Inspection system and non-transitory computer-readable medium

Abstract

To identify a foreign material that may damage a sample in a short time.SOLUTION: The present disclosure provides an inspection system for estimating at least one of an element contained in a sample and the element distribution from data obtained by an image acquisition tool, the inspection system including a computer system and an arithmetic module executed by the computer system. The computer system comprises a learning unit that outputs at least one of the element contained in the sample and the element distribution. The learning unit is trained in advance using teacher data in which image data obtained by the image acquisition tool is used as input and at least one of the element obtained by an X-ray analyzer and element distribution information is used as output. The arithmetic module outputs at least one of the elemental and elemental distribution information to the learning unit by inputting the image data obtained by the image acquisition tool.SELECTED DRAWING: Figure 14

Description

[Technical Field]

[0001] The present disclosure relates to an inspection system and a non-transitory computer-readable medium, for example, a wafer inspection system and a non-transitory computer-readable medium that inspects the periphery of a wafer. [Background technology]

[0002] In semiconductor manufacturing, understanding the condition of the wafer's outer periphery is important for process management. For example, Patent Document 1 discloses an apparatus equipped with an electron microscope and a rotary stage (θ stage), and describes an inspection method for inspecting the wafer's outer periphery by irradiating the wafer's outer periphery with an electron beam. Furthermore, Patent Document 2 discloses an inspection apparatus that, in order to inspect the wafer's edge, is provided with a beam column that irradiates a beam onto the edge, in addition to an electron beam column that irradiates a beam onto the wafer surface. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent No. 7919760 [Patent Document 2] Patent No. 5608208 Summary of the Invention [Problem to be solved by the invention]

[0004] As disclosed in Patent Document 2, in order to determine the presence or absence of defects and their locations, an image showing a normal (defect-free) sample area is registered in advance as a reference image, and an image (inspection image) obtained by irradiating the edge portion with a beam is compared with the reference image, and areas with large differences are determined to be defects.

[0005] However, the boundaries of the multilayer films stacked on the wafer are concentrated on the inclined surface (bevel) formed on the sidewall of the wafer, and the shape of the boundaries is unstable due to peeling of the films near the boundaries. Therefore, it may be difficult to obtain an image suitable as a reference image. For example, if there is a difference between the reference image and the inspection image in the area other than the foreign matter or defect (background), that area will be regarded as a defect, making it difficult to obtain a reference image for defect detection from the bevel. Furthermore, neither Patent Document 1 nor Patent Document 2 discusses a situation in which the reference image includes the boundary of a multilayer film. Furthermore, depending on the material, foreign matter adhering to the bevel may cause damage to semiconductor elements formed in subsequent processes.On the other hand, element identification based on X-ray spectroscopy requires a relatively long processing time compared to image formation based on the detection of secondary electrons or backscattered electrons. In view of these circumstances, the present disclosure proposes an elemental analysis technique that can quickly identify foreign matter that may damage a sample. [Means for solving the problem]

[0006] As one aspect for solving the above problem, the present disclosure proposes an inspection system that estimates at least one of the elements and element distribution contained in a sample from data obtained by an image acquisition tool, the inspection system including a computer system and an arithmetic module executed by the computer system, wherein the computer system is equipped with a learning device that outputs at least one of the elements and element distribution contained in the sample, the learning device having input image data obtained by the image acquisition tool and having previously performed learning using training data that outputs at least one of element and element distribution information obtained by an X-ray analysis device, and the arithmetic module inputs the image data obtained by the image acquisition tool to the learning device, and outputs at least one of the elements and element distribution information.

[0007] Further features related to the present disclosure will become apparent from the description and accompanying drawings of this specification, and aspects of the present disclosure may be realized and realized by the elements and combinations of various elements and aspects set forth in the following detailed description and the appended claims. It should be understood that the descriptions in this specification are exemplary and illustrative only and are not intended to limit the scope or application of the present disclosure in any way. [Effects of the Invention]

[0008] The elemental analysis technique described above makes it possible to quickly identify foreign matter that may damage a sample. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of a semiconductor wafer defect inspection system. [Figure 2] 1 is a diagram showing an example of a control unit and a storage unit that configure the defect observation system. [Figure 3] FIG. 2 is a diagram showing the configuration of a detector of the defect inspection system. [Figure 4] FIG. 10 is a diagram showing an image capturing method using scan rotation. [Figure 5] 10 is a flowchart illustrating a defect inspection process. [Figure 6] FIG. 10 is a diagram showing the relationship between observation position coordinates and observation position azimuth angles. [Figure 7] 10A and 10B are diagrams showing images before and after adjustment of the scan rotation angle. [Figure 8] FIG. 1 is a diagram showing an example of a scanning electron microscope image of a bevel portion of a semiconductor wafer. [Figure 9] FIG. 1 is a diagram showing an example of a bevel inspection system. [Figure 10] FIG. 10 is a diagram showing an example of a GUI (Graphical User Interface) screen for inputting conditions for generating training data. [Figure 11] 10 is a flowchart illustrating a learning process. [Figure 12]FIG. 10 is a diagram showing an example of a GUI screen for inputting conditions for generating training data. [Figure 13] 10 is a flowchart illustrating an estimation process using a learning model. [Figure 14] FIG. 1 is a diagram showing an example of an elemental information estimation system. [Figure 15] 10 is a flowchart showing a learning process for automatically performing labeling processing. [Figure 16] 10 is a flowchart showing a process of re-learning a learning device during an estimation process using the learning device. [Figure 17] 1 is a flowchart illustrating an example of improving the work efficiency of the learning process (step) by automating the labeling work of DOI (Defect of Interest) and Nuisance using elemental analysis results. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of a wafer inspection system will be described with reference to the drawings. The objectives, features, advantages, and ideas of the present disclosure will be apparent to those skilled in the art from the description in this specification. Although some details may be omitted or simplified as appropriate for clarity, those skilled in the art will be able to easily reproduce the present disclosure from the description in this specification. The embodiments and the like described below represent preferred embodiments and are shown for illustrative or explanatory purposes, and are not intended to limit the present disclosure. Therefore, the present disclosure can be implemented in various other forms. It will be apparent to those skilled in the art that various changes and modifications can be made based on the description in this specification within the intent and scope of the present disclosure.

