CN113095438A - Wafer defect classification method and device, system, electronic equipment and storage medium thereof - Google Patents
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Abstract
The invention relates to a wafer defect classification method, which comprises the following steps: acquiring defect characteristic parameters of each defect in a wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value; acquiring a pre-collected sample image of the wafer sample to be detected; and classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected. The classification method reduces the dependence on manual operation, ensures the accuracy and reliability of classification, reduces the production cost to a certain extent, and can be applied to large-batch product detection.
Description
Technical Field
The present invention relates to the field of semiconductor defect classification technologies, and in particular, to a method and an apparatus for classifying wafer defects, a system, an electronic device, and a computer-readable storage medium.
Background
As semiconductor device technology develops, the number of processes for manufacturing semiconductor devices increases, each process has a certain complexity, and the processing of wafers by each process flow may result in some unexpected structures, wherein the wafer defects are called as the defects that the circuits on the chip cannot work properly. Wafer defect detection is typically performed after a number of critical processes in the chip manufacturing process to monitor the critical processes and ensure their accuracy.
Because the process flow of chip manufacturing is extremely complex, the types of wafer defects are numerous, and at present, a unified classification mode does not exist. In general, an engineer classifies wafer defects by combining actual conditions, such as processes performed before wafer inspection and inspection methods.
Currently, the most common detection methods include two methods: the first is manual detection, that is, firstly scanning a wafer to be detected through a defect scanning device to obtain a position on the wafer to be detected, where a defect may occur, and marking the position correspondingly, then automatically photographing through a Scanning Electron Microscope (SEM), an optical microscope, an electron beam microscope or other devices according to the marked position of the defect scanning device to obtain one or more images of the wafer defect, and then classifying the defect by a worker based on the one or more images of the wafer defect; the second method is to classify the wafer defects based on various wafer defect classification models, such as CNN classifier, that is, the wafer defect images captured by SEM are input into a pre-trained classifier, and then the classifier performs automatic classification. However, both of the above-mentioned detection methods have certain problems:
1) the worker detection method only aims at the defects of the wafer to be detected, and the defects on each wafer are not of many kinds; moreover, the accuracy or reliability of defect classification by a worker is positively correlated with the experience of the worker in identifying the defects, i.e., the more abundant the experience is, the more accurate and reliable the classification method is, i.e., the higher the requirement on the professional degree of the worker is. On the other hand, if the worker identifies the defects for a long time, accumulated fatigue may be caused, so that the accuracy and reliability of the defect identification are reduced, and the defect identification method is manually classified and cannot be widely applied to real-time monitoring and classification of wafer defects in the wafer production process.
2) Classifying the classification model, namely classifying the defects by adopting a convolutional neural network algorithm (CNN) under the condition that the wafer defects are of more types, wherein the traditional CNN algorithm is used for classifying the defects based on defect characteristic parameters extracted from defect regions, such as texture characteristics, gray characteristics, morphological characteristics and the like, to form characteristic vectors, and then inputting the characteristic vectors into a classifier for processing to obtain a classification result. Typical classification methods mainly comprise supervised classification and unsupervised classification, and more common unsupervised classification methods comprise an ISODATA method and a t-mean value method; the supervision classification method comprises a minimum distance method, a mahalanobis distance method and a maximum likelihood method. Since the defect classification is performed directly according to the images obtained by automatic photographing through equipment such as a Scanning Electron Microscope (SEM), an optical microscope or an electron beam microscope, such algorithms are mainly limited to whether the defect feature parameters extracted directly from the photographed images can effectively express the differences of different defect types, and good defect feature expression is very important, but the defect feature parameters extracted for specific defect types are difficult to be widely applied to the classification of other defect types, which makes the defect detection and classification more difficult.
Accordingly, the present invention provides a novel wafer defect classification method and apparatus.
Disclosure of Invention
In view of the above technical problems, an object of the present invention is to provide a method and an apparatus for classifying wafer defects, a system, an electronic device and a storage medium thereof, which overcome or alleviate the above-mentioned defects in the prior art to a certain extent.
