TW202030544A - Recognition method of pattern feature - Google Patents

Abstract

A recognition method of pattern characteristics , the recognition result of which is applied to an optical proximity correction, includes providing a plurality of reference images with reference pattern feature, then recognizing and classifying the reference image by the image recognition device, and storing the recognition result. Then, the image with the actual pattern feature is compared with the stored recognition result by the image recognition device to recognize and classify the image with the actual pattern feature. The image recognition device calculates an angle feature value and/or a distance feature value of the actual pattern feature according to the result of the classification to obtain a recognition result of the pattern feature.

Description

Pattern feature recognition method

The present invention relates to a method for identifying pattern features whose identification results can be applied to optical proximity effect correction.

In the semiconductor device manufacturing process, the minimum line width is usually referred to as the critical dimension (CD). With the reduction of design rules and key dimensions, a special tilt or wiggle shape pattern design is sometimes required in the layout. In addition, due to the limitations of the lithography process and the etching process, optical proximity correction (OPC) is usually used to correct the pattern of the photomask to form a precise pattern.

At present, before the optical proximity effect correction is performed, the pattern image obtained by the Scanning Electron Microscope (SEM) is usually manually measured to measure the characteristic value of the pattern (such as the angle of pattern bending, The distance between adjacent patterns, etc.). After that, the measured characteristic value is applied to the optical proximity effect correction as a compensation value to produce a mask that can form a precise pattern.

However, using manual methods to measure the characteristic values of patterns is very time-consuming and prone to human judgment errors, which affects the accuracy of the final pattern formed.

The invention provides a pattern feature recognition method, which uses an image recognition device to obtain a pattern feature recognition result.

A pattern feature recognition method of the present invention, the recognition result of which is applied to optical proximity effect correction. The pattern feature recognition method includes providing a plurality of reference images with reference pattern characteristics, and then identifying the reference image by an image recognition device And classification, and save the identification results. Then, the image with the actual pattern feature is compared with the stored recognition result by the image recognition device to identify and classify the image with the actual pattern feature. In addition, the image recognition device calculates the angle feature value and/or the distance feature value of the actual pattern feature according to the classification result to obtain the recognition result of the pattern feature.

Based on the above, in the present invention, the image recognition device is used to obtain the recognition result of the pattern feature, which can avoid the occurrence of human judgment errors. In addition, by comparing and classifying images with actual pattern features by the image recognition device, multiple angle feature values and/or distance feature values in the image can be calculated at one time, thereby effectively improving the efficiency of the semiconductor device manufacturing process.

In order to make the above-mentioned features and advantages of the present invention more comprehensible, the following specific embodiments are described in detail in conjunction with the accompanying drawings.

In this context, the so-called "pattern feature" refers to the angle of the curved pattern and/or the distance between adjacent patterns. For example, the angle of the curved pattern is the angle presented by the turning point (vertex) of the curved pattern, and it may be an obtuse angle, a right angle, or an acute angle. In addition, the recognition result of the pattern feature can be used to calculate the compensation value required for the correction of the optical proximity effect, for example.

FIG. 1 is a flowchart of a method for identifying pattern features according to an embodiment of the invention.

Please refer to FIG. 1, first, step S100 is performed to provide a plurality of reference images with the characteristics of reference patterns. In this embodiment, the so-called "reference pattern feature" is the shallow trench isolation (STI) pattern of the existing device, the opening pattern required to form the contact window, or the pattern of the wire pattern required for circuit design. However, the present invention is not limited to this. In this embodiment, the reference image is an image captured by a scanning electron microscope for existing components, where the scanning electron microscope is, for example, a critical dimension scanning electron microscope (CD-SEM).

Then, step S102 is performed to provide the multiple reference images to the image recognition device. In the image recognition process, the image recognition device first divides each reference image into multiple secondary images. Generally speaking, the size of a scanning electron microscope photograph is, for example, 960×960 pixels. Therefore, in this embodiment, taking the reference image 200 of 960×960 pixels as an example, the image recognition device uses the reference image 200 The division is performed in units of 10×10 pixels, and 96×96 secondary images 210 can be obtained, as shown in FIG. 2. In this way, one of the secondary images 210 can be identified and classified by the image identification device for subsequent identification steps.

