Image out-of-focus detection method and critical dimension measurement method

Abstract

The invention provides an image out-of-focus detection method and a key size measurement method. In the detection method, the boundary area of the pattern boundary is intercepted from the detection image, so that the sharpness of the boundary area can be acquired in a targeted manner, and the boundary area is divided into a plurality of equal parts, so that the out-of-focus judgment of the image can be performed based on the sharpness of the plurality of equal parts, and the accuracy of the out-of-focus detection of the image is effectively improved. According to the invention, whether the currently acquired detection image has an out-of-focus phenomenon can be judged, and the problem of inaccurate measurement result caused by measurement of the key size based on the out-of-focus detection image is avoided.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a method for detecting image defocus and a method for measuring key dimensions. Background Art

[0002] With the continuous advancement of ultra-large-scale integrated circuit process technology, the critical dimension (CD) of semiconductor devices is getting smaller and smaller, and the process window is getting smaller and smaller. The measurement requirements for critical dimensions (for example, width dimensions) are also getting higher and higher to ensure the accuracy of critical dimension measurement. When measuring, generally after obtaining an image containing the pattern to be measured, the size of the pattern to be measured is measured based on the image. However, in the actual measurement process, the measuring equipment is prone to defocus when acquiring the image, which leads to abnormal measurement results. Summary of the invention

[0003] The purpose of the present invention is to provide a method for detecting image defocus, so as to solve the problem that the acquired detection image is not discovered in time when the defocus phenomenon occurs, resulting in abnormal measurement results.

[0004] In this regard, the present invention provides a method for detecting image defocus, including: obtaining a detection image containing a pattern to be measured, and cutting out a boundary area that at least covers the boundary of the pattern to be measured; dividing the boundary area into several equal parts, and obtaining the sharpness of each equal part according to the grayscale values ​​of multiple pixels in each equal part; and, based on the sharpness of the several equal parts, judging whether the detection image is defocused.

[0005] Optionally, the method for judging whether the detection image is out of focus according to the sharpness of several equal parts includes: combining each adjacent multiple equal parts to form a unit; and judging whether the detection image is out of focus according to the sharpness of the multiple equal parts within each unit.

[0006] Optionally, the method for judging whether the detection image is out of focus according to the sharpness of several equal parts includes: step one, combining each adjacent P equal parts to form a large unit; step two, judging whether the sharpness of each large unit meets the requirements according to the sharpness of multiple equal parts within each large unit, when the sharpness of any large unit does not meet the requirements, judging that the detection image is out of focus; when the sharpness of all large units meets the requirements, executing step three; step three, combining each adjacent Q equal parts to form a small unit, wherein P is greater than Q, and P and Q are both positive integers greater than or equal to 2; step four, judging whether the sharpness of each small unit meets the requirements according to the sharpness of multiple equal parts within each small unit, when the sharpness of any small unit does not meet the requirements, judging that the detection image is out of focus.

[0007] Optionally, dividing the boundary area into a number of equal parts includes: dividing the boundary area into M rows along the first direction and into N columns along the second direction, so as to divide the boundary area into M*N equal parts.

[0008] Optionally, the method for judging whether the detection image is out of focus based on the sharpness of several equal parts includes: combining at least two adjacent rows in M ​​rows into a large unit, and / or combining at least two adjacent columns in N columns into a large unit; obtaining the sharpness of each large unit based on the average value of the sharpness of multiple equal parts in each large unit; and comparing the sharpness of each large unit with the corresponding standard sharpness. When the absolute value of the difference between the sharpness of any large unit and the corresponding standard sharpness exceeds a preset value, it is judged that the detection image is out of focus.

[0009] Optionally, the method for judging whether the detection image is out of focus based on the sharpness of several equal parts includes: combining multiple equal parts in each row into a small unit, and / or combining multiple equal parts in each column into a small unit; obtaining the maximum sharpness, minimum sharpness and average sharpness of the multiple equal parts in each small unit; comparing the maximum sharpness, minimum sharpness and average sharpness of each small unit with the corresponding standard values, and when any of the comparison results does not meet the requirements, it is judged that the detection image is out of focus.