[0011] Furthermore, in the following description, processing performed by executing a program may be described, but the program is executed by a processor (e.g., a CPU or a GPU) to perform the specified processing while appropriately using storage resources (e.g., a memory) and / or interface devices (e.g., a communication port), and therefore the subject of the processing may be the processor. Similarly, the subject of the processing performed by executing a program may be a controller, device, system, computer, or node having a processor. The subject of the processing performed by executing a program may be any computing unit, and may include a dedicated circuit (e.g., an FPGA or an ASIC) that performs a specific processing.

[0012] A program (module) may be installed on a device such as a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. If the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. Also, in the following description, two or more programs may be realized as one program, and one program may be realized as two or more programs.

[0013] (1) Wafer inspection system <Wafer inspection system configuration> An outline of the wafer inspection system will be explained below with reference to the drawings: Figure 1 is a diagram showing an outline of a scanning electron microscope 100, which is a type of image acquisition tool.

[0014] The scanning electron microscope 100 illustrated in FIG. 1 includes an observation device 101 that takes images of wafers, a control unit 102 that controls the entire system, a memory unit 103 that stores information on a magnetic disk, a semiconductor memory, or the like, a calculation unit (which can be configured with a processor, etc.) 104 that performs calculations according to a program, an external storage medium input / output unit 105 that inputs and outputs information to and from an external storage medium connected to the device, a user interface control unit (which can be configured with a processor, etc.) 106 that controls the input and output of information to and from the user, and a network interface unit 107 that communicates with a defect image classification device via a network.

[0015] In addition, an input / output terminal 113 consisting of a keyboard, a mouse, a display, etc. is connected to the user interface control unit 106. The observation device 101 can be equipped with a movable stage 109 movable in the X and Y directions for mounting the sample wafer 108, an electron source 110 as an imaging means for irradiating the sample wafer 108 with an electron beam, a detection device 111 for detecting secondary electrons and reflected electrons generated from the sample wafer, an electron lens (not shown) for converging the electron beam on the sample, and a deflector 112 for scanning the electron beam over the sample wafer.

[0016] In this embodiment, the scanning electron microscope 100 is used as an example of the image acquisition tool, but the present invention is not limited to this, and other image acquisition tools such as a focused ion beam device can also be used.

[0017] Furthermore, the scanning electron microscope 100 illustrated in FIG. 1 can be provided with a separate detector for elemental analysis. When, for example, energy dispersive X-ray spectrometry (EDS) is used for elemental analysis, a detector that detects X-rays generated by receiving an electron beam irradiated onto the object of inspection can be employed. In this case, the control unit 102 controls the ON / OFF of X-ray detection. The detected X-ray spectrum is then subjected to spectral analysis in the calculation unit 104, and information on the contained elements is extracted. Note that a TES (Transition Edge Sensor) type detector with high energy resolution can also be used as the X-ray detector.

[0018] <Internal configuration example> FIG. 2 is a diagram showing an example of the internal configuration of each of the control unit 102, the storage unit 103, and the calculation unit 104.

[0019] The control unit 102 includes, for example, a stage control unit 201, an electron beam scan control unit 202, and a detector control unit 203. The stage control unit 201 controls the movement and stopping of the stage. The electron beam scan control unit 202 controls the deflector 112 so that the electron beam is irradiated within a predetermined field of view. The detector control unit 203 samples the signal from the detection device 111 in synchronization with the scanning of the electron beam, and adjusts the gain, offset, etc. to generate a digital image.

[0020] The storage unit 103 includes an image storage unit 204, a processing parameter storage unit 205, and an observation coordinate storage unit 206. The image storage unit 204 stores the generated digital image together with accompanying information. The processing parameter storage unit 205 stores imaging conditions, defect detection processing parameters, classification processing parameters, etc. The observation coordinate storage unit 206 stores defect inspection coordinates of the observation target input by, for example, a user (operator).

[0021] The calculation unit 104 includes an observation coordinate azimuth angle derivation unit 207, a defect detection unit 208, a defect image classification unit 209, and an image processing unit 210. The observation coordinate azimuth angle derivation unit 207 derives the azimuth angle of the observation coordinates viewed from the wafer center. The defect detection unit 208 detects defect locations from the images. The defect image classification unit 209 classifies the images according to the type of defect or classification criteria specified by the user. The image processing unit 210 performs image preprocessing such as smoothing and contrast adjustment, and transformations such as image movement and rotation (image transformation means), and outputs the processed images to the image storage unit 204, the defect detection unit 208, and the user interface control unit 106 (image output means).