In a first aspect of the present invention, a method for classifying wafer defects is provided, which includes the steps of:
acquiring defect characteristic parameters of each defect in a wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value;
acquiring a pre-collected sample image of the wafer sample to be detected;
and classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
Further, in some exemplary embodiments of the present invention, the step of classifying the defect according to the sample image and the defect feature parameter specifically includes:
classifying the sample images for the first time according to the defect characteristic parameters to obtain at least one group of sample images to be classified;
and carrying out secondary classification on the at least one group of sample images to be classified according to a pre-trained classification model to obtain the type of the defect.
Further, in some exemplary embodiments of the present invention, in order to ensure the accuracy of classification, the step of classifying the defect further includes the following steps: and preprocessing the sample image.
Further, in some exemplary embodiments of the invention, the preprocessing comprises: and (5) filtering.
A second aspect of the present invention provides a defect classification apparatus, including:
the first data acquisition module is used for acquiring defect characteristic parameters of each defect in the wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value;
the second data acquisition module is used for acquiring a pre-acquired sample image of the wafer sample to be detected; and the defect classification module is used for classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
Further, in some exemplary embodiments of the present invention, the defect classification module specifically includes:
the first classification unit is used for carrying out primary classification on the sample images according to the defect characteristic parameters to obtain at least one group of sample images to be classified;
and the second classification unit is used for carrying out secondary classification on the at least one group of sample images to be classified according to a pre-trained classification model to obtain the type of the defect.
Further, in some exemplary embodiments of the invention, the apparatus further comprises: and the image preprocessing module is used for preprocessing the sample image.
Further, in some exemplary embodiments of the present invention, the preprocessing module specifically includes: and the filtering unit is used for performing de-filtering processing on the sample image.
A third aspect of the present invention provides a defect classification system, comprising:
the scanning equipment is used for scanning the wafer sample to be detected to obtain the defect characteristic parameters of each defect in the wafer sample to be detected; the defect characteristic parameters comprise: defect size and signal strength value;
the photographing equipment is used for photographing the wafer sample to be detected to obtain a sample image of the wafer sample to be detected;
and any defect classification device is used for classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
A fourth aspect of the present invention is to provide an electronic device usable for defect classification, the device comprising at least one processor, at least one memory, a communication interface and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory is used for storing a program for executing any one of the above methods; the processor is configured to execute programs stored in the memory.
A fifth aspect of the invention provides a computer-readable storage medium usable for defect classification, storing a computer program which, when executed by a processor, performs the steps of any of the above-described methods.
Advantageous effects
The defect classification method based on the defect characteristic parameters comprises the following steps: acquiring defect characteristic parameters of each defect in a wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value; acquiring a pre-collected sample image of the wafer sample to be detected; and then, classifying the defects according to the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected. Compared with the traditional classification method, the defect classification is carried out based on the defect characteristic parameters, so that the dependence on manpower can be reduced, on one hand, the defect classification efficiency is improved, and meanwhile, the industrial production detection cost of the wafer is reduced; on the other hand, the accuracy and the reliability of defect classification are improved; due to the fact that the technical scheme provided by the invention is high in efficiency and classification result precision, the wafer defect real-time monitoring and detecting method can be used for real-time monitoring and detecting of wafer defects on a production line.
Further, the wafer defects are firstly classified based on the defect attributes (namely defect characteristic parameters such as defect size and signal intensity value), and then the classification sample images to be classified are further accurately classified by using a pre-trained classification model; the invention overcomes the problem that the defect characteristic parameters extracted aiming at specific defect types are difficult to be widely applied to the classification of other defect types to a certain extent through the first classification (in other words, the classification of the wafer can be more standardized through the classification of the parameters of the defect characteristics obtained by wafer scanning, the application scene of the classification standard is wider and more universal, and the manual classification standard is prevented from being difficult to be unified and is easily influenced by the manual subjective judgment); and the dependence on manpower is reduced, so that the classification efficiency is improved, and the production cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale. It is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive exercise.