[0010] Optionally, the difference between the maximum sharpness and the minimum sharpness of each small unit is also compared with a preset value.

[0011] Optionally, the method for judging whether the detection image is out of focus according to the sharpness of several equal parts includes: step one, combining at least two adjacent rows in M ​​rows into a large unit, and / or combining at least two adjacent columns in N columns into a large unit; step two, obtaining the sharpness of each large unit according to the average value of the sharpness of multiple equal parts in each large unit; step three, comparing the sharpness of each large unit with the corresponding standard sharpness, when the absolute value of the difference between the sharpness of any large unit and the corresponding standard sharpness is greater than a preset value, it is judged that the detection image is out of focus; when the sharpness of all large units is greater than the corresponding standard sharpness, it is judged that the detection image is out of focus. When the absolute values ​​of the differences in sharpness are all less than or equal to the preset values, step four is executed; in step four, multiple equal parts in each row are combined into a small unit, and / or multiple equal parts in each column are combined into a small unit; in step five, the maximum sharpness, minimum sharpness and average sharpness in multiple equal parts in each small unit are obtained; in step six, the maximum sharpness, minimum sharpness and average sharpness of each small unit are respectively compared with the corresponding standard values. When any of the comparison results does not meet the requirements, it is determined that the detection image is out of focus; when all the comparison results meet the requirements, it is determined that the corresponding boundary area is not out of focus.

[0012] Optionally, a method for obtaining the sharpness of each equal part according to the grayscale values ​​of multiple pixels in each equal part includes: sharpness = (maximum pixel grayscale value-minimum pixel grayscale value) / (maximum pixel grayscale value+minimum pixel grayscale value).

[0013] Optionally, the method for cutting out a boundary area that at least covers the boundary of a pattern to be measured includes: obtaining scanning information of a first area containing the pattern to be measured, the first area at least covering the boundary area of ​​the pattern to be measured, the scanning information containing a signal peak corresponding to the pattern boundary; and, based on the position of the signal peak in the scanning information, cutting out a second area that at least covers the signal peak from the first area, the second area constituting the boundary area.

[0014] Optionally, the pattern to be tested has at least two boundaries, and each boundary area covering each boundary of the pattern to be tested is cut out in turn; each boundary area is divided into several equal parts in turn, and based on the sharpness of the several equal parts, it is judged whether each boundary area is out of focus; when any of the boundary areas is out of focus, it is judged that the detection image is out of focus; when all boundary areas are not out of focus, it is judged that the detection image is not out of focus.

[0015] The present invention also provides a method for measuring key dimensions, comprising: using the image defocus detection method as described above to determine whether the current detection image has a defocus phenomenon; if so, reacquiring the detection image and determining whether the reacquired detection image has a defocus phenomenon, until the reacquired detection image does not have a defocus phenomenon; and, performing key dimension measurement based on the detection image without a defocus phenomenon.

[0016] In the image defocus detection method provided by the present invention, the boundary area of ​​the pattern boundary is cut out from the detection image, so that the sharpness of the boundary area can be obtained in a targeted manner, and the boundary area is divided into several equal parts, so that the image defocus judgment can be performed based on the sharpness of the several equal parts, which effectively improves the accuracy of image defocus detection. In the present invention, it is possible to judge whether the currently acquired detection image has a defocus phenomenon, so when the detection image has a defocus phenomenon, it can be discovered in time, avoiding the problem of inaccurate measurement results caused by measuring key dimensions based on the defocused detection image. In other words, before measuring the key dimensions, the present invention preferentially judges whether the acquired detection image has a defocus phenomenon, so that the measurement of the key dimensions is based on the detection image without defocus, ensuring the accuracy of the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. 4 is a flow chart of a method for detecting image defocus in one embodiment of the present invention.

[0018] Figure 2 FIG. 4 is a schematic diagram of a pattern to be tested and a corresponding scanning signal in an embodiment of the present invention.

[0019] Figure 3 It is a schematic diagram of dividing a boundary area into multiple equal parts during the process of detecting image defocus in one embodiment of the present invention.

