Patent Application
20180174000
The present invention relates to a defect image classification apparatus and a defect image classification method that automatically classify an image that results from image-capturing a defect which occurs while manufacturing a semiconductor wafer.
In a process of manufacturing a semiconductor, in order to accomplish a yield improvement, it is important to investigate a cause of occurrence of a defect to a semiconductor wafer and thus to take measure to cope with this situation. Analysis of the defect is made using a visual inspection tool and a defect review tool in a manufacturing field. The visual inspection tool is a tool that inspects a wafer using optical means or an electronic beam and outputs positional coordinates of the detected defect. Because the visual inspection tool needs to inspect an entire surface of the wafer at high speed, pixel resolution of a detection image is lowered to such an extent that the defect detection is possible and an amount of image data per unit area is reduced, thereby accomplishing shortening of the inspection time. For this reason, detailed review of the defect using a detection image that is obtained by the visual inspection tool is difficult.
The detailed review of the defect is performed by the defect review tool. The defect review tool is a tool that image-captures a position which is represented by defect coordinates, at high resolution, based on the defect coordinates which are obtained from the defect review tool, and that outputs the captured image. With the miniaturization of a semiconductor manufacturing process, a defect review tool SEM that uses a scanning electron microscope (SEM) is widely used for this review. The defect review SEM has an automatic defect review (ADR) function of automatically capturing and collecting an image of the defect on the wafer based on the defect coordinates that are obtained from the visual inspection tool and an automatic defect classification (ADC) function of automatically classifying the collected images.
The ADC needs to perform learning that uses the defect image in an initial operation for every classification process, but it is difficult to collect a sufficient amount of image layer data on all defect classes (types) and to perform creating of learning data set. In order to maintain initially-obtained performance until after production application, performance evaluation while the production application is in progress and additional learning are necessary. A problem is that this technique has to be established. A method of monitoring ADC learning data set at the time of the production application is disclosed in PTL 1.
Although the defect class is limited to one classification process, there are various classes. Furthermore, in some cases, variations in shape and brightness are included in one class. For this reason, the ADC is difficult to apply to all processes in terms of an operation. For this reason, a method in which the classification process is divided into an ADC application process and a visual classification process, or the use of both the ADC and the visual classification, such as when division into a defect class that uses a result of the ADC and a defect class that does not use the result of the ADC is performed in one classification process to perform visual classification of only the defect class that does not use the result of the ADC, is proposed. A method of setting a recipe for the ADC on the assumption of the use of both the ADC and the visual classification is disclosed in PTL 2.
PTL 1: JP-A-2001-256480
PTL 2: JP-A-2011-155123
Non-patent Literature
NPL 1: W. Tomlinson, B. Halliday, et.al, “In-Line SEM based ADC for Advanced Process Control”, Proc. Of 2000 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 131-137(2000)
In an automatic defect classification technology that is applied to a visual inspection process in a semiconductor process, whether or not a recipe for automatic defect classification process is suitably set for an image that is a target for automatic defect classification is evaluated periodically or at an arbitrary timing after the time elapses and the recipe needs to be updated in a case where the recipe for the automatic defect classification process is unsuitable. Performance evaluation of the automatic defect classification in the related art is performed with a visual defect class (type) assigned by one operator being used as a reference, and recipe update is performed based on this visual defect class. However, there is a problem in that an error due to an individual difference or the like is included in the visual defect class and in that the performance evaluation of the high-precision automatic defect classification and the recipe update for providing the high classification performance cannot be performed.
PTL 1 discloses a technology in which, in order to stably maintain the classification performance of the ADC at the time of application for mass production, a defect image feature that is an ADC classification target and a feature that is registered in learning data set are compared with each other, statistical changes in the image that is the ADC target and in the learning data set are detected, an instruction for learning data set update is performed, and thus a stable operation can be performed without decreasing the classification performance of the ADC also at the time of the application for mass production.
PTL 1 discloses a method of using a result of the ADC classification or a result of the visual classification for a defect class of the image that is the ADC target. However, the use of the result of the ADC classification that is a performance evaluation target cannot exclude erroneous classification due to the ADC at all, a classification accuracy rate of the visual classification, as disclosed in NPT 1, is at most 60%, suitable performance evaluation of the ADC is difficult although any is used.
