimization process.

In some embodiments, establishing a defect-free structure model and a defect structure model of a target defect type in S103 specifically includes:

A binary image is constructed according to the structure and pattern of the wafer, wherein the dark area of the binary image represents the substrate and the bright area represents the pattern;

According to the spatial resolution size of the wafer end, the target defect type is divided into a non-defective binary image and a defective binary image;

Given the substrate material, pattern material, substrate thickness and pattern thickness, the establishment of a defect-free structure model and a defective structure model is completed.

Specifically, considering that only information such as the illumination intensity in the illumination pupil graph is needed, the construction of the defect-free structure model and the defect structure model can be constructed in the form of a binary image, a grayscale image, etc. In the embodiment of the present application, a binary image is used as an example for explanation. Different grayscales in the grayscale image represent different material properties. Similarly, the first aerial image and the second aerial image can also be displayed in the form of a grayscale image.

For example, binary images B _nd and B _wd are constructed respectively, the dark area of the binary image represents the base (Base), and the bright area represents the pattern (Pattern); B _nd and B _wd are divided according to the spatial resolution size of the wafer end to obtain two-dimensional distribution matrices W _nd and W _wd , both of which are of size N _w ×N _w ; given the base material MB and pattern material MP of W _nd and W _wd ; the base thickness DB and pattern thickness DP, the establishment of the defect-free structure model and the defective structure model is completed.

In some embodiments, solving the defect-free near-field distribution set and the defect near-field distribution set corresponding to the illumination pupil in S103 in combination with the light source point information specifically includes:

Based on the wavelength and light source information of the target light source point, the defect-free near-field distribution and defect near-field distribution of the wafer surface under the action of the target light source point are solved;

Based on the defect-free near-field distribution and defect near-field distribution of the wafer surface under the action of each light source point on the illumination pupil, a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil are determined.

Specifically, the ND model and WD model are established, and the defect-free near-field distribution of ND at each light source point is calculated according to the wavelength λ and light source point information of the light source point Q _{(i, j)} . WD defect near-field distribution The size is N _w ×N _w . Each element is a 2×1 vector:

in, The upper index index (i, j) of is consistent with the lower index index of Q _{(i, j)} after the illumination pupil is rasterized, indicating the near-field distribution on the wafer surface under the action of the point light source Q _{(i, j)} ; They are respectively the x- and y-direction components of the electric field at the wafer surface point (x, y) in the global Cartesian coordinate system.

By calculating each point on the illumination pupil Q point by point, the defect-free near field set NF _nd and the defective near field set NF _wd are obtained as follows:

The calculation process of the defect-free near field set NF _nd and the defective near field set NF _wd is further described in detail below. FIG3 is a flow chart of the imaging model provided in an embodiment of the present application. As shown in FIG3 , bright field microscopy imaging is simplified into four modules: a light source module (S), a wafer module (W), a lens module (P) and an imaging module (I).

First, according to the partial coherence theory of Abbe imaging, the illumination source can be divided into several independent point sources. The frequency domain distribution of the light source with a wavelength of λ is rasterized to obtain the third two-dimensional distribution matrix S of the light source frequency domain with a size of _Ns × _Ns . Each position (i,j) on the third two-dimensional matrix S represents a plane wave S _(i,j ). The relationship between the incident angle and azimuth of S _(i,j) and its position (i,j) satisfies:

in, That is, the difference between the position index i and the midpoint position of the row of matrix S (i _d distinguishes between positive and negative signs, and ceil is a round-up operation); is the difference between position index j and the position of the midpoint of the matrix S column (signs are distinguished); step _fs is the step size of the grid division in the frequency domain of the light source. The illumination pupil is defined as the shape of the illumination light source frequency domain, which determines the range of the incident angle and azimuth angle of the illumination light source.

The illumination pupil is rasterized to obtain the first two-dimensional distribution matrix Q of size _Ns × _Ns . By controlling the pixel values at each position of the illumination pupil matrix Q, the shape of the illumination source (incident angle θ and azimuth angle ) and the incident plane wave intensity.

The polarization angle of the illumination source is ρ (in the global Cartesian coordinate system), which can be calculated according to different polarization illumination methods (TE, TM, X, Y, Natural), and is defined here: ρ _Y =0.