FIG. 1 is a flowchart illustrating a wafer defect classification method according to an exemplary embodiment of the invention;
FIG. 2 is a diagram illustrating a partial classification of a wafer according to an exemplary embodiment of the invention;
FIG. 3 is a schematic diagram of a wafer defect classification apparatus according to an exemplary embodiment of the invention;
fig. 4 is a block diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Herein, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the description of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
Example one
Referring to fig. 1, a flowchart of a wafer defect classification method according to an exemplary embodiment of the present invention is shown, and specifically, the wafer defect classification method according to the exemplary embodiment includes the following steps:
s101, acquiring defect characteristic parameters of each defect in the wafer sample to be detected, wherein the defect characteristic parameters are obtained by scanning the wafer sample to be detected in advance.
In some embodiments, the defect feature parameters include: defect size and signal strength value.
In some embodiments, the wafer defect is first identified by a defect scanning device, specifically, the wafer sample to be detected is divided into small pixel points by the defect scanning device, then the gray values or brightness values of the pixel points at the same position of different wafers are compared, some pixel points with larger differences are identified as the wafer defect, then the identified wafer defect is marked, and the defect characteristic parameters of the wafer defect are recorded by the defect scanning device.
Of course, in other embodiments, some standard wafer samples (i.e., standard wafer samples without any defect) may be selected, then the standard wafer samples are scanned to obtain the gray value or the brightness value of each pixel point in the standard wafer samples, the gray value or the brightness value of each pixel point in the standard wafer samples is referred to (e.g., according to the average value of the gray value or the brightness value of the pixel points at the same position in the selected plurality of standard wafer samples), the gray value threshold range or the brightness value threshold range of each pixel point in the wafer samples under the standard condition (i.e., under the condition without defect) is set, then the gray value threshold range or the brightness value threshold range is set as the defect identification standard of the defect scanning device, for example, the pixel points exceeding the gray value threshold range or the brightness value threshold range are identified as the wafer defect when, the identified wafer defects are then marked and defect characterization parameters of the wafer defects are recorded by a defect scanning device.
In some embodiments, the defect characteristic parameter of each defect is obtained by scanning each wafer in advance through various defect scanning devices, such as a laser scanning device, an infrared scanning device, an ultrasonic scanning device, and the like, preferably, a laser scanning device is adopted, and accordingly, a plurality of wafers to be detected can be directly obtained from the defect scanning devicesThe defect characteristic parameters of the sample may be obtained from the defect scanning device by wired communication or wireless communication, for example, and specifically include: the defect size and the signal intensity value, and an identifier (for example, a wafer number to be measured Ip, where p is 1,2, · is) indicating a wafer to be measured where the defect is located, are obtained as defect characteristic parameters of each defect on each wafer to be measured: for the defect parameters of N1(N1 ═ 1,2,3 ·) defects on the wafer sample I1 to be tested: defect (size) size: a. theI1，1，AI1，2，AI1，3，…AI1，N1(ii) a Signal strength value: sI1，1，SI1，2，SI1，3···SI1，N1(ii) a For the defect parameters of N2(N1 ═ 1,2,3 ·) defects on the wafer sample I2 to be tested: defect size: a. theI2，1，AI2，2，AI2，3，…AI2，N2(ii) a Signal strength value: sI2，1，SI2，2，SI2，3···SI2，N2···。
S103, acquiring a sample image of the wafer sample to be detected, which is acquired in advance.
In some embodiments, the sample image is obtained by various defect photographing apparatuses in advance, specifically, the sample image is obtained by scanning each marked defect with a scanning electron microscope, and accordingly, a plurality of sample images can be directly obtained from the defect photographing apparatuses. For example, a plurality of sample images may be acquired from a scanning electron microscope through wired communication or wireless communication, that is, the acquired sample image set includes: b1, B2, B3, B4 … BN.
And S105, classifying the defects according to the defect characteristic parameters acquired in the step S101 and the sample image acquired in the step S103 to obtain the type of each defect in the wafer sample to be detected.
