Source: https://patents.google.com/patent/JP6009956B2/en
Timestamp: 2019-12-12 09:40:57
Document Index: 196588424

Matched Legal Cases: ['art 1', 'art 1', 'art 120', 'art 140', 'art 150', 'art 160']

JP6009956B2 - Defect inspection apparatus and defect inspection method - Google Patents
JP6009956B2
JP6009956B2 JP2013016909A JP2013016909A JP6009956B2 JP 6009956 B2 JP6009956 B2 JP 6009956B2 JP 2013016909 A JP2013016909 A JP 2013016909A JP 2013016909 A JP2013016909 A JP 2013016909A JP 6009956 B2 JP6009956 B2 JP 6009956B2
JP2013016909A
JP2014149177A (en
貴裕 浦野
西山　英利
2013-01-31 Application filed by 株式会社日立ハイテクノロジーズ filed Critical 株式会社日立ハイテクノロジーズ
2013-01-31 Priority to JP2013016909A priority Critical patent/JP6009956B2/en
2014-08-21 Publication of JP2014149177A publication Critical patent/JP2014149177A/en
2016-10-19 Publication of JP6009956B2 publication Critical patent/JP6009956B2/en
The present invention relates to a defect inspection apparatus and a defect inspection method for inspecting minute defects existing on a sample surface with high sensitivity.
Thin film devices such as semiconductor wafers, liquid crystal displays, and hard disk magnetic heads are manufactured through a number of processing steps. In the manufacture of such a thin film device, visual inspection is carried out for each of several series of processes for the purpose of improving yield and stabilization.
Patent Document 1 (Japanese Patent No. 3566589) discloses that “corresponding regions of two patterns originally formed to have the same shape in appearance inspection can be obtained using lamp light, laser light, electron beam, or the like. A method of detecting a defect such as a pattern defect or a foreign substance based on the reference image and the inspection image is disclosed.
Patent Document 2 (Japanese Patent Application Laid-Open No. 2006-98155) states that “in a situation where a small number of DOIs are included in the majority of nuisances, the optimum inspection conditions can be determined by efficiently extracting and teaching DOIs. A possible inspection method "is disclosed.
Furthermore, as a technique for further improving the inspection sensitivity, Patent Document 3 (US Pat. No. 7,221,992) and Patent Document 4 (US Patent Publication No. 2008/0285023) disclose that “images under a plurality of different optical conditions are detected simultaneously. , A method of performing brightness comparison with a reference image for each condition and integrating the comparison value to discriminate between defects and noise is disclosed, but defects acquired at high resolution under each optical condition There are problems in that a high data transfer rate for sending an image to the defect determination unit is necessary and that a processor with high processing performance is required to process images of a plurality of conditions at once.
In Patent Document 5 (US Pat. No. 7,283,659), “Efficient defect classification is performed by two-stage determination of defect candidate classification based on non-image features such as process information and classification based on defect image characteristics. Method "is disclosed.
In Patent Document 6 (Japanese Patent Laid-Open No. 2012-112915), a defect candidate is extracted from each of images detected under a plurality of imaging conditions, and one of the conditions or a feature amount obtained by processing each image is disclosed. Based on the determination result obtained by integrating the image, a narrow region image centered on the defect candidate is processed under all imaging conditions to extract a detailed feature amount, and a method for determining a defect based on the feature amount is disclosed. Yes.
Japanese Patent No. 3656589 JP 2006-98155 A US Pat. No. 7,221,992 US Published Patent No. 2008/0285023 US Pat. No. 7,283,659 JP 2012-112915 A
In the above conventional technique, basically, an inspection image is compared with the same reference image, and an area having a large difference is detected as a defect. There are detection conditions that are easy to detect and detection conditions that are difficult to detect depending on the area where the defect exists or the attribute of the defect in the detection of the defect. It is difficult to realize an inspection that simultaneously detects such types of defects. Therefore, a method using a plurality of optical conditions is required. In the method of Patent Document 3, the detection method is not mentioned, but in the method of Patent Document 4, both a bright field image and a dark field image are acquired simultaneously.
However, in order to detect a defect in a bright-field image, it is possible to detect only a defect having a size equal to or a fraction of the pixel size of the sensor to be detected, whereas a dark-field image has a value of 1 or less. In general, it is possible to detect up to a defect of a size. Since the imaging time of the sensor is generally proportional to the pixel size, a high-speed inspection that is a characteristic of the dark-field image cannot be realized if the pixel size is appropriate for the bright-field image. Then, even in a bright-field image, the pixel must be enlarged due to restrictions on the imaging time of the sensor, and only low-sensitivity inspection can be performed. That is, if a bright-field inspection and a dark-field inspection are simultaneously performed, there is a gap in throughput, and an appropriate inspection cannot be realized. Also, in inspections that combine dark field and dark field, because the defect detected by the azimuth angle, polarization, etc. of illumination changes in dark field inspection, even if equipped with multiple detection systems under one illumination condition, The defect capture rate cannot be sufficiently improved.
For this reason, a method is conceivable in which multiple inspections are performed sequentially and the inspection results are combined. In this case, FIG. It is difficult to simply apply a technique for integrating and inspecting the difference results of a plurality of detection systems as shown in FIG. When integrating a plurality of inspection results, a very large amount of data is acquired in one inspection, and if this is not stored, it is not possible to obtain the correspondence relationship of the plurality of inspections for each pixel. In order to obtain this completely, an external storage such as a large-capacity hard disk is required, resulting in a complicated system and a reduced throughput.
Further, in the method disclosed in Patent Document 5, the integration of the data of a plurality of detection systems for each pixel is performed for each image acquired by each detection system, so that inspections under all detection conditions are performed. The method of integrating the evaluation results of a plurality of detection systems represented by FIG. 7 of Patent Document 4 cannot be used. For the same reason, when the method disclosed in Patent Document 2 is expanded to a plurality of detection inspections, both detection system defects and nuisances will be determined based on image feature amounts calculated after sequential defect determination. However, since both the detection system and the defect or the nuisance are not imaged, a defect candidate in which the feature value cannot be plotted has occurred.
In Patent Document 6, a narrow area image is processed from an accumulated image that has exceeded an evaluation value based on a defect feature amount obtained with an image photographed under a plurality of detection conditions. If the target is inspected multiple times, this determination cannot be made unless it is scanned multiple times, so an enormous image capturing buffer is required, and in reality, images are captured under multiple conditions in parallel. It was difficult to apply only in some cases.
In the present invention, there is provided a defect inspection method and apparatus capable of solving the above-described problems of the prior art and detecting minute defects existing on the surface of a sample with high sensitivity using a plurality of inspection conditions. To do.
In order to solve the above-described problems, in the present invention, in the defect inspection method, the same region of the sample is imaged under different image acquisition conditions, and a plurality of images are acquired for the same region of the sample. Each of the plurality of images of the sample is processed to extract defect candidates in each of the plurality of images, and the defect candidates extracted from the plurality of images acquired based on the position information of the extracted defect candidates and the periphery of the defect candidates A partial image including the image is cut out, the feature amount of the defect candidate in the plurality of cut out partial images is obtained, and the extracted defect candidates are detected under different image acquisition conditions with the defect candidates having the same coordinates on the sample. Associate defect candidates,
Based on the feature vector of the defect candidate extracted in the defect candidate extraction process, a determination process is performed in which a determination boundary is set in the multidimensional space, and a defect is determined for each defect candidate attribute assumed based on the result of the determination process. The defect is extracted from the defect candidates associated with the image feature amount used for determination narrowed down, and the defect candidate associated with the feature amount of the associated defect candidate is selected based on the multi-dimensional feature amount space data. Defects are extracted, and information on the extracted defects is output.
In order to solve the above-described problem, in the present invention, in the defect inspection method, the same region of the sample is imaged under different image acquisition conditions, and a plurality of images are acquired for the same region of the sample. The acquired plurality of images are processed to extract defect candidates in each of the plurality of images, and the defect candidates extracted from the plurality of images acquired based on the extracted defect candidate position information and the periphery of the defect candidates Cutting out a partial image including a plurality of images, extracting a plurality of images, extracting defect candidates, and cutting out a partial image are performed a plurality of times while changing a plurality of image acquisition conditions. Corresponding to defect candidates having the same coordinates on the sample among the defect candidates included in the partial image cut out in the step of cutting out the partial image from the image obtained by imaging a plurality of times Was carried out, a judgment processing in which discrimination boundary multidimensional space based on the feature amount vector of defects candidates extracted in the step of extracting the defect candidate, the attribute of the defect candidate that is assumed based on the result of this determination process A defect is extracted from defect candidates associated with each other by narrowing down the image feature amount used for defect determination for each time, and information on the extracted defect is output.
Furthermore, in order to solve the above-described problems, in the present invention, the defect inspection apparatus captures the same region of the sample under a plurality of different image acquisition conditions and acquires a plurality of images for the same region of the sample. An acquisition unit, a defect candidate extraction unit that processes a plurality of images of the sample acquired by the image acquisition unit and extracts defect candidates in each of the plurality of images, and a position of the defect candidate extracted by the defect candidate extraction unit A partial image cutout unit that cuts out a partial image including a defect candidate extracted from a plurality of images acquired by the image acquisition unit based on information and an image around the defect candidate, and a plurality of portions cut out by the partial image cutout unit The feature amount calculating means for obtaining the feature amount of the defect candidate in the image and the defect candidate having the same coordinates on the sample among the defect candidates extracted by the defect candidate extracting means A defect candidate correlating means obtained conditions to associate the defect candidate is detected under different conditions, determining process setting the discrimination boundary multidimensional space based on the feature amount vector of defects candidates extracted by the defect candidate extracting means First defect determination means for performing defect detection, and image feature quantity used for defect determination for each defect candidate attribute assumed based on the result of determination processing in the first defect determination means, and by defect candidate association means A defect extracting means for extracting defects from the associated defect candidates and an output means for outputting information on defects extracted by the defect extracting means are provided.