PTL 2 discloses a technology in which division into a defect class suitable the ADC and a defect class unsuitable for the ADC is performed, an operation in which the defect class unsuitable for the ADC is checked in the visual classification is assumed, a recipe that can decrease the number of defect images that are transferred to the visual classification, from among a plurality of ADC recipes that are in advance prepared, and thus optimization of a parameter of the ADC is accomplished. In a defect classification operation in which the ADC and the visual classification are both used, stable operation of the ADC and a decrease in visual classification workload are compatible with each other. However, when the parameter of the ADC is optimized, the result of the visual classification is used, and an influence of the visual classification that is at a low classification accuracy rate cannot be avoided. Furthermore, a method of the performance evaluation that is necessary after the application for mass production and a method of updating the ADC learning data set are not disclosed.
The description is provided above using the terms, such as learning data set, ADC recipe, and ADC parameter, that are used in each of the literature documents, when referring to the two patent literature documents. These may be interpreted, in a broad sense, as data necessary for causing the ADC to operate. However, more precisely, the ADC parameter can be defined as a parameter relating to image processing that is necessary until an image feature is calculated. Furthermore, the learning data set can be defined as a parameter set that is used by a classification algorithm of the ADC, which is derived from a teaching image feature. Furthermore, the ADC recipe can be defined as a data set for causing the ADC, which includes both the ADC parameter and the learning data set, to operate.
An object of the present invention is to provide a defect image classification apparatus and a defect image classification method that solves a problem with the visual classification in the related art and then make it possible to perform high-reliability performance evaluation of ADC and update of ADC learning data set, using both the ADC and visual classification, or both the ADC and one other classification apparatus, in a defect classification operation in which the ADC and the visual classification are both used.
In order to accomplish the object described above, according to an aspect of the present invention, there is provided a defect image classification apparatus that classifies defect images, including: a storage unit in which the defect images that are obtained by being captured in separate image capture means are stored; an image selection unit that selects images from among the defect images that are stored in the storage unit, using information on defect classes into which defects are classified in a plurality of separate defect classification means; an image classification unit that classifies the images which are selected in the image selection unit, based on a classification recipe; a classification performance evaluation unit that evaluates classification performance of the image classification unit based on a result of the classification of the images; and a learning update unit that updates the classification recipe of the image classification unit using the images that are selected in the image selection unit in a case where the result of the evaluation in the classification performance evaluation unit does not satisfy a reference that is in advance set.
Furthermore, in order to accomplish the object described above, according to another aspect of the present invention, there is provided a defect image classification apparatus that classifies defect images, including: a storage unit in which the defect images that are obtained by being captured in separate image capture means are stored; an image selection unit that selects images from among the defect images that are stored in the storage unit, using information on defect classes into which defects are classified in a plurality of separate defect classification means; an image classification unit that classifies the images which are stored in the storage unit, based on a classification recipe; and a learning update unit that updates the classification recipe of the image classification unit using the images that are selected in the image selection unit.
Moreover, in order to accomplish the object described above, according to still another aspect of the present invention, there is provided a defect image classification method of classifying defect images, including: storing the defect images that are obtained by being captured in separate image capture means, in a storage unit; selecting images in an image selection unit from among the defect images that are stored in the storage unit, using information on defect classes into which defects are classified in a plurality of separate defect classification means; classifying the images which are selected in the image selection unit, in an image classification unit, based on a classification recipe; evaluating classification performance of the image classification unit, in a classification performance evaluation unit, based on a result of the classification of the images; and updating the classification recipe of the image classification unit in a learning update unit, using the images that are selected in the image selection unit in a case where a result of the evaluation in the classification performance evaluation unit does not satisfy a reference that is in advance set.