Secondly, according to the structure and pattern of the wafer, a binary image B is constructed. The dark area of the binary image represents the substrate and the bright area represents the pattern. According to the spatial resolution size of the wafer end, B is divided to obtain a two-dimensional distribution matrix W with a size of _Nw × _Nw . Given W substrate material MB, pattern material MP; substrate thickness DB, pattern thickness DP. According to the wavelength (λ) and the incident angle (θ _(i,j) ) and azimuth angle of the light source point S _(i,j) Polarization angle (ρ _(i,j) ), using approximate or strict calculation methods to obtain the near-field distribution on the wafer surface under the action of the light source point The size is _Nw × _Nw , Each element is a 2×1 vector;

Among them, (x, y) is the index of the physical size of the wafer surface, (i, j) is the position index of the light source point S _{(i, j)} in the light source distribution matrix S, They are respectively the x- and y-direction components of the electric field at the wafer surface point (x, y) in the global Cartesian coordinate system.

Again, according to the lens properties of the imaging system (here refers to the lens with the maximum NA), it can be equivalent to a maximum cutoff frequency of The transfer function of a low-pass filter is the pupil function of the lens:

Where f _x , f _y are the frequency domain coordinates of the pupil; the frequency domain distribution of the pupil is gridded to obtain a two-dimensional matrix P with a size of N _p ×N _p ; considering the magnification of the lens of the bright field microscope, the polarization offset of the electric field at high NA, and the wave aberration of the lens, each element of the lens transfer function matrix P is a 3×2 vector, that is:

Among them, (px,py) represents the coordinate index of the two-dimensional matrix P, and _pxx , _pxy , _pxz , _pyx , _pyy and _pyy are the directional components of P _(px,py) .

The collection pupil is defined as the shape of the lens exit pupil. The collection pupil is rasterized to obtain a second two-dimensional distribution matrix C of size _Np × _Np . By controlling the pixel values at each position of the collection pupil matrix C, the transmittance of each frequency component on the imaging plane can be controlled. Consider the lens transfer function matrix after the collection pupil is applied: _Pc = C⊙P, ⊙ represents the multiplication of the elements at the corresponding positions. According to the angular spectrum propagation theory, after the near field _ENF on the wafer surface is applied by the lens, the electric field distribution on the imaging surface is: _Eimahe = F ^-1 { _Pc⊙F { _ENF }}

Wherein, F{} represents Fourier transform, F ^-1 {} represents inverse Fourier transform;

Each element of the E _image is a 3×1 vector, that is:

Where (x, y) represents the index of the physical size of the spatial image plane (CCD camera plane); are the x, y, and z components of the electric field at the image point (x, y) in the global Cartesian coordinate system;

Finally, according to the Abbe imaging principle, the light intensity distribution on the imaging surface is equal to the algebraic superposition of the image surface light intensity distributions of all point light sources, that is, the total image surface light intensity distribution:

in, * represents the conjugation operator.

For a given illumination pupil distribution Q and collection pupil distribution C, the light intensity distribution of the spatial image on the image plane is:

Among them, Q _sum =∑ _j ∑ _i Q _(i,j) .

The optimization method for wafer detection configuration parameters provided in the embodiment of the present application, when optimizing using the imaging model, only needs to calculate the near-field information once based on the light source information after initial discretization. During the optimization process, there is no need to repeatedly calculate the near field of the wafer surface due to changes in lighting conditions, which greatly shortens the calculation time of the imaging model during optimization and improves the optimization efficiency.

In some embodiments, determining an evaluation index based on the first aerial image and the second aerial image in S105, and determining an evaluation function based on the evaluation index, specifically includes:

determining a bi-norm of a difference between the first aerial image and the second aerial image, and a mean of the first aerial image;

Determine the ratio of the second norm to the mean, and use the ratio as an evaluation index;

Determine the evaluation function that aims to maximize the evaluation index.

Specifically, the difference between the first aerial image and the second aerial image can be calculated by difference, division, etc. In the embodiment of the present application, the evaluation index and the evaluation function are determined by the second norm of the difference between the first aerial image and the second aerial image and the mean of the first aerial image.

When using the Die-to-Die method to detect wafer defects, if the grayscale value of the detected image is significantly different from the grayscale value of the reference image, it is considered that the probability of a defect here is high. Therefore, in the embodiment of the present application, a model-based method is considered to design a reference wafer (ND) and a wafer containing specific defects (WD), and an evaluation index is defined using defect signal contrast.

