Method and system for detecting and analyzing polluted particles on surface of TSV wafer after CMP cleaning

Abstract

The invention provides a method and a system for detecting and analyzing pollution particles on the surface of a TSV (Through Silicon Via) wafer after CMP (Chemical Mechanical Polishing) cleaning, relates to the technical field of integrated circuits, and aims to solve the technical problem that pollution particle risk assessment is not accurate enough in related technologies. The method comprises the following steps: acquiring a particle distribution diagram of a to-be-detected wafer; obtaining TSV layout data of the wafer to be detected; on the basis of a preset reference mark, aligning the particle distribution diagram with the TSV layout data to generate fusion data under a unified coordinate system; dividing the surface of the wafer to be detected into an analysis grid formed by a plurality of analysis units; and generating a TSV pollution score representing the pollution risk of the target TSV for the target TSV according to the size information of each pollution particle around the target TSV and the spatial distance between each pollution particle and the geometric center of the target TSV based on the fusion data.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of integrated circuits, and in particular to a method and system for detecting and analyzing contamination particles on a TSV wafer surface after CMP cleaning. BACKGROUND

[0002] In three-dimensional (3D) integrated circuit manufacturing technology, through-silicon via (TSV) is a key structure for realizing vertical interconnection of chips. The manufacturing process of TSV is complex, and chemical mechanical polishing (CMP) is a core process step for realizing wafer surface planarization. However, the CMP process and the subsequent cleaning process will inevitably leave micron-level or even nanometer-level contamination particles on the wafer surface. If these contamination particles are located in the TSV structure or its adjacent area, they may cause serious electrical problems, such as short circuit, open circuit, or crosstalk of signal transmission between TSVs, thereby directly affecting the performance and reliability of the chip, and even causing the entire chip to be scrapped. In the prior art, the detection of contamination on the wafer surface after CMP mainly relies on full-wafer surface scanning equipment. This type of equipment can quickly detect the number, size, and position distribution of surface particles and generate a particle distribution map. However, the traditional analysis method usually judges the cleanliness of the wafer based on the total number or density of particles, or simply sets a "kill zone" to determine whether a particle falls directly above the TSV. The defect of this method is that it cannot finely assess the specific risk differences caused by particles of different sizes and positions to specific TSV structures. For example, a particle that is small in size but close to the TSV may have a much greater risk than a particle that is large in size but far away from all TSVs. This one-size-fits-all approach can easily lead to misjudgment of wafer quality, resulting in yield loss or potential reliability risks. SUMMARY

