WO2023065493A1 - Defect detection method and apparatus, and device and storage medium - Google Patents

Abstract

A defect detection method and apparatus, and a device and a storage medium. The method comprises: acquiring at least one scanning image, which is obtained by means of scanning at least one structural area of a wafer product, and a design drawing of each structural area (S101); performing edge feature extraction on each scanning image, so as to obtain an edge feature map of each scanning image (S102); with regard to each scanning image, on the basis of a design drawing of a structural area that corresponds to each scanning image, determining, in the edge feature map, at least one image area to be measured (S103); and by means of measuring patterns in each image area, determining a defect detection result of each image area (S104).

Description

Defect detection method, device, equipment and storage medium

related cross-references

This disclosure is based on the Chinese patent application with the application number 202111211163.0, the filing date is October 18, 2021, and the title of the invention is "defect detection method, device, equipment and storage medium", and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated by reference into this disclosure.

technical field

The present disclosure relates to but not limited to the field of semiconductors, and in particular relates to a defect detection method, device, equipment and storage medium.

Background technique

In the production process of semiconductor components, the scanned image of the wafer product taken by the measuring machine after the process (such as after photolithography treatment, etc.) is usually used as the measurement image, and based on the captured quantity Manually observe the test image to determine whether there are defects in the wafer product. However, defect detection through manual observation has high labor costs, low efficiency, and is prone to missed or false detections.

Contents of the invention

In view of this, the embodiments of the present disclosure provide a defect detection method, device, equipment and storage medium.

The technical scheme of the embodiment of the present disclosure is realized in this way:

An embodiment of the present disclosure provides a defect detection method, the method comprising:

Obtaining at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each said structural region;

performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images;

For each of the scanned images, determine at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image;

The defect detection result of each image area is determined by measuring the pattern in each image area.

In some embodiments, the wafer product includes a dynamic random access memory, and the at least one structural area includes at least one structural area in at least one structural layer preset in the dynamic random access memory.

In some embodiments, the determining at least one image region to be measured in the edge feature map based on the design map of the structural region corresponding to the scanned image includes: calculating the edge feature map of the scanned image and the Registering the design diagram of the structural region corresponding to the scanned image to obtain at least one image region in the edge feature map that does not match the design diagram; and determining each of the mismatched image regions as to be measured image area.

In some embodiments, the determining the defect detection result of each image area by measuring the pattern in each image area includes: determining the measurement index of each image area; For each image area, based on the measurement index of the image area, the pattern in the image area is measured to obtain the measurement result of the measurement index in the image area; based on each of the The measurement result of the measurement index of the image area determines the defect detection result of each said image area.

In some embodiments, the determining the measurement index of each of the image areas includes: determining the pattern type of each of the image areas; for each of the image areas, based on the pattern type of the image area, A measurement index of the image area is determined.

In some embodiments, the pattern type includes at least one of the following: hole type, line block type; the determination of the measurement index of the image area based on the pattern type of the image area includes: When the pattern type in the image area is the hole type, the area of the hole pattern in the image area is determined as the measurement index of the image area; the pattern type in the image area is the hole type In the case of the line block type, the spatial key dimension of the line block pattern in the image area is determined as the measurement index of the image area.

In some embodiments, the hole pattern includes a hole pattern in the cell array area in at least one of the following areas: active area, bit line contact area, capacitor area; the line block pattern includes a metal wiring layer The strip pattern in at least one of the following areas: the sense amplifier area, the sub-word line driver area, and the sub-word line connection area.

In some embodiments, the determining the defect detection result of each image area based on the measurement result of the measurement index of each image area includes: acquiring the measurement index of each image area Reference value area: For each image area, compare the measurement result of the measurement index of the image area with the reference value area, and determine the defect detection result of the image area.

In some embodiments, the defect detection result includes a defect state; comparing the measurement result of the measurement index of the image area with a reference value area, and determining the defect detection result of the image area includes: When determining that the measurement result of the measurement index in the image area is within the reference value area, determine that the defect state of the image area is no defect; determine that the measurement result of the measurement index in the image area is not in the reference value area. In the case of the value area, it is determined that the defect state of the image area is defective.

In some embodiments, the measurement index is the area of the hole pattern in the image area, and the reference value area is a reference area range; the reference value area of the measurement index of each of the image areas is acquired , including: obtaining the area of each hole pattern in each of the scanned images; determining the area distribution information of each hole pattern in each of the scanned images based on the area of each hole pattern in each of the scanned images ; For each image area, based on the area distribution information of the hole pattern in the scanned image corresponding to the image area, determine the reference area range of the hole pattern in the image area.

In some embodiments, the determining the reference area range of the hole pattern in the image region based on the area distribution information of the hole pattern in the scanned image corresponding to the image region includes: When the area distribution information of the hole pattern in the image obeys the normal distribution, determine the mean value of the area of the hole pattern in the scanned image; based on the mean value of the area, determine the reference area of the hole pattern in the image region scope.

In some embodiments, the defect detection result further includes a defect type; comparing the measurement result of the measurement index of the image area with a reference value area to determine the defect detection result of the image area further includes : In the case where the measured area of the hole pattern in the image area is less than the first area threshold, it is determined that the defect type in the image area is missing holes; the measured area of the hole pattern in the image area is not less than When the first area threshold is smaller than the minimum value in the reference area range, it is determined that the defect type in the image area is hole shrinkage; the measurement area of the hole pattern in the image area is greater than the reference area If the maximum value in the range is less than twice the maximum value in the reference area range, it is determined that the defect type in the image area is hole expansion; the measurement area of the hole pattern in the image area is not less than the specified In the case of twice the maximum value in the above reference area range, it is determined that the defect type of the image area is hole bridge.

In some embodiments, the measurement index is the spatial critical dimension of the line block pattern in the image region, and the reference value area is the reference range of the spatial critical dimension; the acquisition of the quantity of each of the image regions The reference value area of the measurement index includes: for each of the image areas, determining the spatial key dimension of the reference pattern corresponding to the line block pattern in the image area in the design drawing corresponding to the image area, and based on The spatial critical dimension determines a reference range of the spatial critical dimension of the line block pattern in the image area.

In some embodiments, the defect detection result further includes a defect type; comparing the measurement result of the measurement index of the image area with a reference value area to determine the defect detection result of the image area further includes : In the case that the spatial critical dimension of the line-block pattern in the image region is smaller than a first size threshold, it is determined that the defect type in the image region is a line-block bridge; the spatial critical dimension of the line-block pattern in the image region is greater than In a case where the spatial critical dimension is twice the maximum value of the reference range, it is determined that the defect type in the image area is a line block break.

In some embodiments, the method further includes: counting the defect types of each image region whose defect state is defective, to obtain the number of image regions corresponding to each defect type.

An embodiment of the present disclosure provides a defect detection device, the device comprising:

An acquisition module configured to acquire at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each structural region;

An extraction module configured to perform edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images;

The first determining module is configured to determine at least one image region to be measured in the edge feature map based on the design map of the structural region corresponding to the scanned image for each of the scanned images;

The second determination module determines the defect detection result of each image area by measuring the pattern in each image area.

