CN110672645A - Boundary feature extraction method and device of semiconductor structure - Google Patents
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- CN110672645A CN110672645A CN201910836958.7A CN201910836958A CN110672645A CN 110672645 A CN110672645 A CN 110672645A CN 201910836958 A CN201910836958 A CN 201910836958A CN 110672645 A CN110672645 A CN 110672645A
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Abstract
The application discloses a boundary feature extraction method and a boundary feature extraction device of a semiconductor structure. The measuring method comprises the steps of obtaining a section image of a semiconductor structure output by a transmission electron microscope, wherein the section image comprises at least one graph; and recognizing the boundary of the figure according to the sectional image, wherein the step of recognizing the boundary of the figure comprises the following steps: and obtaining a second derivative processing result according to the gray value of each pixel in the section image, and obtaining a plurality of boundary point coordinates on the boundary based on the second derivative processing result. The section image of the semiconductor structure output by the transmission electron microscope is obtained, and second derivative processing is carried out on the section image, so that a clear graph boundary is obtained, and high-precision measurement of the semiconductor structure is guaranteed.
Description
Technical Field
The present invention relates to semiconductor technology, and more particularly, to a method and an apparatus for extracting boundary features of a semiconductor structure.
Background
As semiconductor devices are miniaturized, the critical dimension of the semiconductor devices has been reduced to the nanometer level, which means that the critical dimension will determine the performance of the semiconductor devices, and therefore, it has become an essential link to accurately measure the critical dimension and grasp the variation degree of the critical dimension on the nanometer level.
In the prior art, a general simple shape of a semiconductor device or a single and large critical dimension of the semiconductor device can be measured by a measuring tool, but for a complex and small critical dimension structure (such as 3D NAND), the measuring tool in the prior art cannot meet the requirement, mainly because:
1. the measurement needs manual observation, and the difference degree is difficult to distinguish manually for the structure with complexity and smaller key size, so that the problem of low precision exists.
2. Because manual observation is needed, continuous automatic measurement for multiple times cannot be realized, and the problems of low efficiency and poor reliability exist.
Therefore, it is desirable to further improve the boundary feature extraction method of the semiconductor structure and the apparatus thereof, thereby improving the measurement accuracy, efficiency, and reliability.
Disclosure of Invention
The present invention is directed to an improved method and apparatus for extracting boundary features of a semiconductor structure to solve the above-mentioned problems.
According to an aspect of the present invention, there is provided a boundary feature extraction method for a semiconductor structure, including: acquiring a section image of the semiconductor structure output by a transmission electron microscope, wherein the section image comprises at least one figure; and identifying a boundary of the figure from the cross-sectional image, wherein the step of identifying the boundary of the figure comprises: and obtaining a second derivative processing result according to the gray value of each pixel in the section image, and obtaining a plurality of boundary point coordinates on the boundary based on the second derivative processing result.
Preferably, the step of obtaining a second derivative processing result according to the gray-scale value of each pixel in the sectional image comprises: obtaining a function of a plurality of unit lengths and the gray value according to the gray value of each row of pixels of the section image; and performing second derivative processing on each function to obtain the second derivative of each function respectively, and identifying the boundary point of each row of pixels based on the second derivative of each function.
Preferably, the step of identifying the boundary point of each row of pixels comprises: acquiring preset parameters; and judging whether the plurality of values of each second-order derivative are larger than a preset parameter.
Preferably, before performing the second derivative processing on each function, the step of obtaining a second derivative processing result according to the gray-scale value of each pixel in the cross-sectional image further includes performing high-frequency filtering processing on each function.
Preferably, the method further comprises obtaining an intermediate image according to each function after the high-frequency filtering processing.
Preferably, obtaining a boundary image from each of said second derivatives is further included.
According to another aspect of the present invention, there is provided a boundary feature extraction apparatus of a semiconductor structure, including: the acquisition module is used for acquiring a section image of the semiconductor structure output by a transmission electron microscope, and the section image comprises at least one graph; and an identification module for identifying a boundary of the figure from the cross-sectional image, wherein the identification module comprises: the processing unit is used for obtaining a second derivative processing result according to the gray value of each pixel in the section image; and a coordinate conversion unit that obtains a plurality of boundary point coordinates on the boundary based on the second derivative processing result.
