CN113223976B - Microscopic test piece preparation method, device and recording medium - Google Patents

Abstract

The present invention provides a method, device and recording medium for preparing a microscopic specimen. The method includes the following steps: identifying a plurality of test samples in a test image, and selecting a target sample from the test samples according to the identification result; transferring the target sample to a sample support, and obtaining a top view of the transferred target sample to identify the center point of the target sample in the top view; and moving the position of a mask used for cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample to cut the target sample.

Description

Microscopic specimen preparation method, device and recording medium

Technical Field

The embodiments of the present disclosure are related to a method, device and recording medium for preparing a microscopic specimen.

Background Art

In the semiconductor manufacturing process, it is necessary to quantitatively analyze the concentration of specific elements (such as phosphorus, arsenic, boron, etc.) for surface micro-contamination, doping and ion implantation of semiconductor components, so as to control or adjust process parameters and maintain the stability of components/epitaxial growth. For example, in the epitaxy process of silicon phosphide, it is necessary to quantify phosphorus.

Current quantitative analysis techniques include atom probe tomography (APT) and transmission electron microscope (TEM). However, when preparing microscopic specimens for analysis, test personnel need to select samples based on experience, and when cutting samples, test personnel also need to identify images with the naked eye to adjust the electron beam mask used for cutting.

Summary of the invention

The embodiment of the present disclosure provides a method for preparing a microscopic specimen, which is applicable to an electronic device with a processor. The method includes the following steps: identifying multiple test samples in a test image, and selecting a target sample from the test samples according to the identification result; transferring the target sample to a sample support, and obtaining a top view of the transferred target sample to identify the center point of the target sample in the top view; and moving the position of a mask used for cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample to cut the target sample.

The embodiments of the present disclosure provide a device for preparing a microscopic specimen, which includes an image acquisition device, a transfer device, a cutting device, and a processor. The image acquisition device is used to acquire test images of a plurality of test samples. The transfer device is used to transfer the test samples to a sample support. The cutting device is used to cut the test samples. The processor is coupled to the image acquisition device, the transfer device, and the cutting device, and is configured to identify the test samples in the test images, and select a target sample from the test samples according to the identification result, transfer the target sample to the sample support using the transfer device, and acquire a top view of the target sample after transfer using the image acquisition device, so as to identify the center point of the target sample in the top view, and according to the displacement between the center point and the cutting pattern of the target sample, move the position of the mask used for cutting the target sample, so as to cut the target sample using the cutting device.

An embodiment of the present disclosure provides a computer-readable recording medium that records a program, which is loaded by a processor to execute: identifying multiple test samples in a test image, and selecting a target sample from the test samples based on the identification results; transferring the target sample to a sample support, and obtaining a top view of the target sample after the transfer to identify the center point of the target sample in the top view; and moving the position of a mask used for cutting the target sample based on the displacement between the center point and the cutting pattern of the target sample to cut the target sample.

BRIEF DESCRIPTION OF THE DRAWINGS

When read in conjunction with the accompanying drawings, various aspects of the present disclosure are best understood from the following detailed description. It should be noted that, in accordance with standard practice in the industry, various features are not drawn to scale. In fact, the size of various features may be arbitrarily increased or reduced for clarity of discussion.

FIG. 1 is a block diagram of a microscope test piece device according to an embodiment of the present disclosure.

FIG. 2 is a flow chart of a method for preparing and analyzing a microscopic specimen according to an embodiment of the present disclosure.

FIG. 3 is an example of identifying and selecting test samples according to an embodiment of the present disclosure.

FIG. 4 is an example of identifying and selecting test samples according to an embodiment of the present disclosure.

5A to 5C are examples of focus adjustment according to an embodiment of the present disclosure.

FIG. 6 is an example of sample cutting according to an embodiment of the present disclosure.

FIG. 7 is an example of the relationship between the learning sample size and the cutting pattern according to an embodiment of the present disclosure.

8A and 8B are examples of corrected cutting patterns according to an embodiment of the present disclosure.