Optionally, the difference between the first aerial image I _ND of the ND wafer and the second aerial image I _WD of the WD wafer is compared with the average value of the aerial image I _ND of the ND wafer to determine the evaluation index as follows:

Among them, ‖I _WD -I _ND ‖ ₂ is the second norm of the difference between the first aerial image I _ND and the second aerial image I _WD , mean(I _ND ) is the mean of I _ND , and mean(I _ND ) can be regarded as a constant to simplify the calculation; the evaluation function G=max{FOM} optimizes the contrast of the defect signal, that is, solves the maximum FOM.

Optionally, the difference between the first aerial image I _ND of the ND wafer and the second aerial image I _WD of the WD wafer is compared with the average value of the aerial image I _ND of the ND wafer to determine the evaluation index as follows:

Among them, ‖I _WD -I _ND ‖ ₂ is the second norm of the difference between the first aerial image I _ND and the second aerial image I _WD , mean(I _ND ) is the mean of I _ND , and mean(I _ND ) can be regarded as a constant to simplify the calculation; the evaluation function G=min{FOM} optimizes the contrast of the defect signal, that is, solves the minimum FOM.

The method for optimizing wafer inspection configuration parameters provided in the embodiment of the present application also calculates the defect-free spatial image under the same illumination pupil and collection pupil conditions as a reference during the optimization process, thereby ensuring the optimization direction of the optimization process, that is, the contrast of defect signals in defective images is constantly enhanced; and then using the optimized simulation results, defect detection without a reference image can be achieved, thereby avoiding the situation in which systematic defects cannot be detected due to the traditional Die-to-Die inspection method.

In some embodiments, the iterative optimization process in S106 specifically includes:

Determine the gradient matrix of the evaluation function for the selected optimization object;

The variable matrix of the optimization object is updated based on the fastest gradient descent method, and the first spatial image, the second spatial image and the evaluation index under the updated variable matrix are calculated.

Specifically, gradient descent is a type of iterative method, which is suitable for solving unconstrained optimization problems, that is, solving the objective function itself. The evaluation function in some embodiments of the present application aims to maximize an evaluation index, and the evaluation index is the ratio of the second norm of the difference between the first spatial image and the second spatial image to the mean of the first spatial image. The convergence speed of the ordinary gradient descent method is significantly slowed down in the area close to the optimal solution. It actually takes many iterations to solve the evaluation function using the ordinary gradient solution method. Therefore, in the embodiments of the present application, it is considered to use the fastest gradient descent method to update the variable matrix of the optimization object during the iteration process to speed up the convergence speed of the algorithm.

The specific iterative optimization process is: calculate the gradient matrix of the evaluation function G to the optimization object OT Update the optimization object variable matrix using the fastest gradient descent method Calculate the spatial image I _ND , I _WD and evaluation index FOM under the OT update condition.

According to the last iteration of the optimization process Output the optimized result graph and the optimized evaluation index FOM to end the optimization process.

In some embodiments, selecting an optimization object and an optimization method in S106 includes:

When the selected optimization object is the illumination pupil or the collection pupil, the selected optimization method is direct optimization (DO); or,

When the selected optimization objects are the illumination pupil and the collection pupil, the selected optimization method is sequential optimization (Sequential Optimization, SeO) or simultaneous optimization (Simultaneous Optimization, SiO).

Specifically, the optimization object OT and the optimization method OM are selected, wherein the available optimization objects include the illumination pupil Q and the collection pupil C. When there is only a single optimization object (Q or C), the optimization method is direct optimization; when the optimization object includes both the illumination pupil and the collection pupil (Q and C), the optimization methods that can be selected are sequential optimization or collaborative optimization.

Optionally, when the optimization object is only the illumination pupil Q, the illumination pupil C is fixed, and the optimization process only needs to calculate the approximate gradient matrix of the objective function G with respect to Q: The illumination pupil variable matrix is updated using the fastest gradient descent method, and the ND spatial image I _ND , WD spatial image I _WD and evaluation index FOM _so under the new Q condition are recalculated.

The illumination pupil variable matrix is updated as:

Among them, step _s is the iterative step size for setting the illumination pupil;

Approximate gradient matrix The calculation method is:

in, They are the spatial image intensity distributions of ND and WD wafers of a single point source Q _{(i, j)} respectively.

Optionally, when the optimization object is only the collection pupil C, the illumination pupil Q is fixed, and the optimization process only needs to calculate the approximate gradient matrix of the objective function G with respect to C: Update the collection pupil matrix using the steepest gradient descent method:

And recalculate the ND spatial image I _ND , WD spatial image I _WD and evaluation index FOM _po under condition C.