In some embodiments, the first determination module is further configured to: register the edge feature map of the scanned image with the design map of the structural region corresponding to the scanned image to obtain at least An image area that does not match the design drawing; each of the image areas that do not match is determined as an image area to be measured.

In some embodiments, the second determining module is further configured to: determine the measurement index of each of the image regions; for each of the image regions, based on the measurement indexes of the image region, the measuring the pattern in the area to obtain the measurement result of the measurement index in the image area; based on the measurement result of the measurement index of each of the image areas, determining the defect detection of each of the image areas result.

An embodiment of the present disclosure provides a computer device, including a memory and a processor, the memory stores a computer program that can run on the processor, and the processor implements some or all of the steps in the above method when executing the program.

An embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, part or all of the steps in the above method are implemented.

In the embodiment of the present disclosure, at least one scanned image obtained by scanning at least one structural region of the wafer product and a design drawing of each structural region are acquired; edge feature extraction is performed on each scanned image to obtain the Edge feature map; for each scanned image, based on the design map of the corresponding structural area of the scanned image, determine at least one image area to be measured in the edge feature map; by measuring the pattern in each image area, determine each Defect detection results for an image region. In this way, on the one hand, automatic detection of defects in the structural region of the wafer product can be realized, thereby reducing the labor cost caused by manual observation; The image area to be measured in the edge feature map can preliminarily determine the image area that may have defects. On this basis, the defect detection result of the image area is determined by measuring the pattern in the image area to be measured. In this way, it can be Improve the efficiency and accuracy of defect detection in wafer products, and reduce missed or false detections.

The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the present disclosure will be apparent from the description and drawings.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

FIG. 1 is a schematic diagram of an implementation flow of a defect detection method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of an implementation flow of a defect detection method provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an implementation flow of a defect detection method provided by an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of an implementation flow of a defect detection method provided by an embodiment of the present disclosure;

FIG. 5A is a schematic diagram of the implementation flow of a defect detection method provided by an embodiment of the present disclosure;

FIG. 5B is a schematic diagram of implementing defect detection on a hole-shaped scanned image provided by an embodiment of the present disclosure;

FIG. 5C is a schematic diagram of the distribution of the number and area of a hole pattern provided by an embodiment of the present disclosure;

FIG. 5D is a schematic diagram of an implementation of defect detection on a line-block type scanned image provided by an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of the composition and structure of a defect detection device provided by an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of a hardware entity of a computer device provided by an embodiment of the present disclosure.

Detailed ways

The technical solutions of the present disclosure will be further elaborated below in conjunction with the accompanying drawings and embodiments. The described embodiments should not be regarded as limiting the present disclosure. All other embodiments obtained by those of ordinary skill in the art without creative work , all belong to the protection scope of the present disclosure.

In the following description, references to "some embodiments" describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict.

In the following description, the term "first/second/third" is only used to distinguish similar objects, and does not represent a specific order for objects. Understandably, "first/second/third" is used in Where permitted, the specific order or sequence may be interchanged such that the embodiments of the disclosure described herein can be practiced in sequences other than those illustrated or described herein.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terms used herein are for the purpose of describing the present disclosure only, and are not intended to limit the present disclosure.

An embodiment of the present disclosure provides a defect detection method, which can be executed by a processor of a computer device. Among them, the computer device refers to any server, notebook computer, tablet computer, desktop computer, smart TV, set-top box, mobile device (such as mobile phone, portable video player, personal digital assistant, dedicated messaging device, portable game device), etc. Appropriate equipment with data processing capabilities. Fig. 1 is a schematic diagram of the implementation flow of a defect detection method provided by an embodiment of the present disclosure. As shown in Fig. 1, the method includes the following steps S101 to S104:

Step S101 , acquiring at least one scanned image obtained by scanning at least one structural region of a wafer product, and a design drawing of each structural region.

Here, the wafer product may include at least one structural layer, such as a capacitor layer, a metal wiring layer, etc., and each structural layer may include at least one structural area, such as a cell array area, a sense amplifier area, a sub-word line driver area, a sub-word line driver area, and a sub-layer. word line connection area, etc.

During the processing of the wafer product (such as photolithography, etching, etc.), any suitable image acquisition device can be used to scan at least one structure area of the wafer product, which is not limited here. In some embodiments, during the processing of the wafer product, a scanning electron microscope (Scanning Electron Microscope, SEM) machine can be used to scan at least one structural region in the wafer product to obtain at least one scanning image. During implementation, the scanned image of at least one structural region of the wafer product may be directly obtained from the image acquisition device, or may be scanned by the image acquisition device and stored in the database and then retrieved from the database, which is not limited here.

Before the actual production of each wafer product, the product designer will design the wafer product and store the design information of the designed wafer product in the Graphic Design System (GDS) of the wafer product , the design information may include a design drawing of each structural region in the wafer product, and the design drawing may include design width, length, direction, and pattern shape, etc. During implementation, the design drawing of the structure region in the wafer product can be obtained from the graphic design system.

Step S102, performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images.

Here, any suitable edge detection algorithm can be used, such as Canny edge detection algorithm, Sobel edge detection algorithm, etc., to extract the edge features of the pattern in the scanned image to obtain the edge feature map of the scanned image, which is not discussed in the embodiments of the present disclosure. limited. The edges of patterns in different scanned images will be different, and the edge features in the edge feature map may include straight line features or curve features.

Step S103, for each of the scanned images, determine at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image.

Here, in some embodiments, for each scanned image, the design map of the structural region corresponding to the scanned image can be registered with the edge feature map of the scanned image to obtain the difference between the edge feature map and the design map. Matching at least one image area, and determining each unmatched image area as an image area to be measured.

In some embodiments, at least one candidate area in the design drawing of each structural region in the wafer product can be determined in advance, and for each scanned image, based on the design drawing of the corresponding structural region of the scanned image, the design drawing can be determined at least one candidate region, and determine the image region corresponding to the at least one candidate region in the edge feature map of the scanned image as the image region to be measured.

Step S104, by measuring the pattern in each of the image areas, determine the defect detection result of each of the image areas.

Here, the defect detection result of the image area may include, but not limited to, one or more of the defect state, defect type, etc. of the image area. The defect state can be defective or non-defective, and the defect type can include, but not limited to, one or more of missing holes, narrowed holes, enlarged holes, bridged holes, bridged wire blocks, broken wire blocks, and the like. During implementation, measurement may be performed in an appropriate manner according to the actual shape of the pattern in the image area, so as to determine the defect detection result of the image area, which is not limited in this embodiment of the present disclosure.

In some implementations, the area of the pattern in the image area may be measured, and the defect detection result of the image area may be determined based on the measured area result. For example, if the area result is within the preset reference area range, it can be determined that there is no defect in the image area; if the area result is outside the preset reference area range, it can be determined that there is a defect in the image area.

In some embodiments, the critical dimension of the pattern in the image area may be measured, and the defect detection result of the image area may be determined based on the measured critical dimension result. For example, if the critical dimension result is within the preset critical dimension reference range, it can be determined that there is no defect in the image area; defect. During implementation, the critical dimension of the pattern may be determined according to actual conditions, which is not limited in the embodiments of the present disclosure.

