Image processing method and system thereof
An image processing system, including an input interface configured to receive a first direction image corresponding to a view of a semiconductor device in a first direction, and a second direction image corresponding to a view of the semiconductor device in a second direction which intersects the first direction at a first height at which the first direction image is generated; a processor configured to perform an edge detection operation for detecting an edge based on the first direction image, and to perform an image binarization operation on the first direction image; and a learning device configured to compare a first line width obtained based on the image binarization operation, and a second line width obtained based on the second direction image through machine learning, and to learn a condition of the image binarization operation which maximizes a correlation between the first line width and the second line width.
1 . An image processing system comprising:
at least one processor; and
a memory configured to store one or more instructions which, when executed by the at least one processor, cause the image processing system to:
receive a first direction image corresponding to a view of a semiconductor device in a first direction, and a second direction image corresponding to a view of the semiconductor device in a second direction which intersects the first direction at a first height at which the first direction image is generated,
perform an edge detection operation for detecting an edge based on the first direction image,
perform an image binarization operation on the first direction image,
compare a first line width obtained based on the image binarization operation, and a second line width obtained based on the second direction image through machine learning, and
learn a condition of the image binarization operation which maximizes a correlation between the first line width and the second line width.
2 . The image processing system of claim 1 ,
wherein the memory is further configured to store the condition of the image binarization operation.
3 . The image processing system of claim 1 , wherein the one or more instructions, when executed by the at least one processor, further cause the image processing system to:
communicate with an external device which generates the first direction image and the second direction image.
4 . The image processing system of claim 1 , wherein the first direction image and the second direction image are obtained using a scanning electron microscope (SEM) or a transmission electron microscope (TEM).
5 . The image processing system of claim 1 , wherein the edge detection operation is performed based on a gradient value obtained by applying a gradient to brightness information about pixels of the first direction image.
6 . The image processing system of claim 5 , wherein the edge detection operation determines pixels in which the gradient value is greater than a threshold gradient value, to be the edge.
7 . The image processing system of claim 1 , wherein the image binarization operation determines pixels having a brightness greater than a threshold brightness to be an inner region, based on brightness information about pixels of the first direction image.
8 . The image processing system of claim 7 , wherein the at least one processor is further configured to:
calculate an edge detection ratio value by dividing a number of edges included in the inner region by a total number of edges detected in the first direction image; and
perform the image binarization operation such that the edge detection ratio value is equal to a predetermined edge detection ratio value.
9 . An image processing system comprising:
at least one processor; and
a memory configured to store a machine learning model for performing machine learning, and one or more instructions which, when executed by the at least one processor, cause the image processing system to:
receive a first direction image corresponding to a view of a semiconductor device in a first direction, and a second direction image corresponding to a view of the semiconductor device in a second direction intersecting the first direction at a first height at which the first direction image is generated;
perform the machine learning based on the first direction image and the second direction image;
wherein to perform the machine learning, the one or more instructions which, when executed by the at least one processor, cause the image processing system to:
perform an edge detection operation for detecting an edge based on the first direction image,
perform an image binarization operation on the first direction image,
compare a first line width obtained based on the image binarization operation with a second line width obtained based on the second direction image using the machine learning, and
learn a condition of the image binarization operation at which a correlation between the first line width and the second line width is maximized by performing the machine learning.
10 . The image processing system of claim 9 , wherein the memory is further configured to store the condition of the image binarization operation.
11 . The image processing system of claim 9 , wherein the first direction image and the second direction image are obtained using a scanning electron microscope (SEM) or a transmission electron microscope (TEM).
12 . The image processing system of claim 9 , wherein the edge detection operation is performed based on a gradient value obtained by applying a gradient to brightness information about pixels of the first direction image.
13 . The image processing system of claim 12 , wherein the edge detection operation determines pixels in which the gradient value is greater than a threshold gradient value, to be the edge.
14 . The image processing system of claim 9 , wherein the image binarization operation determines pixels having brightness greater than a threshold brightness to be an inner region, based on brightness information about pixels of the first direction image.
15 . The image processing system of claim 9 , wherein the one or more instructions, when executed by the at least one processor, further cause the image processing system to:
calculate an edge detection ratio value obtained by dividing a number of edges included in an inner region by a total number of edges detected in the first direction image; and
perform the image binarization operation such that the edge detection ratio value is equal to a predetermined edge detection ratio value.
16 . An image processing method comprising:
receiving a first direction image corresponding to a view of a semiconductor device in a first direction, and a second direction image corresponding to a view of the semiconductor device in a second direction intersecting the first direction at a first height at which the first direction image is generated through an input interface;
performing an edge detection operation for detecting an edge based on the first direction image, using a processor;
performing an image binarization operation on the first direction image, using the processor; and
comparing a first line width obtained based on the image binarization operation, and a second line width obtained based on the second direction image using machine learning, and learning a condition of the image binarization operation which maximizes a correlation between the first line width and the second line width, using a learning device.
17 . The image processing method of claim 16 , wherein the edge detection operation is performed based on a gradient value obtained by applying a gradient to brightness information about pixels of the first direction image.
18 . The image processing method of claim 17 , wherein the edge detection operation determines pixels in which the gradient value is greater than a threshold gradient value, to be the edge.
19 . The image processing method of claim 16 , wherein the image binarization operation determines pixels having brightness greater than a threshold brightness to be an inner region, based on brightness information about pixels of the first direction image.
20 . The image processing method of claim 19 , wherein an edge detection ratio value is obtained by dividing a number of edges included in the inner region by a total number of edges detected in the first direction image, and
wherein the image binarization operation is performed such that the edge detection ratio value is equal to a predetermined edge detection ratio value.
21 . The image processing system of claim 1 , wherein the condition specifies a relationship between the image binarization operation and at least one edge detected using the edge detection operation.