Information processing systems, information processing methods, and programs

Abstract

To provide an information processing system.SOLUTION: An information processing system comprises: at least one or more control units. The at least one or more control units set an imaged area of a second imaged image on the basis of a defective part of a sample which is included in a first imaged image. The second imaged image is an imaged image that has a higher magnification ratio than the first imaged image. An imaging instruction of the second imaged image in the imaged area of the sample is transmitted to a scan type electron microscope 110.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method, and a program.

Background Art

[0002] There is an etch pit method as a method for evaluating the position of crystal defects (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, since the etch pit method is a chemical treatment, there is a problem that the crystal is destroyed.

Means for Solving the Problems

[0005] According to one aspect of the present invention, an information processing system is provided. This information processing system has at least one or more control units. The at least one or more control units set an imaging area of a second imaging image based on a defective part of a sample included in a first imaging image. The second imaging image is an imaging image with a higher magnification than the first imaging image. An imaging instruction of the second imaging image in the imaging area of the sample is transmitted to a scanning electron microscope.

Brief Description of the Drawings

[0006] [Figure 1] FIG. 1 is a diagram showing an example of the system configuration of an information processing system 1000. [Figure 2] FIG. 2 is a diagram showing an example of the hardware configuration of a PC 100. [Figure 3] FIG. 3 is a diagram showing an example of a CL image and a reflected electron image of the same area. [Figure 4] Figure 4 is a flowchart showing an example of the overall processing performed by the control unit 210. [Figure 5] Figure 5 shows an example of a CL image of a GaN crystal. [Figure 6] Figure 6 is a flowchart showing an example of a defect extraction process. [Figure 7] Figure 7 shows an example of a binarized CL image. [Figure 8] Figure 8 shows an example of an extracted defect. [Figure 9] Figure 9 shows an example of a generated imaging rectangle. [Figure 10] Figure 10 is a flowchart showing an example of the imaging area setting process. [Figure 11] Figure 11 shows an example of each generated imaging rectangle. [Figure 12] Figure 12 shows an example of a captured backscattered electron image. [Modes for carrying out the invention]

[0007] Embodiments of the present invention will be described below with reference to the drawings. The various features shown in the embodiments below can be combined with each other.

[0008] In this specification, "part" may include, for example, hardware resources implemented by circuits in a broad sense, and the information processing of software that can be specifically realized by these hardware resources. Furthermore, Embodiment 1 handles various types of information, which are represented by high and low signal values ​​as a set of binary bits consisting of 0s and 1s, and communication and calculations can be performed on circuits in a broad sense.

[0009] Furthermore, a circuit in a broad sense is a circuit realized by combining at least a suitable combination of circuits, circuits, processors, and memory. In other words, it includes application-specific integrated circuits (ASICs), programmable logic devices (for example, simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)), etc.

[0010] <Embodiment 1> 1. Information Processing System Figure 1 shows an example of the system configuration of the information processing system 1000. The information processing system 1000 includes a PC (Personal Computer) 100 and a scanning electron microscope 110 as its system configuration. The PC 100 performs information processing according to Embodiment 1, including the modified examples shown below. The scanning electron microscope 110 focuses an electron beam based on control from the PC 100 and irradiates the target as an electron beam, and observes the target by detecting backscattered electron images, etc., emitted from the sample. Embodiment 1 will be explained using a GaN crystal as an example sample. The PC 100 and the scanning electron microscope 110 may be connected via a wired connection or via a wireless connection. Figure 1 shows an example in which the PC 100 and the scanning electron microscope 110 are connected via a wired connection.

[0011] The information processing system described in the claims may consist of one device or multiple devices. If it consists of one device, an example of the information processing system described in the claims is PC100. If it consists of multiple devices, an example of the information processing system described in the claims may be a cloud system that provides the functions of PC100, or a combination of PC100 and scanning electron microscope 110. If the example of the information processing system is PC100, an example of one or more control units is the control unit 210 described later. If the example of the information processing system consists of multiple devices, an example of one or more control units is one or more control units for each of the devices.