Furthermore, in order to solve the above-described problem, in the present invention, the defect inspection apparatus captures the same region of the sample under a plurality of different image acquisition conditions and acquires a plurality of images for the same region of the sample. An acquisition unit, a defect candidate extraction unit that processes a plurality of images acquired by the image acquisition unit and extracts defect candidates in each of the plurality of images, and position information of the defect candidates extracted by the defect candidate extraction unit A partial image cutout unit that cuts out a partial image including a defect candidate extracted from a plurality of images acquired by the image acquisition unit and an image around the defect candidate, an image acquisition unit, a defect candidate extraction unit, and a partial image cutout unit; To acquire a plurality of images with the image acquisition means, extract defect candidates with the defect candidate extraction means, and extract partial images with the partial image cutout means A control unit that executes a plurality of times by changing a plurality of image acquisition conditions, and a part from an image obtained by imaging a plurality of times by changing a plurality of image acquisition conditions of the image acquisition unit by controlling the control unit A defect candidate associating means for associating defect candidates having the same coordinates on the sample among defect candidates included in the partial image cut out by the image cutting out means, and a feature vector of the defect candidate extracted by the defect candidate extracting means First defect determination means for performing a determination process in which a determination boundary is set in a multidimensional space based on the above, and defect determination for each attribute of a defect candidate assumed based on the result of the determination process in the first defect determination means out the defect extraction means for extracting a defect from the defect candidates associated by narrowing down defect candidate correlating means an image feature amount, the information of the defect extracted with this defect extraction means used to It was constructed and an output means for.
According to the invention disclosed in the present application, it is possible to provide a defect inspection method and a defect inspection apparatus for inspecting minute defects existing on a sample surface with high sensitivity.
It is a block diagram which shows the whole schematic structure of the defect inspection apparatus which concerns on Example 1 of this invention. It is a block diagram which shows the whole schematic structure of the 1st modification of the defect inspection apparatus which concerns on Example 1 of this invention. It is a block diagram which shows the whole structure of the 2nd modification of the defect inspection apparatus which concerns on Example 1 of this invention. It is a block diagram which shows an example of a structure of the image acquisition part in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention. It is a block diagram which shows an example of a structure of the defect candidate extraction part of the defect inspection apparatus which concerns on Example 1 and Modification 1 and 2 of this invention. It is a three-dimensional scatter diagram explaining the defect candidate extraction method in the defect inspection apparatus according to Example 1 and Modifications 1 and 2 of the present invention. It is a block diagram which shows an example of a structure of the defect candidate detection part in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention. It is the top view of a semiconductor wafer which shows the structure of the die | dye of the semiconductor wafer which is a test object in the defect inspection apparatus which concerns on Example 1 and the modification 1 and 2 of this invention, and the enlarged view of several die | dye. It is the top view of a semiconductor wafer which shows the structure of the die | dye of the semiconductor wafer which is a test object in the defect inspection apparatus which concerns on Example 1 and the modification 1 and 2 of this invention, and the enlarged view of several die | dye. A plan view of a semiconductor wafer showing an example in which a region inside a die formed in the shape of a semiconductor wafer to be inspected in the defect inspection apparatus according to Example 1 and Modifications 1 and 2 of the present invention is divided, and a plurality of dies It is an enlarged view. The area | region inside the die | dye formed in the semiconductor wafer shape which is a test object in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention was divided | segmented, and the inspection conditions for this divided | segmented area | region were put together. It is an example of a list of processing conditions. It is a block diagram which shows the structure of the defect candidate selection part in the defect inspection apparatus which concerns on the modifications 1 and 2 of Example 1 of this invention. It is a block diagram which shows the structure of the modification of the defect candidate selection part in the defect inspection apparatus which concerns on Example 1 and the modifications 1 and 2 of this invention. FIG. 6 is a defect candidate distribution diagram illustrating an example of a coordinate determination rule for determining the regularity of coordinates of a detected defect in the defect inspection apparatus according to Example 1 and Modifications 1 and 2 of the present invention. It is a three-dimensional graph which shows an example of the feature space of the defect determination part in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention. It is a front view of an inspection object pattern showing an example in the case of being detected only under specific conditions. It is a perspective view of an inspection object pattern showing an example in the case of being detected only under a specific condition. It is the graph which plotted the difference of a test | inspection image and a reference image in the three-dimensional space comprised by each illumination condition. It is another graph which shows the distribution in the two-dimensional space of the conditions 1 and 2 of the defect detected by the 1st test | inspection scan. It is another graph which shows the distribution in the two-dimensional space of the conditions 3 and 4 of the defect detected by the 2nd inspection scan. It is a block diagram which shows an example of a structure of the image process part in the structure shown to FIG. 1B of the defect inspection apparatus which concerns on the modification 1 of Example 1 of this invention. It is a flowchart which shows an example of the flow of the defect determination in the defect inspection apparatus which concerns on Example 1 and the modifications 1 and 2 of this invention. It is a figure which shows an example of the flow of the defect determination in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention, Comprising: It is a flowchart in the case of changing an image acquisition condition and acquiring an image in multiple times. . It is a figure which shows an example of the flow of the defect determination in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention, Comprising: It is a case where an image acquisition condition is changed and it acquires an image several times, Comprising: It is a flowchart in the case of memorize | stored once in the common buffer. It is a figure which shows an example of the flow of the defect determination in the defect inspection apparatus which concerns on Example 1 and modification 1 and 2 of this invention, Comprising: Image acquisition conditions are changed and an image is acquired several times, Detailed analysis of a cut-out image It is a flowchart in the case of determining a defect candidate, without performing. It is a figure which shows an example of the flow of the defect determination in the defect inspection apparatus which concerns on the modification 2 of Example 1 of this invention, Comprising: When changing an image acquisition condition and acquiring an image in multiple times, it is obtained by a different inspection. It is a flowchart of the process which performs defect determination after matching the defect candidate which was made. It is a front view of the screen which shows an example of the extended display of GUI for defect candidate teaching in the defect inspection apparatus which concerns on Example 1 and the modification 1 and 2 of this invention. It is a block diagram which shows an example of the structure of the whole schematic of the defect inspection apparatus which concerns on Example 2 of this invention. It is a block diagram which shows an example of a structure of the defect inspection apparatus which concerns on Example 3 of this invention. It is a block diagram which shows an example of a structure of the form of the defect inspection apparatus which concerns on the modification of Example 3 of this invention. It is a block diagram which shows the structure of the outline of the SEM type inspection apparatus which concerns on Example 4 of this invention. It is a figure of the data sheet which shows the example of an output of the inspection data of the defect inspection apparatus which concerns on Example 1 and the modifications 1 and 2 of this invention. It is a figure of the map of a defect candidate which shows an example of the position shift detection / correction in the defect determination part 1 of the defect inspection apparatus which concerns on Example 1 thru | or 3 of this invention.
The present invention extracts defect candidates from each image acquired under a plurality of image acquisition conditions, and integrates and processes the defect candidates extracted between the plurality of images, thereby increasing the minuteness without increasing image data. A defect can be inspected with high sensitivity and at high speed.
Hereinafter, a first embodiment of the defect inspection technique (defect inspection method and defect inspection apparatus) of the present invention will be described in detail with reference to FIGS.
As a first embodiment of the pattern inspection technique of the present invention, a defect inspection apparatus and a defect inspection method using dark field illumination for a semiconductor wafer will be described as an example.
FIG. 1A shows an example of the configuration of the defect inspection apparatus according to the first embodiment. The defect inspection apparatus according to the first embodiment includes an image acquisition unit 110 (image acquisition units 110-1, 110-2, 110-3) and an image storage buffer unit 120 (image storage buffers 120-1, 120-2, 120-). 3, 120-4), feature quantity storage buffer 125 (125-1), defect candidate extraction unit 130 (defect candidate extraction unit 130-1, 130-2, 130-3), defect candidate selection unit 140, control unit 150. , An image transfer unit 160, an image processing unit 170, a defect determination unit 180, and a result output unit 190.
In the image acquisition unit 110, each image acquisition unit 110-1, 110-2, 110-3 acquires inspection image data of a semiconductor wafer that is an inspection object, and each image storage buffer 120-1 in the image storage buffer unit 120. , 120-2, 120-3 and the defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130 are transferred image data. The defect candidate extraction units 130-1, 130-2, and 130-3 of the defect candidate extraction unit 130 are image data transferred from the image acquisition units 110-1, 110-2, and 110-3 of the image acquisition unit 110, respectively. Then, defect candidates are extracted by a process described later, and the defect candidates are transferred to the defect candidate selection unit 140. The defect candidate selection unit 140 removes false information that is false detection of noise or the like from the defect candidates and a nuisance that the user does not want to detect, and transmits the remaining defect candidate information to the control unit 150.
The control unit 150 that has received the defect candidate information from the defect candidate selection unit 140 transmits the coordinate information of the remaining defect candidates to the image storage buffers 120-1, 2, and 3 of the image storage buffer unit 120. In the image storage buffer unit 120, based on the coordinate information of the defect candidate received from the control unit 150 from the image data input from the image acquisition unit 110 stored in each of the image storage buffers 120-1, 2, and 3. An image including the defect candidate is cut out, and the image including the cut out defect candidate is transferred to the image transfer unit 160. The image transfer unit 160 stores, in the buffer 121, images including defect candidates transferred from the image storage buffers 120-1, 2, and 3.
In the pattern inspection in this embodiment, a sample to be inspected is inspected a plurality of times. That is, the buffer 121 illustrated in FIG. 1A stores a cut-out image including defect candidates acquired by a plurality of inspections (a plurality of inspection conditions). The size of the image data is typically a size centered on a 32 × 32 defect, for example, and is reference image data that is an image of the same design portion as the inspection image. The image stored in the buffer 121 is transferred to the image processing unit 170. In the image processing unit 170, the image feature amount of the defect candidate, that is, the difference brightness between the inspection image and the reference image, the brightness of the defect portion of the difference image between the inspection image and the reference image, the texture of the difference image, the shape feature amount of the defect portion, Of the pattern shape of the defective portion of the difference image, a feature vector consisting of one or more features is calculated and stored in the buffer 125-1. This feature vector is transferred to the defect determination unit 180, and defect determination is performed in a multidimensional feature space.
It should be noted that well-known classification methods such as binary tree, support vector machine, Mahalanobis distance method, and neural network are used for discrimination between defects and nuisances in a multidimensional feature space, or Use a combination. Based on this determination, only the defect of interest (DOI) that the user desires to detect is extracted, and the DOI is output to the result output unit 190.