Preferably, the processing unit includes: the function generation subunit is used for obtaining a plurality of functions of unit length and the gray value according to the gray value of each row of pixels of the section image; and the function calculating subunit is used for performing second derivative processing on each function, respectively obtaining the second derivative of each function, and identifying the boundary point of each row of pixels based on the second derivative of each function.
Preferably, the processing unit further comprises: and the frequency filtering subunit is used for performing high-frequency filtering processing on each function.
Preferably, the image processing device further comprises an intermediate image conversion module for obtaining an intermediate image according to each function after the high-frequency filtering processing.
Preferably, the image processing device further comprises a boundary image conversion module for obtaining a boundary image according to each second-order derivative.
Preferably, the boundary feature extraction means automatically performs the steps of acquiring a cross-sectional image of the semiconductor structure output by a transmission electron microscope and recognizing the boundary of the pattern from the cross-sectional image.
According to the method and the device for extracting the boundary characteristics of the semiconductor structure, the detail characteristics of the semiconductor structure can be obtained by acquiring the section image of the semiconductor structure output by the transmission electron microscope, and the pixel size is generally from a few nanometers to dozens of nanometers and even below the nanometer level; the purpose of identifying the boundary of the graph is achieved by obtaining a second derivative processing result for each gray value in the section image and obtaining a plurality of boundary point coordinates on the graph boundary based on the second derivative processing result. The abrupt change of the image gray scale can be highlighted after the second derivative processing, and the area with slowly changing gray scale is not emphasized, so that the boundary positioning capability is stronger; after the clear boundary is obtained, the semiconductor structure is measured based on the boundary of the graph, so that the measurement precision, efficiency and reliability are improved.
Compared with the conventional convolution method, the boundary feature extraction method and the device of the semiconductor structure of the embodiment of the invention realize full-automatic measurement by searching the feature curve in the transmission electron microscope image, and the scheme is feasible and high in reliability because the pixel size of the transmission electron microscope image is in a nanometer level, and the object is easy to find by mistake or miss by the convolution method, and the scheme of the invention is to perform second derivative processing on each gray value of the sectional image, so that the object is difficult to find by mistake or miss.
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The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings.
Fig. 1 is a schematic diagram illustrating steps of a boundary feature extraction method for a semiconductor structure according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of the step S02 in fig. 1.
Fig. 3 shows a schematic step diagram of S021 in fig. 2.
Fig. 4 shows a schematic cross-sectional view of a semiconductor structure according to an embodiment of the invention.
Fig. 5 shows a schematic diagram of the local gray matrix in fig. 4.
Fig. 6 shows a functional image diagram of fig. 5.
Fig. 7 shows a schematic diagram of the functional image after high-frequency filtering of fig. 6.
Fig. 8 shows a schematic diagram of the function of fig. 7 after second derivative processing.
Fig. 9 shows a schematic diagram of the intermediate image after high frequency filtering.
FIG. 10 shows a schematic of a boundary image of an embodiment of the invention.
Fig. 11 is a schematic structural diagram illustrating a boundary feature extraction apparatus of a semiconductor structure according to an embodiment of the present invention.
Fig. 12 is a schematic diagram showing the structure of the boundary identifying module in fig. 11.
FIG. 13 is a schematic diagram showing the structure of the processing unit in FIG. 12
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. Like elements in the various figures are denoted by like reference numerals. For purposes of clarity, the various features in the drawings are not necessarily drawn to scale. In addition, certain well known components may not be shown. For simplicity, the semiconductor structure obtained after several steps can be described in one figure.
It will be understood that when a layer or region is referred to as being "on" or "over" another layer or region in describing the structure of the device, it can be directly on the other layer or region or intervening layers or regions may also be present. And, if the device is turned over, that layer, region, or regions would be "under" or "beneath" another layer, region, or regions.
If for the purpose of describing the situation directly on another layer, another area, the expression "directly on … …" or "on … … and adjacent thereto" will be used herein.