Moreover, in order to accomplish the object described above, according to still another aspect of the present invention, there is provided a defect image classification method of classifying defect images, including: storing the defect images that are obtained by being captured with separate image capture means, in a storage unit; selecting images in an image selection unit from among the defect images that are stored in the storage unit, using information on defect classes into which defects are classified in a plurality of separate defect classification means; classifying the images which are stored in the storage unit, in an image classification unit, based on a classification recipe; and updating the classification recipe of the image classification unit in a learning update unit, using the images that are selected in the image selection unit.
According to the present invention, with comparison among a plurality of visual classification or results that are obtained by a classification apparatus other than an ADC apparatus, high-reliability performance evaluation of ADC is possible using data that has a few defect class errors. Furthermore, with this data, learning update is performed without ADC learning, and thus classification performance of the ADC can be maintained and improved.
In a method of and an apparatus for classifying defect images according to the present invention, images that are selected using information on a defect class into which a defect is classified in a plurality of separate defect classification means are classified, classification performance is evaluated based on a result of the classification, and an image classification recipe is updated in a case where a result of the evaluation does not satisfy a reference that is in advance set.
Furthermore, in a method of and an apparatus for classifying defect images according to the present invention, an image is selected in an image selection unit from among defect images that are stored in a storage unit using information on defect classes into which defects are classified in a plurality of separate defect classification means, and a classification recipe of an image classification unit is updated in a learning update unit using the selected image.
Embodiments of the present invention will be described below with reference to the drawings.
An automatic defect classification (ADC) apparatus 100 according to the present invention is illustrated in
In
The defect image capture apparatus 102 is an apparatus that captures an image of a defect position which is detected in a visual inspection tool (not illustrated), at high magnification and that takes a picture of the external appearance of a defect, such as an optical-type apparatus or a scanning electron microscope (SEM)-type apparatus. The SEM-type device is used for a microscopic device, has a function of automatically capturing the image of the defect position that is detected in the visual inspection tool, and is called a defect review SEM or the like.
The yield management system 103 receives defect coordinates that are output from the visual inspection tool (not illustrated), a defect image that is output from the defect image capture apparatus 102, and defect class (defect type) information that is output from the ADC apparatus 100 and the MDC apparatus 104, and along with this, transmits the defect coordinates according to a request from the defect image capture apparatus 102 and transmits the defect image according to a request from the ADC apparatus 100 and the MDC apparatus 104. The defect coordinates, the defect images, and the defect classes that are accumulated in the yield management system 103 are statistically interpreted, and thus a user can monitor a state of a process.
The MDC apparatus 104 is an apparatus with which an operator performs classification of defect images and assigns defect class information to the defect image. The defect image is received from the ADC apparatus 100, the defect image capture apparatus 102, or the yield management system 103, the defect class is assigned by the operator to the received defect image, and the defect class information is transmitted to the ADC apparatus 100 or the yield management system 103. A plurality of MDC apparatuses 104 from MDC 1 to MDC N are set to be connected.
In the present embodiment, as a defect classification apparatus that is different from the ADC apparatus 100, the MDC apparatus 104 is taken as an example to proceed with the description, but any apparatus that can assign a defect class to a defect that is image-captured in the defect image may be a defect classification apparatus or a defect analysis apparatus other than the MDC apparatus.
The inside of the ADC apparatus 100 will be described. The defect image capture apparatus 102, the yield management system 103, and the MDC apparatus 104 transmits and receives defect image data, the defect class information, and the like, through a data transmission and reception unit 110.
The ADC apparatus 100 includes the data transmission and reception unit 110, a storage unit 111, a defect class comparison unit 112, an image selection unit 113, a classification performance evaluation unit 114, an image classification unit 115, a learning update unit 116, an input and display terminal 117, and a bus 118.
Stored in the storage unit 111 are the defect image data, the defect class information, and the like. The defect class comparison unit 112 performs comparison among a plurality of defect classes for the same defect image that is obtained in other than the ADC apparatus 100, or comparison of a defect class that is obtained as a result of the comparison and a defect class that is assigned in the image classification unit 115. Based on a defect that results from the comparison by the defect class comparison unit 112, the image selection unit 113 makes a selection of a defect image from among defect images that are stored in the storage unit 111.