[0003] Embodiments of the present application provide a method and system for detecting and analyzing contamination particles on a TSV wafer surface after CMP cleaning, to improve the technical problem of insufficient accuracy in risk assessment of contamination particles in related technologies. To achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions: In a first aspect, the application provides a method for detecting and analyzing contamination particles on a TSV wafer surface after CMP cleaning, the method comprising: obtaining a particle distribution map of a wafer to be detected, the particle distribution map comprising position coordinates and size information of each contamination particle on the wafer surface; obtaining TSV layout data of the wafer to be detected, the TSV layout data comprising geometric center position coordinates of each TSV on the wafer surface; aligning the particle distribution map and the TSV layout data based on a preset reference mark to generate fusion data in a unified coordinate system; dividing the surface of the wafer to be detected into an analysis grid composed of multiple analysis units; for each target TSV, generating a TSV contamination score representing the contamination risk of the target TSV based on the fusion data, according to the size information of each contamination particle around the target TSV and the spatial distance between each contamination particle and the geometric center of the target TSV. In a possible implementation manner of the first aspect, the aligning the particle distribution map and the TSV layout data comprises: identifying a first reference mark in the particle distribution map and a second reference mark in the TSV layout data; obtaining a coordinate transformation matrix based on the coordinates of the first reference mark and the second reference mark; and applying the coordinate transformation matrix to the position coordinates of the particle distribution map to align the coordinate system of the particle distribution map with the coordinate system of the TSV layout data. In a possible implementation manner of the first aspect, the step of generating a TSV contamination score for the target TSV comprises: for any contamination particle around the target TSV, determining a basic influence value based on the size information of the contamination particle; obtaining a distance attenuation factor based on the spatial distance between the contamination particle and the target TSV; obtaining a single particle contribution value of the contamination particle to the target TSV based on the basic influence value and the distance attenuation factor; and accumulating the single particle contribution values of all contamination particles to obtain the TSV contamination score of the target TSV. In a possible implementation manner of the first aspect, the distance attenuation factor is obtained by a preset attenuation function, and a standard deviation of the attenuation function is positively correlated with the size information of the contamination particle. In a possible implementation manner of the first aspect, after the TSV contamination score of the target TSV is generated, the method further comprises: obtaining optical characteristic parameters of each contamination particle in the particle distribution map; matching a preset material risk coefficient to each contamination particle according to the optical characteristic parameters of the contamination particle; and correcting the TSV contamination score based on the material risk coefficient to obtain an adjusted TSV contamination score. In a possible implementation manner of the first aspect, the optical characteristic parameters comprise at least one of light scattering intensity and reflectivity of the contamination particle. In a possible implementation manner of the first aspect, the correcting the TSV contamination score based on the material risk coefficient comprises: when the single-particle contribution value is calculated, the material risk coefficient corresponding to the contamination particle is taken as a weight factor, and is combined with the basic influence value and the distance attenuation factor. In a possible implementation manner of the second aspect, the system further comprises: a risk correction module, configured to: after the TSV contamination score is generated by the score generation module, acquire optical characteristic parameters of each contamination particle, match a preset material risk coefficient for each contamination particle according to the optical characteristic parameters, and correct the TSV contamination score based on the material risk coefficient to obtain an adjusted TSV contamination score. In a possible implementation manner of the second aspect, the score generation module is specifically configured to: for any contamination particle around the target TSV, determine a basic influence value based on size information of the contamination particle; acquire a distance attenuation factor based on a spatial distance between the contamination particle and the target TSV; combine the basic influence value and the distance attenuation factor to obtain a single-particle contribution value of the contamination particle to the target TSV; and accumulate the single-particle contribution values of all contamination particles to obtain the TSV contamination score of the target TSV. In a possible implementation manner of the second aspect, the system further comprises: a risk correction module, configured to: after the TSV contamination score is generated by the score generation module, acquire optical characteristic parameters of each contamination particle, match a preset material risk coefficient for each contamination particle according to the optical characteristic parameters, and correct the TSV contamination score based on the material risk coefficient to obtain an adjusted TSV contamination score. BRIEF DESCRIPTION OF DRAWINGS

[0004] Figure 1 A flowchart of a contamination particle detection and analysis method provided for some embodiments of the present application; Figure 2 A structural diagram of a contamination particle detection and analysis system provided for some embodiments of the present application. DETAILED DESCRIPTION