In the following description, numerous specific details of the invention, such as structure, materials, dimensions, processing techniques and techniques of the devices are described in order to provide a more thorough understanding of the invention. However, as will be understood by those skilled in the art, the present invention may be practiced without these specific details.
The present invention may be embodied in various forms, some examples of which are described below.
Fig. 1 shows a schematic step diagram of a method for measuring a semiconductor structure according to an embodiment of the present invention.
In step S01, a cross-sectional image of the semiconductor structure output by a Transmission Electron Microscope (TEM) is acquired. The sectional image is a gray scale image obtained directly or indirectly and includes at least one graphic. In the present embodiment, a cross-sectional image of a channel pillar of a 3D memory device is taken as an example to be specifically described, and as shown in fig. 4, the cross-sectional image of the semiconductor structure includes a plurality of cross-sectional patterns of the channel pillar.
However, the present embodiment is not limited thereto, and those skilled in the art can select the cross-sectional images of other portions of the semiconductor structure as required.
In step S02, the boundary of the figure is identified from the sectional image. As the critical dimensions of semiconductor devices have been reduced to the nanometer scale, one step that is important for measuring device critical dimensions is to obtain precise pattern boundaries to provide accurate parameters for subsequent critical dimension measurements. As shown in fig. 2, the boundary of the figure may be recognized by the following steps S021 to S022.
In step S021, a second derivative processing result is obtained according to the gray-level value of each pixel in the cross-sectional image. The second derivative processing of the section image can highlight the abrupt change of the image gray level, and does not emphasize the area with slowly changing gray level, so that the boundary positioning capability is stronger. As shown in fig. 3, the second derivative processing result can be obtained by the following steps S021a to S021 c.
In step S021a, a plurality of unit lengths and gray-scale values are obtained according to the gray-scale value of each row of pixels of the cross-sectional image. For clarity, the present embodiment only cuts a part of the cross-sectional image, the cut part refers to the white dashed frame part of fig. 4, and the following description will describe in detail the step of extracting the boundary of the graphics in the white dashed frame part.
As shown in fig. 5, the image of the white virtual frame portion is composed of m × n pixels, each pixel has a corresponding gray value, each line of gray values is scanned to obtain a plurality of functions of unit length and gray value, for example, scanning a line of pixels in the virtual frame in fig. 5 to obtain a function as shown in fig. 6, wherein the unit of the abscissa x is nm, and the ordinate y represents the gray value of the unit length corresponding to the line of pixels.
In some preferred embodiments, the gray value of each pixel may be expanded, for example, by the same factor, thereby expanding the difference between the corresponding gray values. However, the embodiment of the present invention is not limited to this, and those skilled in the art may perform different processing on the gray value corresponding to each pixel as needed, for example, perform inverse processing on the gray value, or expand the gray values of different regions by different multiples, so as to expand the difference between the corresponding gray values while preserving the graph characteristic curve, which is beneficial to improving the sensitivity.
In step S021b, high frequency filtering is performed for each function. In this step, the function in fig. 6 is, for example, high-frequency filtered to obtain the function shown in fig. 7. After high frequency filtering, noise in the image can be filtered out.
In step S021c, a second derivative process is performed on each of the functions to obtain a second derivative of each of the functions, and boundary points of each row of pixels are identified based on the second derivative of each of the functions. In this step, for example, the function in fig. 7 is first subjected to second derivative processing to obtain a second derivative as shown in fig. 8. And then acquiring preset parameters, and judging whether a plurality of values of each second derivative are larger than the preset parameters or not, wherein pixels corresponding to the derivative values larger than the preset parameters are boundary points.
In this embodiment, the preset values can be adjusted accordingly as needed, and the number of the obtained boundary points is related to the setting of the preset values.
In step S022, a plurality of boundary point coordinates on the boundary are obtained based on the second derivative processing result. In this step, it is necessary to calculate boundary point coordinates for each line of pixels, and a plurality of boundary point coordinates constitute a boundary curve of the figure.