In some embodiments, the wafer product includes a dynamic random access memory (Dynamic Random Access Memory, DRAM), and the at least one structural region includes at least one structural layer preset in the dynamic random access memory At least one structure area. Here, at least one structural layer may be preset by the user according to actual application scenarios, which is not limited here.

In some embodiments, determining at least one image region to be measured in the edge feature map based on the design map of the structural region corresponding to the scanned image in the above step S103 may include:

Step S111, registering the edge feature map of the scanned image and the design map of the corresponding structural region of the scanned image to obtain at least one image area in the edge feature map that does not match the design map;

Step S112, determining each unmatched image area as an image area to be measured.

Here, the registration may be a process of superimposing and matching the edge feature map of the scanned image and the design map of the corresponding structural region. At least one image region in the edge feature map that does not match the design map can be determined through registration, so that at least one image region to be measured can be quickly determined, thereby improving the efficiency and accuracy of defect detection in wafer products.

In the embodiment of the present disclosure, at least one scanned image obtained by scanning at least one structural region of the wafer product and a design drawing of each structural region are acquired; edge feature extraction is performed on each scanned image to obtain the Edge feature map; for each scanned image, based on the design map of the corresponding structural area of the scanned image, determine at least one image area to be measured in the edge feature map; by measuring the pattern in each image area, determine each Defect detection results for an image region. In this way, on the one hand, automatic detection of defects in the structural region of the wafer product can be realized, thereby reducing the labor cost caused by manual observation; The image area to be measured in the edge feature map can preliminarily determine the image area that may have defects. On this basis, the defect detection result of the image area is determined by measuring the pattern in the image area to be measured. In this way, it can be Improve the efficiency and accuracy of defect detection in wafer products, and reduce missed or false detections.

An embodiment of the present disclosure provides a defect detection method, which can be executed by a processor of a computer device. As shown in Figure 2, the method includes the following steps S201 to S206:

Step S201, acquiring at least one scanned image obtained by scanning at least one structural region of a wafer product, and a design drawing of each structural region.

Step S202, performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images.

Step S203, for each of the scanned images, determine at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image.

Here, the above-mentioned steps S201 to S203 respectively correspond to the above-mentioned steps S101 to S103, and the specific implementation manners of the above-mentioned steps S101 to S103 can be referred to for implementation.

Step S204, determining the measurement index of each of the image regions.

Here, at least one measurement index can be determined for each image area, and the measurement index of the image area can be used to measure the pattern in the image area to determine whether there is a defect in the pattern in the image area. During implementation, an appropriate measurement index may be determined according to the shape or type of the actual pattern in the image area, which is not limited here.

In some implementations, the measurement index of each image area can be determined based on the pattern type of each image area. For example, when the pattern type in the image area is a hole-like type, the area of the hole-like pattern in the image area can be determined as the measurement index of the image area; if the pattern type in the image area is a line-block type In some cases, the spatial key dimension of the line block pattern in the image area can be determined as the measurement index of the image area.

Step S205, for each image area, based on the measurement index of the image area, measure the pattern in the image area, and obtain the measurement result of the measurement index in the image area.

Here, based on the measurement index of the image area, the corresponding measurement index in the pattern in the image area may be measured to obtain the measurement result of the measurement index.

Step S206, based on the measurement results of the measurement indicators of each of the image areas, determine the defect detection results of each of the image areas.

Here, the defect detection result of the image region may be determined in an appropriate manner based on the measurement result of the measurement index of the image region according to the actual situation, which is not limited in this embodiment of the present disclosure.

In some implementations, the defect state and/or defect type of each image region can be determined by comparing the measurement result of the measurement index of each image region with a preset reference result. For example, the measurement index is the area of the hole pattern, and the preset reference result is the preset area range. When the area of the hole pattern in the image area is within the preset area range, it is determined that the image area has no Defect: if the area of the hole pattern in the image area is not within the preset area range, it is determined that the image area is defective. For another example, the measurement index is the width of the groove in the line block pattern, the preset reference result is a preset width range, and the width of the groove in the line block pattern in the image area is within the preset width range In this case, it is determined that the image area is free of defects; in the case that the width of the groove in the line block pattern in the image area is less than the minimum value in the preset width range, it is determined that the image area is defective and the defect type is line block bridging ; If the width of the groove in the line block pattern in the image area is greater than twice the maximum value in the preset width range, it is determined that the image area is defective and the defect type is line block breakage.

In the embodiment of the present disclosure, by determining the measurement index of each image area to be measured, for each image area, based on the measurement index of the image area, the pattern in the image area is measured to obtain the image The measurement result of the measurement index in the area, and determine the defect detection result of each image area based on the measurement result of the measurement index of each image area. In this way, because each image area can be measured with an appropriate measurement index, and the defect detection result of each image area can be determined based on the measurement result of the corresponding measurement index, the crystal can be more accurately measured. Defects in round products are detected.

An embodiment of the present disclosure provides a defect detection method, which can be executed by a processor of a computer device. As shown in Figure 3, the method includes the following steps S301 to S307:

Step S301, acquiring at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each structural region.

Step S302, performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images.

Step S303, for each of the scanned images, based on the design map of the corresponding structural area of the scanned image, at least one image area to be measured in the edge feature map is determined.

Here, the above steps S301 to S303 respectively correspond to the above steps S101 to S103, and the specific implementation manners of the above steps S101 to S103 can be referred to for implementation.

Step S304, determining the pattern type of each image area.

Here, the pattern type of the image area is the type of the pattern included in the image area, including but not limited to one or more of the hole-like type, the line-block type, and the like.

In some implementations, any suitable image recognition algorithm may be used to identify the pattern in each image area and determine the pattern type of each image area. In some embodiments, the pattern type corresponding to at least one structure region of the wafer product can be determined in advance according to actual conditions, and the pattern type of each image region can be determined by determining the structure region corresponding to each image region.

Step S305, for each of the image areas, based on the pattern type of the image area, determine the measurement index of the image area.

Here, for image regions of different pattern types, applicable measurement indexes may be different, and the measurement indexes of each image region may be determined based on the pattern type of each image region. For example, when the pattern type in the image area is a hole-like type, the area of the hole-like pattern in the image area can be determined as the measurement index of the image area; if the pattern type in the image area is a line-block type In some cases, the spatial key dimension of the line block pattern in the image area can be determined as the measurement index of the image area.

Step S306, for each image area, based on the measurement index of the image area, measure the pattern in the image area, and obtain the measurement result of the measurement index in the image area.

Step S307, based on the measurement results of the measurement indicators of each of the image areas, determine the defect detection results of each of the image areas.

Here, the above steps S306 to S307 correspond to the above steps S205 to S206 respectively, and the specific implementation manners of the above steps S205 to S206 can be referred to for implementation.

In the embodiment of the present disclosure, for each image area, by determining the pattern type of the image area, and based on the pattern type of the image area, the measurement index of the image area is determined. In this way, it can support the defect detection of image areas of different pattern types, and can determine the appropriate measurement index for each image area, so that the measurement results of the measurement index can better reflect the defect situation of each image area , which can further improve the accuracy of defect detection in wafer products.