[0005] Clearly, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Hereinafter, the terms "first", "second", and the like are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", and the like can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified. In addition, in the present application, the orientation terms such as "up", "down", "left", "right", etc. can include but not limited to the orientation defined by the relative position of the components in the drawing. It should be understood that these directional terms can be relative concepts, which are used for relative description and clarification, and can be changed accordingly according to the change of the position of the components in the drawing. In the present application, unless otherwise explicitly specified and limited, the term "connection" should be understood broadly, for example, "connection" can be fixed connection, or detachable connection, or integral; can be directly connected, or indirectly connected through intermediate medium. In addition, the term "electrical connection" can be the mode of electrical connection that realizes signal transmission. As used herein, "about", "approximately" or "approximately" includes the stated value and the reference value within the acceptable deviation range of the specific value, characterized by the acceptable deviation range as determined by the person skilled in the art considering the measurement being discussed and the error related to the measurement of the specific quantity (i.e. the limitation of the measurement method). The embodiments of the present application provide a TSV wafer surface contamination particle detection analysis method and a corresponding system based on CMP cleaning. The core of the scheme is to abandon the traditional binary judgment logic based on the total number of particles or simple "kill zone", and to construct a risk assessment model by accurately integrating the physical particle scanning data of the wafer and the electronic design automation (EDA) layout data, which comprehensively considers the particle size, spatial position and material properties. The model can generate a unique, clearly physically meaningful contamination score for each TSV structure on the wafer, thereby realizing accurate positioning, quantitative grading and cause analysis of potential manufacturing defects. As Figure 1 The method comprises: S101, acquiring the particle distribution map and TSV layout data of the wafer to be detected. Exemplarily, a particle map of a wafer to be inspected is acquired. In a semiconductor manufacturing process, after a CMP (Chemical-mechanical polishing) planarization process and a series of subsequent complex cleaning processes (e.g., brushing, spray cleaning using megasonic energy and chemical cleaning liquid, etc.), the wafer to be inspected is sent to a high-precision surface scanning inspection device for full-wafer inspection to ensure that the surface cleanliness meets the standard. Such a device, for example, the Surfscan SPx series of KLA-Tencor Company or the Enlight series of Applied Materials Company, usually uses laser dark-field or bright-field scattering technology. In the dark-field mode, a laser beam is incident on the rotating wafer surface at an oblique angle, and a detector is placed at a position that does not receive specular reflection light; when the light beam encounters a surface foreign matter (i.e., a contamination particle), it will be scattered in all directions, and part of the scattered light is captured by the detector. The device analyzes the intensity, angular distribution, etc. of these scattered light signals, and finally generates a particle map through complex signal processing and image recognition algorithms. The particle map is usually stored in a standardized text file (such as CSV, TSV format) or a binary format specific to the device supplier. Its core information records the detailed attributes of each contamination particle detected on the wafer. Exemplarily, the data structure of a particle map can be a table containing a row record, the total number of particles detected on the wafer. Each row represents a contamination particle (where ), and at least includes the following key fields: an ID for uniquely identifying the particle in data processing; the center position of the particle in the physical scanning coordinate system, represented as , the origin and direction of which are determined by the scanning device during initial calibration; and the size information of the particle. The size of the particle is usually represented by the "Latex Sphere Equivalent" (LSE) diameter, denoted as . The physical meaning of the LSE size is that the scattered light intensity generated by an unknown particle is equivalent to the scattered light intensity generated by a standard latex sphere particle with the same diameter under the same conditions. Therefore, for any particle , its core information can be abstracted as a three-tuple containing position and size: . Meanwhile, the TSV (Through Silicon Via) layout data of the wafer product is also needed. This data comes from the EDA (Electronic Design Automation) stage of the chip. When designing the chip, the chip design engineers use EDA tools (such as Cadence Virtuoso or Synopsys Custom Compiler) to draw the layout containing all the circuit elements and interconnection structures, and finally output in the industry standard format (such as GDSII or OASIS). For the analysis of the present application, the specific layer related to the TSV structure needs to be extracted from it. This extraction process can be automated by a pre-processing script, the steps of which generally include: reading the GDSII / OASIS file; identifying the layer number that defines the main body of the TSV structure (for example, the metal filling layer of the TSV or the isolation layer around it) according to the design rules or technical documents (PDK) of the specific product; traversing all the geometric figures (Polygon or Path, etc.) on this layer; for each geometric figure representing a TSV, using a geometric algorithm (such as the centroid algorithm) to calculate the position coordinates