In FIG. 1A, image storage buffers 120-1, 120-2 corresponding to image acquisition units 110-1, 110-2, 110-3 that perform image acquisition under the acquisition conditions of three different inspection images, one-to-one. A configuration including 120-3 and defect candidate extraction units 130-1, 130-2, and 130-3 is shown. Here, the acquisition condition of the inspection image is the illumination condition for the sample (incident direction of illumination light to the sample, wavelength of illumination light, polarization state of illumination light, etc.) and detection condition (detection direction of reflected scattered light from the sample, Detection wavelength region, polarization state, etc.) and inspection image acquisition with different detection sensitivities.
FIG. 1B is an expanded configuration of the defect inspection in the first embodiment described with reference to FIG. 1A (first modification). A fine DOI, particularly a short short circuit called a groove bottom short circuit or a hair short circuit, makes it difficult to reveal defects, and cannot be actualized unless specific detection conditions are met. In the present invention, the DOI capture rate is improved by inspecting a plurality of times under a plurality of different imaging conditions, but a defect that is difficult to be revealed cannot be detected by all inspections.
The defect inspection configuration in the first embodiment described with reference to FIG. 1A includes three image acquisition units that can be detected in parallel. For example, out of M inspections, a defect is revealed only by N inspections. Otherwise, only 3N cut-out images can be obtained as the defect candidate cut-out images stored in the buffer 121. Since it is unknown how many cutout images can be obtained for each defect candidate, high performance is achieved by applying the Mahalanobis distance method and support vector machine that perform discrimination based on the number of dimensions of a specific feature vector. It is difficult to expect.
Therefore, the configuration of the defect inspection apparatus according to the first modification shown in FIG. 1B includes a multidimensional feature classifier that does not use the cut image of the buffer 121. Reference numeral 125-2 denotes a feature amount storage buffer, which stores feature amounts used for defect candidate extraction in each defect candidate extraction unit 130-1, 2, 3 of the defect candidate extraction unit. This feature amount is based on one of the difference between the inspection image and the reference image, the lightness variation between dies at the same coordinates, the difference image variation in a pattern similar to the defect candidate of the reference image, the edge strength of the reference image, or a combination thereof. Composed. This feature amount data is stored for M inspections. Here, the feature amount storage buffer 125-2 can store more data than the cut-out image stored in the buffer 121.
The image data stored in the buffer 121 typically requires both an inspection image and a reference image with a size of 32 × 32, and therefore requires approximately 2 KB of data for each image acquisition data. For example, if the data stored in the feature amount storage buffer 125-2 is a four-dimensional feature amount vector and each is represented by a fixed point of 2 bytes, 8B of data is sufficient, so a buffer with the same capacity is prepared. In this case, it is possible to store about 150 times as much data. Further, since processing using the image processing unit 170 is not required, the calculation cost is basically not required. Therefore, the defect candidate selection unit 140 selects defect candidates such that the number of defect candidates accumulated in the feature amount storage buffer 125-2 is about two digits greater than the number of defect candidates accumulated in the buffer 121.
The defect determination unit 180 in the defect inspection apparatus shown in FIG. 1A is divided into two in the configuration of the defect inspection apparatus of the first modification shown in FIG. 1B, and the defect determination unit 1: 180-1 is a feature quantity. Based on the feature vector of defect candidates accumulated in the storage buffer 125-2, a determination process is performed in which a determination boundary is set in a multidimensional space. The defect determination unit 2: 180-2 is extracted by the image processing unit 170, and the buffer 125 The processing is performed from the feature amount data stored in -1. The defect determination unit 2: 180-2 narrows down the image feature amount used for the determination for each attribute of the assumed defect candidate based on the determination result of the defect determination unit 1: 180-1, and extracts all the cutouts for each defect candidate. Reduce issues that do not have images. For example, if the defect determination unit 1: 180-1 determines that the possibility of a DOI type scratch is high, the defect determination unit 2: 180-2 determines using only the image feature amount for scratch determination, and the defect determination unit If it is determined that the possibility of a foreign object is high at 1: 180-1, the defect determination unit 2: 180-2 performs determination using only the image feature amount for foreign object determination.
FIG. 1C shows a configuration of a second modification of the defect inspection apparatus. The configuration of the second modification example of the defect inspection apparatus illustrated in FIG. 1C is similar to the configuration of the first modification example of the defect inspection apparatus illustrated in FIG. 1B, but the defect determination unit 1: 181-1 The operation of the defect determination unit 2: 181-2 is different from the operation of the defect inspection apparatus described with reference to FIG. 1B. In the case of FIG. 1B, the defect determination unit 1: 180-1 is pre-processing of the defect determination unit 2: 180-2, whereas in the configuration of FIG. 1C, the defect determination unit 2: 181-2 is defect determination. Part 1: Pre-processing for defect determination of 181-1. Defect determination by the defect determination unit 1: 181-1 is performed by performing defect determination in a multidimensional feature space based on feature amount data accumulated in the buffer 125-1 in a single inspection in a plurality of inspections. The determination result is transferred to the buffer 125-3.
In this case, if a defect candidate is extracted at a certain coordinate in any one of the defect candidate extraction units 130-1, 130-2, and 130-3 of the defect candidate extraction unit 130 in a specific image, even if the defect candidate is extracted. The defect candidate selection unit 140 is set to operate so as to save the clipped image of the corresponding coordinates without exceeding the specific threshold for candidate extraction, and without causing a problem that the cutout images of the defect are not prepared. The defect determination unit 2: 181-2 can execute the defect determination. In this defect determination unit 2: 181-2, an obvious defect such as nuisance is removed and a defect is determined, and the determination result is stored in the buffer 126-3.
The buffer 125-3 also includes the feature amount extracted by the image processing unit 170. In the image processing unit 170, image processing for defect candidate extraction in each defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130 is performed in order to perform image processing based on the cut-out image. Compared with the above, image processing that requires calculation cost, for example, image brightness, contrast correction and noise removal, image quality restoration by image deconvolution, and detailed noise determination by recognizing a pattern are performed. Thereby, even if the feature amount is the same as the feature amount of the defect candidate output by the defect candidate extraction in each defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130, it is more reasonable. The result can be calculated.
Reference numeral 185 denotes a feature amount changing unit. Of the feature amounts accumulated in the feature amount storage buffer 125-2, the feature amount accumulated in the defect determination unit 2: 181-2 is used as the feature amount accumulated in the buffer 125-3. In addition, although the defect determination unit 2: 181-2 determines that the nuisance is determined, the difference value is a result of determining that the difference between the inspection image and the reference image that is an important determination criterion in the defect determination is large. Is multiplied by a gain of 1 or less, or the brightness variation between dies or the difference image difference is multiplied by a gain of 1 or more. Thereby, it is suppressed that the defect candidate determined to be nuisance accumulated in the buffer 125-2 is determined to be a defect. In addition, binary logic may be implemented, for example, by setting the difference signal value of an inspection determined to be nuisance to 0.
Based on the feature quantity stored in the feature quantity storage buffer 125-2 converted by the feature quantity conversion unit 185, the defect determination unit 1: 181-1 is similar to the first modification described with reference to FIG. 1B. The attribute of the defect is determined by the method.
The above-described determination will be described using the data sheet shown in FIG. In FIG. 15, reference numeral 1501 denotes a defect candidate ID, which is an identifier attached to each defect candidate. Reference numeral 1502 denotes an inspection ID, which is an identifier assigned to each of a plurality of sample inspections. The defect candidate ID 1501 assigns an identifier by associating defect candidates at the same position obtained by different inspections with each other based on the defect candidate coordinates 1503 obtained by a plurality of inspections. In addition, even if the defect candidate coordinates 1503 are different, one defect ID is assigned as the same defect from information such as a line of dots arranged in a line.
Reference numeral 1504 in FIG. 15 indicates a defect determination feature amount calculated from the feature amount stored in the feature amount storage buffer 125-2 in FIG. 1C. Typically, the difference between the inspection image and the reference image is calculated for each pixel. It is a value divided by the variation of the difference image to be calculated. 1505 is a provisional defect determination result determined by the defect determination unit 2: 181-2. For example, a flaw defect or a nuisance generated at an edge portion of a pattern having a strong intensity can be clearly understood. The result of judgment is output, and the result of indeterminate is output for other things. Further, a likelihood indicating the probability of being a defect is output, and this is represented by a gain H. As the simplest method, 0 is determined as a nuisance and 1 is set otherwise. In a more advanced implementation, in the multidimensional feature space formed in the defect determination unit 181-2, a boundary surface for identifying a specific defect and nuisance is set to 0.5, and as the distance from the boundary surface increases toward the nuisance side, The likelihood is expressed by a sigmoid function taking the distance as an argument, and this likelihood is set as a gain H.
Reference numeral 1506 in FIG. 15 is obtained by converting the evaluation value of the defect determination feature quantity 1504 based on the gain H obtained by the defect determination unit 181-2. Since the gain H cannot be calculated for the data in which the clipped image is not accumulated, the data is converted by giving a preset gain H0. H0 is generally 0.5 or more and 1 or less, but an appropriate value varies depending on the parameter for determining the profile of the sigmoid function described above, so it is preferable to provide it as a parameter set by the user.
FIG. 2 is a diagram illustrating an example of the configuration of the image acquisition unit 110 ′ using dark field illumination as a specific example of the configuration of the image acquisition unit 110 in the first embodiment and the first and second modifications. The image acquisition unit 110 ′ includes two illumination optical systems 240-1 and 240-2, a stage 220, a mechanical controller 230, and an illumination unit 240, a detection optical system (upper detection system) 250-1, and an oblique detection system 250. -2, and image sensors 260-1, 260-2. The upper detection system 250-1 includes an objective lens 251-1, a spatial frequency filter 252-1, an imaging lens 253-1, and an analyzer 254-1. I have. The oblique detection system 250-2 includes an objective lens 251-2, a spatial frequency filter 252-2, an imaging lens 253-2, and an analyzer 254-2.
The sample 210 is an inspection object such as a semiconductor wafer. The stage 220 mounts the sample 210 and can move and rotate (θ) in the XY plane and move in the Z direction. The mechanical controller 230 is a controller that drives the stage 220. Illumination light emitted from either the illumination optical system 240-1 or 240-2 of the illumination unit 240 is irradiated to the sample 210 obliquely, and the scattered light from the sample 210 is detected by the upper detection system 250-1, oblique detection. Each image is formed by the system 250-2, and the formed optical images are received by the image sensors 260-1 and 260-2 and converted into image signals. At this time, the detection result can be obtained as a two-dimensional image by mounting the sample 210 on the stage 220 driven by XYZ-θ and detecting foreign matter scattered light while moving the stage 220 in the horizontal direction. it can.