[0020] Figure 4 FIG. 4 is a schematic diagram of a specific detection process of a method for detecting image defocus in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] As described in the background art, in the process of measuring key dimensions, the measuring device is prone to defocus when acquiring an image, which in turn leads to abnormal measurement results. Therefore, it is particularly important to determine the defocus phenomenon of the image.

[0022] To this end, the present invention provides a method for detecting image defocus, which can be specifically referred to Figure 1 As shown, the detection method comprises the following steps.

[0023] Step S100, acquiring a detection image containing a pattern to be detected, and cutting out a boundary area that at least covers a boundary of the pattern to be detected.

[0024] Step S200, dividing the boundary area into a plurality of equal parts, and obtaining the sharpness of each equal part according to the grayscale values ​​of a plurality of pixels in each equal part.

[0025] Step S300, judging whether the detection image is out of focus according to the sharpness of the equal parts.

[0026] In the image defocus detection method provided by the present invention, it is used that when the image is out of focus, the boundary of the pattern to be tested therein is blurred, resulting in a deviation in the sharpness of its edge. Therefore, it is possible to judge whether the detection image is out of focus according to the sharpness of the boundary area of ​​the pattern to be tested. Specifically, the boundary area is divided into several equal parts in the present invention, so that the defocus detection result can be obtained more accurately.

[0027] The following is a further detailed description of the image defocus detection method and the key dimension measurement method proposed in the present invention in conjunction with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will become clearer. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. It should be recognized that relative terms such as "above", "below", "top", "bottom", "above" and "below" shown in the drawings can be used to describe the relationship between various elements. These relative terms are intended to cover different orientations of elements other than the orientations depicted in the drawings. For example, if the device is inverted relative to the view in the drawing, an element described as being "above" another element will now be below the element.

[0028] In step S100, a detection image containing a pattern to be detected is acquired, and a boundary area at least covering a boundary of the pattern to be detected is cut out.

[0029] The pattern to be measured may be formed on a substrate. Also, an image acquisition unit of the measuring device may be used to acquire a detection image containing the pattern to be measured, wherein the acquired detection image may display, for example, a top view structure of the pattern to be measured, wherein the top view structure is, for example, a strip-shaped pattern extending in a predetermined direction (e.g. Figure 2 or, the acquired detection image may, for example, display a cross-sectional structure of the pattern to be detected along a height direction.

[0030] In one example, the measuring device may be an electron beam device (e.g., a scanning electron microscope SEM). A substrate formed with a pattern to be measured is placed in the electron beam device, and the pattern to be measured is scanned by an electron beam to obtain scanning information. The imaging system in the electron beam device can obtain a detection image containing the pattern to be measured based on the scanning information. Specifically, when the electron beam scans the surface of the substrate, secondary electrons are excited from the surface of the substrate. The detector in the electron beam device collects the secondary electrons and can further convert them into electrical signals, thereby displaying a scanning image within the scanning field of view. For example Figure 2 As shown, the electron beam scans the portion of the substrate 10 where the pattern to be measured 11 is formed, so that a corresponding scanned image can be displayed.

[0031] In addition, the obtained scanning information has a signal peak corresponding to the pattern boundary. Further, the scanning information can be digitized to obtain corresponding grayscale information, which contains grayscale distribution in a predetermined direction, and the grayscale corresponding to the pattern boundary position reaches a peak. For example, refer to Figure 2 As shown, a stripe pattern (i.e., the pattern to be tested 11) is displayed in the detection image, and the grayscale distribution of the stripe pattern in the width direction can be obtained, wherein the grayscale corresponding to the boundary position of the pattern reaches a peak value. Therefore, according to the peak position in the grayscale distribution, the area in the image that at least covers the peak portion can be cut out, and then the boundary area that at least covers the boundary of the pattern to be tested can be cut out.