[0012] 2. Hardware Configuration Figure 2 shows an example of the hardware configuration of PC100. As shown in Figure 2, the PC100's hardware configuration includes a control unit 210, a storage unit 220, an input unit 230, a display unit 240, and a communication unit 250. The control unit 210 is a CPU (Central Processing Unit) or the like, and controls the entire PC100. The storage unit 220 is one of the following: HDD (Hard Disk Drive), ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drive), or any combination thereof, and stores programs, data used by the control unit 210 when executing processing based on the programs. The control unit 210 executes processing based on the programs stored in the storage unit 220, thereby realizing the functions of the PC100 and the processing of the flowcharts shown in Figures 4, 6, and 10, which will be described later. The input unit 230 is a mouse and / or keyboard or the like, and inputs the operator's operation information of the PC100 to the control unit 210. The display unit 240 is a display or the like that displays the results of processing by the control unit 210 and data transmitted from the scanning electron microscope 110. The communication unit 250 connects the PC 100 to the network and manages communication with other devices.

[0013] 3. Information Processing The information processing of Embodiment 1 will be described below. (CL image and reflection electron image) The control unit 210 enables the discrimination of defects, such as the detection of defects (crystal defects, dislocations) distributed in the GaN crystal, to be evaluated for a large area. The processing of Embodiment 1 is based on the fact that the black dots shown in the cathodoluminescence image (hereinafter referred to as the CL image) and the defect contrast shown in the reflection electron image exist at the same location, and the type of dislocation can be discriminated from the defect contrast of the reflection electron image. FIG. 3 is a diagram showing an example of the CL image and the reflection electron image of the same region. Circles 310 and 320 are dislocation locations, respectively. The CL image is an example of the first captured image. The reflection electron image is an example of the second captured image.

[0014] (Outline of information processing) The control unit 210 sets the imaging region of the reflection electron image based on the defect portions of the GaN crystal included in the CL image. The reflection electron image is a captured image with a higher magnification than the CL image. The control unit 210 transmits an imaging instruction for the reflection electron image in the imaging region of the GaN crystal to the scanning electron microscope. By the control unit 210 executing such processing, it becomes possible to obtain a reflection electron image capable of evaluating the defect portion nondestructively.

[0015] (Details of information processing) FIG. 4 is a flowchart showing an example of the overall processing executed by the control unit 210. In step S401, the control unit 210 transmits a control signal to the scanning electron microscope 110 to capture a CL image of the GaN crystal surface. The scanning electron microscope 110 captures an image at a low magnification (e.g., magnification of 1k times) so that a plurality of black dots are shown for high-magnification imaging of a plurality of defect portions (e.g., about 100 defect portions) based on the control signal. The CL image captured by the scanning electron microscope 110 is transmitted to the PC 100 and stored in the storage unit 220 etc. of the PC 100. FIG. 5 is a diagram showing an example of the CL image of the GaN crystal. The black dots in the CL image correspond to defect portions (crystal defects, dislocations). As shown in FIG. 5, there are a plurality of defect portions in the GaN crystal. Then, the control unit 210 receives the CL image captured by the scanning electron microscope from the scanning electron microscope.

[0016] In step S402, the control unit 210 performs image processing on the CL image and extracts defects from the CL image. Figure 6 is a flowchart showing an example of the defect extraction process. In step S610, the control unit 210 normalizes the luminance value for the CL image. In step S620, the control unit 210 performs binarization of the CL image based on its brightness value. Figure 7 shows an example of a binarized CL image.

[0017] In step S630, the control unit 210 extracts defects from the binarized image based on the binarized image. Figure 8 shows an example of the extracted defects.

[0018] In step S640, the control unit 210 generates an imaging rectangle centered on the extracted defect. The imaging rectangle represents the imaging range of the high-magnification backscattered electron image to be captured later (for example, a rectangle with a ratio of 1280 × 960). Figure 9 shows an example of a generated imaging rectangle. As shown in Figure 9, since one imaging rectangle is assigned to one defect, multiple imaging rectangles are drawn overlapping. In other words, the overlap rate is high. As a result, unnecessary imaging occurs. Therefore, the control unit 210 searches for an imaging rectangle that reduces the overlap rate while capturing many black spots (increasing the coverage rate).