of its geometric center; finally, output all the geometric center coordinates of the TSVs into a simplified and easy-to-process data file. The TSV layout data obtained in this way is a list containing the geometric center coordinates of a TSV (where ). The position information of each TSV (denoted as ) can be represented as . It needs to be emphasized that this coordinate is in the design layout coordinate system, whose origin and direction are defined by the design rules of the layout, and is independent of the aforementioned physical scanning coordinate system, so they cannot be directly compared. S102, aligning the particle distribution map and the TSV layout data based on the preset fiducial marks, and generating fusion data in a unified coordinate system. Since the physical coordinate system of the particle distribution map and the design coordinate system of the TSV layout data usually have differences in origin, direction and scale (microscopic) scaling, before spatial correlation analysis, coordinate alignment must be performed. The alignment process relies on the fiducial marks (Fiducial Marks) on the wafer, which exist in both the physical and design. These marks are specially designed graphics with unique and easily identifiable geometric features (such as L-shaped, cross-shaped or more complex patterns), which exist on the photomask and are accurately transferred to specific locations on the wafer (usually on the edge or scribe lane) through the photolithography process. Exemplarily, the corresponding coordinates of these fiducial markers are identified in both data sources. In the particle distribution map, the physical scan coordinates of at least two (in practice, three or more are usually used to improve precision and perform error checking) first fiducial markers are identified by a pattern recognition algorithm or directly reported by the surface scanning device. For example, the coordinates of marker A are , and the coordinates of marker B are . Correspondingly, in the TSV layout data processing stage, the corresponding design coordinates of the second fiducial markers are also extracted from the layout file. For example, the design coordinates of marker A are , and the design coordinates of marker B are . Based on these pairs of coordinate points (i.e. , etc.), the affine transformation matrix that can map the physical coordinate system to the design coordinate system can be calculated. Affine transformation can handle translation, rotation, and scaling at the same time. A two-dimensional affine transformation can be expressed as: In this expression, is the original physical coordinate, is the desired design coordinate after transformation. Using the known fiducial point pairs, an over-determined linear equation system containing multiple equations can be established, and the six transformation parameters that best fit all fiducial points can be solved by numerical optimization methods such as least squares. By using multiple fiducial points, the measurement error of a single marker point can be effectively smoothed out, and the robustness of the overall registration is improved. The calculated coordinate transformation matrix is systematically applied to the original position coordinates of all contamination particles in the particle distribution map, thereby obtaining their new coordinates in the unified coordinate system (usually the design coordinate system is chosen as the reference). After this step, all contamination particles and all TSVs have position information in the same coordinate system. S103, divide the surface of the wafer to be detected into an analysis grid composed of multiple analysis units. Exemplarily, the effective analysis area of the wafer logically covers a two-dimensional Cartesian grid. The grid is composed of a large number of rectangular or square analysis units (Cells) with uniform size. The size of the analysis unit is a configurable key parameter, and its selection needs to be balanced between query efficiency and memory consumption. If the cell is too large, too many particles will be contained in each cell, resulting in a decrease in query efficiency; if the cell is too small, a large number of empty cells will be generated, wasting memory. Preferably, the side length of the cell is slightly larger than the particle influence radius to be defined in the subsequent steps. After meshing, each entity (i.e., each contamination particle) in the fused data needs to be mapped to its corresponding analysis cell. This mapping process can be accomplished using simple coordinate division and rounding operations. For example, the index of an analysis cell can be determined by its row and column numbers in the mesh. To represent, its calculation method is as follows and ,in These are the aligned coordinates of the particles, and These are the width and height of the cell, respectively. In terms of data structure, a hash table or a two-dimensional array can be used, where each entry corresponds to a cell and stores a list of particle IDs that fall into that cell. This spatial indexing structure allows for subsequent calculations where, when it's necessary to find particles surrounding a specific TSV, it's not necessary to traverse tens of thousands of particles across the entire wafer (complexity O(n log n)). Instead of directly determining the cell containing the TSV, we only need to first identify that cell and then check its immediate neighboring cells (e.g., the eight neighbors in the Moore neighborhood). This significantly reduces the time complexity of the search algorithm, bringing it close to constant time. This ensures the scalability of the entire analysis process for production-level data. S104. For each target TSV, generate a TSV contamination score characterizing its contamination risk. This step is used to calculate a quantitative, comprehensive risk score, i.e., a TSV contamination score, for each TSV on the wafer. This score is not a simple "present / absent" judgment, but a continuous numerical value that reflects the level of risk. For example, on the wafer Any one of the TSVs selected as the target TSV for analysis is denoted as . .for Define a calculation radius