In some embodiments, the step S105 specifically includes the steps of:
firstly, classifying the sample images for the first time according to the acquired defect characteristic parameters to obtain at least one group of sample images to be classified.
In some embodiments, the wafer defects may be first classified by defect size (i.e., the size of the defect), and the wafer defects may be classified into one or more types by using the defect size, for example, the identified wafer defects may be classified into large defects and small defects by using the size characteristic values of the existing defects in the defect scanning device, and the classification result may be shown in fig. 2, where a is small defects and b is large defects.
Of course, in other embodiments, the wafer defects may be classified for the first time by the signal intensity values, and the wafer defects may be classified into one or more types by the signal intensity values, where the signal intensity values are related to the imaging principle of the defect scanning device used, and some of the signal intensity values collected by the defect scanning device are reflected light intensities and some of the signal intensity values are scattered light intensities. Specifically, a classification standard using the signal intensity value as a main parameter may be pre-constructed, the signal intensity value may be divided into different value intervals, and a professional may perform defect type identification (such as a defect type name or codes for identifying various defect types) on the different value intervals, for example, the intensity signal value may be divided into different value intervals: c1, C2, C3 … CX, C1 are labeled as a first defect type identifier, i.e., corresponding to a first type of wafer defect; c2 is labeled as a second defect type identifier, i.e., corresponding to a second wafer defect; c3 is labeled as a third defect type identifier, i.e., corresponding to a third wafer defect; … are collectively expected to be X-wafer defects, and each wafer defect corresponds to a defect class identifier. And then, utilizing a defect classification device to match the signal intensity value of the defect in the sample image with the numerical value interval of the classification standard, thereby identifying and classifying the defect on the sample image. Of course, the signal intensity values can be replaced by gray scale values or brightness values.
In other embodiments, a plurality of preset size threshold ranges are preset in advance according to defect sizes (i.e., sizes of defects), and each preset size threshold range corresponds to at least one preset signal intensity threshold range, then the obtained defect size of each defect on each wafer to be tested is compared with the preset size threshold ranges, a signal intensity value of each defect is compared with the preset signal intensity threshold ranges, and if it is determined that the defect size belongs to the corresponding preset size threshold range and the signal intensity value belongs to the corresponding preset size threshold range, the defect is divided into a group of defects to be classified.
For example, the threshold range of the preset defect size corresponding to the first type of defect is X1-X2, and the threshold range of the preset signal intensity is Y1-Y2; a second type of defect, wherein a corresponding preset defect size threshold range is X3-X4, and a preset signal intensity threshold range is Y3-Y4-; then, the defect feature parameters of the N1 defects on the wafer I1 to be tested, which are obtained in the step S101, are respectively compared with the preset defect size threshold range and the preset signal intensity threshold range, and if a is determinedI1，1Belongs to X3-X4, and SI1，1Belongs to Y3-Y4; and determine AI5，7Belongs to X3-X4, and SI5，7If the defect belongs to Y3-Y4, the defect on the wafer I1 to be detected and the defect on the wafer I5 to be detected are divided into the same group of defects to be classified; in the same way, a plurality of groups of defects to be classified are obtained: m1, M2, M3.
And step two, performing secondary classification on at least one group of sample images to be classified obtained in the step S1051 according to a pre-trained classification model to obtain the types of all defects.
In some embodiments, the classification model may employ a common classifier such as a CNN classifier, an SVM classifier, or the like, and the selected classifier is trained in advance, specifically, the selected classifier is trained by using a sample image labeled with a corresponding defect type in advance as a training sample.