[0022] <Detection device configuration example> The configuration and arrangement of the detection device that detects secondary electrons and backscattered electrons generated from the sample wafer 108 are not particularly limited, and the configuration and number of electron detectors included in the detection device are also not particularly limited, but below, an embodiment of the detection device 111 shown in Fig. 3 will be described in detail. Fig. 3 is a diagram that schematically shows the positional relationship between the detectors 301 to 305 and the sample wafer 108. Fig. 3(A) is a projection view, and Figs. 3(B) and (C) are views as seen from the z-axis and y-axis, respectively (detector 305 is not shown).

[0023] 3, detectors 301 to 304 are configured to selectively detect electrons having specific emission angles (elevation angle and azimuth angle). For example, electrons emitted in the y direction from the sample wafer 108 are mainly detected by detector 301. This makes it possible to acquire an image with contrast as if light were irradiated from the direction of the detector. Also, for example, detector 305 is a detector that mainly detects secondary electrons emitted from the sample wafer 108.

[0024] <Defect inspection> Next, an outline of the defect inspection method will be described. The inspection method described here includes an imaging step of imaging multiple locations including the edge of the semiconductor wafer while moving the semiconductor wafer in the X and Y directions on a plane, an output step of outputting images in which the wafer edges are approximately parallel within each of the multiple images obtained by imaging, and a defect detection step of detecting defects in the semiconductor wafer in the output images.

[0025] 4 is a diagram for explaining the principle of capturing an image. The electron beam scan control unit 202 (see FIG. 2) controls the deflector 112 so that the electron beam is irradiated within a predetermined imaging field of view. Specifically, the electron beam scan control unit 202 controls the deflector 112 to scan in a certain direction within the imaging field of view, and then shift in an orthogonal direction and scan again, repeatedly, so that the electron beam is irradiated over the entire imaging field of view. At this time, the detector control unit 203 digitally converts the signal detected by the detection device 111 in synchronization with the electron beam scanning by the deflector 112, thereby obtaining an image.

[0026] FIG. 4(A) schematically shows the scanning of an electron beam across an imaging field of view 402 within a sample 401, and the resulting image 403. FIG. 4(B) shows an example in which an angle is applied to the imaging region in the coordinate system (x, y) of the sample 401 by controlling the scanning direction of the electron beam, and schematically shows the scanning of a region 404 at an angle R relative to the sample 401, and the resulting image 405. The resulting image 405 is rotated with respect to the image 403. Hereinafter, capturing an image at an angle will be referred to as scan rotation, and the angle R at this time will be referred to as the scan rotation angle. The coordinate system of the sample will be referred to as (x, y), and the coordinate system of the image will be referred to as (u, v).

[0027] <Semiconductor wafer edge sample observation processing> Next, an example of an imaging process for imaging multiple locations including the edge of a semiconductor wafer while moving the semiconductor wafer in the X and Y directions on a plane will be described with reference to Fig. 5. Fig. 5 is a flowchart for explaining the details of the sample observation process according to this embodiment.

[0028] First, the semiconductor wafer to be observed is placed on the stage (or the control unit 102 detects that the semiconductor wafer has been placed on the stage 109 by the robot arm) (step 501), and the control unit 102 reads the processing parameters corresponding to the semiconductor wafer from the storage unit 205 (step 502). Note that semiconductors are manufactured through multiple manufacturing processes, and the appearance may vary significantly between each process, and the sample characteristics, such as how easily it becomes charged, may also vary. For this reason, it is common to adjust and store the imaging conditions and processing parameters for each process and device.

[0029] After reading the processing parameters, the control unit 102 reads a coordinate list (observation coordinates) of positions to be observed in parallel from the storage unit 206 (step 503). The observation coordinates may be defect coordinates output by another defect inspection device (not shown) or may be coordinates that the user inputs to be observed.

[0030] Next, the observation device 101 aligns the position of the semiconductor wafer in response to an instruction from the control unit 102 (step 504). The semiconductor wafer is placed on the stage 109 using a robot arm or the like, and during this process, a positional or rotational deviation of several hundred microns occurs. Therefore, the calculation unit 104 (which may also be the control unit 102) images the edge of the wafer and a unique pattern whose position is known, and then calculates the amount of positional deviation through image processing. This makes it possible to correct the observation coordinates and correctly calculate the center coordinates of the wafer.

[0031] Next, an image of the defect site for the i-th observation coordinate is acquired, and the image is classified according to the defect type or a classification criterion specified by the user. One embodiment of this procedure is described below. First, the stage control unit 201 moves the stage 109 so that the target observation coordinate is within the field of view of the observation device 101 (step 505). At this time, the stage position is measured to absorb any movement error of the stage 109, and the beam irradiation position is adjusted during image capture to cancel out the movement error.

[0032] Next, the electron beam scan control unit 202 rotates the scanning direction of the electron beam by controlling the deflector 112 so that the edges of the semiconductor wafer are parallel in multiple images (step 506). An example of a specific method for this will be described. For example, the azimuth angle θi of the observation coordinates viewed from the center of the semiconductor wafer is derived. FIG. 6 shows a line 603 connecting the observation coordinates 601 and the semiconductor wafer center coordinates 602, and the azimuth angle θ604. Image 606 shows an enlarged image of region 605. According to this definition, the azimuth angle θi in the observation coordinates (dxi, dyi) with the semiconductor wafer center as the origin can be found by Equation 1. θi= atan(dyi,dxi) (Formula 1)

[0033] The function atan(y, x) returns the arctangent of y / x in the range of [-π, π] radians, and the quadrant can be determined appropriately from the arguments (y, x). In this embodiment, the azimuth angle θ is defined as the angle between the line 603 and the x-axis, but other definitions may be used as long as they can specify the rotation of the scan direction. The azimuth angle calculation of the observation coordinates is performed by the observation coordinate azimuth angle derivation unit 207 of the calculation unit 104.