In some embodiments, the pattern type includes at least one of the following: a hole type, a line block type; the determination of the measurement index of the image area based on the pattern type of the image area described in the above step S305 , including the following steps S311 to S312:

Step S311, if the pattern type in the image area is the hole type, determine the area of the hole pattern in the image area as the measurement index of the image area;

Step S312, if the pattern type in the image area is the line block type, determine the spatial key dimension of the line block pattern in the image area as the measurement index of the image area.

Here, the hole-like type means that the pattern in the image area is hole-like, that is, the image area includes a hole-like pattern. The line-block type refers to that the pattern in the image area is line-block, that is, the image area includes line-block patterns, such as patterns such as grooves and lines. The spatial key dimension of the line block pattern may be determined according to the actual pattern shape in the line block pattern, for example, may include but not limited to the width of patterns such as grooves or lines.

In some embodiments, the hole pattern includes a hole pattern in a cell array (Cell Array) area in at least one of the following areas: active area (Active Area, AA), bit line contact area (Bitline Contact, BLC ), a capacitance (Capacitance, CAP) area; the line block pattern includes a strip pattern in at least one of the following areas of the metal wiring layer: sub-word-line connection area (Sub Word-line Conjunction, SWC), sense amplifier (Sense Amplify, SA) area, Sub Word-line Driver (Sub Word-line Driver, SWD) area. In some embodiments, the sub-word line connection area, the sense amplifier area, and the sub-word line driver area may be respectively the sub-word line connection area, the sense amplifier area, and the sub-word line driver area in the memory cell transistor.

In the embodiment of the present disclosure, when the pattern type in the image area is a hole type, the area of the hole pattern in the image area is determined as the measurement index of the image area; the pattern type in the image area is line block In the case of the type, the spatial key dimension of the line block pattern in the image area is determined as the measurement index of the image area. In this way, since the area can better reflect the defect situation of the hole pattern, and the spatial critical dimension can better reflect the defect situation of the line block pattern, so based on the measurement result of the hole pattern area, it is possible to more accurately determine each The defect detection results of the image area of the hole type, based on the measurement results of the spatially critical dimensions of the line block pattern, can more accurately determine the defect detection result of the image area of each line block pattern, which can further improve the quality of wafer products. Accuracy of defect detection.

An embodiment of the present disclosure provides a defect detection method, which can be executed by a processor of a computer device. As shown in Figure 4, the method includes the following steps S401 to S407:

Step S401, acquiring at least one scanned image obtained by scanning at least one structural region of a wafer product, and a design drawing of each structural region.

Step S402, performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images.

Step S403, for each of the scanned images, determine at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image.

Step S404, determining the measurement index of each of the image regions.

Step S405, for each image area, based on the measurement index of the image area, measure the pattern in the image area, and obtain the measurement result of the measurement index in the image area.

Here, the above-mentioned steps S401 to S405 correspond to the above-mentioned steps S201 to S205 respectively, and the specific implementation manners of the above-mentioned steps S201 to S205 can be referred to for implementation.

Step S406, acquiring a reference value area of the measurement index of each image area.

Here, the reference value area is a value area used to refer to the measurement result of the measurement index, and may be a continuous area, or may include multiple interval areas. During implementation, the reference value area can be pre-set by the user according to the specifications of the wafer product, or it can be the default, or it can be automatically determined according to the design drawing of the structural area corresponding to the image area, or it can be the current The image area is obtained through statistical analysis of the measurement results of the measurement indicators of the historical image area corresponding to the same structural area, and is not limited here.

Step S407 , for each of the image regions, comparing the measurement result of the measurement index of the image region with the reference value region, and determining the defect detection result of the image region.

Here, in some implementations, the reference value area of the measurement index of the image area may be a normal value area of the measurement index, and may be in the case where the measurement result of the measurement index of the image area is within the reference value area, It is determined that the defect detection result of the image area is no defect, and when the measurement result of the measurement index of the image area is not in the reference value area, it is determined that the defect detection result of the image area is defective.

In other embodiments, the reference value area of the measurement index of the image area may be an abnormal value area of the measurement index, and it may be determined when the measurement result of the measurement index of the image area is within the reference value area. The defect detection result of the image area is defective, and if the measurement result of the measurement index of the image area is not in the reference value area, it is determined that the defect detection result of the image area is no defect.

In some embodiments, the defect detection result includes a defect state; the measurement result of the measurement index of the image area described in the above step S407 is compared with the reference value area to determine the defect detection of the image area As a result, the following steps S411 to S412 are included:

Step S411, if it is determined that the measurement result of the measurement index in the image area is within the reference value area, determine that the defect state of the image area is no defect.

Step S412, if it is determined that the measurement result of the measurement index in the image area is not in the reference value area, determine that the defect state of the image area is defective.

In the embodiment of the present disclosure, by comparing the measurement result of the measurement index of the image area with the corresponding reference value area, the defect detection result of the image area can be quickly and accurately determined, thereby further improving the accuracy of defect detection in wafer products. accuracy and efficiency.

In some embodiments, the measurement index is the area of the hole pattern in the image area, and the reference value area is a reference area range; the above step S406 includes the following steps S421 to S423:

Step S421, acquiring the area of each hole pattern in each of the scanned images.

Step S422, based on the area of each hole pattern in each scan image, determine the area distribution information of each hole pattern in each scan image.

Here, the area distribution information of the hole pattern in the scanned image may include, but not limited to, the mean, maximum, minimum, median, quantile, variance, standard deviation, etc. of the area of each hole pattern in the scanned image. One or more of them, which are not limited in the embodiments of the present disclosure.

Step S423, for each image area, based on the area distribution information of the hole pattern in the scanned image corresponding to the image area, determine the reference area range of the hole pattern in the image area.

Here, the reference area range of the hole pattern may be a value range of the area when the hole pattern is normal. Since the area of the hole pattern in the image area of the hole type may be adjusted according to the process, the normal value is not necessarily equal to the area of the hole pattern in the design drawing of the corresponding structure area. During implementation, the reference area range of the hole pattern in the image area may be determined in an appropriate manner according to the area distribution information of the hole pattern in the scanned image, which is not limited here. For example, the mean value of the area of each hole-shaped pattern in the scanned image corresponding to the image area can be determined as the reference area range of the hole-shaped pattern in the image area; The range between the 25% quantile and the 75% quantile of the area is determined as the reference area range of the hole pattern in the image area.

In some embodiments, the determination of the reference area range of the hole pattern in the image region based on the area distribution information of the hole pattern in the scanned image corresponding to the image region in the above step S423 includes the following steps S431 to Step S432:

Step S431 , if the area distribution information of the hole pattern in the scanned image corresponding to the image area follows a normal distribution, determine the mean value of the area of the hole pattern in the scanned image.

Step S432, based on the mean value of the area, determine a reference area range of the hole pattern in the image area.

In the embodiment of the present disclosure, based on the area of each hole pattern in each scanned image, the area distribution information of the hole pattern in each scanned image is determined, and for each image region, based on the hole shape in the scanned image corresponding to the image region The area distribution information of the pattern determines the reference area range of the hole pattern in the image area. In this way, since the area of the hole pattern in the image area of the hole type may be adjusted according to the process, the reference area range of the hole pattern in the image area can be more accurately determined according to the area distribution information of the hole pattern in the scanned image , so that the accuracy and efficiency of defect detection in wafer products can be further improved.

