CN108109131B - Image processing of semiconductor devices - Google Patents
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- CN108109131B CN108109131B CN201611048584.5A CN201611048584A CN108109131B CN 108109131 B CN108109131 B CN 108109131B CN 201611048584 A CN201611048584 A CN 201611048584A CN 108109131 B CN108109131 B CN 108109131B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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Abstract
Embodiments of the present disclosure relate to image processing of semiconductor devices. A method of processing an image of a semiconductor device, comprising: segmenting the measurement image of the semiconductor device to determine a target region associated with the target structural element; refining the target region to determine a skeleton of the target region; and determining a plurality of points to be measured on the measurement profile of the target structural unit based on the gradient of variation in the intensity of the measurement image at a plurality of initial points on the skeleton.
Description
Technical Field
Embodiments of the present disclosure relate to image processing of inspection in a semiconductor device manufacturing process, and more particularly to sub-pixel edge inspection to obtain a measurement image of an inspected semiconductor device to determine errors caused by the manufacturing process.
Background
With the rapid development of semiconductor device manufacturing process technology, the critical dimension of semiconductor devices is continuously reduced, and the structural design is increasingly complex. In the fabrication process of integrated circuits, it is becoming an increasingly challenging challenge to determine the quality of the finally manufactured semiconductor devices and thus whether to comply with design requirements. More and more systematic errors and random errors affect the quality of the devices that are ultimately manufactured.
In the traditional method, part of boundary points of the semiconductor device in the acquired measurement image are determined manually, so that the measurement efficiency is seriously influenced, and the measurement error is high and the repeatability is poor. In addition, in the case of comparing with a design image to determine a process error, only a partial region is generally selected to be performed, the entire condition of the semiconductor device to be tested cannot be reflected, and a quantitative index cannot be provided.
Therefore, there is a need for a fast, accurate and efficient method of image inspection in modern integrated circuit manufacturing processes to confirm the quality of semiconductor device manufacture.
Disclosure of Invention
Embodiments of the present disclosure aim to provide methods and apparatus that at least partially address the above-mentioned disadvantages of the prior art and meet the requirements of modern integrated circuit manufacturing process inspection.
In a first aspect, a method for processing an image of a semiconductor device is provided. The method comprises the following steps: segmenting the measurement image of the semiconductor device to determine a target region associated with a target structural unit; refining the target region to determine a skeleton of the target region; and determining a plurality of points to be measured on the measurement profile of the target structural unit based on the gradient of variation in intensity of the measurement image at a plurality of initial points on the skeleton.
In a second aspect, an apparatus for processing an image of a semiconductor device is provided. The apparatus comprises: a segmentation component configured to segment a measurement image of the semiconductor device to determine a target region associated with a target structural unit; a refining component configured to refine the target region to determine a skeleton of the target region; and a first determination unit configured to determine a plurality of points to be measured on the measurement profile of the target structural unit based on a gradient of variation in intensity of the measurement image at a plurality of initial points on the skeleton.
In a third aspect, an apparatus for processing an image of a semiconductor device is provided. The device comprises a processing unit configured to: segmenting the measurement image of the semiconductor device to determine a target region associated with a target structural unit; refining the target region to determine a skeleton of the target region; and determining a plurality of points to be measured on the measurement profile of the target structural unit based on the gradient of variation in intensity of the measurement image at a plurality of initial points on the skeleton.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure, in which:
fig. 1 shows a flowchart of an image processing method of a semiconductor device according to an embodiment of the present disclosure;
fig. 2 shows a schematic diagram of an image of a semiconductor device according to an embodiment of the present disclosure;
FIG. 3 shows a schematic diagram of an interpolation method according to an embodiment of the present disclosure;
fig. 4 shows a schematic diagram of a method for determining a process error of a semiconductor device according to an embodiment of the present disclosure; and
fig. 5 shows a block diagram of an environment for processing an image of a semiconductor device according to an embodiment of the present disclosure.
Detailed Description
The principles and spirit of the present disclosure will be described with reference to a number of exemplary embodiments shown in the drawings. It should be understood that these embodiments are described merely to enable those skilled in the art to better understand and to implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way.
Fig. 1 shows a flow chart of an image processing method 100 of a semiconductor device according to an embodiment of the present disclosure. The term "semiconductor device" as used herein may refer to a discrete semiconductor device, an integrated circuit, or a portion thereof.