In a specific embodiment, the traditional Convolutional Neural Networks (CNN) method is used for secondary classification, and the Convolutional Neural network algorithm that can be used includes: AlexNet network, ZFNet network improved based on AlexNet, VGG network, google lenet network, etc., for example, in the present embodiment, ZFNet can be used for defect classification. Before secondary classification, a scanning electron microscope is used for acquiring sample images of a plurality of wafer materials, and corresponding defect types are marked in advance to obtain a training set. The defect type can be divided into three types of redundancy, crystal defect, mechanical damage and the like according to morphological characteristics of the wafer defect type in advance by combining with actual conditions (certainly, the wafer defect can be divided into types of fouling, cracking, unfilled corner, edge cracking, incomplete defect, protrusion and the like, and can also be divided into types of point defect, dislocation, primary defect, impurity and the like, and because the wafer defect is various in actual production work, the classification method of the defect is not limited to the classification, and can be flexibly classified according to actual application scenes), further, the position and the defect type of the sample defect of the training set are marked, and meanwhile, in order to improve the generation efficiency of the training set, the data set can be expanded by means of carrying out horizontal and vertical overturning, random change of contrast and the like on the sample image. Then, training a patch-based ZFNET detector, wherein when the patch-based ZFNET classifier is trained, a data set is a sample image of the wafer materials, the sample image already contains defect positions and types, a plurality of groups of data are obtained through a series of data expansion operations, 60% of data are randomly selected as a training set, and 40% of data are selected as a testing set. On the basis of the trained patch-based ZFNET detector, sample images of the wafer to be detected are sequentially sent to the ZFNET detector, and the ZFNET detector classifies defects according to set defect types.
In some embodiments, a sample image to be classified may be randomly selected from each group, and then classified by the classifier to obtain a defect type of the sample image to be classified, and all sample images to be classified in the group of images to be classified where the sample image to be classified is currently located are all marked as the defect type. Of course, further, in order to improve the accuracy of classification, at least two images to be classified may be selected from each group for secondary classification.
In other embodiments, a sample image to be classified with the largest defect (size) size and/or the largest signal intensity value may be selected from each group for secondary classification. Furthermore, in order to improve the accuracy of classification, all the sample images to be classified in each group can be sorted according to the order of the sizes of the defects (sizes) from large to small, and then the sample images to be classified with the sizes of the defects (sizes) sorted in the first two or three bits can be selected from each group for secondary classification; or, all the sample images to be classified in each group can be sorted according to the sequence of the signal intensity values from large to small, and then the sample images to be classified with the signal intensity values sorted in the first two bits or the first three bits are selected from each group for secondary classification; or selecting at least one sample image to be classified with the defect (size) size and the signal intensity value ranked in the first few bits (for example, 3 bits) from each group for secondary classification.
Further, in some embodiments, in order to remove noise interference in the sample image and improve the accuracy of defect classification, the sample image is also preprocessed before the step of defect classification.
In some embodiments, the preprocessing is a filtering processing; specifically, the filtering process includes: median filtering, mean filtering, block filtering, gaussian filtering, bilateral filtering. In this embodiment, a median filter may be used to perform filtering processing on a sample image, and a pixel point in the sample image is first divided into an isolated noise point, an edge detail, and a flat noise, where the isolated noise point and the flat noise are processed using the median filter, and the edge detail is directly output without being processed, and then a sample image that can be accurately classified is obtained.
Example two
Fig. 3 is a schematic view of a wafer defect classification apparatus according to an exemplary embodiment of the invention. Specifically, the wafer defect classification apparatus of the present exemplary embodiment includes:
the first data acquisition module 14 is configured to acquire a defect characteristic parameter of each defect in a wafer sample to be detected, which is obtained by scanning the wafer sample to be detected in advance; wherein, the defect characteristic parameters comprise: defect size and signal strength value; specifically, the first data acquisition module 14 may be a laser scanning device, an infrared scanning device, an ultrasonic scanning device;
the second data acquisition module 16 is configured to acquire a pre-acquired sample image of the wafer sample to be detected; specifically, the second data acquisition module 16 employs a scanning electron microscope;
and the defect classification module 18 is configured to perform defect classification according to the sample image and the defect characteristic parameters to obtain a type of each defect in the wafer sample to be detected.