Generally speaking, due to the difference in the surface structure of the semiconductor components, the scanning electron microscope photo changes the secondary electron signal received by the scanning electron microscope, and the white edge shown in Figure 2 is generated at the unevenness of the secondary electron signal. (White wall). In this embodiment, as shown in FIG. 2, the white side 216 of the reference image 200 presents a curved wave pattern, and the white side 216 includes a boundary 212 and a vertex 214, wherein the vertex 214 is the turning point of the curved wave pattern, and the two A boundary 212 is formed between adjacent vertices 214. After segmentation, the reference image 200 includes a secondary image 210a including a boundary 212, a secondary image 210b including a vertex 214, and the remaining secondary images 210c.

Then, step S104 is performed to classify the secondary image including the boundary or vertex of the reference pattern feature as the first type by the image recognition device, and classify the remaining secondary images as the second type. In this embodiment, the secondary image 210a and the secondary image 210b are classified into the first type, and the secondary image 210c is classified into the second type by the image recognition device. In this embodiment, the second type is regarded as image noise and is not referred to. In other words, in the subsequent identification steps, the first type is mainly used as the analysis basis for identifying pattern features. In this embodiment, the image recognition device performs the above-mentioned image recognition and classification by, for example, a K-Means clustering algorithm.

Then, step S106 is performed to store the identification result. Specifically, after the reference image 200 is identified and classified through the above steps, the identification result (including the classification result) is stored. Accordingly, the subsequent identification steps can be performed by the secondary images 210 of the first type and the second type to replace the subjective judgment of the engineering personnel, thereby avoiding the occurrence of human judgment errors.

Then, step S108 is performed to compare and recognize the image with the actual pattern characteristics and the stored recognition result. In this embodiment, the so-called "actual pattern feature" is similar to the above-mentioned reference pattern feature, which can be the shallow trench isolation pattern of the actual device, the opening pattern required to form the contact window, or the pattern of the wire pattern required for circuit design. However, the present invention is not limited to this. In this embodiment, the image with actual pattern features includes, for example, an image obtained by a scanning electron microscope photographing an actual element, where the scanning electron microscope is, for example, a critical dimension scanning electron microscope (CD-SEM).

In this step, the image with the actual pattern characteristics is compared with the first type of secondary image by the image recognition device, that is, for example, using the boundary or vertex of the first type of secondary image in the recognition result stored in step S106 Fitting images with actual pattern characteristics.

Then, according to the fitting result of the image with the actual pattern feature, the image with the actual pattern feature is identified, the image with the closed pattern feature and the actual pattern feature is classified as the first type of image, and the image with the open pattern feature is classified Images with actual pattern characteristics are classified as the second type of images. For example, if an image with actual pattern features presents a closed pattern such as a square or a circle, the actual pattern feature is regarded as a closed pattern feature; on the contrary, the actual pattern feature is regarded as an open pattern feature. In this embodiment, the closed pattern feature is an opening pattern or an opening pattern, and the open pattern feature is a wire pattern. After that, the image recognition device can be used to calculate the angle feature value and/or the distance feature value of the actual pattern feature according to the classification result to obtain the recognition result of the pattern feature. The following will give a detailed definition of the angle characteristic value and the distance characteristic value.

Hereinafter, the calculation of the angle feature value and/or the distance feature value of the actual pattern feature in the first type image or the second type image will be described respectively.

If the image with the actual pattern feature is classified as the first type of image, the following steps are performed to calculate the angle feature value and the distance feature value of the actual pattern feature.

1 and 3A at the same time, proceed to step S110 to find the center of gravity of each closed pattern feature. For example, the calculation unit in the image recognition device or the calculation unit external to the image recognition device can be used to find the center of gravity 310 of each closed pattern feature in the first type image 300, but the invention is not limited to this. In this embodiment, the closed pattern features of the first type image 300 take an elliptical pattern as an example, and the elliptical patterns are arranged in a staggered manner, for example. In other embodiments, circular patterns, square patterns, etc. may also be present, or arranged in different ways, but the present invention is not limited thereto.