[0032] That is, in a specific example, the method of cutting out a boundary region that at least covers the boundary of the pattern to be measured may include: first, obtaining scanning information of a first region of the substrate, the first region at least covers the boundary region of the pattern to be measured, and the scanning information has a signal peak corresponding to the pattern boundary; then, according to the position of the signal peak in the scanning information, further cutting out a second region (for example, Figure 2 ), the second area is a boundary area that at least covers the boundary of the pattern to be measured. In a further solution, after obtaining the scanning information of the first area, the scanning information in the first area can also be digitized to obtain corresponding grayscale information, and the grayscale information contains grayscale distribution in a predetermined direction, wherein the grayscale corresponding to the boundary position of the pattern reaches a peak value; and when the second area is cut out, the boundary area of ​​the pattern to be measured can be cut out according to the grayscale peak value in the grayscale information.

[0033] In this embodiment, based on the position of the signal peak of the scanning signal (or the grayscale peak in the grayscale information), the boundary area of ​​the pattern to be tested can be captured more accurately, so that the grayscale information in the boundary area can be processed specifically to make a defocus judgment on the image.

[0034] In step S200, the boundary area is divided into a number of equal parts, and the sharpness of each equal part is obtained according to the grayscale values ​​of a plurality of pixels in each equal part.

[0035] Sharpness is an important parameter for measuring image quality. An image with high sharpness has more detailed information than an image with low sharpness; conversely, an image with low sharpness often has blurred parts. In this embodiment, whether the detection image is out of focus is determined based on the sharpness of the boundary area of ​​the pattern to be detected.

[0036] For a specific example, refer to Figure 3 As shown, the boundary area 20 can be divided into M rows along the first direction and N columns along the second direction, and then divided into M*N equal parts, each of which corresponds to a plurality of pixels. That is, after the boundary area of ​​the pattern to be measured is cut out, the boundary area is further divided into multiple equal parts, so that the sharpness comparison can be performed based on several equal parts of small size, which is conducive to improving the accuracy of the judgment result.

[0037] Furthermore, when obtaining the sharpness of each equal part according to the grayscale values ​​of multiple pixels in each equal part, it can be specifically obtained according to the following formula: sharpness = (maximum grayscale value of pixel - minimum grayscale value of pixel) / (maximum grayscale value of pixel + minimum grayscale value of pixel).

[0038] In step S300, it is determined whether the detection image is out of focus according to the sharpness of the equal parts.

[0039] When performing sharpness comparison, multiple equal parts can be combined into a unit for comparison, and based on the sharpness of the multiple equal parts in each unit, it can be judged whether the detection image is out of focus. For example, based on the difference between the maximum sharpness, minimum sharpness and / or average sharpness of the multiple equal parts in each unit and the corresponding standard sharpness, it can be judged whether the boundary area is out of focus, and then it can be judged whether the detection image is out of focus.

[0040] In a specific scheme, each adjacent P equal parts can be preferentially combined to form a large unit for sharpness comparison to determine whether the sharpness of each large unit meets the requirements; when the comparison results of each large unit meet the requirements (for example, when the absolute value of the difference between the sharpness of all large units and the standard sharpness is less than or equal to the preset value), the adjacent Q equal parts are combined to form a small unit for sharpness comparison. When the sharpness of any small unit does not meet the requirements, it is determined that the corresponding boundary area is out of focus; when the sharpness of all small units meets the requirements, it is determined that the corresponding boundary area is not out of focus. Among them, P is greater than Q, and P and Q are both positive integers greater than or equal to 2. In practical applications, P can be set to a positive integer multiple of Q, that is, large unit comparison is prioritized, followed by small unit comparison.

[0041] The sharpness comparison method for the large unit and the sharpness comparison method for the small unit can be the same or different. For example, when comparing the sharpness of the large unit, the average sharpness of multiple equal parts in the large unit can be directly compared with the corresponding standard sharpness; and when comparing the sharpness of the small unit, the maximum sharpness R of multiple equal parts in the small unit can be directly compared with the corresponding standard sharpness. MAX-X , minimum sharpness R MIN-X and the average sharpness R AVE-X Etc. are compared with the corresponding standard sharpness.