As shown in fig. 1, the method 100 begins at step 102. At step 102, the measurement image of the semiconductor device is segmented to determine a target region associated with the target structural unit. For example, the measurement image may be obtained by scanning the semiconductor device by a Scanning Electron Microscope (SEM). It will be appreciated that the measurement image may also be obtained by various other physical measurement methods, both now known and developed in the future, such as optical microscopy and electron microscopy, among others. In some embodiments, the measured image intensity is a gray scale map representation, in which case the intensity of each pixel point may also be referred to as gray scale.
The target structural element may be a structural element of interest of the semiconductor device, such as a dielectric strip, a metal line, or the like. As semiconductor processes continue to advance, the widths of these structural elements are gradually reduced, comparable to or smaller than the pixel size of the measurement equipment (e.g., SEM) used, and thus, means for determining the edges of sub-pixels is urgently needed.
The measurement image of the semiconductor device may be segmented using various image segmentation methods. One possible image segmentation method is a thresholding method. In the method, the measurement image is first converted into a binary image according to a predetermined threshold intensity, wherein the region having the first value in the binary image may be a target region and the region having the second value may be a background region.
Fig. 2 shows a schematic diagram of a measurement image 200 of a semiconductor device according to an embodiment of the present disclosure. As shown in fig. 2, the measurement image 200 includes a target region 202 and a background region 204, which may be obtained by step 102 in fig. 1.
Furthermore, to overcome noise in the measurement image and differences in contrast of different regions, in some embodiments, a blocking method may be used to implement the binarization operation. Specifically, the measurement image is first divided into a plurality of sub-images, which may be in the form of a matrix, for example. Then, a respective local threshold intensity is determined for each sub-image. These local threshold intensities may be determined, for example, by means of cluster analysis. Clustering analysis is a common unsupervised machine learning method, and is often used for threshold processing of images, and is not described herein again.
Then, the sub-images are respectively converted into binary sub-images based on the local threshold intensities. For example, for a sub-image, pixels with intensity values greater than the local threshold intensity may be defined as having a first intensity, while pixels with intensity values less than the local threshold intensity may be defined as having a second intensity. These binary sub-images are then combined to form a binary image.
With continued reference to FIG. 1, at step 104, the target region is refined to determine a skeleton of the target region. The thinning operation is an operation known in the art of image processing that is used to determine the skeleton, also called the central axis, of a particular region. Therefore, the detailed operation of step 104 is not described again. In fig. 2, a skeleton 206 is shown that results from the refinement operation in the target region 202.
Then, in step 106, a plurality of points to be measured on the measurement profile of the target structural unit are determined based on gradients of intensity changes of the measurement image at the plurality of points on the skeleton. The direction of the gradient of the intensity variation of the measurement image at a plurality of points on the skeleton 206, for example the direction 208, is shown in fig. 2. Based on the intensities of the plurality of pixels in the direction of the gradient, a plurality of points to be measured on the measurement profile of the target structural unit can be determined. The number of pixels in the direction of the gradient may be determined on a case-by-case basis, for example 2 in the case of fig. 3.
Fig. 3 shows a schematic diagram of a difference method 300 according to an embodiment of the present disclosure. In fig. 3, the horizontal axis represents a position in the image along a certain gradient direction, where 0 represents a pixel point at the skeleton; the vertical axis represents the intensity of the corresponding position. For example, points 302 and 304 represent two pixel points at positions 0 and 1, respectively, with position 0 representing the skeleton. The straight line 306 represents an interpolation function obtained by performing an interpolation operation between two pixel points. In this embodiment, linear interpolation is used. It will be appreciated that any suitable other interpolation function may also be used. The line 308 represents a predetermined threshold intensity, which may be set as the case may be. The threshold intensity may be a specific value or may be a ratio to the peak intensity (e.g., the intensity at 302 points). The intersection 310 of the straight lines 306 and 308 represents the point to be measured on the determined measurement profile. The coordinate location of the point is then determined based on its projected point 312. The distance between the proxel 312 and the origin (i.e., its position) may be defined as d. The point to be measured on the corresponding measurement profile can be determined by equation (1).
Wherein xiAnd yiRespectively represent coordinates, x 'of a corresponding point (origin in FIG. 3) on the skeleton'iAnd y'iRespectively representing the coordinates of the points to be measured on the determined measurement profile, and theta represents the gradient direction.
Mask fidelity analysis plays an increasingly important role in the lithographic processing of modern integrated circuit production. The fidelity of the mask is the basis and guarantee of the final quality and performance of the lithographic process during the production process. In recent years, with rapid development of semiconductor manufacturing process technology, the device size is continuously reduced, and meanwhile, the structural design of an integrated circuit device is gradually complicated. Therefore, the fidelity requirements between the design pattern for the mask and the measurement image of the final fabricated device obtained by a measurement device such as SEM become more and more stringent. The design pattern of the mask is typically stored in a graphical manner or may be converted to a graphical manner and is therefore referred to herein as the design image.