The illumination light sources of the two illumination optical systems 240-1 and 240-2 of the illumination unit 240 may use lasers or lamps. Moreover, the light of the wavelength of each illumination light source may be a short wavelength, or may be light of a broad wavelength (white light). In the case of using light of a short wavelength, in order to increase the resolution of an image to be detected (detect a fine defect), light having a wavelength in the ultraviolet region (Ultra Violet Light: UV light) can also be used. When a laser is used as a light source, if it is a single wavelength laser, means (not shown) for reducing coherence is provided for each of the two illumination optical systems 250-1 and 250-2 of the illumination unit 240. It is also possible to prepare for.
In addition, the image acquisition unit 110 illustrated in FIG. 2 includes an epi-illumination light source 240-3 that illuminates the sample 210 via the objective lens 251-1 of the upper detection system 250-1, and the spatial filter 251. The optical path is changed by a folding mirror (not shown) at the position, so that illumination light can be incident from above. Furthermore, by providing a wavelength plate (not shown) between each illumination optical system 240-1, 240-2, 240-3 of the illumination unit 240 and the sample 210, the polarization state of illumination incident on the sample 210 can be changed. Allows you to change. The rotation angle of the wave plate can be moved by a control unit 270 so that the illumination polarization can be switched for each inspection condition.
Further, a time delay integration type image sensor (Time Delay Integration Image Sensor: TDI image sensor) constituted by arranging a plurality of one-dimensional image sensors two-dimensionally in each of the image sensors 260-1 and 260-2 is a stage. By transferring and adding the signals detected by each one-dimensional image sensor to the next-stage one-dimensional image sensor in synchronization with the movement of 220, it becomes possible to obtain a two-dimensional image with relatively high speed and high sensitivity. . By using a parallel output type sensor having a plurality of output taps as the TDI image sensor, outputs from the sensors can be processed in parallel, and detection at higher speed becomes possible. Further, when a backside illumination type sensor is used for the image sensors 260-1 and 260-2, the detection efficiency can be increased as compared with the case where a frontside illumination type sensor is used.
The detection results output from the image sensors 260-1 and 260-2 are supplied to the image storage buffers 120-1 and 120-2 and the defect candidate extraction units 130-1 and 130 of the defect candidate extraction unit 130 via the control unit 270. -2.
FIG. 3A is a diagram illustrating an example of the configuration of the defect candidate extraction unit 130-1 of the defect candidate extraction unit 130 in the first embodiment and the first and second modifications. The defect candidate extraction unit 130-1 includes a preprocessing unit 310-1, an image memory unit 320-1, a defect candidate detection unit 330-1, a parameter setting unit 340-1, a control unit 350-1, a storage unit 360-1. An input / output unit 370-1 is provided. The defect candidate extraction unit 130-2 has the same configuration.
First, the preprocessing unit 310-1 performs image correction such as shading correction, dark level correction, and bit compression on the image data input from the image acquisition unit 110-1, and divides the image data into images of a certain unit size. And stored in the image memory 320-1. A digital signal of an image of an area (hereinafter referred to as a reference image) corresponding to an image of the area to be inspected (hereinafter referred to as a detected image) stored in the image memory 320-1 is read out.
Here, the reference image may be an image of an adjacent chip, or may be an ideal image that is created from a plurality of adjacent chip images and has no defect in the image. Further, a correction amount for aligning the positions in the plurality of adjacent chip defect candidate detection units 330-1 is calculated, and the detected image and the reference image are aligned using the calculated correction amount of the position, and correspondingly performed. A pixel having a deviation value in the feature space is output as a defect candidate using the feature amount of the pixel.
The parameter setting unit 340-1 sets inspection parameters such as the type of feature amount and threshold value used when extracting defect candidates, which are input from the outside, and supplies the inspection parameters to the defect candidate detection unit 330-1. The defect candidate detection unit 330-1 outputs the extracted defect candidate images, feature amounts, and the like to the defect candidate selection unit 140 via the control unit 350-1. The control unit 350-1 includes a CPU that performs various controls, and a display unit that accepts changes in inspection parameters (type of feature amount, threshold value, etc.) from the user, and displays detected defect information. An input / output unit 351-1 having an input unit is connected to a storage device 352-1 that stores the feature amounts and images of detected defect candidates.
Here, the control unit 150 described in FIGS. 1A to 1C, the control unit 270 described in FIG. 2, and the control units 350-1 and 350-2 described in FIG. 3A may all be the same control unit. However, they may be configured by different control units, and each may be connected to each other.
A typical feature amount for extracting defect candidates will be described. Here, the in-die coordinates (x, y), the detection condition identifier of the detected image is k, the difference Diff between the inspection image and the reference image, the differential filter strength of the reference image at the defect candidate position is edge, and the same in-die coordinates Assuming that the variation in brightness of the pattern is Δdie, the pattern attribute at the position of the defect candidate is id (x, y), and the variation in the difference between the inspection image and the reference image in this pattern portion is Δcateg, the basic shape satisfies (Equation 1) Are extracted as defect candidates.
A (k), B (k), and C (k) are parameters that are set for each inspection image acquisition condition, and the setting of this value differs depending on the criteria for defect candidates to be left at 120 and 125. As will be described later, this value A (k), B (k), and C (k) may be changed for each coordinate or for each assumed defect type. When A, B, and C are changed for each defect type, the most sensitive boundary surface is selected in the multidimensional space.
FIG. 3B is an explanatory diagram of an outlier detection method of the defect candidate detection unit. A variation of (Equation 1) is (Equation 2), and 360 is a scatter diagram illustrating the state of (Equation 2). 361 and 362 are DOI, and 363 which is other distribution is a nuisance.
Note that, here, as a typical example, the determination formula is described based on the sum of squared feature amounts, but the feature amount may be used without being squared.
The following equation (Equation 3) is normalized by the threshold value of (Equation 1) and multiplied by the gain G (k) for each detection condition, and is a determination equation for a plurality of detection conditions.
(Equation 3) cannot be accurately calculated unless a defect candidate is selected in (Equation 2). Therefore, A, B, and C in (Equation 2) are set to small values for the defect detection only from one general image. However, if A, B, and C are set too small, there is a problem that the number of defect candidates becomes too large and a huge buffer is required, or the processing time of the subsequent stage is greatly increased. Since this inspection method is a method in which N inspections are integrated and inspected, a position where a defect candidate is selected is input in advance to a defect candidate detection unit, and within a tolerance range of deviations between a plurality of inspections. Thus, an exceptionally small threshold value is set, or a mechanism for outputting a pixel having the highest possibility of being a defect as a defect candidate is provided so that the parameters A, B, and C do not need to be extremely lowered.
FIG. 4 shows an example of the configuration of the defect candidate detection unit 330-1 in the first embodiment and the first and second modifications. The defect candidate detection unit 330-1 includes an alignment unit 430-1, a feature amount calculation unit 440-1, a feature space formation unit 450-1, and an outlier pixel detection unit 460-1. The alignment unit 430-1 detects and corrects the positional deviation between the detection image 410-1 and the reference image 420-1 input from the image memory unit 320-1. In the feature amount calculation unit 440-1, the feature amount is calculated from the corresponding pixels of the detected image 440-1 and the reference image 420-1 in which the positional deviation is corrected by the alignment unit 430-1. The feature amount calculated here is a lightness difference between the detected image 440-1 and the reference image 420-1, a total sum or variation of the lightness differences in a certain region, and the like.
The feature space forming unit 450-1 forms a feature space corresponding to 360 in FIG. 3B based on the arbitrarily selected feature amount. The outlier pixel detection unit 460-1 outputs a pixel at a position outside the feature space as a defect candidate. In the feature space forming unit 450-1, normalization may be performed based on the variation of each defect candidate. Here, in addition to (Equation 1) and (Equation 2), the criterion for determining the defect candidate can be a variation of data points in the feature space, a distance from the center of gravity of the data points, or the like. At this time, the determination criterion may be determined using the parameter input from the parameter setting unit 340-1.
5A and 5B are diagrams showing an example of the configuration of a chip to be inspected in the defect inspection apparatus according to Example 1 and Modifications 1 and 2 of the present invention, and the defect in the defect candidate detection unit 330-1 The detection of candidates will be described. A sample (also referred to as a semiconductor wafer or wafer) 210 to be inspected is regularly arranged with a large number of chips 500 having the same pattern including a memory mat portion 501 and a peripheral circuit portion 502. In the control unit 270, the semiconductor wafer 210, which is a sample, is continuously moved by the stage 220, and in synchronization with this, illumination of 503 and 504 is performed sequentially, whereby the image of the chip is image sensor 260-1, 260-. 2, with respect to the detected image, the same position of the regularly arranged chips, for example, the detected image area 530 in FIG. Compared with the corresponding pixel of the image or other pixels in the detected image, a pixel having a large difference is detected as a defect candidate. Here, the sample 509 in FIG. 5B is obtained by rotating the wafer of the sample 210 in FIG. 5A by 90 degrees.
In FIGS. 5A and 5B, the pattern of the area 502 becomes periodic in the longitudinal direction of the illumination 503 by the illumination of 503, and the pattern of the area 501 becomes periodic in the longitudinal direction of the illumination direction by the illumination of 504. Pattern emerges. These periodic patterns can cut the scattered light from the pattern by the spatial filters 252-1 and 252-2 shown in FIG. 2, but the periodic pattern in the short direction of illumination is scattered by the spatial filter. It is difficult to cut light. As described above, since the area where noise can be cut changes depending on the combination of the direction of illumination and the spatial filter, it is necessary to inspect the image acquisition conditions a plurality of times.