[0042] by Figure 3 For example, the boundary area of ​​the pattern to be tested is divided into M*N equal parts, so each row contains N equal parts, and each column contains M equal parts. At this time, the N equal parts in each row can be combined into one unit for sharpness comparison; or, the M equal parts in each column can be combined into one unit for sharpness comparison; or, at least two adjacent rows in the M rows can be combined into one unit for sharpness comparison; or, at least two adjacent columns in the N columns can be combined into one unit for sharpness comparison.

[0043] In a specific example, at least two adjacent rows in M ​​rows can be combined into a large unit, and at least two adjacent columns in N columns can be combined into a large unit, and then compared with the corresponding standard values ​​respectively; then, each row can be a small unit, and each column can be a small unit, and then compared with the corresponding standard values ​​respectively. That is, the large unit comparison (multi-row comparison and multi-column comparison) is performed first, and then the small unit comparison (single row comparison and single column comparison) is performed.

[0044] When at least two adjacent rows in the M rows are combined into a large unit for comparison, the sharpness R of each large unit can be further obtained. X , and the sharpness of each large unit RX And the corresponding standard sharpness BSL X For comparison, when the sharpness of any large unit R X With standard sharpness BSL X The absolute value of the difference exceeds the corresponding preset value spec1 (i.e., │R X -BSL X │>spec1), the boundary area is judged to be out of focus, and the detected image is out of focus accordingly. Among them, the sharpness of each large unit R X It can be obtained based on the average value of the sharpness of multiple equal parts in the large unit. For example, if three adjacent rows are combined into a large unit, the sharpness of each large unit is R X That is, the average sharpness of the 3*N equal parts.

[0045] When at least two adjacent columns in N columns are combined into a large unit for comparison, the sharpness R of each large unit can be further obtained. Y , and the sharpness of each large unit R Y And the corresponding standard sharpness BSL Y For comparison, when the sharpness of any large unit R Y With standard sharpness BSL Y The absolute value of the difference exceeds the corresponding preset value spec2 (i.e., │R Y -BSL Y │>spec2), the boundary area is judged to be out of focus, and the detected image is out of focus accordingly. Similarly, the sharpness of each large unit R Y It can be obtained based on the average sharpness of multiple equal parts in a large unit. For example, if three adjacent columns are combined into a large unit, the sharpness of each large unit is R Y That is, the average sharpness of the 3*M equal parts.

[0046] In a specific example, the standard sharpness level BSL for comparison can be obtained based on the detection image that is not out of focus. X / BSL Y That is, for the detection image that is not out of focus, cut out the boundary area that at least covers the boundary of the pattern to be tested, and divide the boundary area into several equal parts (M*N equal parts), then use pixel digitization processing to obtain the grayscale value of each pixel in each equal part, so as to obtain the sharpness of each equal part; then, combine at least two adjacent rows in the M rows into a large unit, and obtain the average sharpness of multiple equal parts in each large unit to constitute the sharpness of the corresponding large unit, which is used as the standard sharpness BSL for comparison X; and, combining at least two adjacent columns in the N columns into a large unit, and obtaining the average sharpness of multiple equal parts in each large unit to form the sharpness of the corresponding large unit, which is used as the standard sharpness BSL for comparison Y .

[0047] When multiple equal parts in each row are combined into a small unit for sharpness comparison, the maximum sharpness R in the multiple equal parts in the row can be MAX-X , minimum sharpness R MIN-X and the average sharpness R AVE-X The image is then compared with the corresponding standard values. When any comparison result does not meet the requirements, it is determined that the boundary area is out of focus, and the detected image is out of focus. In a further solution, the difference R between the maximum sharpness and the minimum sharpness in the row can be used. MAX-X -R MIN-X , and whether the corresponding boundary area is out of focus can be determined by the difference R between the maximum sharpness and the minimum sharpness MAX-X -R MIN-X When it is greater than the preset value range spec1, the corresponding boundary area is judged to be out of focus. That is, in an optional solution, the maximum sharpness R in each row can be compared respectively. MAX-X , minimum sharpness R MIN-X , average sharpness R AVE-X , the difference between the maximum sharpness and the minimum sharpness R MAX-X -R MIN-X , when the four comparison results are all within the specification range and meet the following requirements, it is judged that there is no defocus phenomenon in the boundary area.