[0019] Returning to the explanation of Figure 4. In step S403, the control unit 210 searches for and generates an imaging rectangle that satisfies a low overlap rate and a high coverage rate. That is, the control unit 210 sets the imaging region for the backscattered electron image based on the sample defects contained in the received CL image. That is, the control unit 210 sets the imaging region based on the sample defects extracted from the CL image. Figure 10 is a flowchart showing an example of the imaging area setting process. In step S1001, the control unit 210 performs an initialization process as follows: Target number of images = number of defects The maximum evaluation value = a large value (for example, a predetermined threshold) Let's assume that.

[0020] In step S1002, the control unit 210 calculates the imaging positions (imaging position list) for the target number of images. More specifically, the control unit 210 calculates the imaging position list by performing k-means clustering on the coordinates of the defective area.

[0021] In step S1003, the control unit 210 generates imaging rectangles from the coordinates of each imaging position included in the calculated imaging position list. Figure 11 shows an example of each generated imaging rectangle. As will be described later, the control unit 210 sets the imaging area based on the rectangular region surrounding the defect in the sample.

[0022] In step S1004, the control unit 210 determines the evaluation value of the imaging position list. The control unit 210 determines the evaluation value based on the following formula. • Evaluation value = overlap rate - weight × coverage rate • Overlap rate = Number of sunspots covered by two or more imaging rectangles / Number of sunspots included in the CL image • Weight = predetermined value Coverage = Number of black spots included in the imaging rectangle / Number of black spots included in the CL image

[0023] In step S1005, the control unit 210 compares the calculated evaluation value of the imaging position list with the minimum evaluation value and determines whether the evaluation value is smaller than the minimum evaluation value. If the control unit 210 determines that the evaluation value is smaller than the minimum evaluation value, it proceeds to step S1006. If the control unit 210 determines that the evaluation value is not smaller than the minimum evaluation value, it proceeds to step S1007.

[0024] In step S1006, the control unit 210 assigns 0 to the counter and assigns the evaluation value to the evaluation minimum value. In step S1007, the control unit 210 increments the value of the counter by 1.

[0025] In step S1008, the control unit 210 determines whether the counter value is equal to or greater than a threshold. If the control unit 210 determines that the counter value is equal to or greater than a threshold, it proceeds to step S1010. If the control unit 210 determines that the counter value is not equal to or greater than a threshold, it proceeds to step S1009.

[0026] In step S1009, the control unit 210 decreases the value of the target number of images by 1. In step S1010, the control unit 210 converts each imaging position coordinate included in the imaging position list into the position coordinate of the scanning electron microscope 110 stage. This process is an example of a process that converts the coordinate of the center of the imaging region on the GaN crystal into the coordinate of the scanning electron microscope stage. Note that the method for calculating the imaging position is not limited to the method shown in Figure 10. For example, the control unit 210 may use a counter to determine early termination, and if the minimum evaluation value is not updated even after repeating the process, it may terminate the repeated process on the grounds that no further improvement can be expected. The process described above is an example of the process by which the control unit 210 sets the imaging area based on the overlap rate and coverage rate of each rectangular area of ​​a plurality of defects. Furthermore, the process described above is also an example of the process by which the control unit 210 sets the imaging area such that the evaluation value based on the overlap rate and coverage rate is minimized.

[0027] Returning to the explanation of Figure 4. In step S404, the control unit 210 generates an automatic imaging instruction for the scanning electron microscope 110 from the imaging position list and imaging conditions (magnification, scan speed, resolution, focus, detector, etc.) stored in the storage unit 220, and transmits it to the scanning electron microscope 110. This process is an example of the process by which the control unit 210 transmits an instruction to the scanning electron microscope 110 to capture a backscattered electron image in the imaging area. Upon receiving the automatic imaging instruction, the scanning electron microscope 110 moves the stage to the position indicated in the imaging position list and performs imaging of a backscattered electron image at high magnification (e.g., 20kx). Figure 12 shows an example of an captured backscattered electron image. Because it is imaged at high magnification, it is configured to allow identification of the type of dislocation.