The collected pupil variable matrix is updated as:

Among them, step _p is to set the iteration step size of the collection pupil;

in:

Wherein, ΔI=a(I _WD -I _ND ); a is a coefficient of the size of the iteration accuracy of the collected pupil; and P ^* is the conjugate of the pupil distribution matrix.

Optionally, when the optimization objects are the illumination pupil Q and the collection pupil C, and the optimization method is collaborative optimization (SiO), it is necessary to calculate the approximate gradient matrix of the objective function G for Q: and the approximate gradient matrix for C The illumination pupil variable matrix is updated simultaneously using the steepest gradient descent method:

Collect the pupil variable matrix:

And recalculate the ND spatial image I _ND , WD spatial image I _WD and evaluation index FOM _sio under Q and C conditions.

Optionally, when the optimization objects are Q and C, and the optimization method is sequential optimization (SeO), when the order is to optimize the illumination pupil first and then the collection pupil, the illumination pupil is directly optimized, and when the illumination pupil optimization is completed, the collection pupil is directly optimized, and when the iteration condition is met, the optimized illumination pupil and collection pupil graphics and the evaluation index FOM _seo1 are output; if the order is to optimize the collection pupil first and then the illumination pupil, the collection pupil is optimized, and when the collection pupil optimization is completed, the illumination pupil is optimized, and when the iteration condition is met, the optimized illumination pupil and collection pupil graphics and the evaluation index FOM _seo2 are output.

The wafer detection configuration parameter optimization method provided in the embodiment of the present application provides a variety of illumination pupil and collection pupil optimization process solutions, which can adapt to the optimization of different scenarios. These optimization process solutions can improve the contrast of defect signals to different degrees. By adopting different optimization processes for different defects, the illumination pupil and collection pupil shapes that are most suitable for the specified defect type can be selected to achieve the best detection contrast for the specified defect type.

The technical solution provided in the embodiments of the present application is further illustrated below with several specific examples.

Example 1:

FIG. 4 is a second flow chart of a method for optimizing wafer detection configuration parameters provided in an embodiment of the present application, and the specific steps are as follows:

Step 401: rasterize the illumination pupil to obtain an illumination pupil matrix Q; rasterize the collection pupil to obtain a collection pupil matrix C.

The image of the illumination pupil is rasterized into a two-dimensional distribution matrix Q of size _Ns × _Ns , and the image of the collection pupil is rasterized into a two-dimensional distribution matrix C of size _Np × _Np ; the variable matrix of the illumination pupil of size _Ns × _Ns is initialized with the intensity distribution of the illumination pupil The value range is between [0,1]; whether to initialize the variable matrix of size _Np × _Np collected pupil in the collected pupil graph When the point (i _p ,j _p ) is within the collection pupil pattern, When point (i _p ,j _p ) is outside the collection pupil pattern, Where _Ns and _Np are both integers;

Step 402: Divide the grid according to the polarized illumination mode and the illumination pupil, and calculate the incident angle, azimuth angle and polarization angle information of each light source point.

According to the Y polarization illumination mode and the two-dimensional matrix Q of the illumination pupil, each grid point after rasterization is calculated to represent an illumination light source point, and the polarization angle of each light source point is ρ _Y =0; the incident angle (θ), azimuth The calculation is as follows.

in, That is, the difference between the position index i and the midpoint position of the row of matrix Q (i _d distinguishes between positive and negative signs, and ceil is a round-up operation); is the difference between the position index j and the midpoint position of the matrix Q column (distinguishing between positive and negative signs); step _fs is the step size of the light source frequency domain grid division.

Step 403: Divide the light source point information according to the illumination pupil, and calculate the near-field distribution sets of the defect-free structure and the defective structure of each light source point respectively.

Establish the structural model of defect-free structure and defective structure, construct binary images Bnd and Bwd respectively, the dark area of the binary image represents the substrate, and the bright area represents the pattern; divide _Bnd and _Bwd according to the spatial resolution size of the wafer end, and obtain the two-dimensional distribution matrix _Wnd and _Wwd , both of which are _Nw × _Nw ; given the substrate material MB and pattern material MP of _Wnd and _Wwd ; substrate thickness DB, pattern thickness DP. According to the information of wavelength λ and light source point Q _(i,j) , the strict near field of ND of each light source point is calculated by using Finite Difference Time Domain (FDTD) or Rigorous Coupled Wave Analysis (RCWA) WD's Strict Near Field The size is N _w ×N _w . Each element is a 2×1 vector:

in, The upper index index (i, j) of is consistent with the lower index index of Q _{(i, j)} after the illumination pupil is rasterized, indicating the near-field distribution of the near-field on the wafer surface under the action of the point light source Q _{(i, j)} .