In some embodiments, defect classification module 18 specifically includes: the first classification unit is used for carrying out primary classification on the obtained sample images according to the defect characteristic parameters to obtain at least one group of sample images to be classified; and the second classification unit is used for carrying out secondary classification on the at least one group of sample images to be classified according to a pre-trained classification model to obtain the type of each defect.
In some embodiments, the second classification unit may classify the wafer defect by using a CNN algorithm.
In some embodiments, the apparatus further comprises an image preprocessing module for preprocessing the sample image. Specifically, the preprocessing module specifically includes:
a filtering unit, configured to perform filtering processing on the sample image subjected to background processing; specifically, the filtering unit may perform denoising processing on the sample image by using any one or more of median filtering, mean filtering, block filtering, gaussian filtering, bilateral filtering, and the like.
Accordingly, based on the above wafer defect classification apparatus, a wafer defect classification system is provided, which comprises:
the scanning equipment is used for scanning the wafer sample to be detected to obtain a sample image of the wafer sample to be detected and a defect characteristic parameter of each defect in the wafer sample to be detected; wherein, the defect characteristic parameters comprise: defect size and signal strength value;
the photographing equipment is used for photographing the wafer sample to be detected to obtain a sample image of the wafer sample to be detected;
and the defect classification device in the second embodiment is configured to classify the defects according to the sample image and the defect characteristic parameters to obtain a type of each defect in the wafer sample to be detected.
EXAMPLE III
A third aspect of the present invention is to provide an electronic device comprising a memory 502, a processor 501 and a computer program stored on the memory 502 and executable on the processor 501, wherein the processor 501 executes the program to implement the steps of the method as described above. For convenience of explanation, only the parts related to the embodiments of the present specification are shown, and specific technical details are not disclosed, so that reference is made to the method parts of the embodiments of the present specification. The electronic device may be any electronic device including various electronic devices, a PC computer, a network cloud server, and even a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, a desktop computer, and the like.
Specifically, fig. 4 is a block diagram of an electronic device according to an exemplary embodiment of the present invention. The bus 500 may include any number of interconnected buses 500 and bridges that link together various circuits including one or more processors, represented by the processor 501, and memory, represented by the memory. The bus 500 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The communication interface 503 provides an interface between the bus and the receiver and/or transmitter 504, and the receiver and/or transmitter 504 may be a separate independent receiver or transmitter or the same element 504 may be a transceiver, providing a means for communicating with various other devices over a transmission medium. The processor 501 is responsible for managing the bus 500 and general processing, and the memory 502 may be used for storing data used by the processor 501 in performing operations.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a computer-readable storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring defect characteristic parameters of each defect in a wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value; acquiring a pre-acquired sample image of the wafer sample of the chip to be detected; and classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a computer terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (11)
1. A defect classification method based on defect characteristic parameters is characterized by comprising the following steps:
acquiring defect characteristic parameters of each defect in a wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value;
acquiring a pre-collected sample image of the wafer sample to be detected;
and classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
2. The method according to claim 1, wherein the step of classifying the defect according to the sample image and the defect feature parameter specifically comprises:
classifying the sample images for the first time according to the defect characteristic parameters to obtain at least one group of sample images to be classified;
and carrying out secondary classification on the at least one group of sample images to be classified according to a pre-trained classification model to obtain the type of the defect.
3. The method of claim 2, wherein the step of classifying the defects is preceded by the step of:
and preprocessing the sample image.
4. The method of claim 3, wherein the pre-processing comprises: and (5) filtering.
5. A defect classification apparatus, comprising:
the first data acquisition module is used for acquiring defect characteristic parameters of each defect in the wafer sample to be detected, which are obtained by scanning the wafer sample to be detected in advance; the defect characteristic parameters comprise: defect size and signal strength value;
the second data acquisition module is used for acquiring a pre-acquired sample image of the wafer sample to be detected;
and the defect classification module is used for classifying the defects according to the sample image and the defect characteristic parameters to obtain the type of each defect in the wafer sample to be detected.