[0034] After deriving the azimuth angle θi, the control unit 102 determines the scan rotation angle Ri during image capture using the following equation 2, and sets it as an imaging parameter (step 507). Ri=θi+α (Equation 2)

[0035] α is a predetermined parameter that is determined in advance so that the edges of the semiconductor wafer are approximately parallel in multiple images. For example, if it is set to -π / 2 radians (-90 degrees), the background area can be adjusted to be positioned on top in the image.

[0036] Next, the detector control unit 203 scans the imaging field of view at the set scan rotation angle Ri and acquires an image (step 508). Area 607 in Fig. 6 is the field of view area for imaging the observation coordinate 601, and shows how the scan rotation angle R608 is set. Note that, in deriving the azimuth angle θ and the scan rotation angle R, it is also possible to refer to a database that stores the azimuth angle θ or the scan rotation angle R for each observation coordinate, without performing the calculations using Equations 1 and 2.

[0037] After capturing an image (step 508), the defect detection unit 208 detects defects from the captured image (step 509) and classifies the defect images (step 510). Here, the defect detection method will be described. FIG. 7 is a schematic diagram of images captured at five observation coordinates. Images 701 to 705 are images captured without scan rotation, while images 706 to 710 are images captured after a scan rotation angle is set. In each image, the black area represents the off-wafer area (background area), and the white area represents the on-wafer area. Images 701 to 705 differ in the positional relationship between the on-wafer area and the background area. For example, even if images 701 and 702 are aligned in the (u, v) direction, the inclinations of the area boundaries are different, making it difficult to find an appropriate solution. Therefore, defect detection by comparative inspection is difficult. On the other hand, images 706 to 710 were captured by adjusting the scan rotation angle so that the background area was positioned at the top of the image.

[0038] <Why beveled images are not suitable as reference images> As described above, by performing scan rotation, the beam can be scanned in the same direction as the edge (perpendicular to the edge) regardless of the edge orientation. However, the image obtained by scanning the beam onto the bevel may not be suitable as a reference image for comparison inspection. The reason for this is explained in detail below.

[0039] Figure 8 is a diagram illustrating the shape of a bevel and the state of an image acquired by the bevel. A bevel is a slope formed on the sidewall of a wafer. Recently, semiconductor wafers have become increasingly multi-layered, with multiple layers stacked on top of each other. Since the end 801 of each layer is formed on the bevel, an image 802 obtained by scanning a beam on the bevel includes the ends of multiple layers, as shown in the example in Figure 8. The shape of this end (boundary) often differs from place to place, and in some places, film peeling or other problems may occur.

[0040] That is, even if no defects or foreign matter are present, the edge shapes contained in the image vary depending on the location, and the image acquired here is not suitable as a reference image to be used in comparative inspection.

[0041] (2) Foreign object detection system Below, a system (foreign matter detection system 900) that enables detection of foreign matter, defects, etc. on a bevel without using a reference image, and a non-transitory computer-readable medium that stores a program executed by a computer system will be described.

[0042] FIG. 9 is a diagram showing an example of the configuration of a foreign matter detection system 900 that detects foreign matters on a bevel. The system shown in FIG. 9 is composed of one or more computer systems. The inspection process described in this example may be performed by a calculation unit 104 provided in the observation device 101, or may be performed by an external computer system communicatively connected to the observation device 101. FIG. 9 is expressed as a functional block diagram. The computer system 901 shown in FIG. 9 is a machine learning system, includes one or more processors, and is configured to execute one or more calculation modules (not shown) stored in a predetermined storage medium.

[0043] 9 includes an input unit 904 to which training data to be used for learning and data required for estimation processing are input from an SEM image storage unit (storage medium) 902 and an input / output device 903.

[0044] A learning device 905 built into the computer system 901 receives as training data at least one of image data of the bevel input from the input unit 904 and image features extracted by an image processing device or the like (not shown), and a set of information (data set) relating to foreign matter or scratches on the bevel input from the input / output device 903.

[0045] The foreign substance defect estimation unit 907 reads out the learning model learned by the learning device 905 and stored in the learning model storage unit 906, and executes estimation processing using the learning model.

[0046] <Configuration example of GUI for inputting conditions for generating training data> FIG. 10 is a diagram showing an example of the configuration of a GUI (Graphical User Interface) that is displayed on the display screen of the input / output device 903 when conditions for generating teacher data are input.

[0047] The GUI screen illustrated in FIG. 10 includes, as its constituent areas, an additional information display field 1001 that displays information about image data input from the SEM image storage unit (storage medium) 902, an image display field 1002 that displays the SEM image, and a setting field 1003 that sets the type of foreign matter or defect.

[0048] The additional information display field 1001 displays the acquisition location (Location) of the SEM image and the sampling number, which are additional information of the SEM image displayed in the image display field 1002. In the example of Fig. 10, the azimuth angle explained in Fig. 6 is displayed as the position information, but x and y coordinates may also be displayed.

[0049] The bevel image is displayed in an image display field 1002. The user (operator) can view the displayed bevel image and set the type of foreign matter or defect in a setting field 1003.