of influence And relevant particles are screened out. This radius is an empirical value determined based on process sensitivity; its physical meaning lies in defining the maximum range within which a particle might have a significant impact on TSV (such as stress, electric field, or chemical contamination diffusion). For example, it can be set to... .by geometric center With the center as the center, Using a radius of 1, define a circular potential influence region in the fused data space. Using the analysis grid established in the previous step, identify all contamination particles whose center points fall within this influence region. Assume a total of 100 particles are identified. A set of related particles. . for each of the relevant particles , needs to be calculated , denoted as , the single-particle contribution value . This contribution value is jointly determined by the intrinsic hazard of the particle (determined by its size) and its extrinsic spatial influence (determined by its distance to the TSV). Exemplarily, the potential hazard of the particle itself is quantified as a base impact value . Since the hazard level of a particle is proportional to the area it can cover, as a larger coverage area implies a higher probability of short or open circuit. Therefore, the base impact value can be expressed as the square of the particle’s equivalent diameter : In this model, a larger particle is more likely to physically bridge the TSV and other structures, or break into more small particles during subsequent high-temperature process steps, causing a larger range of contamination. After determining the base impact of a particle, its spatial position also needs to be considered. The farther a particle is from the TSV, the less of a threat it poses. This decay relationship of the influence can be represented by a distance decay factor : In this function, is the Euclidean distance between the center of the particle and the center of the TSV. is the standard deviation of the function, which controls how fast the influence field decays, i.e., the width of the influence range. In this application, the standard deviation is set to be positively correlated with the size of the particle itself, rather than a fixed value for all particles. The larger the particle, the wider its potential influence range. Exemplarily, a linear relationship can be established to determine : where is a dimensionless scaling constant, which can be understood as an “influence range scaling factor”; and is a base standard deviation, used to provide a minimum influence halo for particles of extremely small size, to avoid their influence range becoming zero. and are empirical parameters calibrated and optimized through statistical regression or machine learning methods by analyzing a large number of known failure cases (i.e., TSV failures known to be caused by a certain particle) and good product data.​ For example, in order to determine the parameters and The following calibration process can be performed: First, select several known particles of a specific size. At a specific distance The TSV samples that caused the failure are identified, and their risk values ​​are assigned a high normalized value (e.g., 1.0). Simultaneously, a large number of good TSV samples are selected, and their risk values ​​are set to 0. Then, optimization algorithms such as least squares or gradient descent are used to solve for the parameters that best fit these known risk values. and For example, by performing linear regression analysis on a set of data containing 10 failed samples and 1000 good samples, a set of empirical values, such as a dimensionless proportionality constant, may be obtained. and the basic standard deviation These calibrated parameters are then embedded into the model of the analysis system for risk assessment of unknown wafers. When the base influence value and distance decay factor Once all the particles are identified, they can be combined to obtain particles. right The final : It will affect all areas within the region. The related particles Perform linear accumulation to obtain the final target TSV. This accumulation process reflects the superposition effect of risks, that is, if there are multiple particles around a TSV, the total risk it faces is the sum of the individual risks of these particles. By analyzing all of the wafer Each TSV repeatedly executes the above S104 process, eventually generating a This is a result set that corresponds one-to-one with the TSV layout data, where each TSV is assigned a unique, quantified attribute. Value. Based on this map, various analyses can be performed, such as: analyzing all TSVs by... The values ​​are sorted in descending order to directly identify the top-N TSVs with the highest risk, guiding subsequent costly physical failure analysis; alternatively, all TSVs can be mapped onto the wafer map in the form of a heatmap. The values ​​are visualized and rendered to intuitively identify process areas (Hot Spots) with systemic contamination problems. In a real semiconductor manufacturing environment, a 0.5 pm copper (Cu) particle, due to its high conductivity and easy diffusion, poses a much greater threat to the electrical performance of TSV than a same-size particle of chemically stable silicon dioxide (SiO2) or insulating organic polymer residue. Therefore, the consideration of particle material properties needs to be introduced into the risk assessment model, so as to improve the accuracy and practicability of the model. Exemplarily, in the data acquisition stage, in addition to the position and size information of the particle, the optical characteristic parameters of the contaminated particle also need to be acquired. The optical characteristic parameters can include light scattering intensity and / or surface reflectivity. The parameters can serve as an effective indirect "fingerprint" for inferring the material composition thereof. After the optical characteristic parameters are acquired, a preset material risk coefficient can be matched for each particle according to the parameters. For example, a mapping relationship from the optical characteristic parameter space to the material category and