In step S03, an intermediate image is obtained from each function subjected to high-frequency filtering, as shown in fig. 9. In this step, a plurality of function values in each function, each corresponding to a row of pixels, need to be converted into a gray scale, respectively.
In step S04, a boundary image is obtained from each second-order derivative, as shown in fig. 10. In this step, a plurality of derivative values in each derivative, each corresponding to a row of pixels, need to be converted into gray, respectively. Because the boundary 10 is composed of boundary points and has a large difference value with the gray values of the pixels on the two sides of the boundary, a clear and accurate graph boundary can be obtained according to the boundary image.
After a clear and accurate graph boundary is obtained, the semiconductor structure is measured based on the graph boundary. For example, critical dimensions of the trench pillar may be measured. In some other embodiments, other critical dimensions of the semiconductor structure may also be measured according to different cross-sectional images, such as the dimensions of the sidewall of the cylindrical structure in a 3D memory, the dimensions of the gate Stack structure (N-O Stack), and the dimensions of each layer (PONO) in the trench pillar.
Fig. 11 shows a schematic structural diagram of a boundary feature extraction apparatus of a semiconductor structure according to an embodiment of the present invention, fig. 12 shows a schematic structural diagram of a boundary identification module in fig. 11, and fig. 13 shows a schematic structural diagram of a processing unit in fig. 12.
As shown in fig. 11 to 13, the boundary feature extraction apparatus of the semiconductor structure according to the embodiment of the present invention includes: an acquisition module 110, a recognition module 120, an intermediate image conversion module 130, and a boundary image conversion module 140.
The acquiring module 110 is used for acquiring a cross-sectional image of the semiconductor structure output by the transmission electron microscope, wherein the cross-sectional image comprises at least one pattern. The recognition module 120 is configured to recognize a boundary of the graph according to the cross-sectional image. The intermediate image conversion module 130 is configured to obtain an intermediate image according to each of the functions after the high-frequency filtering process. The boundary image conversion module 140 is configured to obtain a boundary image according to each of the second derivatives.
The recognition module 120 includes a processing unit 121 and a coordinate conversion unit 122. The processing unit 121 is configured to obtain a second derivative processing result according to the gray-level value of each pixel in the cross-sectional image. The coordinate conversion unit 122 obtains a plurality of boundary point coordinates on the boundary based on the second derivative processing result.
The processing unit 121 includes a function generation subunit 1211, a frequency filtering subunit 1212, and a function calculation subunit 1213. The function generation subunit 1211 is configured to obtain a plurality of functions of unit lengths and the gray-scale values according to the gray-scale value of each row of pixels of the cross-sectional image. The frequency filtering subunit 1212 is configured to perform a second derivative processing on the cross-sectional image, and further perform a high-frequency filtering processing on each of the functions. The function calculating subunit 1213 is configured to perform second derivative processing on each of the functions, obtain second derivatives of each of the functions, and identify boundary points of each row of pixels based on the second derivatives of each of the functions.
The boundary feature extraction device of the semiconductor structure of the embodiment of the invention automatically completes the steps of acquiring the section image of the semiconductor structure output by the transmission electron microscope and identifying the boundary of the graph according to the section image, thereby realizing the boundary feature extraction method, and the description is omitted here.
According to the method and the device for extracting the boundary characteristics of the semiconductor structure, the detail characteristics of the semiconductor structure can be obtained by acquiring the section image of the semiconductor structure output by the transmission electron microscope, and the pixel size is generally from a few nanometers to dozens of nanometers; the purpose of identifying the boundary of the graph is achieved by obtaining a second derivative processing result for each gray value in the section image and obtaining a plurality of boundary point coordinates on the graph boundary based on the second derivative processing result. The abrupt change of the image gray scale can be highlighted after the second derivative processing, and the area with slowly changing gray scale is not emphasized, so that the boundary positioning capability is stronger; after the clear boundary is obtained, the semiconductor structure is measured based on the boundary of the graph, so that the measurement precision is improved.
The boundary feature extraction device is automatically completed in batches, so that the efficiency and the reliability of measurement are improved.