In some embodiments, the defect detection result also includes the defect type; the measurement result of the measurement index of the image area described in the above step S407 is compared with the reference value area to determine the defect of the image area The detection result may also include the following steps S441 to S444:

Step S441 , if the measured area of the hole pattern in the image area is smaller than a first area threshold, determine that the defect type in the image area is a missing hole.

Here, the first area threshold may be pre-set by the user according to the specifications of the wafer product, or it may be a default value, or it may be automatically determined according to the design drawing of the structural area corresponding to the image area, or it may be determined according to the corresponding area of the image area. The area distribution information of the hole pattern in the scanned image is determined, which is not limited here.

In some embodiments, when the measured area of the hole pattern in the image region is 0, it may be determined that the defect type in the image region is a missing hole.

Step S442, if the measured area of the hole pattern in the image area is not smaller than the first area threshold and smaller than the minimum value in the reference area range, determine that the defect type of the image area is hole shrinkage.

Here, the first area threshold is smaller than the minimum value in the reference area range. In the case where the measured area of the hole pattern in the image area is not less than the first area threshold and is less than the minimum value in the reference area range, the area of the hole in the pattern is smaller than the reference area range, so that it can be determined that the defect type in the image area is The hole shrinks.

Step S443, when the measured area of the hole pattern in the image area is greater than the maximum value in the reference area range and less than twice the maximum value in the reference area range, determine the defect in the image area The type is hole enlargement.

Step S444, when the measured area of the hole pattern in the image area is not less than twice the maximum value in the reference area range, determine that the defect type in the image area is hole bridge.

Here, in the case where the measured area of the hole-like pattern in the image area is not less than twice the maximum value in the reference area range, the area of the hole in the pattern is too large, which may be caused by the bridging of two holes, so that the image can be determined The defect type of the region is hole bridging.

In the embodiment of the present disclosure, the defect detection result also includes the defect type. If the measured area of the hole pattern in the image area is smaller than the first area threshold, it is determined that the defect type in the image area is missing holes; in the image area, the hole pattern When the measurement area of the pattern is not less than the first area threshold and is less than the minimum value in the reference area range, it is determined that the defect type in the image area is a hole shrinkage; the measurement area of the hole pattern in the image area is greater than that in the reference area range. When the maximum value is less than twice the maximum value in the reference area range, it is determined that the defect type in the image area is hole expansion; the measurement area of the hole pattern in the image area is not less than twice the maximum value in the reference area range case, determine the defect type of the image area as hole bridging. In this way, various types of defects in the hole pattern can be identified, and further analysis of the defects and optimization of the process can be facilitated, thereby improving the progress of research and development of wafer products.

In some embodiments, the measurement index is the spatial critical dimension of the line block pattern in the image area, and the reference value area is the reference range of the spatial critical dimension; the above step S406 includes:

Step S451, for each image area, determine the spatial critical dimension of the reference pattern corresponding to the line block pattern in the image area in the design drawing corresponding to the image area, and determine based on the spatial critical dimension The reference range of the spatial key dimension of the line block pattern in the image area.

Here, the space critical dimension of the line block pattern may be determined according to the actual pattern shape in the line block pattern, for example, may include but not limited to the width of patterns such as grooves or lines. The reference pattern is the design pattern corresponding to the line block pattern in the design drawing corresponding to the image area. The reference pattern corresponding to the line-block pattern in the image area can be determined by means of image registration or coordinate comparison, and then the spatial key dimension of the reference pattern can be determined.

The reference range of the spatially critical dimension of the line block pattern can be the value range of the space critical dimension when the line block pattern is normal. For example, when the spatial critical dimension is the width of the groove in the line block pattern, the reference range of the spatial critical dimension of the line block pattern can be the width range of the corresponding groove in the reference pattern corresponding to the line block pattern . During implementation, those skilled in the art may determine the reference range of the spatial critical dimension of the line block pattern in the image region in an appropriate manner based on the actual situation, which is not limited in the embodiments of the present disclosure. For example, in the design drawing corresponding to the image area, the spatial key dimension of the reference pattern corresponding to the line block pattern in the image area can be determined as the spatial key dimension reference range of the line block pattern in the image area; The value obtained after reducing the first offset of the spatial critical dimension is used as the minimum value of the reference range of the spatial critical dimension, and the value obtained after the second offset is added to the spatial critical dimension is used as the maximum value of the reference range of the spatial critical dimension, so that The range between the minimum value and the maximum value is determined as the reference range of the spatial critical dimension.

In the embodiment of the present disclosure, based on the spatial key dimension of the line block pattern in the design drawing, the reference range of the key dimension of the corresponding line block pattern in the image area can be accurately and directly obtained, thereby further improving the defect detection accuracy of the line block pattern rate and efficiency.

In some embodiments, the defect detection result also includes the defect type; the measurement result of the measurement index of the image area described in the above step S407 is compared with the reference value area to determine the defect of the image area The detection result may also include the following steps S461 to S462:

Step S461 , in the case that the spatial critical size of the line block pattern in the image area is smaller than a first size threshold, determine that the defect type in the image area is line block bridging.

Here, the first size threshold may be pre-set by the user according to the specifications of the wafer product, or it may be the default, or it may be automatically determined according to the design drawing of the structural region corresponding to the image region, or it may be determined according to the image region corresponding to The distribution information of the spatial key dimensions of the line block pattern in the scanned image is determined, which is not limited here.

Step S462, in the case that the spatial critical dimension of the line block pattern in the image area is greater than twice the maximum value of the reference range of the spatial critical dimension, determine that the defect type in the image area is a line block break.

Here, in the case that the spatial critical dimension of the line block pattern in the image area is greater than twice the maximum value of the reference range of the spatial critical dimension, the spatial critical dimension in the pattern is too large, which may be caused by the breakage of the line block, so that the image can be determined The defect type of the area is line block break.

In the embodiment of the present disclosure, the defect detection result also includes the defect type. If the spatial key size of the line block pattern in the image area is smaller than the first size threshold, it is determined that the defect type in the image area is line block bridging. In the image area, the line block pattern In the case that the spatial critical dimension of the shape pattern is greater than twice the maximum value of the reference range of the spatial critical dimension, it is determined that the defect type in the image area is a line block break. In this way, various types of defects in the line-block pattern can be identified, and further analysis of the defects and process optimization can be facilitated, thereby improving the research and development progress of wafer products.

In some embodiments, the above method further includes: step S471 , counting the defect types of each image area whose defect state is defective, to obtain the number of image areas corresponding to each defect type.

Here, for each defect type, the number of image regions with the defect type can be counted, so as to obtain the number of image regions corresponding to each defect type.

In the embodiment of the present disclosure, the number of image regions corresponding to each defect type is obtained by counting the defect types of the image regions whose defect state is defective, and the number of image regions corresponding to each defect type can be quickly and accurately counted. The quantity is convenient for further analysis of defects and process optimization, which in turn helps to improve the research and development progress of wafer products.