[0050] The setting field 1003 allows the user to set foreign matter on the bevel, scratches, peeling film, etc. Furthermore, regarding foreign matter, the user can select whether it is metallic or non-metallic. If the user can distinguish between metallic and non-metallic matter based on their experience when visually inspecting an SEM image, the configuration may allow the user to select based on that information. This is because metallic substances generated by EUV (Extra Ultra Violet) exposure may adhere to patterns generated in subsequent processes, causing electrical continuity between patterns and resulting in device destruction. If such metallic substances are generated during EUV exposure and are carried into subsequent processes while adhering to the bevel, they may cause device destruction. Therefore, inspection for the presence of metallic foreign matter, in particular, is extremely important for improving the yield of semiconductor devices.

[0051] <Learning process details> FIG. 11 is a flowchart for explaining the learning process (steps) executed by the calculation unit 104 or the computer system 901.

[0052] First, the observation device 101 acquires an image along the bevel (step 1101). The computer system 901 or the like displays the acquired image on a GUI screen such as the one shown in Fig. 10. Then, a user (operator) looks at the image displayed on the GUI screen and sets (labels) the types of foreign particles or defects contained in the image, and the input unit 904 acquires the labeled information (step 1102). In the labeling step, the defect type is identified by selecting the defect type in a setting field 1003 on the GUI screen shown in Fig. 10, for example.

[0053] The learning device 905 etc. generates a learning model using the identified defect type data and a data set of image data or image data of a ROI (Region Of Interest) selected within the image data as training data (step 1103), and stores the learning model in the learning model storage unit 906 (step 1104).

[0054] The setting field 1003 of the GUI screen shown in Fig. 10 allows the user to set an unknown contaminant as the type of contaminant. If an unknown contaminant is selected, it is possible to perform EDS analysis to identify the element of the unknown contaminant later, and generate training data using information on metals and non-metals identified by the EDS analysis. In the computer system 901, for example, an inspection recipe can be generated based on the setting of the unknown contaminant and the position information at that time, and the recipe can be stored in a predetermined storage medium, thereby generating training data based on accurate element identification. Furthermore, when an unknown foreign particle is selected on the GUI screen, an inspection recipe may be generated that selectively performs EDS analysis.

[0055] Furthermore, a setting field 1003 allows the selection of a boundary of a film that is not a defect. The end 1004 of the film is displayed in the beveled portion, but its shape is not uniform. The fact that this end 1004 of the film is reflected in the image makes comparative inspection difficult. However, since the end 1004 of the film is not a foreign object or a scratch, it is possible to improve the reliability of the estimation by learning that this structure is normal.

[0056] 10, an example has been described in which ROI 1006 is set by pointer 1005 that is moved by operating a pointing device or the like, and a defect type within the ROI is set, but labeling may also be performed by classifying the image data itself, as exemplified in Fig. 12. GUI screen 1201 is provided with a left column 1205 that displays a plurality of thumbnails 1204 each of which is a set of bevel image 1202 and supplementary information 1203 of the bevel image, and a right column 1208 that is provided with input columns 1206 and 1207 for each defect type.

[0057] A user (operator) can update the learning data by looking at the bevel image 1202, determining the presence or absence of foreign matter, the type of foreign matter, the presence or absence of scratches, the presence or absence of film peeling, etc., and using a pointing device or the like to move the thumbnail 1204 to the input field for the corresponding defect type in the right field 1208. The input unit 904 inputs the bevel image 1202 included in the thumbnail 1204, generates a data set in which the defect type in the input field into which the thumbnail 1204 has been input is output, and uses this data set as training data for the learning device 905.

[0058] By making an estimation using the learning model generated through the above-described learning process, it becomes possible to determine the presence or absence of foreign matter on the bevel. Note that a learning model may be prepared for each manufacturing process. This is because the number of boundaries (number of multilayer films) included in the bevel portion varies depending on the manufacturing process, and therefore, highly accurate estimation can be achieved by preparing a model according to the manufacturing process (model according to the number of films). Furthermore, highly accurate estimation may be performed by using process information as input data in addition to the bevel image.

[0059] <Regulatory treatment of foreign matter, etc.> FIG. 13 is a flowchart for explaining a process (step) of estimating foreign matter, etc. using the generated learning model. First, as shown in FIG. 8, an observation device (scanning electron microscope) 101 is used to acquire an image along the bevel (step 1301).

[0060] Next, the foreign substance defect estimation unit 907 executes estimation processing using the learning device 905 obtained in the learning process (step 1302). Specifically, the foreign substance defect estimation unit 907 illustrated in FIG. 9 reads out a learning model learned by the learning device 905 and stored in the learning model storage unit 906, and executes estimation processing using the learning model. Based on process information input from the input / output device 903, the foreign substance defect estimation unit 907 reads out a learning model stored in association with the process information from the learning model storage unit 906. When using a learning model that uses process information as input data, the foreign substance defect estimation unit 907 executes estimation processing by acquiring the input process information and a bevel image.

[0061] The foreign substance / defect estimation unit 907 outputs the estimation result to at least one of the estimation result storage unit 908 and a display device provided in the input / output device 903 (step 1303).

[0062] 9, it is possible to detect foreign matter on the bevel without using a reference image for comparison inspection. As the learning device, for example, a neural network, a regression tree, a Bayesian classifier, etc. can be used.