FIG. 6A shows a pattern obtained by dividing an area with respect to the patterns shown in FIGS. 5A and 5B. Reference numerals 601 to 606 denote areas for setting inspection conditions. As described above, when an inspection is performed using the image acquisition unit 110 having the configuration illustrated in FIG. 2, how noise is generated varies depending on the region due to the relationship between the pattern to be inspected and illumination. For this reason, the feature space formed by the feature space forming unit 450-1 is changed for each region, and the defect determination criterion is changed. Furthermore, since the noise generation state and the defect generation state of the sample 210 change depending on the center 650, the periphery 660, and the end 670 of the sample 210, these are combined with the regions in the die such as 601 to 607, respectively. As shown in the figure, the feature amount space is set by dividing into regions 651 to 656, 661 to 666, and 671 to 676. Furthermore, since the image acquisition conditions to be manifested are different for each defect type, and the detection priority of the user is also changed, this defect determination criterion is provided for each defect type.
This criterion includes the following criteria for evaluation values.
(A) Evaluation value of defect candidate detection unit 330-1 shown in FIG. 3A (b) Evaluation value for storing cut-out image in buffer 121 in defect candidate selection unit 140 of FIG. 1B (c) Feature feature data Evaluation value (d) stored in the quantity storage buffer 125-2 (d) In the defect determination unit 1: 180-1, an evaluation value for separating the defect and nuisance (e) In the defect determination unit 1: 180-2, the defect and nuisance are separated. Evaluation value
FIG. 7A is a diagram illustrating an example of the configuration of the defect candidate selection unit 140 in the defect inspection apparatus according to Modifications 1 and 2 of Embodiment 1 of the present invention. The defect candidate selection unit 140 includes a misalignment detection / correction unit 710, a defect candidate association unit 720, and an outlier detection unit 730. The misregistration detection / correction unit 710 inputs images and feature amounts of a plurality of defect candidates and detection positions on the wafer from the defect candidate extraction units 130-1, 130-2, and 130-3, and detects each defect candidate. Detect and correct wafer coordinate misalignment.
The defect candidate associating unit 720 groups defect candidate groups from images acquired under different conditions at the same position by associating defect candidates whose detection positions are corrected by the misalignment detection / correction unit 710. . Correlation is performed by a method of determining whether or not defect candidates overlap within a preset range on wafer coordinates. If the defect candidates are not in the overlapping range, the defect candidate associating unit 720 accesses the evaluation data of the defect candidate extracting units 130-1, 130-2, and 130-3 of the defect candidate extracting unit 130, and the feature amount It is good to get data. In order to avoid the complexity of the system, the outlier detection unit 730 may be executed except for an image acquisition condition in which a specific defect candidate cannot be detected. The same defect candidate associating unit as the defect candidate associating unit 720 is also incorporated in the defect determining unit 180-1 (181-1), and the feature amount from the same defect candidate is obtained in a plurality of inspections. So that they can be collected.
The outlier detection unit 730 sets a threshold value for the defect candidate associated with the defect candidate associating unit 720, detects a defect candidate that is out of the feature space, and detects the defect candidate feature. The amount, the detection position, and the like are output to the control unit 150. At this time, typically, what satisfies the determination formula shown in (Expression 3) is determined as a defect. Each element of the feature vector in the multidimensional space is typically expressed as (Equation 4).
When grouping defect candidates with the same coordinates, if there is no defect candidate in a specific image, it is impossible to accurately obtain (Equation 3), but from the relationship between (Equation 1) and (Equation 3) The feature quantity of the image k that could not be detected is G (k) or less. G (k) can be reduced by reducing the denominator of (Expression 3), that is, the right side of (Expression 1).
Generally, since the right side of (Equation 1) is set to X times the standard deviation of Diff, which is a defect signal value, the feature of the position where no defect candidate is detected is a value such as G (k) 0.5 / X Can be set to In this way, by normalizing the defect signal of the feature amount with the defect detection threshold value, the feature amount can be estimated without a large error even when a defect candidate cannot be detected. Further, as the right side of (Equation 1) is reduced, that is, as the above-described X is reduced, G (k) is reduced. Therefore, even if the feature of the portion where there is no defect candidate is set to 0, no large error is caused. Here, the example in which (Equation 1), (Equation 2), and (Equation 3) are in the form of a square is shown, but the defect signal Diff is a threshold for detecting a defect candidate even if it is a linear expression. If the threshold value is normalized by the defect signal Diff, the same feature amount can be estimated.
The table shown in FIG. 6B typically stores the parameters A (k), B (k), C (k), and G (k) in (Equation 3), and separates each defect type and nuisance. Parameter. If any of the defect types satisfies (Equation 4), it is provisionally determined as a defect candidate for that defect type. The outlier detection unit 730 outputs to the control unit 150 and the feature amount storage buffer 125-2, but the values of the parameters A (k), B (k), and C (k) are input to the control unit 150 and the feature amount storage buffer 125. This is realized by providing for -2.
The control unit 150 sets the values of A (k), B (k), C (k), and G (k) to be large with respect to the parameters for outputting to the feature amount storage buffer 125-2, so that the buffer 120 The number of cutout images stored in each of the buffers 120-1, 120-2, and 120-3 is typically smaller than the number of feature amounts stored in the feature amount storage buffer 125-2. In addition, an upper limit may be set for the number of defect candidates stored in each buffer, and when the upper limit is exceeded, defect candidates are output to the control unit 150 from defects greatly deviated from the boundary surface of (Equation 3). Also good. Note that (Equation 2) and (Equation 3) are formulated in a form in which each feature quantity is squared, but this is not squared, other modifications, and increase / decrease in the number of feature quantities However, this configuration can be implemented.
FIG. 7B is an example in which the defect candidate selection unit 140 described with reference to FIG. 7A is modified, and each defect candidate of the defect candidate extraction unit 130 is added to the defect candidate selection unit 140 ′ described with reference to FIG. Coordinate rule decision units 705-1, 2-2 that perform nuisance removal or defect extraction on the coordinates of defect candidates output from the extraction units 130-1, 130-2, and 130-3 based on the regularity of the coordinates. A coordinate rule determination unit 705 having 3 is added.
FIG. 7C is a distribution diagram of defect candidates showing an example of a coordinate determination rule, which discriminates scratches from nuisances at circuit pattern boundaries. When a straight line detection algorithm such as Hough transform is applied to the coordinates of the defect candidate, for example, a point sequence 750, 751, 752 arranged in a straight line surrounded by a dotted line from the defect candidate line and other point groups are identified. be able to.
There are two main reasons for the linear alignment of defect coordinates: when a line defect actually occurs and when a boundary of a circuit pattern with a large variation in brightness is detected as a nuisance. Conceivable. In the case of nuisance, the lines are generally arranged in the horizontal and vertical directions with respect to the arrangement of the circuit patterns. On the other hand, in the case of a flaw defect, the lines are not easily generated in a specific direction. Therefore, the point rows 750 and 751 arranged in a straight line horizontally or vertically have a high possibility of nuisance, and the point row 752 arranged in a straight line in an oblique direction has a high possibility of a flaw defect. In addition, the scratch defect can be distinguished from noise and nuisance because of the shape feature that the point sequence continues, but it is often very difficult to distinguish from the noise and nuisance only by the point of each defect candidate. Therefore, the defect candidates arranged in the horizontal and vertical directions with respect to the circuit pattern such as the point series 750 and 751 arranged in a straight line in the horizontal or vertical direction in advance are determined as nuisance, and the point series 752 arranged in the diagonal direction in a straight line are determined. In this way, a point sequence arranged in a direction other than horizontal and vertical is determined as a flaw defect.
The defect candidate group arranged in a straight line is grouped as one defect candidate, the feature amounts are integrated, and the start point and the end point are transferred to the subsequent outlier detection unit 730. As an integration method, the difference between the inspection image of the integrated feature amount and the reference image may be the sum of the point sequence (defect candidate group) arranged in a straight line, or more preferably the square. It may be the square root of the sum. The integrated defect candidate data is determined by the outlier detection unit 730 together with defect candidate data that are not arranged in a straight line, such as points 753, 754, and 755. The defect candidate group determined to be nuisance is excluded from the subsequent processing.
In the configuration shown in FIG. 7B, the coordinate rule determination unit 705 operates the coordinate rule independently of the coordinates of the defect candidates extracted by the defect extraction units 130-1, 130-2, and 130-3 of the defect candidate extraction unit 130. Although the configuration for performing the determination is described, the coordinate rule determination is performed after merging the coordinates output from the defect candidate extraction units 130-1 to 130-3 of the defect candidate extraction unit 130 after the defect candidate association unit 720. You may make it the structure to implement.
The defect determination unit 180 of Example 1 and the defect determination unit 1: 180-1 in Modifications 1 and 2 or the defect determination concept of the defect determination unit 1: 181-1 are centered on conditions 1 to 3 shown in FIG. 8A. A three-dimensional feature space graph will be described. The defect determination unit 180, the defect determination unit 1: 180-1, or the defect determination unit 1: 181-1 performs defect determination based on the defect candidates accumulated in the feature amount storage buffer 125-2. The configuration of the defect determination unit 180, the defect determination unit 1: 180-1, or the defect determination unit 1: 181-1 is the same as that of the defect candidate selection unit 140 illustrated in FIG. 7A or 7B. However, since the defect determination unit 180, the defect determination unit 1: 180-1, or the defect determination unit 1: 181-1 performs determination on defect candidates accumulated by a plurality of inspection results, the number of dimensions of the feature space Becomes larger and the inspection is performed sequentially.
Therefore, in the defect candidate associating unit 720, when there is no defect candidate at the same coordinates in the image acquisition under different conditions, it is possible to access the evaluation data of the defect candidate extracting unit 130 and acquire this information. However, this cannot be performed by the defect determination unit 180, the defect determination unit 1: 180-1, or the defect determination unit 1: 181-1. Therefore, the feature amount represented by (Equation 3) is calculated in a state where there is no defect candidate, and defect determination is performed. Here, for ease of explanation, the feature amount is illustrated only in a three-dimensional feature space based on the feature amount of the defect candidate stored in the feature amount storage buffer 125-2 in which the results of three inspections are accumulated. However, typically, it has 3N dimensions when processed with the configuration of FIG. The coordinates of each dimension are expressed by (Equation 5).
In each defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130, a defect candidate that is a threshold value 830-1, 2, 3 or more which is a threshold value of a single defect Among these, defects 810-1 and 810-2 are detected only under some image acquisition conditions.
On the other hand, the defect determination unit 180, the defect determination unit 1: 180-1, or the defect determination unit 181-1 has a threshold for a certain image such as general 130-1, 130-2, 130-3. In addition to the value, a certain threshold value 850 is provided at a distance from the origin of the feature space formed under a plurality of detection conditions, and outliers are also determined using this threshold value. That is, a defect candidate deviating from the origin with respect to this threshold value is set as an outlier (defect candidate surrounded by a circle in the figure).