An embodiment of the present disclosure provides a defect detection method, which can be executed by a processor of a computer device. Fig. 5A is a schematic diagram of the implementation flow of a defect detection method provided by an embodiment of the present disclosure. As shown in Fig. 5A, the method includes the following steps S501 to S505:

Step S501, acquiring at least one scanned image output by the SEM machine;

Step S502, performing edge feature extraction on at least one scanned image to obtain an edge feature map of each scanned image;

Step S503, obtaining the design drawing of the structural region corresponding to each scanned image from the GDS;

Step S504, registering the edge feature map of each scanned image with the corresponding design map to obtain at least one image region that does not match the corresponding design map in each edge feature map;

Step S505, by measuring the area or key dimension of the pattern in each image area, determine the defect state of each image area and the defect type of the defective image area, and use the computer to automatically output the image corresponding to each defect type The number of regions.

Referring to FIG. 5B, for a dense hole-like scanned image 511, such as the pattern area corresponding to the structure of the active area, the bit line contact area, the cell array area of the capacitor area, etc., the image processing algorithm is used to extract the edge features of the scanned image 511 , the edge feature map 512 of the scanned image 511 can be obtained. After the edge feature map 512 is registered with the design map 513 corresponding to the scanned image 511 in the GDS, at least one image region 514 that does not match the design map 513 can be determined. By calculating each The area of the hole pattern 10 in the image area 514, according to the size of the calculated area, classify the defects in the image area 514 to obtain the defect type 20, and count the number of each type of defect. The defect type 20 can include hole loss, hole shrinkage, hole Enlarging or hole bridging; the area of the hole pattern 11 in the image area 521 is 0, then it is determined that the defect type of the image area 521 is a hole missing (missing); the area of the hole pattern 12 in the image area 522 is less than the minimum of the reference area range value, then it is determined that the defect type of the image area 522 is shrinkage (shrinkage); the area of the hole pattern 13 in the image area 523 is greater than the maximum value of the reference area range, then it is determined that the defect type of the image area 523 is hole expansion (expanding) ); the area of the hole pattern 14 in the image area 524 is more than twice the maximum value in the reference area range, then it is determined that the defect type of the image area 524 is a hole bridge (bridge).

Since the area of the hole pattern may be adjusted according to the process, the reference area range is not necessarily equal to the area of the corresponding area in the design drawing in GDS. During implementation, the reference area range may be determined according to the number and area distribution of the hole patterns. In some embodiments, the distribution of the number and area of hole patterns is as shown in Figure 5C, the horizontal axis X is the area of hole patterns, and the vertical axis Y is the number of hole patterns. Since the overall number of hole patterns is large, the holes The distribution of the quantity and the area of the hole pattern is close to a normal distribution, and the mean value of the area can be approximated as the normal value of the area of the hole pattern, therefore, the preset range containing the mean value can be determined as the reference area range, and the reference area range can include The mean value of the area and the range of values around the mean value. In addition, continuing to refer to Figure 5C, the defect type can be determined according to the area of the hole pattern corresponding to the X coordinate, and the Y coordinate corresponding to the area is the number of image regions of the corresponding defect type, as shown in Figure 5C, the area range The defect type corresponding to A1 is missing holes, and the cumulative value of Y coordinates within this range is 128, so the number of image areas with missing holes is 128; the defect type corresponding to the area range A2 is hole shrinkage, and the cumulative value of Y coordinates within this range is 434, the number of image areas with hole reduction is 434; the defect type corresponding to the area range A3 is hole expansion, and the cumulative value of Y coordinates within this range is 890, then the number of image areas with hole expansion is 890; the area range A4 The corresponding defect type is hole bridge, and the cumulative value of the Y coordinate in this range is 140, so the number of image regions of hole bridge is 140.

Referring to FIG. 5D, for a scan image 531 of a line block type, such as a pattern area corresponding to a structure such as a sub-word line connection area, an inductive amplifier area, and a sub-word line driver in a memory cell transistor, the scan image 531 is processed using an image processing algorithm. Edge feature extraction can obtain the edge feature map 532 of the scanned image 531. After the edge feature map 532 is registered with the design map 533 corresponding to the scanned image 531 in the GDS, at least one image region 534 that does not match the design map 533 can be determined. By Calculate the spatial critical dimension B of the line block pattern 30 in each image area 534, classify the defects in the image area 534 according to the size of the spatial critical dimension B, obtain the defect type 40, and count the number of each type of defect, the defect type 40 may include Line block bridging or line block breaking; the spatial critical dimension B1 of the line block pattern 31 in the image area 541 is 0, then the defect type of the image area 541 is determined to be line block bridging; the spatial critical dimension of the line block pattern 32 in the image area 542 If B2 is more than twice the maximum value C of the reference range of the spatial critical dimension, it is determined that the defect type of the image region 542 is a line block break. The reference range of the spatial critical dimension of the line block pattern can be determined based on the critical dimension of the corresponding area in the design drawing in the GDS, which can be the same as the critical dimension, or include the critical dimension and the value range around the critical dimension.

In the embodiment of the present disclosure, the defect types of the structural layer in the wafer product and the number of image regions corresponding to each defect type can be output quickly and accurately by computer equipment, which can reduce errors and omissions statistics and human subjective judgments when manually identifying defects. The errors brought about can improve the accuracy and efficiency of the defect detection of the structural layer in the wafer product, and can provide a basis for the evaluation of the process such as the mask.

FIG. 6 is a schematic diagram of the composition and structure of a defect detection device provided by an embodiment of the present disclosure. As shown in FIG. 6 , the defect detection device 600 includes: an acquisition module 610, an extraction module 620, a first determination module 630 and a second determination module 640 ,in:

An acquisition module 610, configured to acquire at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each structural region;

The extraction module 620 is configured to perform edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images;

The first determining module 630 is configured to determine at least one image region to be measured in the edge feature map based on the design map of the structural region corresponding to the scanned image for each of the scanned images;

The second determination module 640 determines the defect detection result of each image area by measuring the pattern in each image area.

In some embodiments, the wafer product includes a dynamic random access memory, and the at least one structural area includes at least one structural area in at least one structural layer preset in the dynamic random access memory.

In some embodiments, the first determination module is further configured to: register the edge feature map of the scanned image with the design map of the structural region corresponding to the scanned image to obtain at least An image area that does not match the design drawing; each of the image areas that do not match is determined as an image area to be measured.

In some embodiments, the second determining module is further configured to: determine the measurement index of each of the image regions; for each of the image regions, based on the measurement indexes of the image region, the measuring the pattern in the area to obtain the measurement result of the measurement index in the image area; based on the measurement result of the measurement index of each of the image areas, determining the defect detection of each of the image areas result.

In some embodiments, the second determination module is further configured to: determine the pattern type of each of the image regions; for each of the image regions, based on the pattern type of the image region, determine the pattern type of the image region Metrics.

In some embodiments, the pattern type includes at least one of the following: hole type, line block type; the second determination module is further configured: the pattern type in the image area is the hole type In the case of , the area of the hole pattern in the image area is determined as the measurement index of the image area; in the case of the pattern type in the image area being the line block type, the image The spatial key dimension of the line block pattern in the area is determined as the measurement index of the image area.

