JPH05128237A - Pattern inspecting method - Google Patents

Abstract

PURPOSE:To improve a fault detecting capability by attaining the detection of a fault in a complicated-shaped or an adjacent and close pattern. CONSTITUTION:The picture of an object to be inspected in which a synchronous pattern is prepared is fetched in right and left screens by a television camera (step 1). The fetched multilevel pictures 5 and 6 are binarized by a prescribed threshold value so that them, contour part of the pattern can be extracted (step 2). The exclusive logical sum operation of the same coordinate picture elements between those binary pictures 7 and 8 is performed (step 3). A part 9b concerned with a fine difference less than a prescribed reference of an obtained exclusive logical sum picture 9 is eliminated from the original binary pictures 7 and 8 (step 4). Then, obtained binary pictures 10 and 11 are comparison-operated so that the unmatching of the pattern can be detected (step 5).

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a pattern inspection method for inspecting a defect on a pattern of a reticle used for forming a pattern of a semiconductor device.

[0002]

2. Description of the Related Art Conventionally, as a pattern inspection method of this kind, two TV cameras capture images of two objects to be inspected, and the captured multi-valued images are vertically, horizontally and horizontally.
It is known to perform differentiation and binarization in an oblique direction, extract an edge component, and perform a comparison operation. In this method, for example, as shown in FIG. 6, first, two television cameras (not shown) capture images of two inspection objects (step). Here, the portion 20a of the captured right screen image 20 is a defect to be detected. Then, the vertical direction differential processing is performed to emphasize the horizontal edges of the images 20 and 21 on both screens (step), and only the horizontal edge component is extracted by the binarization processing (step). Finally, the left screen and the right screen are compared and calculated for each 8 × 8 pixel, and the defect 22
(Step). After that, similar processing is performed in the horizontal and diagonal directions to extract the defects of the pattern on the two inspection objects, that is, the reticle. According to this method,
Since the edge portion is emphasized by the differential processing, a fairly small defect can be clearly detected.

[0003]

However, in such a conventional example, in the case where the pattern is dense or has a convoluted shape, the defect portion is buried in the peripheral edge component when the image differentiation is performed, There is a problem that it is difficult to detect defects. That is, for example, as shown in FIG. 7, when the normal pattern 23 and the pattern 24 in which the patterns are connected to each other to cause a defect are vertically differentiated and binarized, approximate patterns 25 and 26 are extracted, There was a problem that it was difficult to distinguish.

The present invention has been made in view of the above points of the conventional example, and an object of the present invention is to improve the defect detection capability by making it possible to detect defects in a convoluted shape, a proximity, or a dense pattern. It is to provide a pattern inspection method that can achieve the above.

[0005]

According to the present invention, as shown in, for example, FIGS. 1 and 2, images of two inspected objects 1 on which synchronous patterns are formed are taken into corresponding screens (steps). ), Each of these captured multi-valued images 5,
6 is binarized with a predetermined threshold value to extract the contour portion of the pattern (step), and the exclusive OR operation is performed on the same coordinate pixels between these binary images 7 and 8 (step). The minute difference portion 9b of the obtained exclusive OR image 9 which is equal to or smaller than a predetermined reference is deleted from the original binary images 7 and 8 (step), and the binary image 1 thus obtained
The calculation is performed by comparing 0 and 11 with each other to detect pattern irregularity (step).

[0006]

According to the present invention, the multivalued images 5 and 6 captured on each screen are not binarized by differentiation, but simply binarized with a predetermined threshold value to extract the contour portion of the pattern. Therefore, even defects in a complicated shape, close proximity, or a dense pattern can be clearly extracted on the screen without being buried in the peripheral edge component. Then, the minute difference 9b between the binary images 7 and 8 is extracted by exclusive OR, and each binary image 7 based on this is extracted.
By deleting the data from No. 8, it is possible to suppress the occurrence of pseudo defects due to unevenly binarized portions and misalignment between images.

[0007]

Embodiments of the pattern inspection method according to the present invention will be described below with reference to the drawings.

FIG. 2 shows a schematic structure of an apparatus used in this embodiment. As shown in the figure, this device
It has a pair of television cameras 2 for picking up an image of a reticle 1 as an inspection object. The television camera 2 is a CCD camera with a microscope, and is configured to capture images of the same portion of the reticle 1 on which the same pattern is formed. Then, each television camera 2 is connected to a CPU 3 including an A / D converter, a memory and the like for arithmetically processing the captured image signal. Furthermore, CPU
Display means 4 for displaying the position of the detected defect is connected to 3.

Next, the pattern inspection method of this embodiment will be described with reference to FIG.
It will be explained based on. Here, the images from step to step show a process of taking an image of the pattern on the reticle 1 on the same screen and performing digital image processing.

In the method of the present embodiment, similarly to the method of the conventional example, first, the image of each reticle 1 is captured by the television camera 2 to obtain multivalued images 5 and 6 (step).
In this case, the protruding portion 6a of the multi-valued image 6 captured on the right screen is a defect to be detected.

Next, the multi-valued images 5 and 6 of the patterns captured on the left and right screens are simply binarized at a predetermined threshold level, and only the contours of the patterns are extracted (step). Thereby, the binary images 7 and 8 are obtained.

Generally, when a multi-valued image is captured and binarized, image distortion due to the configuration of the optical system, non-uniformity of illumination light amount on the screen, quantization due to A / D conversion, etc. Due to the influence, it is difficult to faithfully binarize the original pattern, and in most cases, a jagged image is extracted. In particular, in the case where different images are taken in from different optical systems and compared as in this embodiment, the jaggedness occurs as a difference between the images and becomes a pseudo minute defect. Therefore, in the present embodiment, in order to reduce the effect of such jaggedness of the binary image, the processing of minute difference deletion as described below is added. That is, pattern defects often appear as lumps when images are compared with each other, while jaggedness due to binarization tends to appear as fine dots or thin lines. Therefore, a process of deleting a difference smaller than a predetermined standard by a kind of filtering is performed.