In addition to the continuous boundary surface such as the threshold value 850, a logical determination method can be adopted at the same time. For example, for the image acquisition condition 2 (condition 2 in FIG. 8A), a threshold value indicated by a threshold value 830-3 is set, and the threshold value of the common defect threshold value 850 is set to be larger than the boundary surface. A logical operation is made possible based on the strength of the defect candidate with respect to the boundary surface set so as to be strong or stronger than the threshold value 830-3. Furthermore, this logical operation can be converted into a fuzzy logic operation. In this case, if a defect is detected only under a specific image acquisition condition, there arises a problem that the distance from the origin in the feature space cannot be accurately calculated.
Defects detected only under specific conditions, such as defects 810-2 and 810-2 in FIG. 8A, will be described with reference to FIGS. 8B to 8F. In FIG. 8B, 1801 is a pattern of a sample, and 1802 is a defect. In the case of the optical inspection apparatus shown in FIG. 2, it is possible to illuminate by switching the polarization of illumination. However, in the case of S-polarized light or TE-polarized light whose direction of the electric field is orthogonal to the groove of the pattern 1801, the illumination reaches the depth of the groove. Therefore, it is easy to detect the defect, but it is difficult to detect the defect because the illumination does not reach with TM polarized light or P polarized light orthogonal thereto.
FIG. 8C shows a non-opening defect 1805 generated in a hole formed on the pattern as indicated by 1804 in a pattern 1803. In TE polarized light, light reaches the bottom of the groove, but there is a tendency to detect large noise due to the roughness of the groove portion. As a result, the non-opening defect 1805 tends to be manifested only by the TM polarized light that does not reach the bottom of the groove. A surface 1810 surrounded by a dotted line shown in the three-dimensional space in FIG. 8D shows each illumination condition and a defect signal, that is, a difference between the inspection image and the reference image. Defects are only manifest under certain conditions. If boundary surfaces 8301 to 4 as shown in FIG. 8A are set for each defect type using this property, sensitivity can be set for each defect type, and the detection sensitivity of unnecessary defects can be suppressed, and nuisance can be set. It is possible to suppress the number of detections.
FIG. 8A shows the feature space for the entire plurality of examinations, but FIGS. 8E and 8F show the feature space in FIG. 8A as the feature space for each examination (first examination and second examination). This is a process that is separated and performed by the outlier detection unit 730. In the examples shown in FIGS. 8A, 8E, and 8F, the points detected at a plurality of threshold values 830-1, 830-2, 830-3, and 840-4, and the points detected only at one of them. Both are shown. For data that does not exceed the threshold, the coordinates in the feature space are generally unknown, but the coordinates can be obtained by the defect candidate associating unit 720 in FIG. 7A. That is, for data that exceeds the threshold under any of the conditions, the data of the defect candidate extraction unit 130 is accessed for data detected at the same imaging timing, and there is no data that exceeds the threshold In addition, it is possible to adopt a configuration in which the corresponding position is output as a defect candidate.
By this mechanism, it is possible to eliminate data detected by only one of them, and it is possible to obtain an accurate position in the feature space. For example, when N inspections are performed with an inspection apparatus equipped with three detection systems, in a different detection system in a certain I-th inspection, if any detection system detects a defect, the other two detection systems However, it becomes possible to find a defect corresponding to the defect candidate. However, in the J-th inspection different from I, there is a case where there is no data corresponding to this defect candidate. That is, in the inspection under a plurality of conditions where the inspection is not performed at the same timing, there may occur a case where even if a defect candidate is extracted under a certain condition, it cannot be detected as a defect under another condition.
A surface 860 in FIG. 8E and a surface 870 in FIG. 8F are defect discrimination surfaces in each inspection, and are discrimination surfaces used for determination by the outlier detection unit 730. When the discrimination surface is exceeded, image clipping and feature amount output are performed. Based on the identification surface 850 determined by all inspections and all inspection conditions shown in FIG. 8A, defect discrimination surfaces 860 and 870 are set in a feature space obtained by each inspection as a partial space.
The discriminant plane expressed in the feature space shown in FIG. 8A cannot uniquely represent the discriminant plane in the feature space of a lower dimension than that shown in FIGS. 8E and 8F. As a result, depending on the position of the defect candidate in the feature space shown in FIG. 8A, a case may occur in which the boundary surface of 850 exists on the origin side with respect to the set defect discrimination surfaces 860 and 870. This means that the defect discrimination plane set in the partial space is set far from the origin, and the sensitivity is lowered. Therefore, it is desirable that the boundary surface selected as a defect candidate in the feature space of FIGS. 8E and 8F is set on the inner side of the boundary where the boundary surface of 850 intersects the space represented by FIGS. 8E and 8F. However, if the boundary surface is set too much inside, the number of defect candidates increases extremely, and therefore, the number of defect candidates is limited.
Even if the data 820 of the second inspection shown in FIG. 8F is shifted to the outside (the arrow side of the condition 3 and the condition 4) so that the defect discrimination surface (boundary surface) 870 position is detected more, Unless the number of detected nuisances is significantly increased, it is difficult to detect more defects. Therefore, in the first inspection as shown in FIG. 8E, the coordinates on the sample surface of the defect candidate deviating from the defect discrimination boundary surface are stored as shown in the data 820, and the coordinates are the same. On the other hand, the subsequent data shown in FIG. 8F is secured by leaving it as a defect candidate.
FIG. 9 is a diagram illustrating an example of the configuration of the image storage buffer 120 and the image processing unit 170 in the defect inspection apparatus according to the first modification of the first embodiment of the present invention. The control unit 150 receives position information of defect candidates determined to be outliers from the defect candidate selection unit 140, and partially cuts out from the inspection image acquired by the image acquisition unit 110 and stored in the image storage buffer unit 120. The image cut-out position for this is set. Defect extraction is performed in all the buffers 120-1, 120-2, and 120-3 of the image storage buffer unit 120. For each defect candidate, the detected image of the inspection area including the defect candidate and the comparison target The reference image is cut out. At this time, the same image cutout position is set for all the buffers 120-1, 120-2, and 120-3 even for the defect candidate determined as a single defect by the defect candidate selection unit 140.
The image transfer unit 160 receives partial image data at the image cut-out position determined by the control unit 150 from each of the buffers 120-1, 120-2, and 120-3 of the image storage buffer unit 120, and stores this image in the buffer 120- 4 and transferred to a pre-processing unit 910 constituting the image processing unit 170 by a control signal (not shown) of the control unit 150. The pre-processing unit 910 performs sub-pixel image alignment and correction of image brightness deviation between the image data for the partial image data in the image storage buffer 120 for the input partial image data. To do.
The feature amount extraction unit 920 receives the detected image and partial image data of the reference image under each image acquisition condition from the preprocessing unit 910, and calculates the feature amount of the defect candidate. The feature quantities to be calculated are (1) brightness, (2) contrast, (3) contrast difference, (4) brightness dispersion value of neighboring pixels, (5) correlation coefficient, and (6) brightness with neighboring pixels. And (7) secondary differential value.
The feature amount extraction unit 920 stores the feature amount in the buffer 125-1 until the fixed number of defect candidates is reached or the defect candidate extraction unit 130 completes a plurality of inspections. The defect determination unit 180-2 inputs the feature amount of the defect candidate stored in the buffer 125-1, creates a feature space, and performs classification based on the distribution of the defect candidate in the feature space.
Here, the defect determination units 2 and 180-2 use the provisional determination result determined by the defect determination unit 180-1 to select a defect type or a feature amount used for determination in 180-1 for each defect area. The feature space is calculated. This is because (a) the determination using the feature space in 180-1 indicates that the number of defect candidates stored in the buffer 125-2 is much larger than that stored in 125-1. b) The defect determination boundary surface satisfies a specific relationship in advance as shown in (Equation 3) and (Equation 4), and (c) an equation in which the feature amount used is formulated, Because there is a feature that it can be easily estimated from statistics other than the candidate position, it is easy to detect a defect as a defect candidate in a large number of inspection conditions in a plurality of inspections, particularly in an actual defect, This is because even if it is not detected, it is easy to make a determination in a multidimensional space by estimating the feature amount.
On the other hand, the image-based feature quantity stored in 125-1 does not satisfy this. Therefore, based on the result determined in advance in 180-1, features used in the feature space are reduced, that is, defect determination is performed without using feature amounts from inspection conditions that are not necessary for determination. This makes it possible to perform defect determination stably even when there are defect candidates whose correspondences cannot be obtained in the inspection under a plurality of conditions. Here, 180-2 is connected to the user interface 950, and can input a user's teaching. The user can teach the DOI desired to be detected via the user interface 950. The result output unit 190 outputs the result of determination or defect classification by the defect determination unit 2: 180-2.
Teaching with the user interface 950 is also performed simultaneously with teaching of the defect determination unit 1: 180-1 to facilitate recipe setting. For example, as a method for optimizing the boundary surface of the defect determination unit 1: 180-1, a support vector machine algorithm is used, and G (k) in (Expression 3) is optimized. In the same manner, the boundary surface 730 of the outlier detection unit is calculated by optimizing G (k), and by optimizing A (k), B (k), and C (k) in (Equation 2), The boundary surface of 130-1 is set.
FIG. 10A is a diagram illustrating an example of a defect inspection processing flow in the first embodiment and the first and second modifications of the defect inspection apparatus according to the present invention. Here, the image acquisition condition is two conditions. The processing flow of is shown. First, the image acquisition unit 110 simultaneously acquires images at the same location on the sample under each image acquisition condition (S1000-1, S1000-2), and stores them in the image storage buffers 120-1, 120-2 (S1010). -1, S1010-2). Next, defect candidates are extracted from the images acquired under the respective conditions by the defect candidate extraction unit 130 (S1020-1, S1020-2). Next, the defect candidate selection unit 140 performs defect candidate selection by associating defect candidates of each image acquisition condition with outlier calculation (S1030). Next, the defect candidate selection unit 140 sets a partial image cutout position in the image storage buffer unit 120 via the control unit 150 (S1040), and the image processing unit from the image storage buffer unit 120 via the image transfer unit 160. The partial image data is transferred to 170 (S1050-1 and S1050-2). In the image processing unit 170, the images of the respective conditions are integrated and the image feature amount of the defect candidate is extracted (S1060). Next, the defect determination unit 180 associates defect candidates detected under different acquisition conditions with the same defect candidate from the coordinates of the defect candidate for each inspection (S1065). Next, the defect candidate is plotted in the multi-dimensional feature amount space, and the defect candidate plotted in the multi-dimensional feature amount space is compared with a preset threshold value to determine the defect. Classification of defect candidates determined to be defects or defects is performed (S1070), and a defect detection result is output from the result output unit 190 (S1080).