In some embodiments, the hole pattern includes a hole pattern in the cell array area in at least one of the following areas: active area, bit line contact area, capacitor area; the line block pattern includes a metal wiring layer The strip pattern in at least one of the following areas: the sense amplifier area, the sub-word line driver area, and the sub-word line connection area.

In some embodiments, the second determination module is further configured to: obtain a reference value area of the measurement index of each of the image regions; for each of the image regions, the measurement index of the image region The measurement result is compared with the reference value area to determine the defect detection result of the image area.

In some embodiments, the defect detection result includes a defect state; the second determination module is further configured to: determine that the measurement result of the measurement index in the image area is within the reference value area, The defect state of the image area is non-defective; if it is determined that the measurement result of the measurement index in the image area is not in the reference value area, it is determined that the defect state of the image area is defective.

In some embodiments, the measurement index is the area of the hole pattern in the image area, and the reference value area is a reference area range; the second determination module is further configured to: acquire each of the scanned images The area of each hole-shaped pattern in each of the scanned images; based on the area of each hole-shaped pattern in each of the scanned images, determine the area distribution information of the hole-shaped patterns in each of the scanned images; for each of the image regions, based on The area distribution information of the hole pattern in the scanned image corresponding to the image area determines the reference area range of the hole pattern in the image area.

In some embodiments, the second determining module is further configured to: determine the hole pattern in the scanned image when the area distribution information of the hole pattern in the scanned image corresponding to the image area obeys a normal distribution The mean value of the area; based on the mean value of the area, determine the reference area range of the hole pattern in the image area.

In some embodiments, the defect detection result further includes a defect type; the second determining module is further configured to: determine that the measured area of the hole pattern in the image area is smaller than a first area threshold The defect type of the image area is missing holes; when the measured area of the hole pattern in the image area is not less than the first area threshold and is less than the minimum value in the reference area range, determine the image area The type of defect is hole reduction; in the case where the measured area of the hole pattern in the image area is greater than the maximum value in the reference area range and less than twice the maximum value in the reference area range, it is determined that the The defect type of the image area is hole enlargement; when the measured area of the hole pattern in the image area is not less than twice the maximum value in the reference area range, it is determined that the defect type of the image area is hole bridging .

In some embodiments, the measurement index is the spatial critical dimension of the line block pattern in the image area, and the reference value area is the reference range of the spatial critical dimension; the second determining module is further configured to: For each image area, determine the key spatial dimension of the reference pattern corresponding to the line block pattern in the image area in the design drawing corresponding to the image area, and determine the image area based on the spatial key dimension Spatially critical dimension reference ranges for midline block patterns.

In some embodiments, the defect detection result further includes a defect type; the second determining module is further configured to: if the spatially critical size of the line block pattern in the image area is smaller than a first size threshold, determine the The defect type of the image area is line block bridging; in the case where the spatial critical dimension of the line block pattern in the image area is greater than twice the maximum value of the spatial critical dimension reference range, determine the defect type of the image area For line block breaks.

In some embodiments, the device further includes: a statistical module configured to count defect types of each image region whose defect state is defective, to obtain the number of image regions corresponding to each defect type.

The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

It should be noted that, in the embodiment of the present disclosure, if the above-mentioned defect detection method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the embodiments of the present disclosure or the part that contributes to the related technology can be embodied in the form of a software product, the software product is stored in a storage medium, and includes several instructions to make a A computer device (which may be a personal computer, a server, or a network device, etc.) executes all or part of the methods described in various embodiments of the present disclosure. The aforementioned storage medium includes: various media that can store program codes such as U disk, mobile hard disk, read-only memory (Read Only Memory, ROM), magnetic disk or optical disk. As such, embodiments of the present disclosure are not limited to any specific combination of hardware and software.

Correspondingly, an embodiment of the present disclosure provides a computer device, including a memory and a processor, the memory stores a computer program that can run on the processor, and the processor implements the steps in the above method when executing the program.

Correspondingly, an embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method are implemented.

Correspondingly, an embodiment of the present disclosure provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is read and executed by a computer, the above method can be implemented. some or all of the steps. The computer program product can be specifically realized by means of hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK) etc. wait.

It should be pointed out here that: the above descriptions of the storage medium, computer program product, and device embodiments are similar to the descriptions of the above method embodiments, and have similar beneficial effects to those of the method embodiments. For technical details not disclosed in the storage medium, computer program product, and device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

It should be noted that FIG. 7 is a schematic diagram of a hardware entity of a computer device in an embodiment of the present disclosure. As shown in FIG. 7 , the hardware entity of the computer device 700 includes: a processor 701, a communication interface 702, and a memory 703, wherein:

Processor 701 generally controls the overall operation of computer device 700 .

The communication interface 702 enables the computer device to communicate with other terminals or servers through the network.

The memory 703 is configured to store instructions and applications executable by the processor 701, and can also cache data to be processed or processed by each module in the processor 701 and the computer device 700 (for example, image data, audio data, voice communication data and video data) Communication data), which can be realized by flash memory (FLASH) or random access memory (Random Access Memory, RAM).

It should be understood that reference throughout the specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic related to the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that in various embodiments of the present disclosure, the sequence numbers of the above-mentioned processes do not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, rather than by the embodiments of the present disclosure. The implementation process constitutes any limitation. The serial numbers of the above-mentioned embodiments of the present disclosure are for description only, and do not represent the advantages and disadvantages of the embodiments.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

In the several embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods may be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or May be integrated into another system, or some features may be ignored, or not implemented. In addition, the coupling, or direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms of.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed to multiple network units; Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may be used as a single unit, or two or more units may be integrated into one unit; the above-mentioned integration The unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

Those of ordinary skill in the art can understand that all or part of the steps to realize the above method embodiments can be completed by hardware related to program instructions, and the aforementioned programs can be stored in computer-readable storage media. When the program is executed, the execution includes The steps of the foregoing method embodiments; and the foregoing storage media include: removable storage devices, read-only memory (Read Only Memory, ROM), magnetic disks or optical disks and other media that can store program codes.

Alternatively, if the above-mentioned integrated units of the present disclosure are realized in the form of software function modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present disclosure or the part that contributes to related technologies can be embodied in the form of software products, the computer software products are stored in a storage medium, and include several instructions to make a A computer device (which may be a personal computer, a server, or a network device, etc.) executes all or part of the methods described in various embodiments of the present disclosure. The aforementioned storage medium includes various media capable of storing program codes such as removable storage devices, ROMs, magnetic disks or optical disks.

The above is only the embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope of the present disclosure, and should within the protection scope of the present disclosure.

Industrial Applicability

In the embodiment of the present disclosure, at least one scanned image obtained by scanning at least one structural region of the wafer product and a design drawing of each structural region are acquired; edge feature extraction is performed on each scanned image to obtain the Edge feature map; for each scanned image, based on the design map of the corresponding structural area of the scanned image, determine at least one image area to be measured in the edge feature map; by measuring the pattern in each image area, determine each Defect detection results for an image region. In this way, on the one hand, automatic detection of defects in the structural region of the wafer product can be realized, thereby reducing the labor cost caused by manual observation; The image area to be measured in the edge feature map can preliminarily determine the image area that may have defects. On this basis, the defect detection result of the image area is determined by measuring the pattern in the image area to be measured. In this way, it can be Improve the efficiency and accuracy of defect detection in wafer products, and reduce missed or false detections.