[0028] According to Embodiment 1, comprehensive, high-magnification imaging with minimal overlap can be performed on the defects in GaN to generate a backscattered electron image. Furthermore, the control unit 210 can determine the type of dislocation from the contrast of the defects in the captured backscattered electron image.

[0029] (Variation 1) A modified example of Embodiment 1 will now be described. In Embodiment 1, the control unit 210 extracted defective areas from the CL image through image processing. However, in Modification 1, the control unit 210 generates trained data by learning the CL image as input data and the defective areas contained in the CL image as output data. Then, the control unit 210 inputs the CL image to the trained data and obtains the defective areas as output data.

[0030] According to Modification 1, defective areas can be extracted from the CL image using machine learning.

[0031] (Modification 2) A modified example of Embodiment 1, Part 2, will now be described. In Embodiment 1, the control unit 210 generated an imaging position list by optimization based on an objective function. However, in Modification 2, the control unit 210 may learn a Sequence-to-Sequence type network that obtains an imaging position list from an imaging candidate position list, using the imaging candidate position list extracted based on the defect and the imaging position list obtained by advanced optimization described later as training datasets. Here, advanced optimization refers to considering the order of the imaging position list in the calculation of the evaluation value. For example, in order to reduce the amount of stage movement, the total stage movement distance is added to the evaluation value, as shown below. Evaluation value = Overlap rate - Weight × Coverage rate + Weight × Total stage travel distance

[0032] The imaging location list can also be generated by the modified example 2.

[0033] (Variation 3) A third modification of Embodiment 1 will be described. In Embodiment 1, the defect extraction process and the imaging position list calculation process were described separately. However, in Modification 3, the control unit 210 may be configured to learn an image-to-sequence network that performs both processes end-to-end.

[0034] The effects shown in Embodiment 1 can also be achieved by the embodiment of Modification 3.

[0035] <Note> The invention may be provided in the following embodiments.

[0036] (1) An information processing system comprising at least one control unit, wherein the at least one control unit sets an imaging region for a second imaging image based on a defect in a sample included in a first imaging image, the second imaging image is an imaging image with a higher magnification than the first imaging image, and transmits an imaging instruction to a scanning electron microscope for the second imaging image in the imaging region of the sample.

[0037] (2) An information processing system as described in (1) above, wherein at least one control unit receives the first image captured by a scanning electron microscope and sets the imaging region of the second image based on the defects of the sample included in the received first image.

[0038] (3) An information processing system according to (1) or (2) above, wherein at least one control unit performs image processing on the first captured image, extracts the defective portion from the first captured image, and sets the imaging region based on the extracted defective portion.

[0039] (4) An information processing system according to any one of (1) to (3) above, wherein at least one control unit is set to the imaging area based on a rectangular area surrounding the defective part of the sample.

[0040] (5) An information processing system as described in (4) above, wherein the sample has a plurality of defects, and at least one control unit sets the imaging area based on the overlap rate and coverage rate of each of the plurality of defects, wherein the overlap rate is the number of defects covered by two or more rectangular areas / the number of defects included in the first imaging image, and the coverage rate is the number of defects included in the rectangular area / the number of defects included in the first imaging image.

[0041] (6) An information processing system as described in (5) above, wherein at least one control unit sets the imaging area such that the evaluation value based on the overlap rate and the coverage rate is minimized.

[0042] (7) An information processing system according to any one of (1) to (6) above, wherein at least one control unit converts the coordinates of the center of the imaging area on the sample into the coordinates of the stage of the scanning electron microscope, and transmits an instruction to the scanning electron microscope to take the second image in the imaging area.

[0043] (8) An information processing system according to any one of (1) to (7) above, wherein the sample is a GaN crystal.

[0044] (9) An information processing system according to any one of (1) to (8) above, wherein the first image is a cathodoluminescence image and the second image is a backscattered electron image.