[0048] │R MAX-X -BSL MAX-X │≤MAX spec1

[0049] │R MIN-X -BSL MIN-X │≤Min spec1

[0050] │R AVE-X -BSL AVE-X │≤AVE spec1

[0051] R MAX-X -R MIN-X ≤range spec1

[0052] Similarly, when multiple equal parts in each column are combined into a small unit for sharpness comparison, the maximum sharpness R of the multiple equal parts in the column can be calculated. MAX-Y , minimum sharpness R MIN-Yand the average sharpness R AVE-Y The image is then compared with the corresponding standard values. If any of the comparison results does not meet the requirements, it is determined that the boundary area is out of focus, and the detected image is out of focus. In a further solution, the difference R between the maximum sharpness and the minimum sharpness in the column can be used. MAX-Y -R MIN-Y , and whether the corresponding boundary area is out of focus can be determined by the difference R between the maximum sharpness and the minimum sharpness MAX-Y -R MIN-Y When it is greater than the preset value range spec2, the corresponding boundary area is judged to be out of focus. That is, in an optional solution, the maximum sharpness R in each column can be compared respectively. MAX-Y , minimum sharpness R MIN-Y , average sharpness R AVE-Y , the difference between the maximum sharpness and the minimum sharpness R MAX-Y -R MIN-Y , when the four comparison results are all within the specification range and meet the following requirements, it is judged that there is no defocus phenomenon in the boundary area.

[0053] │R MAX-Y -BSL MAX-Y │≤MAX spec2

[0054] │R MIN-Y -BSL MIN-Y │≤Min spec2

[0055] │R AVE-Y -BSL AVE-Y │≤AVE spec2

[0056] R MAX-Y -R MIN-Y ≤range spec2

[0057] In a specific example, the above standard value for comparison can also be obtained based on the detection image that is not out of focus. That is, for the detection image that is not out of focus, the boundary area that at least covers the boundary of the pattern to be tested is cut out, and the boundary area is divided into several equal parts (M*N equal parts), and then the grayscale value of each pixel in each equal part is obtained by pixel digitization processing, so as to obtain the sharpness of each equal part; then, the maximum sharpness R in multiple equal parts in each row is obtained. MAX-X , minimum sharpness R MIN-X and the average sharpness R AVE-X , the maximum sharpness R within multiple equal parts in each column MAX-Y , minimum sharpness R MIN-Y and the average sharpness R AVE-Y, to be used as the corresponding standard value for sharpness comparison.

[0058] It should be noted that spec1 and spec2 are preset values ​​used for comparison when judging the sharpness of small cells in the row direction and small cells in the column direction, respectively, wherein the preset values ​​set for the same category in spec1 and spec2 can be the same or different. For example, the values ​​of MAX spec1 and MAX spec2 can be the same or different; the values ​​of Minspec1 and Min spec2 can also be the same or different.

[0059] It should be recognized that, in practical applications, only large unit comparison (i.e., multi-row comparison and multi-column comparison) may be performed. Alternatively, only small unit comparison (i.e., single-row comparison and single-column comparison) may be performed. Alternatively, large unit comparison (i.e., multi-row comparison and multi-column comparison) may be performed preferentially. When any comparison result in the large unit comparison does not meet the requirements, it can be determined that the boundary area has a defocusing phenomenon; when all comparison results in the large unit comparison meet the requirements, small unit comparison (i.e., single-row comparison and single-column comparison) may be continued. If any comparison result in the small unit comparison does not meet the requirements, it can be determined that the boundary area has a defocusing phenomenon; conversely, if all comparison results in the small unit comparison meet the requirements, it is determined that the boundary area does not have a defocusing phenomenon.

[0060] In one example, the pattern to be tested has two opposite boundaries, such as Figure 2 The strip pattern has two boundaries in the width direction. At this time, each boundary area of ​​the pattern to be tested can be divided into several equal parts and the sharpness can be judged by the method as described above. When the sharpness comparison results of each boundary area meet the requirements, it is judged that the test image does not have a defocus phenomenon; when the sharpness comparison result of any boundary area does not meet the requirements, it is judged that the test image has a defocus phenomenon.