6. The apparatus of claim 5, wherein the defect classification module specifically comprises:
the first classification unit is used for carrying out primary classification on the sample images according to the defect characteristic parameters to obtain at least one group of sample images to be classified;
and the second classification unit is used for carrying out secondary classification on the at least one group of sample images to be classified according to a pre-trained classification model to obtain the type of the defect.
7. The apparatus of claim 5, further comprising: and the image preprocessing module is used for preprocessing the sample image.
8. The apparatus according to claim 7, wherein the preprocessing module specifically comprises:
and the filtering unit is used for performing de-filtering processing on the sample image.
9. A defect classification system, comprising:
the scanning equipment is used for scanning the wafer sample to be detected to obtain the defect characteristic parameters of each defect in the wafer sample to be detected; the defect characteristic parameters comprise: defect size and signal strength value;
the photographing equipment is used for photographing the wafer sample to be detected to obtain a sample image of the wafer sample to be detected; and
the defect classification device according to any one of claims 5 to 8, configured to perform defect classification according to the sample image and the defect feature parameters to obtain a type of each defect in the wafer sample to be detected.
10. An electronic device comprising at least one processor, at least one memory, a communication interface, and a bus; wherein,
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory is used for storing a program for executing the method of any one of claims 1 to 4;
the processor is configured to execute programs stored in the memory.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of one of claims 1 to 4.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113222967A (en) * | 2021-05-28 | 2021-08-06 | 长江存储科技有限责任公司 | Wafer detection method and system |
| CN113610788A (en) * | 2021-07-27 | 2021-11-05 | 上海众壹云计算科技有限公司 | Fault monitoring method and device for image acquisition device, electronic equipment and storage medium |
| CN115360116A (en) * | 2022-10-21 | 2022-11-18 | 合肥晶合集成电路股份有限公司 | Wafer defect detection method and system |
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| CN117197617A (en) * | 2023-09-19 | 2023-12-08 | 芯率智能科技(苏州)有限公司 | Defect classification method and system for repeated defects |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030212469A1 (en) * | 2002-05-08 | 2003-11-13 | Sheng-Jen Wang | Method for automatically controlling defect -specification in a semiconductor manufacturing process |
| KR20090053274A (en) * | 2007-11-23 | 2009-05-27 | 삼성전자주식회사 | Reviewing apparatus of wafer defect and method thereof |
| CN101996855A (en) * | 2009-08-21 | 2011-03-30 | 中芯国际集成电路制造(上海)有限公司 | Wafer defect analysis method |
| CN104062305A (en) * | 2014-07-28 | 2014-09-24 | 上海华力微电子有限公司 | Defect analysis method for integrated circuit |
| CN108133900A (en) * | 2017-12-21 | 2018-06-08 | 上海华力微电子有限公司 | A kind of Defect Scanning board and its automatic defect classification method |
| CN109977808A (en) * | 2019-03-11 | 2019-07-05 | 北京工业大学 | A kind of wafer surface defects mode detection and analysis method |
| WO2020057644A1 (en) * | 2018-09-21 | 2020-03-26 | Changxin Memory Technologies, Inc. | Method and apparatus for classification of wafer defect patterns as well as storage medium and electronic device |
| CN110969598A (en) * | 2018-09-28 | 2020-04-07 | 台湾积体电路制造股份有限公司 | Wafer inspection method and wafer inspection system |
| CN111819676A (en) * | 2018-03-28 | 2020-10-23 | 科磊股份有限公司 | Training neural networks for defect detection in low resolution images |