[0045] (10) An information processing method to be performed by an information processing system, comprising setting an imaging region for a second imaging image based on a defect portion of a sample included in a first imaging image, the second imaging image being an imaging image with a higher magnification than the first imaging image, and transmitting an imaging instruction for the second imaging image of the sample in the imaging region to a scanning electron microscope.

[0046] (11) A program that causes a computer to function as an information processing system as described in any one of (1) through (9) above. Of course, this is not always the case.

[0047] For example, it may be provided as a computer-readable, non-temporary storage medium for storing the aforementioned program. All or part of the embodiments and modifications described above may be implemented in any combination.

[0048] Finally, various embodiments of the present invention have been described, but these are presented as examples only and are not intended to limit the scope of the invention. Novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. Embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0049] 100: PC 110: Scanning electron microscope 210: Control Unit 220: Storage section 230: Input section 240: Display section 250: Communications Department 1000: Information Processing System

Claims

1. An information processing system, Having at least one control unit, The at least one control unit is Based on the defects in the sample included in the first image, the imaging region of the second image is set. The second captured image is a higher magnification image than the first captured image. An instruction to capture the second image of the sample in the imaging region is transmitted to the scanning electron microscope. The imaging area is set based on the rectangular region surrounding the defective part of the sample. The aforementioned sample has multiple defects, The imaging area is set based on the overlap rate and coverage rate of each of the rectangular regions of the plurality of defective parts. The overlap rate is the number of defective areas covered by two or more rectangular regions / the number of defective areas included in the first captured image. The coverage ratio is the number of defects included in the rectangular region / the number of defects included in the first captured image. Information processing system.

2. In the information processing system described in claim 1, The at least one control unit is The imaging region is set such that the evaluation value based on the overlap rate and the coverage rate is minimized. Information processing system.

3. An information processing system, Having at least one control unit, The at least one control unit is Based on the defects in the sample included in the first image, the imaging region of the second image is set. The second captured image is a higher magnification image than the first captured image. An instruction to capture the second image of the sample in the imaging region is transmitted to the scanning electron microscope. The first image described above is a cathodoluminescence image, The second image described above is a backscattered electron image. Information processing system.

4. In the information processing system described in claim 1, The at least one control unit is The first image captured by the scanning electron microscope is received, Based on the defects in the sample included in the received first image, the imaging region of the second image is set. Information processing system.

5. In the information processing system described in claim 1, The at least one control unit is Image processing is performed on the first captured image, the defective portion is extracted from the first captured image, and the imaging region is set based on the extracted defective portion. Information processing system.

6. In the information processing system described in claim 1, The at least one control unit is The coordinates of the center of the imaging region on the sample are converted to the coordinates of the scanning electron microscope stage, and an instruction to capture the second image in the imaging region is transmitted to the scanning electron microscope. Information processing system.

7. In the information processing system described in claim 1, The aforementioned sample is a GaN crystal. Information processing system.

8. An information processing method performed by an information processing system, Based on the defects in the sample included in the first image, the imaging region of the second image is set. The second captured image is a higher magnification image than the first captured image. An instruction to capture the second image of the sample in the imaging region is transmitted to the scanning electron microscope. The imaging area is set based on the rectangular region surrounding the defective part of the sample. The aforementioned sample has multiple defects, The imaging area is set based on the overlap rate and coverage rate of each of the rectangular regions of the plurality of defective parts. The overlap rate is the number of defective areas covered by two or more rectangular regions / the number of defective areas included in the first captured image. The coverage ratio is the number of defects included in the rectangular region / the number of defects included in the first captured image. Information processing methods.

9. In the information processing method described in Claim 8, The imaging region is set such that the evaluation value based on the overlap rate and the coverage rate is minimized. Information processing methods.

10. An information processing method performed by an information processing system, Based on the defects in the sample included in the first image, the imaging region of the second image is set. The second captured image is a higher magnification image than the first captured image. An instruction to capture the second image of the sample in the imaging region is transmitted to the scanning electron microscope. The first image described above is a cathodoluminescence image, The second image described above is a backscattered electron image. Information processing methods.

11. It is a program, A program for causing a computer to function as an information processing system according to any one of claims 1 to 7.

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