Specifically, first, in step 2,
Exclusive OR operation is performed on the binarized binary data between the same coordinate pixels on the left and right screens to extract the difference between the binary images 7 and 8. In this case, as can be understood from the figure, in the exclusive OR image 9, in addition to the portion 9a to be extracted as a defect, a portion 9b related to a minute difference due to the above-described reason appears. Then, if this portion 9b is less than or equal to a predetermined amount, a process of deleting the portion 9b from the binarized left and right binary images 7 and 8 is performed (step).
As a result of this processing, for example, as shown in FIG. 3, the data of the pixels of the portion 9b relating to the minute difference among the portions where the data extracted as the contour of the pattern is 1 becomes 0, and as a result, except for the defective portion 11a, Binary images 10 and 11 having no difference are obtained.

The conditions for deleting the minute difference in the exclusive OR image 9 are as follows. That is, as shown in FIG. 4, 1 located at the center of any 5 × 5 pixel
When paying attention to one pixel I, if the surrounding pixels satisfy any one of the expressions (1) to (12), the pixel I is deleted. In this case, according to equations (1) to (4), a pixel having a width of one pixel in the vertical, horizontal, and diagonal directions is deleted, and according to equations (5) to (12), vertical, horizontal, and diagonal directions are eliminated. Pixels having a width of 2 pixels in the direction are deleted. Note that this deletion is performed by rewriting both the corresponding pixels on the left and right binary screens to 0 (or 1).

If such processing is performed for the entire range on the screen, the difference between the binary images 7 and 8 within the width of 2 pixels is deleted. As a result, when the exclusive OR is taken, the defective portion in which the difference appears in a lump remains, and the minute difference such as the positional deviation that causes the pseudo defect is removed.

After that, the left and right binary images 10 and 11 obtained by the above-mentioned processing are subjected to the following comparison operation to extract the defect 12 (step). That is, as shown in FIG. 5, an arbitrary 8 × 8 pixel area is cut out from the left and right screens, and each pixel having the same coordinates is compared. here,
The portion of "1" is a portion extracted as the contour of the pattern. In this case, the combinations of the values of the left and right pixels are (0,0), (0,1), (1,0), (1,1)
There are four possible ways. Usually the left and right values are equal (0,
0) and (1,1) are normal, and left and right values are different (0,1) and (1,0) are abnormal, but either of the values obtained by the following two formulas is considered. If it is equal to or higher than the predetermined threshold value, there is an abnormality in this area, and if not, it is determined to be normal.

[0017]

[Equation 1]

Then, the area of 8 × 8 pixels is sequentially cut out by shifting vertically and horizontally one pixel by one pixel over the entire area of the screen, and finally one screen is judged. The detected defect is displayed on the display means 4 of the apparatus shown in FIG.

As described above, according to the present embodiment, since the enhancement processing such as differentiation is not performed, it is possible to reliably detect the defect in the convoluted shape or the close or dense pattern. Then, as shown in Table 1, defects that could not be detected by the conventional method can be detected by the method of this embodiment. In the table, A on the horizontal axis
1 to A8 and B1 to B8 indicate types of defects, and the vertical axis 1 to
10 indicates the size of the defect. The smaller the number, the smaller the defect size.

[0020]

[Table 1]

Although the method of this embodiment is effective when used alone, various defects can be detected more reliably when used in combination with the conventional method of performing edge enhancement processing by differentiation. Is.

Further, although the reticle used for forming the pattern of the semiconductor device has been described as an example in the above-mentioned embodiment, the present invention is not limited to this, and can be applied to inspection of various patterns.

[0023]

As described above, according to the present invention, the edge enhancement processing by differentiation is not performed on the image captured on each screen, and the image is simply binarized at a predetermined threshold to outline the pattern. After extracting a portion and further removing a minute difference by exclusive OR, the binary images are compared and calculated to detect pattern irregularities, so that a complicated shape, a close proximity, or a dense pattern can be detected. Defects can be reliably detected, and as a result, defect detection capability can be improved.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing a processing procedure of this embodiment.

FIG. 2 is a block diagram showing a schematic configuration of an apparatus used in the embodiment.

FIG. 3 is an explanatory diagram showing a process of deleting a minute difference between binary images in the same embodiment.

FIG. 4 is an explanatory diagram showing a condition for deleting a minute difference of an exclusive OR image in the example.

FIG. 5 is an explanatory diagram showing pixels cut out for defect extraction in the example.

FIG. 6 is an explanatory diagram showing a processing procedure of a conventional example.

FIG. 7 is an explanatory diagram showing a problem in the differential processing of the conventional example.

[Explanation of symbols]

 1 Inspected object 5,6 Multi-valued image 7,8,10,11 Binary image 9 Exclusive OR image 12 Defect

Claims (1)

[Claims] 1. An image of two objects to be inspected on which synchronous patterns are formed is captured on respective corresponding screens,
Each captured image is binarized with a predetermined threshold value to extract the contour portion of the pattern, and the exclusive-or operation of the same-coordinate pixels between these binary images is performed. A pattern inspection method characterized in that a minute difference portion of a logical sum image which is less than a predetermined standard is deleted from the original binary image, and the binary images obtained thereby are compared and calculated to detect pattern irregularities. ..