FIG. 10B is an example of a flow in which a plurality of inspections are executed and integrated to output an inspection result, whereas FIG. 10A shows an inspection result based on a single inspection result. Only portions different from FIG. 10A will be described. After the image transfer to the image processing unit 170 in S1050-1 and S1050-2 is completed, an end determination is performed in S1055. If the inspection is not completed, the image acquisition condition is changed in S1059. The image acquisition of S1000-1 and S1000-2 is performed again. When the inspection is ended as a result of the determination in S1055, defect candidates obtained by different inspections are associated (S1065), and integrated defect candidate selection based on the feature amount executed in 180-1 is performed in S1090. In step S1060, defect candidates for performing defect determination by performing feature extraction by partial image detailed analysis, a feature amount used for the determination, or a conditional image used for calculating the feature amount are selected. In the same manner as described above, defect determination / classification processing based on features (S1070) and classification result output processing (S1080) are performed.
FIG. 10C shows a case where the defect candidate extraction steps S1020-1 and S1020-2 in FIG. 10B are implemented by one processing unit in hardware (S1020). Accordingly, in the outlier detection unit in FIG. 7A, defect candidates from images detected under a plurality of conditions can be detected without missing feature amounts, and sensitivity can be improved. . Other than this, the flow is the same as in FIG. 10B.
FIG. 10D is a flow in which the steps from the selection of the image cutout defect candidate (S130) to the image transfer to the post-detail analysis processing unit (S1050) in the flow of FIG. 10C are not performed. In the processing flow described with reference to FIG. 10C, the integrated defect candidate selection (S1090) is positioned as the pre-processing of the detailed analysis of the extracted image. However, in the processing flow illustrated in FIG. 10D, the integrated defect candidate selection is performed. In (S1090), the final defect determination is performed. In the process flow described with reference to FIGS. 10A and 10B, similarly, a mode in which the determination based on the cut image (from S1030 to S1050-1 and S1050-2) is omitted, and the determination based on S1090 is the final determination. Prepare.
The processing flow shown in FIG. 10E is a processing flow diagram in the configuration of FIG. 1C described in the second modification of the first embodiment, until the image is cut out and transferred to the image processing unit 170 that performs detailed analysis (S1000- 1, S1000-2 to S1050-1, and S1050-2) are the same sequence as the process described in FIG. 10B, but the feature extraction process by the partial image detailed analysis in S1060 is the process of S1050-1 and S1050-2. After each inspection (for each image acquisition condition), instead of the final defect determination of S1070 in the processing flow of FIG. 10B, in FIG. 10E, the defect temporary determination / classification based on the partial image feature of S1075 is performed for each inspection ( (Each image acquisition condition) In S1075, the defect likelihood is calculated together with the temporary determination of the defect. While the image capturing conditions are changed in S1059, the inspection is performed a plurality of times, the defect candidates obtained in the respective inspections are associated in S1065, and then the feature amount conversion performed in 185 of FIG. 1C is performed in S1094. In step S1090, integrated defect determination is performed based on the feature amounts obtained by the plurality of inspections, and the classification result is output in step S1080.
FIG. 11 is a diagram showing an example of a graphic user interface (GUI) 1100 displayed on the user interface 950 in the first embodiment and the first and second modifications of the defect inspection apparatus according to the present invention. The GUI: 1100 includes each inspection defect determination result display area 1101, a defect candidate selection result display area 1102, and an integrated defect determination result display area 1103.
The user displays the wafer map 1110 indicating the result obtained by the defect determination in the defect determination unit 2: 180-2 in each inspection defect determination result display area 1101 of the GUI: 1100, and displays the defect candidate selection result display area 1102 in the defect candidate selection result display area 1102. For each inspection ID input by the user in the inspection ID input area 1125, the defect determination unit 1 or 140 for each inspection condition uses a feature space 1120 for determining an outlier of the defect candidate or a defect candidate selection. After integrating the results, a wafer map 1130 indicating defect candidates output to the image processing unit 170 is displayed. Also, in the integrated defect determination result display area 1103, a wafer map 1140 showing the results when the inspection results obtained in each inspection are integrated and the defect determination unit 180-1 classifies real defects and false reports. Is displayed.
The evaluation / teaching result display area 1135 is an area for displaying each defect candidate ID, its evaluation result, and the taught defect attribute, and is input from the inspection ID input unit 1145 by designating the screen with a GUI pointer. The defect candidate image corresponding to the inspection ID specified in the above is displayed in the image display area 1150 for each detection condition. In the figure, no image is displayed under condition 3 where no defect candidate is detected. In the illumination / imaging condition display area 1146, the illumination conditions used in the inspection ID and imaging conditions such as the rotation direction of the wafer are displayed.
A teaching type selection area 1160 is an area for selecting a defect attribute for teaching. One of the defect attributes displayed in the teaching type selection area 1160 is specified with a pointer, and is specified by clicking a set button 1170 with the pointer. The defect attribute is taught to the defect ID. 1180 is a teaching button. Based on the result taught by clicking with the GUI pointer, the defect candidate detection unit 330 discriminates between defect and false information for each defect attribute via the parameter setting unit 340. Set the boundaries. As a determination boundary setting method, a support vector machine or linear determination is used. The result of teaching can be confirmed by the display content of the inspection condition display area 1195.
The information displayed in the inspection condition display area 1195 indicates the degree of contribution given to the inspection under each inspection condition of each inspection ID. The data 1196 and 1197 displayed in the inspection condition display area 1195 are typically G (k) values obtained by teaching. In the parameter input area 1190, the user can also manually input parameters, and input the degree of deviation from the reference variation of detection conditions for each inspection, which is known a priori, or a specific detection condition. Information that does not contribute to determination of a specific defect attribute can be input.
According to the present embodiment, by extracting and integrating local image data including defect candidates from a large amount of image data detected by a plurality of detection systems, it is possible to detect defects without extremely increasing the data capacity. The sensitivity can be increased, and the imaging of the sample and the image processing can be executed in parallel, so that a minute defect can be inspected with high sensitivity and at high speed.
The configuration illustrated in FIG. 12 is a block diagram illustrating a schematic configuration of the defect inspection apparatus according to the second embodiment, and illustrates a configuration obtained by partially changing the configuration described with reference to FIG. 1B in the first embodiment. . About the structure which attached | subjected the same number as Example 1, it has a function similar to having demonstrated in Example 1. FIG. In the configuration shown in FIG. 1B described in the first embodiment, the image data acquired by each of the image acquisition units 110-1, 110-2, and 110-3 of the image acquisition unit 110 is different from each other in the defect candidate extraction unit 130. Although defect candidates are extracted by the candidate extraction units 130-1, 130-2, and 130-3, in the configuration shown in FIG. 12 in the second embodiment, this is performed by the integrated defect candidate extraction unit 135.
The integrated defect candidate extraction unit 135 includes a configuration for processing image data corresponding to the defect extraction units 130-1, 130-2, and 130-3 in the first embodiment. When outputting defect candidates detected in images from any of the image acquisition units 110-1, 110-2, and 110-3 of the image acquisition unit 110, images acquired by another image acquisition unit Therefore, the feature amount of the defect candidate is output even if it is not selected as a defect candidate. As a flow, FIG. 10C or FIG. 10D is typically applied.
The configuration illustrated in FIG. 13A in the third embodiment is an example in which the configuration described with reference to FIG. 12 in the second embodiment is modified. In the configuration illustrated in FIG. 13A, the inputs of the image acquisition units 110-1, 110-2, and 110-3 of the image acquisition unit 110 are stored in the buffers 120-1, 120-2, and 120-3 of the buffer unit 120, respectively. Then, the cut-out images are transferred from the respective buffers 120-1, 120-2, and 120-3 to the image transfer unit 160. This is because the defect candidate is extracted independently by each defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130 in the configuration shown in FIG. If any of the defect candidate extraction units 130-1, 130-2, 130-3 determines a defect candidate, it is stored in each buffer 120-1, 120-2, 120-3 of the buffer unit 120. The image cut out from the previously stored image can be transferred to the image transfer unit 160.
On the other hand, the defect candidate extraction unit 130-1, 130-2, 130-3 of the defect candidate extraction unit 130 described in the first embodiment is replaced with an integrated defect candidate extraction unit 135 shown in FIG. 13A. In the configuration, defect candidates are extracted from the images sent from the image acquisition units 110-1, 110-2, and 110-3 in the integrated defect candidate extraction unit 135, and the extracted peripheral images including the defect candidates are extracted. cut. Then, the integrated defect candidate extraction unit 135 is configured to transfer the extracted defect candidate and the surrounding image to the defect candidate selection unit 140 and store the extracted image in the buffer 120-5.
The configuration shown in FIG. 13B is a modification of the configuration in FIG. 13A. The configuration shown in FIG. 13A is an embodiment different from the configuration described in FIG. 1B in the first embodiment. However, the configuration shown in FIG. 13B is different from the configuration described in FIG. As in the configuration described with reference to FIG. 1C, the defect determination unit 2: 180-2 is executed as preprocessing of the defect determination unit 1: 180-1, and the defect determination unit 2: 180-1 is final. Determine the defect. The newly added buffer 125-3 and the feature amount changing unit 185 perform the same operations as those described in FIG.
FIG. 16 shows an example of the positional deviation correction of defect candidates in the defect determination unit 1: 180-1. From the defect candidate map 1610 detected from the image acquired under the image acquisition condition 1 and the defect candidate map defect candidate 1620 detected from the image acquired under the image acquisition condition 2, defect candidates 1630 and 1640 used for misregistration detection are used. And the amount of deviation is calculated from the selected defect candidate. Based on the obtained shift amount, a map 1650 in which the position shift of the defect candidates 1610 and 1620 in the image acquisition conditions 1 and 2 is corrected is created (1650). However, it is not always necessary to create the map 1650, and it is only necessary to store data in which positional deviation is corrected for each defect candidate.