[0063] <Elemental analysis system> An elemental analysis system 1400 (see FIG. 14) including an EDS analyzer attached to the observation device (scanning electron microscope) 101 exemplified in FIG. 1 and one or more computer systems communicably connected to the EDS analyzer can perform map analysis (area analysis) within the field of view of the scanning electron microscope. Specifically, the elemental analysis system 1400 divides the field of view into one or more predetermined pixels (unit areas) and measures the X-ray intensity of each pixel using an X-ray detector to obtain elemental map data (information on the distribution of elements).

[0064] Elemental map data is data containing information on the two-dimensional distribution of elements, including two-dimensional positions (coordinates) and information on the X-ray intensity at each position. Furthermore, by converting the X-ray intensity at each position into the concentration of the element at each position, elemental map data can be obtained that indicates the position on the sample and the concentration of the element at each position. Elemental map data is obtained for each element.

[0065] As described above, depending on the material, foreign particles adhering to the bevel may cause damage to semiconductor elements formed in subsequent processes. On the other hand, element identification based on X-ray spectrum analysis requires a relatively long processing time compared to image formation based on detection of secondary electrons or backscattered electrons. Therefore, this embodiment proposes an element analysis system (defect inspection system) 1400 that can quickly identify foreign particles that may cause damage to semiconductor elements.

[0066] FIG. 14 is a diagram showing an example of the configuration of a system (elemental analysis system) 1400 that derives elemental map data based on image data obtained by an image acquisition tool. The elemental analysis system 1400 shown in FIG. 14 is a system that estimates at least one of the elements contained in a sample and the element distribution from data obtained by an image acquisition tool (observation device (scanning electron microscope) 101). The elemental analysis system 1400 includes a computer system 901 and a calculation module (not shown: for example, a module including an input unit 904, a learning device 905, and an elemental information estimation unit 1406) executed by the computer system 901. The computer system 901 includes a learning device 905 that outputs at least one of the elements and element distribution contained in a sample wafer 108. The learning device 905 has previously performed learning using training data that receives an image obtained by the image acquisition tool as input and outputs at least one of element and element distribution information obtained by the elemental analysis system 1400. The calculation module is a module that inputs image data obtained by the image acquisition tool to the learning device 905 and outputs at least one of element and element distribution information.

[0067] Although images formed based on the detection of charged particles (especially backscattered electrons) obtained by irradiating a charged particle beam are inferior to elemental maps obtained by an EDS analyzer (X-ray analyzer 1405), differences in elemental composition are expressed as brightness differences (contrast). Furthermore, images formed based on the detection of charged particles can be obtained in a shorter time than X-ray spectrum analysis using an EDS analyzer. Therefore, by inputting images obtained based on the detection of charged particles (or features extracted from the images) and performing estimation using a learning machine trained using training data in which elemental information and elemental maps obtained by an EDS analyzer or the like are output, it becomes possible to quickly identify the elements of a sample.

[0068] 14, in the learning process (step) of the learning model, image data is generated by an image processing device 1403 based on a signal output from an electron detector 1402, and X-rays detected by an X-ray detector 1404 are subjected to EDS analysis by an X-ray analysis device 1405, thereby generating data to be input to an input unit 904 of a computer system 901. Furthermore, the computer system 901 causes a learner 905 to learn using the data set of image data and X-ray analysis results as training data.

[0069] In the estimation process (step), the elemental information estimation unit 1406 estimates elemental information from input image data using the learning model stored in the learning model storage unit 906. The elemental information is, for example, information on contained elements identified by EDS analysis or element distribution information.

[0070] The elemental analysis system 1400 having the above-described configuration and functions enables high-speed elemental analysis. Furthermore, by generating a learning model trained using training data that inputs elemental map information and SEM images (images formed based on the detection of secondary electrons and backscattered electrons) and outputs elemental information of interest, and inputting the elemental map information, elemental information, and SEM images into the learning model, the labor involved in labeling during learning can be simplified. Preparing such a labeling learning model allows automatic updating of the learning model without manual labeling. Furthermore, by configuring the system to selectively perform EDS analysis depending on the type of defect or foreign particle estimated based on machine learning, inspection time can be reduced.

[0071] <Labeling process> FIG. 15 is a flowchart for explaining the process of automatically performing labeling (labeling process).

[0072] Using an elemental analysis system 1400 such as the one shown in FIG. 14, the field of view of the electron beam is positioned at a plurality of positions on the bevel portion, and electron microscope images are acquired and EDS analysis is performed (step 1501).

[0073] Element information of a defect to be treated as a DOI (Defect of Interest) is set in advance via the input / output device 903. When the element to be treated as a DOI is detected by the X-ray analysis device 1405, the input unit 905 performs labeling of the electron microscope image (step 1502).

[0074] The computer system 901 generates training data based on the labeled electron microscope image (step 1503), trains the learning device 905 using the training data, and stores the generated training model in the training model memory unit 906 (step 1504).

[0075] By performing estimation using the learning model generated as described above, it becomes possible to detect the DOI or the coordinates (field of view position) containing the DOI without performing X-ray analysis, which takes a relatively long time. Note that labeling may be performed not only on elements that can be DOI, but also on other elements that can be detected by the X-ray analysis device 1405.

[0076] Furthermore, datasets of elemental maps and electron microscope images may be used as training data. By generating a learning machine capable of estimating elemental maps, it becomes possible to identify the size and position of DOIs contained within the field of view.