Fig. 4 shows a schematic diagram of a method for determining a process error of a semiconductor device according to an embodiment of the present disclosure. In fig. 4, the image 400 may represent a measurement image of the semiconductor device, a design image thereof, or a superposition of both. The measurement image and the design image of the semiconductor device may be identical in terms of magnification, direction, position, and the like. This may be accomplished by various methods known in the art or developed in the future, and the present disclosure is not limited thereto. Curve 402 may represent a design profile of a target building block in a design image, with the plurality of points represented by three different patterns (e.g., points 404, 406, and 408) representing a plurality of points to be measured on a measurement profile determined by the method shown in fig. 1. It will be appreciated that the curve 402 shown in fig. 4 is merely an example, and that the method of fig. 4 may also be applied to complex design patterns including horizontal lines, vertical lines, and diagonal lines. Further, the number of various points is also for example only, and not limiting.
As shown in fig. 4, a plurality of relevant points on the design contour may be determined in the design image based on the gradient of variation of the intensity of the measurement image at a plurality of points on the skeleton. For example, at each point to be measured on the determined measurement profile, extending along the gradient direction, an intersection point with the design profile in the design image is determined, i.e. the associated point on the design profile can be determined. In the example of fig. 4, for example, the intersection of the point 406 on the measurement profile and the curve 402 may be identified as a point on the design profile.
The distance between corresponding points on the measured profile and the design profile can then be calculated to determine the deviation. In addition, the type of deviation may also be determined. As shown in FIG. 4, the deviations may include three types, for example, a solid circle pattern (e.g., point 406) indicating that the point to be measured on the measurement profile is outside the design profile, a solid box pattern (e.g., point 404) indicating that the point to be measured on the measurement profile substantially coincides with the design profile, and an open circle pattern (e.g., point 408) indicating that the point to be measured on the measurement profile is inside the design profile.
In some embodiments, statistical analysis can be performed on a plurality of point deviations, and statistical data such as the average value and standard deviation of the deviations can be determined, so that the process errors of the semiconductor device can be globally analyzed and quantified.
According to the embodiment of the disclosure, the whole image of the semiconductor device can be analyzed, the error condition of the mask design can be determined, and specific quantitative indexes can be given. Accordingly, embodiments of the present disclosure provide a fast, accurate, and convenient method for determining sub-pixel edges of a measurement image of a semiconductor device and, in turn, its final manufacturing process error.
Fig. 5 illustrates a block diagram of an environment 500 for processing an image of a semiconductor device, in accordance with an embodiment of the present disclosure. Environment 500 includes a measurement device 502 and a processing device 504. The measurement device 502 may be, for example, a Scanning Electron Microscope (SEM). Processing device 504 receives measurement images of the semiconductor device from measurement device 502 and optionally design images from a design device. In addition, the design image may also be designed by the user at processing device 504. As shown in fig. 5, the processing device 504 may include a segmentation component 506, a refinement component 508, and a first determination component 510.
The segmentation component 506 is configured to segment the measurement image of the semiconductor device to determine a target region associated with the target structural unit. The refining component 508 is configured to refine the target region to determine a skeleton of the target region. The first determination section 510 is configured to determine a plurality of points to be measured on the measurement profile of the target structural unit based on the gradient of variation in the intensity of the measurement image at a plurality of initial points on the skeleton.
In some embodiments, the segmentation component 506 comprises: a conversion module configured to convert the measurement image into a binary image based on the threshold intensity, wherein a region having a first value in the binary image is a target region.
In some embodiments, the conversion module is configured to: dividing the measurement image into a plurality of sub-images; determining a plurality of local threshold intensities for a plurality of sub-images; and converting the plurality of sub-images into binary sub-images based on the plurality of local threshold intensities to form a binary image. In some embodiments, determining the plurality of local threshold intensities for the plurality of sub-images comprises: according to a method of cluster analysis, a plurality of local threshold intensities are determined.
In some embodiments, the first determining means comprises: a first determination module configured to determine a gradient of change in intensity of the measurement image at a plurality of initial points on the skeleton; and a second determination module configured to determine a plurality of points to be measured on the measurement profile of the target structural unit based on intensities of the plurality of pixels in the direction of the gradient. In some embodiments, the second determination module is configured to: performing an interpolation operation on a relationship between intensities and positions of a plurality of pixels to obtain an interpolation function; and determining a plurality of points to be measured on the measurement profile based on the interpolation function and the predetermined threshold intensity.