Description of Figure Numbers:

10: Microscope specimen device

12: Image acquisition equipment

14: Transfer device

16: Cutting device

18: Processor

30, 40, 52-56, 60: Image

32, 34, 36: Transistors

42, 44: Area

62: Sample

70, 80: Top view

72: Target sample

74, 82: Cutting pattern

C: Center point

d: diameter

X: Displacement

S202～S206: Steps

DETAILED DESCRIPTION

The following disclosure provides many different embodiments or examples for implementing the different features of the provided themes. Specific examples of components and arrangements are described below to simplify the present disclosure. Of course, these components and arrangements are only examples and are not intended to be restrictive. For example, in the following description, the formation of a first feature above or on a second feature may include an embodiment in which the first feature and the second feature are directly contacted, and may also include an embodiment in which an additional feature may be formed between the first feature and the second feature so that the first feature and the second feature may not be directly contacted. In addition, the present disclosure may repeat reference numerals and/or letters in various examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and/or configurations discussed.

Additionally, for ease of description, spatially relative terms such as "below," "beneath," "lower," "above," "upper," etc. may be used herein to describe the relationship of one element or feature to another element or feature as illustrated in the figures. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

FIG1 is a block diagram of a microscopic test piece device according to an embodiment of the present disclosure. Referring to FIG1 , the microscopic test piece device 10 of the present embodiment includes an image acquisition device 12, a transfer device 14, a cutting device 16, and a processor 18 coupled to the image acquisition device 12, the transfer device 14, and the cutting device 16, and its functions are described as follows:

The image acquisition device 12 is, for example, a transmission electron microscope (TEM) or a scanning electron microscope (SEM), which projects an accelerated and focused electron beam onto a sample or scans the sample surface to generate an image of the sample surface, with a resolution of, for example, 0.1 nanometers.

The transfer device 14 is, for example, a micromanipulator, which can transfer the sample to the sample support. The sample is, for example, a strip or sheet obtained by trenching, cutting, etching, etc., using a focused ion beam (FIB) to perform operations such as grooving, cutting, etching, etc. on a component to be tested (such as a semiconductor component). The material of the sample support is, for example, tungsten, carbon, platinum, etc., which is not limited here. In one embodiment, the transfer device 14 welds the sample sheet to the sample support and cuts it into strips, so as to subsequently make a needle-shaped test sample.

The cutting device 16 is, for example, a focused ion beam system, which uses a high-energy gallium ion beam (or helium ion beam, neon ion beam) to cut the test sample from top to bottom to produce a nanostructure. The cutting device 16 uses a patterned ion beam mask to shield the focused ion beam to retain the shielded portion of the test sample and remove the unshielded portion, thereby cutting the test sample into a desired shape (such as a needle tip). In one embodiment, the mask is, for example, dug out a donut-shaped pattern, and its inner diameter is, for example, greater than or equal to the diameter of the sample to be produced. That is, the mask can protect samples within the inner diameter range from being cut, and only cut samples within the range from the inner diameter to the outer diameter.

The processor 16 is, for example, a central processing unit (CPU), a programmable general-purpose or special-purpose microprocessor, a digital signal processor (DSP), a programmable controller, an application specific integrated circuit (ASIC), a programmable logic device (PLD), other similar devices or a combination thereof, and is used to execute instructions stored in a computer-readable recording medium such as a random access memory (RAM), a read-only memory (ROM), a flash memory, a hard disk, etc., to implement the microscope specimen preparation method of the embodiment of the present disclosure.

Specifically, FIG2 is a flow chart of a method for preparing and analyzing a microscopic specimen according to an embodiment of the present disclosure. Please refer to FIG1 and FIG2 simultaneously. The method of this embodiment is applicable to the microscopic specimen preparation device 10 shown in FIG1. The detailed steps of the method of this embodiment are described below with reference to various components in the microscopic specimen preparation device 10.

In step S202 , the processor 18 of the microscope specimen preparation device 10 identifies a plurality of test samples in the test image, and selects a target sample from the test samples according to the identification result.

In some embodiments, the processor 18 controls the image acquisition device 12 to acquire a test image of a component to be tested (e.g., a semiconductor component), and uses a trained or learned learning model to identify a test sample in the test image. The learning model is established, for example, using a machine learning algorithm, and by inputting sample images of different test samples and their corresponding sample parameters, the learning model can learn the relationship between the sample images of these test samples and the corresponding sample parameters. The sample parameters include at least one of a sample yield, a sample size, or a sample shape, which are not limited here.