Compared with the conventional convolution method, the boundary feature extraction method and the device of the semiconductor structure of the embodiment of the invention realize full-automatic measurement by searching the feature curve in the transmission electron microscope image, and the scheme is feasible and high in reliability because the pixel size of the transmission electron microscope image is in a nanometer level, and the object is easy to find by mistake or miss by the convolution method, and the scheme of the invention is to perform second derivative processing on each gray value of the sectional image, so that the object is difficult to find by mistake or miss.
In the above description, the technical details of patterning, etching, and the like of each layer are not described in detail. It will be appreciated by those skilled in the art that layers, regions, etc. of the desired shape may be formed by various technical means. In addition, in order to form the same structure, those skilled in the art can also design a method which is not exactly the same as the method described above. In addition, although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination.
The embodiments of the present invention have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the invention, and these alternatives and modifications are intended to fall within the scope of the invention.
Claims (12)
1. A method for extracting boundary features of a semiconductor structure is characterized by comprising the following steps:
acquiring a section image of the semiconductor structure output by a transmission electron microscope, wherein the section image comprises at least one figure; and
identifying a boundary of the figure from the cross-sectional image,
wherein the step of identifying the boundary of the graph comprises: and obtaining a second derivative processing result according to the gray value of each pixel in the section image, and obtaining a plurality of boundary point coordinates on the boundary based on the second derivative processing result.
2. The boundary feature extraction method according to claim 1, wherein the step of obtaining a second derivative processing result according to the gray-scale value of each pixel in the cross-sectional image comprises:
obtaining a function of a plurality of unit lengths and the gray value according to the gray value of each row of pixels of the section image; and
and performing second derivative processing on each function to respectively obtain the second derivative of each function, and identifying the boundary point of each row of pixels based on the second derivative of each function.
3. The boundary feature extraction method according to claim 2, wherein the step of identifying the boundary point of each line of pixels comprises:
acquiring preset parameters; and
and judging whether the plurality of numerical values of each second-order derivative are larger than a preset parameter.
4. The boundary feature extraction method according to claim 2, wherein before performing the second derivative processing on each function, the step of obtaining a second derivative processing result according to the gray-scale value of each pixel in the cross-sectional image further includes performing high-frequency filtering processing on each function.
5. The boundary feature extraction method according to claim 4, further comprising obtaining an intermediate image from each of the functions subjected to the high-frequency filtering process.
6. The boundary feature extraction method according to claim 2, further comprising obtaining a boundary image from each of the second-order derivatives.
7. An apparatus for extracting boundary features of a semiconductor structure, comprising:
the acquisition module is used for acquiring a section image of the semiconductor structure output by a transmission electron microscope, and the section image comprises at least one graph; and
an identification module for identifying a boundary of the graph from the cross-sectional image,
wherein the identification module comprises: the processing unit is used for obtaining a second derivative processing result according to the gray value of each pixel in the section image; and a coordinate conversion unit that obtains a plurality of boundary point coordinates on the boundary based on the second derivative processing result.
8. The boundary feature extraction device according to claim 7, wherein the processing unit includes:
the function generation subunit is used for obtaining a plurality of functions of unit length and the gray value according to the gray value of each row of pixels of the section image; and
and the function calculating subunit is used for performing second-order derivative processing on each function, respectively obtaining the second-order derivative of each function, and identifying the boundary point of each row of pixels based on the second-order derivative of each function.
9. The boundary feature extraction device according to claim 8, wherein the processing unit further includes:
and the frequency filtering subunit is used for performing high-frequency filtering processing on each function.
10. The boundary feature extraction device according to claim 9, further comprising an intermediate image conversion module configured to obtain an intermediate image from each of the functions subjected to the high-frequency filtering processing.
11. The boundary feature extraction device according to claim 8, further comprising a boundary image conversion module configured to obtain a boundary image according to each second-order derivative.
12. The boundary feature extraction apparatus according to any one of claims 7 to 11, wherein the boundary feature extraction device automatically performs the steps of acquiring a cross-sectional image of the semiconductor structure output by a transmission electron microscope and recognizing a boundary of the pattern from the cross-sectional image.
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