1 and 3B at the same time, proceed to step S112 to calculate the slope of the line between the centers of gravity of two adjacent closed pattern features. In this embodiment, as shown in FIG. 3B, the center of gravity 312 and the center of gravity 314 that are interlaced and adjacent to each other are connected to obtain a line 316, and the slope of the line 316 is calculated by the above calculation unit. In addition, the closed pattern features are classified by the slope of the connection line 316 obtained by calculation. For example, if the slope of the line 316 is greater than 0, the closed pattern feature with the center of gravity 312 and the closed pattern feature with the center of gravity 314 are classified as the first group; if the slope of the line 316 is less than or equal to 0, then The closed pattern feature with the center of gravity 312 and the closed pattern feature with the center of gravity 314 are classified into the second group. That is, two adjacent closed pattern features with a slope greater than 0 are classified as the first group, and two adjacent closed pattern features with a slope less than or equal to 0 are classified as the second group.

Then, select the first group or the second group to calculate the feature values in the subsequent steps to avoid repeated calculations. For example, in the case of the same group of slopes, by selecting any two slopes not to exceed a certain deviation value (threshold) to calculate the characteristic value in the subsequent steps, the calculation error can be reduced. In one embodiment, a certain deviation value is 0.01, but the present invention is not limited to this. In this embodiment, as shown in FIG. 3B, for example, two adjacent closed pattern features with a slope less than 0, that is, the second group, are selected for calculation.

After that, step S114 is performed to calculate the distance between the centers of gravity of two adjacent closed pattern features. In this embodiment, as shown in FIG. 3B, the length of the connection 316, that is, the distance D1 between the center of gravity 312 and the center of gravity 314, is calculated by the calculation unit.

And proceed to step S116 to calculate the angle feature value θ1 and the distance feature values D2, D3 according to the distance D1. In this embodiment, the calculation unit is used to calculate the angle feature value θ1 of two adjacent closed pattern features, and the distance D1 between the center of gravity 312 and the center of gravity 314 and the angle feature value θ1 obtained by the above calculation are based on Inverse trigonometric function method to obtain distance characteristic values D2, D3. In this embodiment, the distance characteristic value D2 is substantially equal to the horizontal distance between the center of gravity 312 and the center of gravity 314, and the distance characteristic value D3 is substantially equal to the vertical distance between the center of gravity 312 and the center of gravity 314. In other words, the distance D1 and the distance characteristic values D2 and D3 are roughly the lengths of the three sides of the right triangle, and the angle characteristic value θ1 is, for example, one of the included angles of the right triangle. In this embodiment, the angle feature value θ1 and the distance feature values D2 and D3 are the recognition results of the pattern features that can be applied to the optical proximity effect correction.

For the convenience of description, the above is an example of calculating a set of angle characteristic values and distance characteristic values. In other embodiments, multiple sets of closed pattern features can be calculated simultaneously by the image recognition device, and multiple angle feature values and/or distance feature values of the actual pattern features in the first type of image can be obtained at one time , Thereby effectively improving the efficiency of the semiconductor device manufacturing process.

In another embodiment, if the feature value of the actual pattern feature in the second type of image is calculated, step S118 is performed as shown in FIGS. 1, 4A and 4B to calculate the angle feature value of the actual pattern feature.

Please refer to FIGS. 1 and 4A at the same time. In this embodiment, the open pattern feature of the second-type image 400 takes a curved wave pattern as an example, and the curved wave patterns are arranged in parallel to each other. In other embodiments, other wire patterns may also be present or arranged in different ways, but the present invention is not limited thereto.

For example, the second-type image 400 is compared with the first-type secondary image by the image recognition device, that is, the boundary or vertex of the first-type secondary image in the recognition result stored in step S106 is used to fit the second image The second-type image 400 more clearly marks the boundary or vertex of the open pattern feature of the second-type image 400, which can replace the subjective judgment of the engineer, thereby avoiding the occurrence of human judgment error.

Please refer to FIG. 1 and FIG. 4B at the same time, the calculation unit in the image recognition device or the calculation unit external to the image recognition device is used to calculate the angle between the two adjacent borders and the vertices connecting the two borders. , To obtain the angle feature value θ2 of the open pattern feature of the second type image 400. In other embodiments, multiple sets of open pattern features can be calculated by the image recognition device at the same time, and multiple angular feature values of the open pattern features can be calculated at one time, thereby effectively improving the efficiency of the semiconductor device manufacturing process. In this embodiment, the angle feature value θ2 is the recognition result of the pattern feature applicable to the optical proximity effect correction method.