For example, FIG. 3 is an example of identifying and selecting test samples according to an embodiment of the present disclosure. Referring to FIG. 3 , image 30 is a SEM image of a Fin Field-effect transistor (FinFET) array. Among them, the epitaxy (EPI) layer (i.e., the black shadow area at the top) of the source/drain of transistors 32 and 34 is relatively thin, and its yield is also low (e.g., below 30%), while the epitaxy layer of transistor 36 is relatively thick and full, and its yield can reach more than 80%. In the embodiment of the present disclosure, by inputting a large number of sample images (such as images of transistors 32, 34, and 36) and their corresponding sample parameters (such as yield, size, shape) and other information into the learning model, the learning model can recognize the pattern of each sample in the test image and automatically judge the quality of the sample. Taking image 30 as an example, the learning model can automatically judge that the yield of transistor 36 is better, and then select transistor 36 as the sample for subsequent preparation of the test piece.

In some embodiments, the processor 18 may further identify the type of the test sample according to the image contrast of each test sample in the test image, and select the target sample according to the identification result.

For example, FIG. 4 is an example of identifying and selecting test samples according to an embodiment of the present disclosure. Referring to FIG. 4 , image 40 is also a SEM image of a FinFET array. The image contrast of the sample (black column) in region 42 is low, and the type of the sample can be identified as a P-type Metal-Oxide-Semiconductor (PMOS) field effect transistor. In contrast, the image contrast of the sample (black column) in region 44 is high, and the type of the sample can be identified as an N-type Metal-Oxide-Semiconductor (NMOS) field effect transistor.

In step S204, the processor 18 controls the transfer device 14 to transfer the target sample selected in step S202 to the sample support, and uses the image acquisition device 12 to obtain a top view of the target sample after transfer, so as to identify the center point of the target sample in the top view. The processor 18, for example, uses the cutting device 16 to perform operations such as trenching, cutting, etching, etc. on the component to be tested to obtain a long strip or thin sheet including the test sample (such as the transistor 36 in FIG. 3), and transfers and welds it to the sample support.

In some embodiments, when the processor 18 uses the image acquisition device 12 to acquire a top view of the target sample, the processor 18 adjusts the focus of the image acquisition device 12 so that the focus is located at the center point of the target sample to obtain a clear sample image.

For example, FIG5A to FIG5C are examples of focus adjustment according to an embodiment of the present disclosure. Referring to FIG5A to FIG5C, images 52 to 56 are images obtained by SEM photographing the sample from above the sample, and during the image photographing, the SEM adjusts the focus of the acquired image by, for example, grayscale/color blur comparison, so that the captured image gradually changes from the originally blurry image 52 to a clearer image 54, and then to an image 56 with a clearly visible center point C.

In step S206 , the processor 18 moves the position of the mask used for cutting the target sample according to the displacement between the identified center point of the target sample and the cutting pattern of the target sample, so as to cut the target sample.

In some embodiments, when preparing a sample, a protective layer with a thickness of about 10 nanometers to 1 micron can be deposited on the surface of the sample by evaporation or sputtering, for example, to protect the surface of the sample from damage. The material of the protective layer includes, for example, gold, platinum, silicon, etc., which are not limited here. In the process of cutting the sample, the height and width of the sample (including the protective layer) will gradually decrease. If the cutting is excessive so that the protective layer is too thin and loses its effectiveness, scratches may appear on the surface of the sample, resulting in a reduction in the yield. In this regard, in some embodiments, the processor 18 uses the image acquisition device 12 to obtain a side view of the target sample to calculate the ratio of the height to the width of the target sample in the side view, so that when the calculated ratio is less than a default value, the cutting of the target sample is stopped to avoid the above-mentioned defects. The default value is, for example, between 1 and 2, which are not limited here.

For example, FIG6 is an example of sample cutting according to an embodiment of the present disclosure. Referring to FIG6 , image 60 is an image obtained by using a SEM to photograph the side of a sample 62 during the sample cutting process of the microscopic specimen preparation device. During the process of cutting the sample 62, the height and width of the sample will gradually decrease, and when the calculated ratio is less than a default value (e.g., 2), the microscopic specimen preparation device will automatically stop cutting the target sample to ensure that the sample surface is not damaged.