[0077] When estimation is performed using the learner 905, EDS analysis can be selectively performed when the DOI detection accuracy is low or when it is estimated that a DOI is clearly included (when accuracy is high), thereby making it possible to evaluate whether the estimation using machine learning was performed properly. That is, for example, when the DOI detection accuracy is low (when it is lower than the first threshold), re-learning is necessary to improve the estimation accuracy through learning. Also, when the DOI detection accuracy is high (when it is higher than the second threshold (> the first threshold)), re-learning can be performed to further improve the estimation accuracy through learning.

[0078] <Relearning process> FIG. 16 is a flowchart for explaining details of the re-learning process in which re-learning is performed as necessary during the estimation process using the learning device 905. After starting, the electron beam scan control unit 202 first moves the field of view of the electron microscope so that the field of view is positioned on the bevel (step 1601).

[0079] Next, the detector control unit 203 generates an image based on the beam scanning (step 1602 ), and inputs the generated image to the learning device 905 via the image processing device 1403 and the input unit 904 .

[0080] Then, the elemental information estimation unit 1406 performs a process of estimating foreign matter, etc. (determining the presence or absence of foreign matter, etc.) using the learning device 905 (step 1603). The learning device 905 used here is assumed to have undergone appropriate learning in advance.

[0081] Furthermore, the elemental information estimation unit 1406 evaluates an index value such as accuracy output from the learning device 905 at this time (step 1604). If the accuracy is equal to or greater than (or higher than) a predetermined value (second threshold), or equal to or less than (or lower than) a predetermined value (first threshold (<second threshold)) (Yes in step 1605), the process proceeds to step 1606. Also, for example, if the index value is between the first threshold and the second threshold (No in step 1605), the process proceeds to step 1609.

[0082] In step 1606, elemental analysis is performed using the X-ray analyzer 1405 for additional learning. If the accuracy is high, the set of electron microscope image and elemental information is considered suitable as training data. On the other hand, if the accuracy is low, it is considered that unknown foreign matter or defects are contained. By selectively performing elemental analysis when certain conditions are met, additional learning can be performed to create a learning machine capable of more reliable estimation while reducing the time required for elemental analysis. Since analysis using an elemental analyzer takes a considerable amount of time compared to electron microscope image formation, the system that automatically executes the process illustrated in FIG. 16 can perform appropriate re-learning of the learning machine while suppressing an increase in inspection time. Note that the learning machine 905 can be configured to estimate multiple types of objects that may be present in the field of view, such as foreign matter, scratches, peeling film, and film edges, and if the accuracy of these estimation objects is low, it can be determined that an unknown object is contained in the field of view and selectively perform elemental analysis.

[0083] Next, the learning device 905 generates training data from the elemental analysis results (element names, element maps, etc.) and the data set of electron microscope images (step 1607), and then re-trains the learning device 905 using the generated training data (step 1608). Note that, as a predetermined accuracy condition, for example, training data may be selectively generated when a specific element (e.g., a metal that affects subsequent processes) is detected. Furthermore, for example, to enable manual assistance, training data may be generated by displaying a GUI screen such as that shown in FIG. 10 together with the elemental analysis information and selecting the type of foreign matter or artifact other than foreign matter. By displaying the elemental map and electron microscope image together on the display device (the display screen of the input / output device 903) according to this difference learning process, it becomes possible to identify artifacts other than critical foreign matter such as metallic foreign matter. Using the data set of the artifacts and defect types as training data makes it possible to generate a learning device 905 with excellent ability to distinguish between critical metallic foreign matter and other artifacts.

[0084] A system programmed to automatically repeat steps 1601 to 1608 for multiple inspection points can improve the discrimination function of the learning device while efficiently inspecting for foreign objects, etc. (step 1609 → step 1601).

[0085] Unlike the wafer surface, the bevel portion is an inclined surface, and therefore the height may vary depending on the field of view position. When the height varies, the focusing conditions of the electron beam change depending on the field of view position. Since changes in the focusing conditions result in changes in image quality, multiple models may be prepared according to the focusing conditions or position information stored in association with the height of the bevel portion, and estimation processing may be performed based on the selection of a learning model according to the focusing conditions or position information.

[0086] <Automated labeling> FIG. 17 is a flowchart for explaining an example of improving the work efficiency of the learning process (step) by automating the labeling work of DOI (Defect of Interest) and Nuisance using the elemental analysis results.

[0087] First, the electron beam scan control unit 202 moves the field of view of the observation device (electron microscope) 101 so that the field of view is positioned on the bevel (step 1701). Next, the detector control unit 203 generates an image based on the beam scanning (step 1702).

[0088] The defect detection unit 208 executes a defect candidate detection process (step 1703). The defect candidate detection process may be executed using the learning device 905. When using the learning device 905, the defect candidate detection process is executed by inputting the acquired image into the learning device 905. When using the learning device 905, it is necessary that the learning device 905 has been subjected to appropriate learning in advance. In this case, the time required for elemental analysis can be reduced by limiting defect candidates that require detailed analysis. The higher the learning accuracy, the more accurately defect candidates can be limited, thereby reducing the time required for elemental analysis.

[0089] Next, the elemental analysis system 1400 performs elemental analysis on the detected defect candidates (step 1704). If the result of the elemental analysis is DOI, the elemental analysis system 1400 labels it as DOI (step 1706), and if it is Nuisance, it labels it as Nuisance (step 1707). The elemental analysis results include the coordinates and regions of the elemental analysis, so by matching these with the SEM image, the elementally analyzed regions and elements can be automatically labeled on the SEM image. By labeling using the elemental analysis results, more accurate and stable labeling results can be expected than if an operator judges DOI / Nuisance based on experience alone, using the SEM image.