Based on the comparison of the defect class that is obtained as a result of the comparison in the defect class comparison unit 112 and the defect class that is assigned in the image classification unit 115, the classification performance evaluation unit 114 makes an evaluation of classification performance of the image classification unit 115. Based on an evaluation result of the classification performance of the image classification unit 115 that is evaluated in the classification performance evaluation unit 114, the learning update unit 116 updates a recipe for ADC processing that is performed in the image classification unit 115. The input and display terminal 117 displays processing detail, and along with this, receives input of a setting value of the operator and the like.
The bus 118 performs information transmission and reception between each of the data transmission and reception unit 110, the storage unit 111, the defect class comparison unit 112, an image selection unit 113, a classification performance evaluation unit 114, the image classification unit 115, the learning update unit 116, and a display terminal 117, within the ADC apparatus 100. The ADC apparatus 100 may be equipped with any of the defect image capture apparatus 102, the yield management system 103, and the manual defect classification apparatus 104.
A flow for operation of the ADC apparatus 100 will be described in detail below with reference to
When the defect image is determined, the defect image is transmitted to a plurality of MDC apparatuses 104 (S201). The defect images are classified by the operator in each of the MDC apparatuses 104 (MDCs 1 To N), and the defect class is assigned. The assigned defect class information is received from the MDC apparatus 104 through the data transmission and reception unit 110, and is stored in the storage unit 111 (S202). Next, the defect classes that are assigned by the operator in each of the MDC apparatus 104 (MDCs 1 to N) are compared (S203). A method of the comparison will be described with reference to
On the other hand, with defect ID 2 under the defect ID field 301, information that is defect class A is received from all the MDC apparatuses that are shown under the MDC apparatus field 302, and the defect classes in the MDC apparatuses 1, 2, 3, 4, and 5 are consistent with each other. In a case where consistency is present, ο is placed under a “consistency” field 303 corresponding to each defect ID, which belongs to the classification result field 310. In a case where inconsistency is present, ο is placed under an “inconsistency” field 304.
It can be considered that the defect ID, the defect classes with which are consistent with each other has higher reliability of the defect class in the MDC than the defect ID, the defect classes with which are inconsistent with each other. It is noted that five MDC apparatuses are illustrated in
First, S203 in a flowchart in
The defect class that is assigned by each of the MDC apparatuses 104 (MDCs 1 to N) is read from the storage unit 111 into the defect class comparison unit 112 and the comparison is performed (S203). A defect number in which as a result of the comparison, the defect classes are consistent with each other is specified, and a defect image corresponding to the specified defect number is selected in the image selection unit 113 (S204).
Next, the defect image that is selected in the image selection unit 113 is automatically classified in the image classification unit 115 (S205), and in the classification performance evaluation unit 114, performance evaluation of the ADC is performed using a defect class that is obtained with the automatic classification in the image classification unit 115 and an MDC defect class of a selection image (S206). Steps from S200 to S206 are collectively set to be S210 for reference in
Whether defect class “A” in the vertical column 401, which is assigned by the MDC, can be correctly classified in the ADC in the horizontal row 402 can be evaluated as accuracy performance 404, that is, (the number of true positives in defect class “A” that is assigned by the MDC)/(a total of the number of true positives and the number of false negatives in defect class “A” that is assigned by the MDC)=55/(55+2+3)×100=92%. This serves as an index indicating to what accuracy the ADC can perform the classification by comparison with the MDC. The same is true for other defect classes “B” and “C” in terms of accuracy and purity.
A total accuracy rate is shown under a field 405 in
The total accuracy rate that is illustrated in the field 405 in
In the method in
In the table in
Accordingly, the ADC classification robustness of the defect class can be improved and a decrease in unknown can be accomplished. In a case where the number of images that are determined, as being unknown, with the MDC defect classes is equal to or greater than the setting value, an alarm is issued to the display terminal 116, and in the case where the input instruction from the user is present, the proceeding to the learning update of the ADC (S801) may take place.