Claims (20)

1. A defect detection method, the method comprising:

   Obtaining at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each said structural region;

   performing edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images;

   For each of the scanned images, determining at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image;

   The defect detection result of each image area is determined by measuring the pattern in each image area.
2. The method according to claim 1, wherein the wafer product includes a dynamic random access memory, and the at least one structural region includes at least one structure in at least one structural layer preset in the dynamic random access memory area.
3. The method according to claim 1, wherein the determining at least one image area to be measured in the edge feature map based on the design map of the structural area corresponding to the scanned image comprises:

   Registering the edge feature map of the scanned image with the design map of the corresponding structural region of the scanned image to obtain at least one image region in the edge feature map that does not match the design map;

   Each of the non-matching image areas is determined as an image area to be measured.
4. The method according to any one of claims 1 to 3, wherein said determining the defect detection result of each said image area by measuring the pattern in each said image area comprises:

   determining a measurement index for each of said image regions;

   For each of the image areas, based on the measurement index of the image area, the pattern in the image area is measured to obtain the measurement result of the measurement index in the image area;

   Based on the measurement result of the measurement index of each of the image regions, the defect detection result of each of the image regions is determined.
5. The method according to claim 4, wherein said determining the measurement index of each said image region comprises:

   determining a pattern type for each of said image regions;

   For each image area, based on the pattern type of the image area, the measurement index of the image area is determined.
6. The method according to claim 5, wherein the pattern type comprises at least one of the following: hole-like type, line-block type;

   The determining the measurement index of the image area based on the pattern type of the image area includes:

   In the case that the pattern type in the image area is the hole type, determining the area of the hole pattern in the image area as the measurement index of the image area;

   In the case that the pattern type in the image area is the line block type, the spatial key dimension of the line block pattern in the image area is determined as the measurement index of the image area.
7. The method of claim 6, wherein,

   The hole pattern includes the hole pattern of the cell array area in at least one of the following areas: active area, bit line contact area, capacitor area;

   The line block pattern includes a strip pattern in at least one of the following areas of the metal wiring layer: a sense amplifier area, a sub-word line driver area, and a sub-word line connection area.
8. The method according to claim 4, wherein the determination of the defect detection result of each of the image regions based on the measurement results of the measurement indicators of each of the image regions comprises:

   Acquiring a reference value area of the measurement index of each of the image areas;

   For each image area, the measurement result of the measurement index of the image area is compared with the reference value area to determine the defect detection result of the image area.
9. The method according to claim 8, wherein the defect detection result comprises a defect state;

   The comparison of the measurement result of the measurement index of the image area with the reference value area to determine the defect detection result of the image area includes:

   When it is determined that the measurement result of the measurement index in the image area is within the reference value area, determine that the defect state of the image area is no defect;

   If it is determined that the measurement result of the measurement index in the image area is not within the reference value area, it is determined that the defect state of the image area is defective.
10. The method according to claim 9, wherein the measurement index is the area of the hole pattern in the image area, and the reference value area is a reference area range;

    The acquisition of the reference value area of the measurement index of each of the image areas includes:

    Acquiring the area of each hole pattern in each of the scanned images;

    determining area distribution information of each hole pattern in each of the scanned images based on the area of each hole pattern in each of the scanned images;

    For each image area, based on the area distribution information of the hole pattern in the scanned image corresponding to the image area, a reference area range of the hole pattern in the image area is determined.
11. The method according to claim 10, wherein the determining the reference area range of the hole pattern in the image region based on the area distribution information of the hole pattern in the scanned image corresponding to the image region includes:

    When the area distribution information of the hole pattern in the scanned image corresponding to the image area obeys a normal distribution, determine the mean value of the area of the hole pattern in the scanned image;

    Based on the mean value of the area, a reference area range of the hole pattern in the image area is determined.
12. The method according to claim 10, wherein the defect detection result further comprises a defect type;

    The comparing the measurement result of the measurement index of the image area with the reference value area, and determining the defect detection result of the image area also includes:

    When the measured area of the hole pattern in the image area is smaller than a first area threshold, it is determined that the defect type of the image area is missing holes;

    When the measured area of the hole pattern in the image area is not less than the first area threshold and is smaller than the minimum value in the reference area range, determine that the defect type of the image area is hole shrinkage;

    In the case where the measured area of the hole pattern in the image area is greater than the maximum value in the reference area range and less than twice the maximum value in the reference area range, it is determined that the defect type in the image area is a hole expand;

    In the case that the measured area of the hole pattern in the image area is not less than twice the maximum value in the reference area range, it is determined that the defect type in the image area is a hole bridge.
13. The method according to claim 9, wherein the measurement index is the spatial critical dimension of the line block pattern in the image area, and the reference value area is the reference range of the spatial critical dimension;

    The acquisition of the reference value area of the measurement index of each of the image areas includes:

    For each image area, determine the spatial critical dimension of the reference pattern corresponding to the line block pattern in the image area in the design drawing corresponding to the image area, and determine the image based on the spatial critical dimension Spatially critical dimension reference ranges for line block patterns in the region.
14. The method according to claim 13, wherein the defect detection result further includes a defect type;

    The comparing the measurement result of the measurement index of the image area with the reference value area, and determining the defect detection result of the image area also includes:

    In the case that the spatially critical dimension of the line block pattern in the image area is smaller than a first size threshold, determining that the defect type in the image area is line block bridging;

    In the case that the spatial critical dimension of the line block pattern in the image area is greater than twice the maximum value of the reference range of the spatial critical dimension, it is determined that the defect type of the image area is a line block break.
15. The method according to claim 12 or 14, wherein the method further comprises:

    The defect type of each image area whose defect state is defective is counted to obtain the number of image areas corresponding to each defect type.
16. A defect detection device, comprising:

    An acquisition module configured to acquire at least one scanned image obtained by scanning at least one structural region of the wafer product, and a design drawing of each structural region;

    An extraction module configured to perform edge feature extraction on each of the scanned images to obtain an edge feature map of each of the scanned images;

    The first determining module is configured to determine at least one image region to be measured in the edge feature map based on the design map of the structural region corresponding to the scanned image for each of the scanned images;

    The second determination module determines the defect detection result of each image area by measuring the pattern in each image area.
17. The device according to claim 16, wherein the first determining module is further configured to:

    Registering the edge feature map of the scanned image with the design map of the corresponding structural region of the scanned image to obtain at least one image region in the edge feature map that does not match the design map;

    Each of the non-matching image areas is determined as an image area to be measured.
18. The device according to claim 16 or 17, wherein the second determination module is further configured to:

    determining a measurement index for each of said image regions;

    For each image area, based on the measurement index of the image area, the pattern in the image area is measured to obtain the measurement result of the measurement index in the image area;

    Based on the measurement result of the measurement index of each of the image regions, the defect detection result of each of the image regions is determined.
19. A computer device, comprising a memory and a processor, the memory stores a computer program that can run on the processor, and the processor implements the steps in the method of any one of claims 1 to 15 when executing the program .
20. A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the method of any one of claims 1 to 15 are realized.

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