By calculating each point on the illumination pupil Q point by point, the defect-free near field set NF _nd and the defective near field set NF _wd are obtained as follows:

Step 404: using a bright field microscopic imaging model, respectively calculate the defective wafer aerial image and the non-defective wafer aerial image under the current illumination pupil and collection pupil conditions.

Initialize the parameters of the bright field microscopy imaging model illumination, wafer, and lens module, and calculate the spatial image I _ND of the ND wafer and the spatial image I _WD of the WD wafer under the conditions of the illumination pupil Q and the collection pupil C.

in,

Step 405: construct evaluation indexes and evaluation functions of the optimization object.

The defect signal contrast is used to define the evaluation index, that is, the difference between the aerial image I _ND of the ND wafer and the aerial image I _WD of the WD wafer is compared with the average value of the aerial image I _ND of the ND wafer. The evaluation index is as follows:

Wherein, ‖I _WD -I _Nd ‖ ₂ is the second norm of the difference between the aerial image I _ND and the aerial image I _WD , mean(I _ND ) is the mean of I _ND ; the evaluation function G=max{FOM} optimizes the contrast of the defect signal, that is, solves the maximum FOM.

Step 406: Select the optimization object and optimization method.

The optimization object (OT) selected includes both the illumination pupil and the collection pupil (Q and C), the optimization method is sequential optimization (SeO), and the optimization order is: optimize the illumination pupil first and then optimize the collection pupil.

Step 407: Execute the iterative optimization process.

① When the optimization object (OT) is first the illumination pupil Q, the approximate gradient matrix of the objective function G with respect to Q is calculated The illumination pupil variable matrix is updated using the fastest gradient descent method, and the ND spatial image I _Nd , WD spatial image I _WD and the evaluation index FOM _seo1 under the new Q condition are recalculated.

The illumination pupil variable matrix is updated as:

Among them, step _s is the iterative step size for setting the illumination pupil;

Approximate gradient matrix The calculation method is:

in, They are the spatial image intensity distributions of ND and WD wafers of a single point source Q _{(i, j)} respectively.

② When OT is next collecting pupil C, the illumination pupil Q is fixed and optimized, and the approximate gradient matrix of the objective function G for C is calculated. The pupil matrix is updated using the fastest gradient descent method, and the ND spatial image I _ND , WD spatial image I _WD and evaluation index FOM _seo2 under C conditions are recalculated.

The collected pupil variable matrix is updated as:

Among them, step _p is to set the iteration step size of the collection pupil;

in:

Wherein, ΔI=a(I _WD −I _ND ); a is a coefficient of the size of the iterative precision of the collected pupil; and P ^* is the conjugate of the pupil distribution matrix P.

Step 408: If the value of the evaluation index FOM in step 407 meets the set threshold or When the number of iterations reaches the maximum number, proceed to step 409 , otherwise return to step 407 .

Step 409: According to the last Output the graphics of the optimized illumination pupil and collection pupil, as well as the optimized evaluation index FOM _seo2 , and end the optimization process.

Example 2:

Figure 5 is a schematic diagram of the initial illumination pupil, defect-free wafer binary image, initial collection pupil and imaging results provided by an embodiment of the present application. As shown in Figure 5, from left to right are: the result after the initialization illumination pupil is rasterized, the grayscale value represents the intensity value of the light source point; the defect-free wafer binary image, the bright area represents the pattern, and the dark area represents the substrate; the result after the initialization collection pupil is rasterized, the bright area represents the frequency component in the area can reach the imaging plane, and the gray or dark area represents the frequency component in the area will be attenuated or blocked; the defect-free wafer spatial image under the conditions of the initialization illumination pupil and the collection pupil.

Figure 6 is a schematic diagram of the initial illumination pupil, defective wafer binary image, initial collection pupil and imaging results provided in an embodiment of the present application. As shown in Figure 6, from left to right are: initial illumination pupil, defective wafer binary image, initial collection pupil and schematic diagram of defective wafer imaging results; FOMinit of the defect signal in the initial defective spatial image is 0.19761.