then to the specific risk coefficient value is established. To enable those skilled in the art to implement, a specific mapping rule example is provided below. It is assumed that the surface scanning device can provide the normalized reflectivity and the normalized scattering intensity of each particle. By calibrating particles of known materials, a look-up table (Look-up Table) can be established to determine the material risk coefficient as follows: When performing analysis, after the system acquires the optical parameters and of the first particle, a unique material risk coefficient can be matched for the particle according to the look-up table. It should be noted that the mapping relationship (i.e., the look-up table) can be fixed in a preset database or "recipe". The establishment of the database usually needs to be performed through controlled experiments (for example, intentionally depositing particles of known types on a clean wafer) and combined with physical confirmation by high-order analysis tools (such as a scanning electron microscope / energy dispersive X-ray spectrometer, i.e., SEM / EDX) to construct a reliable training data set. Then, by using the data set, a classification rule is established and optimized through statistical methods or machine learning models (such as support vector machines, decision trees, etc.). Finally, the obtained material risk coefficient reflects the potential harm degree of the material, and will be used as a key weight factor for correcting the basic TCS value. Therefore, the corrected is denoted as , and the calculation formula thereof is updated as: ​ Accordingly, the final adjusted TSV contamination score considering the material properties is obtained , and the calculation formula is the sum of the adjusted contribution values of all related particles: By introducing the correction mechanism, in the risk assessment model, the contribution value of a particle with small size but high-risk material will be significantly amplified, and vice versa, the risk contribution of a large benign particle will be moderately reduced, so that the final risk assessment result has a stronger correlation with the electrical failure probability. The application also provides a TSV wafer surface contamination particle detection and analysis system. The system can be an independent software package deployed on a server, or a functional module integrated into a wafer factory manufacturing execution system (MES) or yield management system (YMS). As shown in Figure 2 , the system comprises: a data acquisition module for acquiring particle distribution map and TSV layout data of a wafer to be detected; a data alignment module for aligning the particle distribution map and the TSV layout data based on a preset reference mark to generate fusion data in a unified coordinate system; a grid division module for dividing the surface of the wafer to be detected into an analysis grid composed of multiple analysis units; and a score generation module for generating, for each target TSV, a TSV contamination score representing the contamination risk of the target TSV based on the fusion data, according to the size information of each contamination particle around the target TSV and the spatial distance between each contamination particle and the geometric center of the target TSV. Exemplarily, the score generation module is specifically configured to: for any contamination particle around the target TSV, determine a basic influence value based on the size information of the contamination particle; obtain a distance attenuation factor based on the spatial distance between the contamination particle and the target TSV; combine the basic influence value and the distance attenuation factor to obtain a single particle contribution value of the contamination particle to the target TSV; and accumulate the single particle contribution values of all contamination particles to obtain the TSV contamination score of the target TSV. Exemplarily, the system can further comprise a risk correction module for obtaining optical characteristic parameters of each contamination particle after the score generation module generates the TSV contamination score, matching a preset material risk coefficient for each contamination particle according to the optical characteristic parameters, and correcting the TSV contamination score based on the material risk coefficient to obtain an adjusted TSV contamination score. Those skilled in the art can clearly understand the above-mentioned technical solutions from the description of the above embodiments. For the convenience and brevity of description, only the division of the above functional modules is taken as an example, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms. The units described as separate components can or can not be physically separate, and the components shown as units can be one physical unit or multiple physical units, that is, can be located in one place, or can be distributed to multiple different places. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware. The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting and analyzing contaminant particles on the surface of a TSV wafer after CMP cleaning, characterized in that, The method includes: Obtain a particle distribution map of the wafer to be inspected, the particle distribution map including the position coordinates and size information of each contaminant particle on the wafer surface; The TSV layout data of the wafer to be tested is obtained, and the TSV layout data includes the geometric center coordinates of each TSV on the wafer surface; Based on preset reference markers, the particle distribution map and the TSV layout data are aligned to generate fused data in a unified coordinate system; The surface of the wafer to be tested is divided into an analysis grid consisting of multiple analysis units; For each target TSV, based on the fused data, a TSV pollution score characterizing its pollution risk is generated according to the size information of each pollution particle around the target TSV and the spatial distance between each pollution particle and the geometric center of the target TSV.