In the first to third embodiments, the dark field inspection device is used as the inspection device. However, the present invention can be applied to all types of inspection devices such as a bright field inspection device and an SEM type inspection device, and includes a plurality of images. As acquisition conditions, images can be acquired by the above-described inspection apparatuses of a plurality of methods, and defect determination can be performed.
FIG. 14 is a diagram illustrating an example of the configuration of an SEM type inspection apparatus 110 ″ as another example of the configuration of the image acquisition unit 110. The darkness described with reference to FIG. 2 in the first embodiment and the first and second modifications. Parts that perform the same or equivalent operations as the visual field inspection apparatus are given the same numbers.
In the configuration of the SEM type inspection apparatus 110 ″ shown in FIG. 14, 1410 is an electron beam source for emitting an electron beam, 1420 and 1430 are condenser lenses for converging the electron beam emitted from the electron beam source 1410, and 1440 is for converging. An electron beam axis adjuster for adjusting astigmatism and misalignment of the electron beam; 1480, a reflector having a primary electron beam passage hole for allowing the electron beam adjusted for astigmatism and misalignment to pass; 1450 1460 is a scanning unit for scanning an electron beam, 1470 is an objective lens, and 1490 is a reflection reflected by a reflector 1480 having a primary electron beam passage hole among the reflected electrons generated on the wafer 210 irradiated with the electron beam. Backscattered electron detectors for detecting electrons 1491 and 1492 are emitted from the wafer 210 irradiated with the electron beam. Secondary electron detectors 1500, 1501, 1502 for detecting the secondary electrons detected are detection signals obtained by detecting the backscattered electrons or secondary electrons by the backscattered electron detector 1490, the secondary electron detectors 1491, 1492, respectively. A / D converters 270 convert the signals into digital signals, 270 receives the signals output from the respective D / C converters 1500, 1501, 1502 and sends this detection signal to the defect candidate extraction unit 130 and the entire SEM inspection apparatus 110 " It is a control part which controls.
In such a configuration, the SEM inspection apparatus 110 ″ allows the electron beam irradiated from the electron beam source 1410 to pass through the condenser lenses 1420 and 1430, and then the astigmatism and misalignment are corrected by the electron beam axis adjuster 1440. After the electron beam is deflected by the scanning units 1450 and 1460 and the position where the electron beam is irradiated is controlled, the electron beam is converged by the objective lens 1470 and irradiated to the imaging target region 1400 of the wafer 210. As a result, secondary electrons and reflected electrons are emitted from the target region 1400, and the secondary electrons and the reflected electrons collide with a reflector having a primary electron beam passage hole 1410, and the secondary electrons generated there are reflected electrons. The secondary electrons generated from the imaging target region are detected by the detector 1490. The secondary electron detector 14 It is detected at 1,1492. As a result, detected by electron detector disposed in each of three different directions position as the imaging target region 1400.
The secondary electrons and the reflected electrons detected by the reflected electron detector 1490 and the secondary electron detectors 1491 and 1492 are converted into digital signals by the A / D converters 1500, 1501 and 1502, respectively, and sent to the control unit 270. It is transferred to the defect candidate extraction unit 130. The combination of the electronic detectors 1490, 1491, and 1492 and the A / D converters 1500, 1501, and 1502 is based on each image acquisition unit 110- of the image acquisition unit 110 illustrated in FIGS. 1A to 1C, 12, 13A, and 13B. 1, 110-2, and 110-3 can be regarded as the same. In addition, in the case of SEM, the detection condition is changed by changing the conditions including the acceleration voltage of the electron beam and the potential difference between the wafer 210 and the objective lens 1470, and the same inspection object is inspected a plurality of times, as in the past. By this method, inspection sensitivity can be improved.
As mentioned above, although the invention made by the present inventor has been specifically described based on the embodiments, it is needless to say that the present invention is not limited to the above embodiments and can be variously modified without departing from the gist thereof. Yes.
DESCRIPTION OF SYMBOLS 110 ... Image acquisition part 120 ... Image storage buffer 125 ... Feature-value storage buffer 130 ... Defect candidate extraction part 140 ... Defect candidate selection part 150 ... Control part 160 ... Image transfer Unit 170 ... Image processing unit 180 ... Defect determination unit 190 ... Result output unit 210 ... Wafer 220 ... Stage 230 ... Controller 240 ... Illumination system 250 ... Detection system 310 ... Pre-processing unit 320 ... Image memory unit 330 ... Defect candidate detection unit, 340 ... Parameter setting unit, 350 ... Control unit, 410 ... Detection image 420 ... Reference image 430 Alignment unit 440 Feature amount calculation unit 450 Feature space formation unit 460 Displacement pixel detection unit 71 ... Misalignment detection / correction unit 720 ... Defect candidate association unit 730 ... Outlier detection unit 910 ... Pre-processing unit 920 ... Feature quantity extraction unit 930 ... Feature quantity storage unit 940 ... Defect classification unit 950 ... User interface.
Capture the same region of the sample under different image acquisition conditions to acquire a plurality of images for the same region of the sample,
Processing each of the acquired plurality of images of the sample to extract defect candidates in each of the plurality of images;
Cut out a partial image including the extracted defect candidate and an image around the defect candidate from the plurality of acquired images based on the position information of the extracted defect candidate,
Obtain the feature amount of the defect candidate in the cut out partial images,
Corresponding defect candidates detected under the condition that the image acquisition condition is different with the defect candidate having the same coordinate among the extracted defect candidates,
Based on the feature vector of the defect candidate extracted in the step of extracting the defect candidate, a determination process is performed in which a determination boundary is set in a multidimensional space,
Extracting defects from the associated defect candidates by narrowing down the image feature amount used for defect determination for each defect candidate attribute assumed based on the result of the determination process ,
A defect inspection method characterized by outputting the extracted defect information.
The defect inspection method according to claim 1, wherein acquiring a plurality of images for the same region of the sample is performed a plurality of times while changing the image acquisition condition, and outputting the defect information, A defect inspection method, wherein defect candidates extracted from images acquired a plurality of times under different image acquisition conditions are associated and integrated, and information on the integrated defects is output.
Processing each of the acquired plurality of images to extract defect candidates in each of the plurality of images;
Obtaining the plurality of images, extracting the defect candidates, and cutting out the partial image are performed a plurality of times while changing the plurality of image acquisition conditions,
Correspondence of defect candidates having the same coordinates on the sample among the defect candidates included in the partial image cut out in the step of cutting out the partial image from the image obtained by imaging a plurality of times while changing the plurality of image acquisition conditions And
The defect inspection method according to claim 1, wherein acquiring a plurality of images for the same region of the sample is acquired using a plurality of image acquisition units having different image acquisition conditions. Defect inspection method.
The defect inspection method according to claim 1 or 3, wherein the false information is removed from the associated defect candidates based on the coordinate information of the defect candidates, and the defect candidates from which the false information has been removed are subjected to the multidimensional inspection. A defect inspection method characterized by providing feature amount space information.
4. The defect inspection method according to claim 1, wherein the extracted defects are classified and information on the classified defects is output.
Image acquisition means for imaging the same region of the sample under different image acquisition conditions and acquiring a plurality of images for the same region of the sample;
Defect candidate extraction means for processing each of the plurality of images of the sample acquired by the image acquisition means and extracting defect candidates in each of the plurality of images;
Partial image cutout means for cutting out a partial image including the extracted defect candidate and an image around the defect candidate from a plurality of images acquired by the image acquisition means based on the position information of the defect candidate extracted by the defect candidate extraction means When,
Feature quantity calculating means for obtaining feature quantities of defect candidates in a plurality of partial images cut out by the partial image cut-out means;
A defect candidate associating means for associating defect candidates detected under the condition in which the image acquisition condition is different with the defect candidate having the same coordinate among the defect candidates extracted by the defect candidate extracting means;
First defect determination means for performing a determination process in which a determination boundary is set in a multidimensional space based on a defect candidate feature amount vector extracted by the defect candidate extraction means;
A defect is selected from the defect candidates associated with the defect candidate associating unit by narrowing down the image feature amount used for defect determination for each defect candidate attribute assumed based on the result of the determination process in the first defect determining unit. Defect extraction means for extracting,
A defect inspection apparatus comprising: output means for outputting information on defects extracted by the defect extraction means.
The defect inspection apparatus according to claim 7, wherein the image acquisition unit performs a plurality of times to acquire a plurality of images for the same region of the sample while changing the image acquisition condition, and in the defect extraction unit, Defect candidates extracted from images acquired a plurality of times by changing the image acquisition conditions by the image acquisition means are associated and integrated, and the output means outputs the defect information integrated by the defect extraction means from the output means A defect inspection apparatus characterized by:
Defect candidate extraction means for processing each of the plurality of images acquired by the image acquisition means and extracting defect candidates in each of the plurality of images;
Wherein the extracting the defect candidate by said defect candidate extracting means for acquiring a plurality of images in front Kiga image acquisition means to control the said image acquisition means and said defect candidate extracting means and the partial image cut-out unit section A control means for cutting out a partial image by the image cutout means and changing the plurality of image acquisition conditions a plurality of times;
Of the defect candidates included in the partial image cut out by the partial image cutout unit from the image obtained by imaging a plurality of times by changing the plurality of image acquisition conditions of the image acquisition unit under the control of the sample acquisition unit Defect candidate associating means for associating defect candidates having the same coordinates in;
10. The defect inspection apparatus according to claim 7, wherein the image acquisition unit includes a plurality of image acquisition units having different image acquisition conditions.
The defect inspection apparatus according to claim 7 or 9, wherein the feature amount calculation unit removes false information from the defect candidates associated by the defect candidate association unit based on the coordinate information of the defect candidate. The defect inspection apparatus, wherein the multidimensional feature amount space information is added to the defect candidate from which the false information is removed.
The defect inspection method according to claim 7 or 9, wherein the defect extraction unit further classifies the extracted defect, and the output unit outputs information on the classified defect. Inspection device.
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US10262408B2 (en) 2017-04-12 2019-04-16 Kla-Tencor Corporation System, method and computer program product for systematic and stochastic characterization of pattern defects identified from a semiconductor wafer
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