FIG7 is a schematic diagram of an imaging structure when only the illumination pupil is optimized according to an embodiment of the present application. As shown in FIG7 , when only the illumination pupil is optimized (IPO), from left to right are: an illumination pupil after optimization, a binary image of a defective wafer, an initial collection pupil, and a schematic diagram of an imaging result of a defective wafer; FOMipo of a defect signal in a defective spatial image after only illumination pupil optimization (IPO) is 0.33930; compared with FIG6 , the defect signal contrast is enhanced by 71% after optimization, and the defect signal contrast is greatly improved;

FIG8 is a schematic diagram of an imaging structure when only the collection pupil is optimized according to an embodiment of the present application. As shown in FIG8 , when only the collection pupil is optimized (CPO), from left to right are: an initial illumination pupil, a defective wafer binary image, an optimized collection pupil, and a schematic diagram of imaging results of a defective wafer; FOMcpo of a defect signal in a defective spatial image after only the collection pupil optimization (CPO) is 0.21963; compared with FIG6 , the defect signal contrast is enhanced by 11% after optimization, and the improvement of the defect signal contrast is small;

Figure 9 is one of the schematic diagrams of the imaging structure when the illumination pupil and the collection pupil are optimized according to an embodiment of the present application. As shown in Figure 9, when the illumination pupil and the collection pupil are synchronously optimized (SiO), from left to right are: the illumination pupil after optimization, the binary image of the defective wafer, the collection pupil after optimization, and a schematic diagram of the imaging results of the defective wafer; the FOMsio of the defect signal in the defective spatial image after synchronous optimization of the illumination pupil and the collection pupil is 0.45886; compared with Figure 6, the defect signal contrast is enhanced by 130% after optimization, and the improvement in the defect signal contrast is large, which is better than the result of optimizing the illumination pupil or the collection pupil alone.

Figure 10 is the second schematic diagram of the imaging structure when optimizing the illumination pupil and the collection pupil provided in an embodiment of the present application. As shown in Figure 10, when the illumination pupil and the collection pupil are optimized sequentially (SeO), the optimization order is to optimize the illumination pupil first and then optimize the collection pupil. From left to right, they are: the illumination pupil after optimization, the binary image of the defective wafer, the collection pupil after optimization, and a schematic diagram of the imaging results of the defective wafer; the FOMseo of the defect signal in the defective spatial image after synchronously optimizing the illumination pupil and the collection pupil is 0.58654; compared with Figure 6, the defect signal contrast is enhanced by 196% after optimization, and the improvement in the defect signal contrast is the largest; under this condition, sequential optimization (IPO first and then CPO) can obtain the best defect signal contrast in the example.

Comparing the results of Figures 5 to 10, it can be seen that after the discrete illumination and collection pupils, the degree of freedom of optimization is improved. When optimizing a single object, the defect signal contrast is improved to a certain extent. Combining the optimization of the illumination pupil and the collection pupil, choosing the appropriate optimization method can further improve the defect contrast. The selection of multiple optimization methods is conducive to the global optimal solution required in various complex scenarios. These implementation cases prove that after the optimization of the illumination pupil and the collection pupil, the detection sensitivity and detection capability of the defect detection system are improved.

FIG. 11 is a schematic diagram of the structure of a device for optimizing wafer detection configuration parameters provided in an embodiment of the present application. As shown in FIG. 11 , the device at least includes:

A rasterization unit 1101 is used to rasterize the graphics of the illumination pupil and the collection pupil respectively to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initialize a first variable matrix corresponding to the illumination pupil based on the intensity distribution of the illumination pupil, and initialize a second variable matrix corresponding to the collection pupil based on whether the collection pupil is within the graphics of the collection pupil;

A light source point information determining unit 1102, configured to determine light source point information of a light source point corresponding to each grid point after rasterization based on the polarized illumination mode and the first two-dimensional distribution matrix;

A near-field distribution determination unit 1103 is used to establish a defect-free structure model and a defect structure model of the target defect type, and solve a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil in combination with light source point information;

An aerial image determination unit 1104 is used to initialize a bright field microscopic imaging model and calculate a first aerial image of a non-defective wafer and a second aerial image of a defective wafer;

An evaluation index determining unit 1105, configured to determine an evaluation index based on the first aerial image and the second aerial image, and determine an evaluation function based on the evaluation index;

An iterative optimization unit 1106 is used to select an optimization object and an optimization method, perform an iterative optimization process based on the selected optimization object and optimization method, update a variable matrix of the optimization object, and an evaluation index under the updated variable matrix; the optimization object is an illumination pupil and/or a collection pupil;

The optimization output unit 1107 is used to determine the optimized graph of the optimization object with the variable matrix of the optimization object updated by the last iterative optimization process when the updated evaluation index meets the preset threshold or the number of iterations reaches the maximum number.