In one embodiment, the image recognition device can be used to simultaneously calculate multiple sets of angular feature values of the open pattern features in the second type of image, and can be automatically marked in the image as shown in FIG. 5.

In summary, by using the image recognition device to calculate multiple sets of angle feature values and/or distance feature values according to the classification results, the occurrence of human judgment errors can be avoided. In addition, multiple sets of angle feature values and/or distance feature values are applied to the optical proximity effect correction, and the compensation value required for the optical proximity effect correction is derived by methods such as inverse trigonometric function method to correct the pattern of the mask, and then Form a precise pattern. In this way, human judgment errors can be avoided, and the efficiency of the semiconductor device manufacturing process can be effectively improved.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be determined by the scope of the attached patent application.

200: Reference image 210, 210a, 210b, 210c: secondary image 212: border 214: Vertex 216: White Edge 300: The first type of image 310, 312, 314: center of gravity 316: connection 400: Second category image S100, S102, S104, S106, S108, S110, S112, S114, S116, S118: steps θ1, θ2: Angle characteristic value D1: distance D2, D3: distance characteristic value

FIG. 1 is a flowchart of a method for identifying pattern features according to an embodiment of the invention. Figure 2 is a scanning electron microscope (SEM) photograph of the segmented reference image. Figure 3A is a scanning electron microscope (SEM) photograph of the first type of image. Fig. 3B is a schematic diagram showing the first type of image. Figure 4A is a scanning electron microscope (SEM) photo of the second type of image. Fig. 4B is a schematic diagram showing the second type of image. Fig. 5 is a scanning electron microscope (SEM) photograph showing the recognition result of pattern features.

S100, S102, S104, S106, S108, S110, S112, S114, S116, S118: steps

Claims (9)

A method for identifying pattern features, the result of which is applied to optical proximity effect correction, and the method for identifying pattern features includes: Provide multiple reference images with features of reference patterns; Recognizing and classifying the reference image by the image recognition device, and storing the recognition result; Comparing the image with the actual pattern feature with the stored recognition result by the image recognition device to identify and classify the image with the actual pattern feature; and The image recognition device calculates the angle feature value and/or the distance feature value of the actual pattern feature according to the classification result to obtain the recognition result of the pattern feature. The method for identifying pattern features as described in item 1 of the scope of patent application, wherein identifying and classifying the reference image includes: Dividing the reference image into multiple secondary images; and The secondary images including the boundaries or vertices of the reference pattern feature are classified as a first type, and the remaining secondary images are classified as a second type. The method for identifying pattern features as described in item 2 of the scope of patent application, wherein the comparison and classification of the images with actual pattern features include: Using the image recognition device to compare the image with the actual pattern feature with the secondary image of the first type to identify the image with the actual pattern feature, and the image with the closed pattern feature The image with the actual pattern feature is classified as a first type of image and the image with the open pattern feature is classified as the second type of image. According to the method for identifying pattern features described in item 3 of the scope of patent application, wherein the closed pattern features include open hole patterns or opening patterns, and the open pattern features include wire patterns. The method for identifying pattern features as described in item 3 of the scope of patent application, wherein calculating the characteristic value of the actual pattern feature in the first type image includes: Find the center of gravity of each of the enclosed pattern features; Calculating the distance between the centers of gravity of two adjacent closed pattern features; and The angle characteristic value and the distance characteristic value are calculated according to the distance. The method for identifying pattern features as described in item 5 of the scope of patent application, wherein after finding the center of gravity of each of the enclosed pattern features and before calculating the distance, it further includes: Calculating the slope of the line between the centers of gravity of two adjacent closed pattern features; and Classify the two adjacent closed pattern features with the slope greater than 0 into a first group, and classify the two adjacent closed pattern features with the slope less than or equal to 0 into a second group And When calculating the angle characteristic value and the distance characteristic value, the first group or the second group is selected for calculation. The method for identifying pattern features as described in item 3 of the scope of patent application, wherein calculating the characteristic value of the actual pattern feature in the second type image includes: Calculating the angle feature value of the actual pattern feature. According to the method for identifying pattern features described in item 1 of the scope of patent application, the reference image includes an image taken by a scanning electron microscope. According to the method for identifying pattern features described in item 1 of the scope of patent application, the image with actual pattern features includes an image taken by a scanning electron microscope.

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