[0090] The learning device 905 performs learning by using the labeling results necessary for learning (step 1709). As described above, by using the elemental analysis results, the labeling work in the learning process (step) can be performed automatically and with high accuracy. [Explanation of symbols]

[0091] 101 Observation Device 102 Control section 103 Storage section 104 Arithmetic section 105 External storage medium input / output section 106 User interface control section 107 Network Interface Unit 108 sample wafer 109 Movable Stage 110 Electron source 111 Detection equipment 112 Deflector 113 Input / Output Terminal 201 Stage control unit 202 Electron beam scan control unit 203 Detector control unit 204 Image storage unit 205 Processing parameter storage unit 206 Observation coordinate memory unit 207 Observation coordinate azimuth angle derivation part 208 Defect detection unit 209 Defect Image Classification Unit 210 Image processing section 301~305 Detectors 401 Samples 402 imaging field of view 403 images 404 Area with angle R 405 images 601 Observation Coordinates 602 Semiconductor wafer center coordinates 603 straight line 604 Azimuth 608 scan rotation angle 701~705 Images taken without scan rotation 706~710 Images taken after setting the scan rotation angle 900 Foreign object detection system 901 Computer Systems 902 SEM image storage unit 903 Input / Output Devices 904 Input section 905 Learning Machine 906 Learning Model Memory Unit 907 Foreign matter defect estimation department 908 Estimation result storage unit 1400 Elemental Analysis System 1402 Electron Detector 1403 Image Processing Device 1404 X-ray detector 1405 X-ray analyzer 1406 Elemental Information Estimation Department

Claims

1. An inspection system that estimates at least one of elements contained in a sample and a distribution of elements from data obtained by an image acquisition tool, comprising: a computer system and a computing module executed by the computer system, the computer system includes a learning device that outputs at least one of elements contained in the sample and an element distribution; the learning device has previously performed learning using training data in which image data obtained by an image acquisition tool is input and element distribution information obtained by an X-ray analysis device is output; the calculation module inputs the image data obtained by the image acquisition tool to the learning device, and outputs element distribution information; the inspection system further comprises an image acquisition tool communicatively connected to the computer system; The image acquisition tool includes: An X-ray detector that detects X-rays emitted from the sample when the sample is irradiated with a beam; and an X-ray analyzer that identifies elements contained in the sample based on the output of the X-ray detector. Equipped with the image acquisition tool acquires image data of the sample based on a signal output from an electron detector that detects electrons emitted from the sample by the beam irradiation; the X-ray analysis device performs EDS analysis on the X-rays detected by the X-ray detector; the sample is a semiconductor wafer; the image acquisition tool selectively performs an analysis using an X-ray analysis device depending on the type of foreign matter attached to the semiconductor wafer or the type of damage formed on the semiconductor wafer; the computer system outputs the element distribution information using a learning model corresponding to the manufacturing process of the semiconductor wafer. Inspection system.

2. In claim 1, The computer system receives image data from the image acquisition tool and elemental information from the X-ray analysis device, and performs a labeling process on the received image data using the received elemental information.

3. In claim 1, An inspection system in which the learning device performs learning using teacher data including manufacturing process information of the semiconductor wafer, and the calculation module outputs at least one of the element and the element distribution information by inputting the manufacturing process information of the sample to the learning device.

4. In claim 1, The image acquisition tool outputs at least one of the element and the element distribution information of the foreign material attached on the bevel of the edge of the semiconductor wafer.

5. In claim 1, An inspection system wherein the image acquisition tool acquires the image data by imaging the sample at an angle to the sample.

6. In claim 3, The computer system retrains the learning device when an index value of an estimation result of at least one of the element and the element distribution information satisfies a predetermined threshold condition.

7. In claim 6, The computer system retrains the learner using training data generated using a predetermined artifact.

8. A non-transitory computer-readable medium storing a computer program for causing a computer to execute a process for estimating at least one of elements and a distribution of elements contained in a sample based on data obtained by an image acquisition tool, the process comprising: The computer program comprises: A process of performing learning using training data in which an image obtained by an image acquisition tool is used as input and element distribution information obtained by an X-ray analysis device is used as output; and using the results of the learning, outputting element distribution information included in other image data obtained by the image acquisition tool; the computer includes an image acquisition tool communicatively connected to the computer; The image acquisition tool includes: An X-ray detector that detects X-rays emitted from the sample when the sample is irradiated with a beam; and an X-ray analyzer that identifies elements contained in the sample based on the output of the X-ray detector. Equipped with the image acquisition tool acquires image data of the sample based on a signal output from an electron detector that detects electrons emitted from the sample by the beam irradiation; the X-ray analysis device performs EDS analysis on the X-rays detected by the X-ray detector; the computer program executes a process of performing the learning using the image data and the result of the EDS analysis as the training data; the sample is a semiconductor wafer; the image acquisition tool selectively performs an analysis using an X-ray analysis device depending on the type of foreign matter attached to the semiconductor wafer or the type of damage formed on the semiconductor wafer; the computer program executes a process of outputting the element distribution information by using a learning model corresponding to the manufacturing process of the semiconductor wafer. Non-transitory computer-readable medium.

Images