2. The method according to claim 1, characterized in that, Aligning the particle distribution map with the TSV layout data includes: Identify the first reference marker in the particle distribution map and the second reference marker in the TSV layout data; Based on the coordinates of the first reference mark and the second reference mark, obtain the coordinate transformation matrix; The coordinate transformation matrix is ​​applied to the position coordinates of the particle distribution map to align the coordinate system of the particle distribution map with the coordinate system of the TSV layout data.

3. The method according to claim 1, characterized in that, The step of generating a TSV contamination score for the target TSV includes: For any contamination particle surrounding the target TSV, its basic impact value is determined based on the particle's size information; Based on the spatial distance between the contaminant particles and the target TSV, the distance attenuation factor is obtained; Based on the basic influence value and the distance attenuation factor, the single-particle contribution value of the contamination particle to the target TSV is obtained; The TSV contamination score of the target TSV is obtained by summing the individual particle contribution values ​​of all contaminant particles.

4. The method according to claim 3, characterized in that, The distance attenuation factor is obtained through a preset attenuation function, and the standard deviation of the attenuation function is positively correlated with the size information of the pollutant particles.

5. The method according to any one of claims 1 to 4, characterized in that, After generating the TSV contamination score for the target TSV, the method further includes: Obtain the optical characteristic parameters of each pollutant particle in the particle distribution map; Based on the optical properties of each pollutant particle, a preset material risk coefficient is matched to it; Based on the material risk coefficient, the TSV pollution score is corrected to obtain the adjusted TSV pollution score.

6. The method according to claim 5, characterized in that, The optical characteristic parameters include at least one of the light scattering intensity and reflectivity of the pollutant particles.

7. The method according to claim 5, characterized in that, The correction of the TSV pollution score based on the material risk coefficient includes: When calculating the contribution value of a single particle, the material risk coefficient corresponding to the contamination particle is used as a weighting factor and combined with the basic impact value and the distance attenuation factor.

8. A method for detecting and analyzing contaminant particles on the surface of a TSV wafer after CMP cleaning, characterized in that, The system includes: The data acquisition module is used to acquire the particle distribution map and TSV layout data of the wafer to be inspected; The data alignment module is used to align the particle distribution map and the TSV layout data based on a preset reference mark, and generate fused data in a unified coordinate system. The mesh generation module is used to divide the surface of the wafer to be inspected into an analysis mesh composed of multiple analysis units; The score generation module is used to generate a TSV pollution score for each target TSV based on the fused data, according to the size information of each pollution particle around the target TSV and the spatial distance between each pollution particle and the geometric center of the target TSV.

9. The system according to claim 8, characterized in that, The score generation module is specifically used for: For any contamination particle surrounding the target TSV, its basic impact value is determined based on the particle's size information; Based on the spatial distance between the contaminant particles and the target TSV, a distance attenuation factor is obtained; The basic influence value is combined with the distance attenuation factor to obtain the single-particle contribution value of the contaminant particle to the target TSV. The TSV contamination score of the target TSV is obtained by summing the individual particle contribution values ​​of all contaminant particles.

10. The system according to claim 8 or 9, characterized in that, The system also includes: The risk correction module is used to obtain the optical characteristic parameters of each pollutant particle after the score generation module generates the TSV pollution score, match a preset material risk coefficient for each pollutant particle according to the optical characteristic parameters, and correct the TSV pollution score based on the material risk coefficient to obtain an adjusted TSV pollution score.

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