| CN112129772A (en) * | 2019-06-24 | 2020-12-25 | 杭州元色科技有限公司 | Defect detection system and method |
| CN112529873A (en) * | 2020-12-09 | 2021-03-19 | 深圳市芯汇群微电子技术有限公司 | Wafer defect detection method based on ART neural network |
-
2021
- 2021-04-30 CN CN202110478016.3A patent/CN113095438B/en active Active
- 2021-04-30 CN CN202410073784.4A patent/CN117893817A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030212469A1 (en) * | 2002-05-08 | 2003-11-13 | Sheng-Jen Wang | Method for automatically controlling defect -specification in a semiconductor manufacturing process |
| KR20090053274A (en) * | 2007-11-23 | 2009-05-27 | 삼성전자주식회사 | Reviewing apparatus of wafer defect and method thereof |
| CN101996855A (en) * | 2009-08-21 | 2011-03-30 | 中芯国际集成电路制造(上海)有限公司 | Wafer defect analysis method |
| CN104062305A (en) * | 2014-07-28 | 2014-09-24 | 上海华力微电子有限公司 | Defect analysis method for integrated circuit |
| CN108133900A (en) * | 2017-12-21 | 2018-06-08 | 上海华力微电子有限公司 | A kind of Defect Scanning board and its automatic defect classification method |
| CN111819676A (en) * | 2018-03-28 | 2020-10-23 | 科磊股份有限公司 | Training neural networks for defect detection in low resolution images |
| WO2020057644A1 (en) * | 2018-09-21 | 2020-03-26 | Changxin Memory Technologies, Inc. | Method and apparatus for classification of wafer defect patterns as well as storage medium and electronic device |
| CN110969598A (en) * | 2018-09-28 | 2020-04-07 | 台湾积体电路制造股份有限公司 | Wafer inspection method and wafer inspection system |
| CN109977808A (en) * | 2019-03-11 | 2019-07-05 | 北京工业大学 | A kind of wafer surface defects mode detection and analysis method |
| CN112129772A (en) * | 2019-06-24 | 2020-12-25 | 杭州元色科技有限公司 | Defect detection system and method |
| CN112529873A (en) * | 2020-12-09 | 2021-03-19 | 深圳市芯汇群微电子技术有限公司 | Wafer defect detection method based on ART neural network |
Non-Patent Citations (4)
| Title |
|---|
| FANG XIN 等: "Wafer Defect Detection and Classification Algorithms Based on Convolutional Neural Network", 《COMPUTER ENGINEERING》, vol. 44, no. 08, pages 218 - 423 * |
| 涂政乾 等: "基于光散射理论的玻璃晶圆表面缺陷检测方法研究", 《光散射学报》, vol. 32, no. 03, pages 245 - 250 * |
| 陈治杉: "基于机器视觉的晶圆缺陷检测系统分析与设计", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 09, pages 135 - 73 * |
| 马磊: "IC晶圆表面缺陷检测技术研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 03, pages 138 - 7398 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| WO2023095505A1 (en) * | 2021-11-24 | 2023-06-01 | 東レエンジニアリング株式会社 | Automatic defect classifier |
| CN115360116A (en) * | 2022-10-21 | 2022-11-18 | 合肥晶合集成电路股份有限公司 | Wafer defect detection method and system |
| CN115360116B (en) * | 2022-10-21 | 2023-01-31 | 合肥晶合集成电路股份有限公司 | Wafer defect detection method and system |
| CN115753813A (en) * | 2022-11-01 | 2023-03-07 | 太原国科半导体光电研究院有限公司 | Method, device and system for detecting wafer defects, storage medium and electronic equipment |
| CN115753813B (en) * | 2022-11-01 | 2023-10-31 | 太原国科半导体光电研究院有限公司 | Method, device and system for detecting wafer defects, storage medium and electronic equipment |
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| CN115876823A (en) * | 2023-01-19 | 2023-03-31 | 合肥晶合集成电路股份有限公司 | Film defect detection method, film defect detection device and film defect detection system |
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| CN116130377B (en) * | 2023-04-17 | 2023-09-29 | 西安奕斯伟材料科技股份有限公司 | Method, device and system for detecting defects of epitaxial wafer and manufacturing method thereof |
| CN117197617A (en) * | 2023-09-19 | 2023-12-08 | 芯率智能科技(苏州)有限公司 | Defect classification method and system for repeated defects |
| CN117197617B (en) * | 2023-09-19 | 2024-05-28 | 芯率智能科技(苏州)有限公司 | Defect classification method and system for repeated defects |
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