In some embodiments, the processing device 504 may further include: a second determination section configured to determine a plurality of relevant points on the design contour of the target structural unit in the design image of the semiconductor device based on a gradient of variation of the intensity of the measurement image at a plurality of points to be measured on the skeleton; and a third determining section configured to determine a process error of the finally manufactured semiconductor device based on the plurality of points to be measured on the measurement profile and the plurality of relevant points on the design profile.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (14)
1. A method of processing an image of a semiconductor device, comprising:
segmenting the measurement image of the semiconductor device to determine a target region associated with a target structural unit;
refining the target region to determine a skeleton of the target region;
determining a gradient of change in intensity of the measurement image at a plurality of points on the skeleton; and
determining a plurality of sub-pixel points on the measured profile of the target structural element based on intensities of a plurality of pixels in the direction of the gradient.
2. The method of claim 1, wherein segmenting the measurement image of the semiconductor device comprises:
converting the measurement image into a binary image based on a threshold intensity, wherein a region having a first value in the binary image is the target region.
3. The method of claim 2, wherein converting the measurement image to a binary image comprises:
dividing the measurement image into a plurality of sub-images;
determining a plurality of local threshold intensities for the plurality of sub-images; and
converting the plurality of sub-images to binary sub-images based on the plurality of local threshold intensities to form the binary image.
4. The method of claim 3, wherein determining a plurality of local threshold intensities for the plurality of sub-images comprises:
according to a method of cluster analysis, the plurality of local threshold intensities is determined.
5. The method of claim 1, wherein determining a plurality of sub-pixel points on the measurement profile of the target structural element based on intensities of a plurality of pixels in the direction of the gradient comprises:
performing an interpolation operation on a relationship between the intensity and the position of the plurality of pixels to obtain an interpolation function; and
determining a plurality of sub-pixel points on the measurement profile based on the interpolation function and a predetermined threshold intensity.
6. The method of claim 1, further comprising:
determining a plurality of relevant points on a design contour of the target structural unit in a design image of the semiconductor device based on gradients of intensities of the measurement image at a plurality of sub-pixel points on the skeleton; and
determining a process error of the semiconductor device based on the plurality of sub-pixel points on the measurement profile and the plurality of correlation points on the design profile.
7. The method of claim 1, wherein the measurement image is acquired by a Scanning Electron Microscope (SEM).
8. An apparatus for processing an image of a semiconductor device, comprising:
a segmentation component configured to segment a measurement image of the semiconductor device to determine a target region associated with a target structural unit;
a refining component configured to refine the target region to determine a skeleton of the target region; and
a first determination unit configured to determine a plurality of sub-pixel points on the measurement profile of the target structural unit based on a gradient of variation in intensity of the measurement image at a plurality of points on the skeleton, wherein the first determination unit includes:
a first determination module configured to determine a gradient of change in intensity of the measurement image at the plurality of points on the skeleton; and
a second determination module configured to determine a plurality of sub-pixel points on the measurement profile of the target structural element based on intensities of a plurality of pixels in the direction of the gradient.
9. The apparatus of claim 8, wherein the segmentation component comprises:
a conversion module configured to convert the measurement image into a binary image based on a threshold intensity, wherein a region having a first value in the binary image is the target region.
10. The device of claim 9, wherein the conversion module is configured to:
dividing the measurement image into a plurality of sub-images;
determining a plurality of local threshold intensities for the plurality of sub-images; and
converting the plurality of sub-images to binary sub-images based on the plurality of local threshold intensities to form the binary image.
11. The apparatus of claim 10, wherein determining a plurality of local threshold intensities for the plurality of sub-images comprises:
according to a method of cluster analysis, the plurality of local threshold intensities is determined.
12. The device of claim 8, wherein the second determination module is configured to:
performing an interpolation operation on a relationship between the intensity and the position of the plurality of pixels to obtain an interpolation function; and
determining a plurality of sub-pixel points on the measurement profile based on the interpolation function and a predetermined threshold intensity.
13. The apparatus of claim 8, further comprising:
a second determination unit configured to determine a plurality of relevant points on the design contour of the target structural unit in the design image of the semiconductor device based on gradients of intensities of the measurement image at a plurality of sub-pixel points on the skeleton; and
a third determining component configured to determine a process error of the semiconductor device based on the plurality of sub-pixel points on the measurement profile and the plurality of correlation points on the design profile.
14. The apparatus of claim 8, wherein the measurement image is acquired by a Scanning Electron Microscope (SEM).
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