In some embodiments, the near-field distribution determining unit 1103 is specifically configured to:

A binary image is constructed according to the structure and pattern of the wafer, wherein the dark area of the binary image represents the substrate and the bright area represents the pattern;

According to the spatial resolution size of the wafer end, the target defect type is divided into a non-defective binary image and a defective binary image;

Given the substrate material, pattern material, substrate thickness and pattern thickness, the establishment of a defect-free structure model and a defective structure model is completed.

In some embodiments, the near-field distribution determining unit 1103 is specifically configured to:

Based on the wavelength and light source information of the target light source point, the defect-free near-field distribution and defect near-field distribution of the wafer surface under the action of the target light source point are solved;

Based on the defect-free near-field distribution and defect near-field distribution of the wafer surface under the action of each light source point on the illumination pupil, a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil are determined.

In some embodiments, the aerial image determination unit 1104 is specifically configured to:

determining a bi-norm of a difference between the first aerial image and the second aerial image, and a mean of the first aerial image;

Determine the ratio of the second norm to the mean, and use the ratio as an evaluation index;

Determine the evaluation function that aims to maximize the evaluation index.

In some embodiments, the iterative optimization unit 1106 is specifically configured to:

Determine the gradient matrix of the evaluation function for the selected optimization object;

The variable matrix of the optimization object is updated based on the fastest gradient descent method, and the first spatial image, the second spatial image and the evaluation index under the updated variable matrix are calculated.

In some embodiments, the iterative optimization unit 1106 is specifically configured to:

When the selected optimization object is the illumination pupil or the collection pupil, the selected optimization method is direct optimization; or,

When the selected optimization objects are the illumination pupil and the collection pupil, the selected optimization method is sequential optimization or collaborative optimization.

It is understandable that the detailed functional implementation of each of the above-mentioned units/modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

Based on the method in the above embodiment, an embodiment of the present application provides an electronic device. The device may include: at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the above embodiment.

FIG12 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in FIG12 , the electronic device may include: a processor 1201, a communications interface 1220, a memory 1203, and a communication bus 1204, wherein the processor 1201, the communications interface 1202, and the memory 1203 communicate with each other through the communication bus 1204. The processor 1201 may call the software instructions in the memory 1203 to execute the method described in the above embodiment.

In addition, the logic instructions in the above-mentioned memory 1203 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present application.

Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

It is easy for those skilled in the art to understand that the above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims (7)

1. The optimization method of the wafer detection configuration parameters is characterized by comprising the following steps:

respectively rasterizing patterns of an illumination pupil and a collection pupil to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initializing a first variable matrix corresponding to the illumination pupil based on intensity distribution of the illumination pupil, and initializing a second variable matrix corresponding to the collection pupil based on whether the first variable matrix is initialized in the pattern of the collection pupil;

Determining light source point information of light source points corresponding to each grid point after rasterization based on a polarized illumination mode and the first two-dimensional distribution matrix;

Establishing a defect-free structure model and a defect structure model of a target defect type, and solving a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil by combining the light source point information;

Initializing a bright field microscopic imaging model, and calculating a first space image of a defect-free wafer and a second space image of the defect wafer;

Determining an evaluation index based on the first aerial image and the second aerial image, and determining an evaluation function based on the evaluation index;

selecting an optimization object and an optimization mode, executing an iterative optimization flow based on the selected optimization object and the optimization mode, updating a variable matrix of the optimization object, and updating an evaluation index under the variable matrix; the optimization object is the illumination pupil and/or the collection pupil;

when the updated evaluation index meets a preset threshold value or the iteration number reaches the maximum number, determining an optimized graph of the optimized object by using a variable matrix of the optimized object updated by the iterative optimization flow for the last time;

wherein the establishing a defect-free structural model and a defect structural model of the target defect type comprises:

constructing a binary image according to the structure and the pattern of the wafer, wherein a dark area of the binary image represents a substrate, and a bright area represents the pattern;

Dividing a defect-free binary image and a defect binary image of the target defect type according to the space resolution of the wafer end;

giving a substrate material, a pattern material, a substrate thickness and a pattern thickness, and completing the establishment of the defect-free structural model and the defect structural model;

wherein the determining an evaluation index based on the first aerial image and the second aerial image, and determining an evaluation function based on the evaluation index, comprises:

Determining a second norm of the difference between the first aerial image and the second aerial image, and a mean value of the first aerial image;

Determining the ratio of the two norms to the average value, and taking the ratio as the evaluation index;

Determining an evaluation function targeting maximizing the evaluation index;

The iterative optimization flow comprises the following steps:

determining a gradient matrix of the evaluation function for the selected optimization object;

updating a variable matrix of the optimization object based on a steepest gradient descent method, and calculating a first aerial image, a second aerial image and an evaluation index under the updated variable matrix.