In order to deepen the learning of the ADC, the fixed number of defect images or greater needs to be taught for every defect class. This method is illustrated in
In
For consideration of the weight, the weight field 1103 is shown in the table that is illustrated in
On the other hand,
In a case where there is no example in which MDC defect classes are consistent with each other and where a few of such samples are present, one technique is to perform the performance evaluation of the ADC from results of a plurality of MDCs from the perspective of the classification without inclining to a specific person's preference. The performance of the ADC that has to be enhanced is not seen from a result in
When it comes to a timing at which the performance evaluation and the learning data set update of the ADC that is illustrated in
Next, in the processing flow that is illustrated in
That is, in the first embodiment, the ADC is performed on the image that is selected based on the result of the MDC and thus the performance evaluation of the ADC is performed, and in a case where the total accuracy rate of the ADC is equal to or lower than a setting value, the ADC is learning-updated based on the image that is selected based on the result of the MDC. In contrast, in the present embodiment, the ADC is set to be immediately learning-updated based on the image that is selected based on the result of the MDC without performing the performance evaluation of the ADC.
A configuration of an automatic defect classification apparatus in the present embodiment is a configuration that results from excluding the classification performance evaluation unit 114 from the configuration that is described in the first embodiment with reference to
Because Steps from S1400 to S1404 in
In the present embodiment, in S1403, a defect number in which the defect classes are consistent with each other is specified with the comparison method as described with reference to
According to the first embodiment, the result from the majority decision that uses a plurality of defect classification means or a plurality of defect classification results is automatically reflected in a database of an automatic defect classification unit, from the standpoint in the past that the result of the defect determination by a human being is correct. In contrast, in the present embodiment, the database of the automatic defect classification apparatus is improved based on the result of the majority decision, from the completely-opposite standpoint that a human being has an individual difference and a result varies depending on the human difference, and thus, an exceedingly remarkable operation that can realize the accuracy of the defect classification which is such that the human visual defect classification is exceeded can be accomplished with the automatic defect classification.
In the first and second embodiments, the processing is performed on the assumption that the MDC is first performed on the defect image, but in the present embodiment, processing that performs the MDC only in a case where the ADC is first performed on the defect image in the preceding processing, and thus where the number of unknown defects that cannot be classified into defects is equal to or greater than a setting value, and periodic activation will be described. Because a configuration of the automatic defect classification apparatus in the present embodiment is the same as that which is described with reference to
As described above, with the comparison among a plurality of MDC defect classes, because the MDC defect class that results from excluding the individual difference in visual review can be obtained, high-reliability ADC performance evaluation and the additional learning of the ADC are possible.
In a fourth embodiment, a method of using the inconsistency among the MDC defect classes, which is described with reference to
A processing flow in the fourth embodiment will be described with reference to
Next, the defect class that is assigned by each of the MDC apparatuses 104 (MDCs 1 to N) is read from the storage unit 111 into the defect class comparison unit 112 and the comparison is performed (S1703). A defect number in which as a result of the comparison, the defect classes are inconsistent with each other is specified, and a defect image corresponding to the specified defect number is selected (S1704). A method of extracting the inconsistency among the defect classes as a result of the comparison is as described with reference to
In the steps from S1700 to S1704, which are described above, the same processing operations as in the steps from S200 to S204, which are described with reference to
With regard to image data in which the selected MDC defect classes are inconsistent with each other, it is checked whether the defect class information is also transmitted (S1705). In a case where a defect class that is determined by a skilled operator is not disclosed as a response to an operator (in the case of NO in S1705), the defect image that is again selected is transmitted to the MDCs 1 to N and the operator is requested to perform an MDC operation (S1606). In a case where the defect class that is determined by the skilled operator is disclosed as the response to the operator (in the case of YES in S1705), the selected defect image and defect class information of a selection image are transmitted to the MDCs 1 to N, and the operator is caused to check these (S1607).
Using this result, the performance evaluation of the ADC is performed according to the processing flow that is described with reference to
According to the present embodiment, the image in which the MDC defect classes are inconsistent with each other is caused to be checked by the operator, and thus the variation in the operational level of the operator can be reduced, and the standardization of the operational level can be accomplished.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2015/066244 | 6/4/2015 | WO | 00 |