In some embodiments, the processor 18 learns the relationship between the sample size in the image and the cutting pattern used to mill the sample, so that when it is determined that the cutting pattern deviates from the center point of the sample, the mask of the cutting device 16 can be automatically moved to correct the position of the cutting pattern.

For example, FIG. 7 is an example of the relationship between the learning sample size and the cutting pattern according to the embodiment of the present disclosure. Referring to FIG. 7 , the microscopic specimen preparation device of the embodiment of the present disclosure, for example, reads the scale of the top view 70 of the target sample 72 and the diameter d of the target sample 72 in the top view 70 , and adjusts the position of the mask accordingly so that the inner diameter of the cutting pattern 74 (the donut shape shown in FIG. 7 ) projected by the mask on the top view 70 is larger than the diameter d of the target sample 72 , thereby ensuring that the target sample 72 is not damaged during the cutting process. Based on the read scale, the microscopic specimen preparation device, for example, calculates the conversion relationship between the displacement (number of pixels) of the cutting pattern 74 in the top view 70 and the moving distance (nanometers) of the mask, so as to correctly move the position of the mask when cutting the target sample 72 , so that the cutting pattern 74 can be aligned with the target sample 72 (center point alignment).

FIG8A and FIG8B are examples of correcting the cutting pattern according to the embodiment of the present disclosure. Referring to FIG8A, the microscopic specimen preparation device of the embodiment of the present disclosure, for example, identifies the center point C of the target sample from the top view 80 of the target sample, and calculates the displacement X between the center point C of the target sample and the cutting pattern 82 according to the position of the cutting pattern 82 projected on the top view 80 by the mask used to cut the target sample. Next, referring to FIG8B, the microscopic specimen preparation device calculates the moving distance of the mask corresponding to the displacement X according to the conversion relationship between the displacement (number of pixels) of the cutting pattern and the moving distance (nanometers) of the mask learned previously (as shown in FIG7), and uses it to move the mask so that the cutting pattern 82 projected on the top view 80 by the mask after the movement is aligned with the center point C of the target sample, thereby completing the correction of the cutting pattern 82.

Through the method, the present disclosure provides the following advantages: (1) by automatically identifying and selecting samples in the test image through a pre-established learning model, samples with specifications that meet the set test conditions can be screened out for test piece preparation; (2) by machine learning the conversion relationship between the sample center point displacement and the mask movement distance, the position of the electron beam mask can be determined by identifying the sample center point.

According to some embodiments, a method for preparing a microscopic specimen is provided, which is applicable to an electronic device having a processor. The method includes the following steps: identifying a plurality of test samples in a test image, and selecting a target sample from the test samples according to the identification result; transferring the target sample to a sample support, and obtaining a top view of the transferred target sample to identify the center point of the target sample in the top view; and moving the position of a mask used for cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample to cut the target sample.

According to some embodiments, a microscopic specimen preparation device is provided, which includes an image acquisition device, a transfer device, a cutting device and a processor. The image acquisition device is used to acquire test images of multiple test samples. The transfer device is used to transfer the test samples to a sample support. The cutting device is used to cut the test samples. The processor is coupled to the image acquisition device, the transfer device and the cutting device, and is configured to identify the test samples in the test images, and select a target sample from the test samples according to the identification result, transfer the target sample to the sample support using the transfer device, and acquire a top view of the target sample after transfer using the image acquisition device to identify the center point of the target sample in the top view, and according to the displacement between the center point and the cutting pattern of the target sample, move the position of the mask used for cutting the target sample, so as to cut the target sample using the cutting device.

According to some embodiments, a computer-readable recording medium is provided, recording a program, which is loaded by a processor to execute: identifying multiple test samples in a test image, and selecting a target sample from the test samples based on the identification results; transferring the target sample to a sample support, and obtaining a top view of the target sample after the transfer to identify the center point of the target sample in the top view; and moving the position of a mask used for cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample to cut the target sample.

The foregoing summarizes the features of several embodiments so that those skilled in the art can better understand the various aspects of the present disclosure. Those skilled in the art will appreciate that they can easily use the present disclosure as a basis for designing or modifying other processes and structures for performing the same purposes and/or obtaining the same advantages of the embodiments introduced herein. Those skilled in the art will also recognize that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions, and modifications herein without departing from the spirit and scope of the present disclosure.