2. The method according to claim 1, wherein the solving the defect-free near-field distribution set and the defect near-field distribution set corresponding to the illumination pupil in combination with the light source point information comprises:

Solving defect-free near-field distribution and defect near-field distribution of the surface of the wafer under the action of a target light source point based on the wavelength of the target light source point and the light source point information;

And determining a defect-free near-field distribution set and a defect near-field distribution set corresponding to the illumination pupil based on the defect-free near-field distribution and the defect near-field distribution of the wafer surface under the action of each light source point on the illumination pupil.

3. The method of optimizing wafer inspection configuration parameters according to claim 1, wherein the selecting optimization objects and optimization methods comprises:

in the case that the selected optimization object is the illumination pupil or the collection pupil, selecting an optimization mode to be direct optimization; or alternatively

In case the selected optimization object is the illumination pupil and the collection pupil, the selected optimization mode is a sequential optimization or a collaborative optimization.

4. An optimizing apparatus for detecting configuration parameters of a wafer, comprising:

A rasterizing unit, configured to respectively rasterize a pattern of an illumination pupil and a pattern of a collection pupil to obtain a first two-dimensional distribution matrix and a second two-dimensional distribution matrix, initialize a first variable matrix corresponding to the illumination pupil based on intensity distribution of the illumination pupil, and initialize a second variable matrix corresponding to the collection pupil based on whether the second variable matrix is initialized in the pattern of the collection pupil;

The light source point information determining unit is used for determining light source point information of light source points corresponding to each grid point after rasterization based on a polarized illumination mode and the first two-dimensional distribution matrix;

The near field distribution determining unit is used for establishing a defect-free structure model and a defect structure model of a target defect type, and solving a defect-free near field distribution set and a defect near field distribution set corresponding to the illumination pupil by combining the light source point information;

The space image determining unit is used for initializing a bright field microscopic imaging model and calculating a first space image of the defect-free wafer and a second space image of the defect wafer;

An evaluation index determination unit configured to determine an evaluation index based on the first aerial image and the second aerial image, and determine an evaluation function based on the evaluation index;

The iterative optimization unit is used for selecting an optimization object and an optimization mode, executing an iterative optimization flow based on the selected optimization object and the optimization mode, updating a variable matrix of the optimization object and an evaluation index under the updated variable matrix; the optimization object is the illumination pupil and/or the collection pupil;

the optimization output unit is used for determining an optimized graph of the optimized object according to the variable matrix of the optimized object updated by the iterative optimization flow for the last time when the updated evaluation index meets a preset threshold or the iteration number reaches the maximum number;

the near field distribution determining unit is specifically configured to:

constructing a binary image according to the structure and the pattern of the wafer, wherein a dark area of the binary image represents a substrate, and a bright area represents the pattern;

Dividing a defect-free binary image and a defect binary image of the target defect type according to the space resolution of the wafer end;

giving a substrate material, a pattern material, a substrate thickness and a pattern thickness, and completing the establishment of the defect-free structural model and the defect structural model;

wherein, the aerial image determining unit is specifically configured to:

Determining a second norm of the difference between the first aerial image and the second aerial image, and a mean value of the first aerial image;

Determining the ratio of the two norms to the average value, and taking the ratio as the evaluation index;

Determining an evaluation function targeting maximizing the evaluation index;

the iterative optimization unit is specifically configured to:

determining a gradient matrix of the evaluation function for the selected optimization object;

updating a variable matrix of the optimization object based on a steepest gradient descent method, and calculating a first aerial image, a second aerial image and an evaluation index under the updated variable matrix.

5. An electronic device, comprising:

At least one memory for storing a computer program;

At least one processor for executing the memory-stored program, which processor is adapted to perform the method according to any of claims 1-3, when the memory-stored program is executed.

6. A computer readable storage medium storing a computer program, characterized in that the computer program, when run on a processor, causes the processor to perform the method according to any one of claims 1-3.

7. A computer program product, characterized in that the computer program product, when run on a processor, causes the processor to perform the method according to any of claims 1-3.