Claims (10)

1. A method of preparing a microscopic test strip suitable for use in an electronic device having a processor, wherein the method comprises the steps of:

identifying a plurality of test samples in a test image, and selecting a target sample from the test samples according to an identification result;

transferring the target sample to a sample support column, and obtaining a top view of the transferred target sample so as to identify a center point of the target sample in the top view; and

The position of a mask used to cut the target sample is moved to cut the target sample according to the displacement between the center point and the cutting pattern of the target sample.

2. The method of claim 1, wherein the step of identifying a plurality of test samples in the test image and selecting the target sample from the test samples based on the identification result comprises:

identifying the test samples in the test image by using a learning model, and selecting the target samples according to the sample parameters corresponding to each identified test sample, wherein

The learning model is established by using a machine learning algorithm, and learns the relation between sample images of different test samples and corresponding sample parameters, wherein the sample parameters comprise at least one of sample yield, sample size or sample shape.

3. The method of claim 1, wherein the step of identifying a plurality of test samples in the test image and selecting the target sample from the test samples based on the identification result comprises:

Identifying the type of the test sample according to the image contrast of each test sample, and selecting the target sample according to the identification result.

4. The method of claim 1, wherein in the step of cutting the target sample, further comprising:

And obtaining a side view of the target sample, calculating the ratio of the height to the width of the target sample in the side view, and stopping cutting the target sample when the ratio is smaller than a default value.

5. The method of claim 1, wherein prior to the step of moving the position of a mask used in cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample, further comprising:

reading the dimension of the top view and the diameter of the target sample in the top view;

Adjusting the position of the mask such that the inner diameter of the cutting pattern projected by the mask on the top view is greater than the diameter of the target sample; and

According to the scale, calculating a conversion relation between the displacement of the cutting pattern in the test image and the moving distance of the mask, so as to move the position of the mask when cutting the target sample.

6. A microscopic test piece preparing apparatus, comprising:

the image acquisition equipment acquires test images of a plurality of test samples;

a transfer device for transferring the test sample to a sample column;

a cutting device for cutting the test sample; and

A processor coupled to the image acquisition device, the transfer device, and the cutting device, configured to:

Identifying the test sample in the test image, and selecting a target sample from the test samples according to the identification result;

transferring the target sample to a sample support column by utilizing the transferring device, and acquiring a top view of the transferred target sample by utilizing the image acquisition equipment so as to identify a center point of the target sample in the top view; and

And moving the position of a mask used for cutting the target sample according to the displacement between the center point and the cutting pattern of the target sample so as to cut the target sample by using the cutting device.

7. The microscopic test strip preparation device of claim 6, wherein the processor recognizes the test samples in the test image using a learning model and selects the target samples according to sample parameters corresponding to each of the recognized test samples, wherein the learning model is established by the processor using a machine learning algorithm and learns a relationship between sample images of different plurality of test samples and corresponding sample parameters, wherein the sample parameters include at least one of sample yield, sample size, or sample shape.

8. The microscopic test strip preparation apparatus of claim 6, wherein the processor further obtains a side view of the target sample with the image obtaining device to calculate a ratio of a height to a width of the target sample in the side view, and stops cutting the target sample with the cutting device when the ratio is less than a default value.

9. The microscopic patch preparation apparatus of claim 6, wherein the processor further reads a dimension of the top view and a diameter of the target sample in the top view, and adjusts a position of the mask such that an inner diameter of the cutting pattern projected by the mask on the top view is larger than the diameter of the target sample, and calculates a conversion relationship between a displacement of the cutting pattern in the test image and a moving distance of the mask according to the dimension to move a position of the mask when cutting of the target sample is performed.

10. A computer-readable recording medium recording a program, wherein the program is loaded by a processor to execute:

identifying a plurality of test samples in a test image, and selecting a target sample from the test samples according to an identification result;

transferring the target sample to a sample support column, and obtaining a top view of the transferred target sample so as to identify a center point of the target sample in the top view; and

The position of a mask used to cut the target sample is moved to cut the target sample according to the displacement between the center point and the cutting pattern of the target sample.