[0061] In order to more clearly explain the image defocus detection method provided in this embodiment, the following is combined with Figure 4 And for Figure 2 The specific detection process of the pattern to be tested shown is taken as an example for description.

[0062] First, obtain a test image containing the pattern to be tested. In this example, Figure 2 As shown, the detection image shows a pattern 11 to be detected formed on a substrate 10 .

[0063] Specifically, an electron beam device (eg, a scanning electron microscope SEM) may be used to scan the substrate 10 having the pattern to be tested formed thereon to obtain scanning information, and an imaging system in the electron beam device acquires a detection image containing the pattern to be tested according to the scanning information.

[0064] Next, a boundary area 20 at least covering the boundary of the pattern to be detected 11 is cut out from the detection image.

[0065] In one example, the boundary region 20 that at least covers the boundary of the pattern to be tested 11 can be cut out according to the position of the signal peak in the scanning information of the detection image. Specifically, the scanning information of the first region of the substrate can be obtained first, the first region at least covers the boundary region of the pattern to be tested, and the scanning information has a signal peak corresponding to the pattern boundary. For example, the grayscale information in the first region can be obtained, and the grayscale information has a grayscale peak value corresponding to the pattern boundary; then, according to the position of the signal peak in the scanning information, a second region (for example, a second region) that at least covers the signal peak is further cut out from the first region. Figure 2 As shown in the dotted box in FIG. 1 , the second area is a boundary area 20 that at least covers the boundary of the pattern to be measured.

[0066] Next, the boundary area is divided into several equal parts. In this example, the boundary area is specifically divided into M rows and N columns, that is, the boundary area is divided into M*N equal parts, each of which corresponds to a plurality of pixels. For example, Figure 3 shown.

[0067] Next, at least two adjacent rows are combined into a large unit, and at least two adjacent columns are combined into a large unit, for example Figure 3 In the example, three adjacent rows are combined into a large unit, and three adjacent columns are combined into a large unit, wherein the large unit formed by the combination of three adjacent rows has 3*N equal parts, and the large unit formed by the combination of three adjacent columns has 3*M equal parts.

[0068] Next, obtain the sharpness R of each large unit X / R Y That is, the sharpness R of each large unit formed by the combination of at least two adjacent rows is obtained. X , obtain the sharpness R of each large unit formed by the combination of at least two adjacent columns Y .

[0069] In one example, the sharpness R of each large unit X / R Y It can be obtained based on the average value of the sharpness of multiple equal parts in the unit. The sharpness of each equal part can be obtained according to the following formula: sharpness of each equal part = (maximum grayscale value of pixel - minimum grayscale value of pixel) / (maximum grayscale value of pixel + minimum grayscale value of pixel). Figure 3 In the example, every three adjacent rows are combined into a large unit, and accordingly M / 3 large units in the row direction can be divided. The sharpness of each large unit in the row direction is R X It is the average value of the sharpness of 3*N equal parts; each adjacent three columns are combined into a large unit, and correspondingly, N / 3 large units in the column direction can be divided. The sharpness of the large unit in each column direction is R Y That is, the average sharpness of the 3*M equal parts.

[0070] Next, the sharpness R of each large unit X / R Y Corresponding standard sharpness level BSL X / BSL Y When the absolute value of the difference between the sharpness of any large unit and the standard sharpness exceeds the corresponding preset value spec1 / spec2, it is determined that the boundary area is out of focus, and the detected image is correspondingly out of focus.

[0071] Specifically, the sharpness R of each large unit formed by combining at least two adjacent rows X Corresponding standard sharpness level BSL X When the comparison result of any large unit does not meet the following requirements, it is determined that the boundary area is out of focus, and the detected image is correspondingly out of focus; the sharpness R of each large unit formed by combining at least two adjacent columns is Y Corresponding standard sharpness level BSL Y When the comparison result of any large unit does not meet the following requirements, it is determined that the boundary area is out of focus, and the detection image is correspondingly out of focus.

[0072] │R X -BSL X │≤spec1

[0073] │R Y -BSL Y │≤spec2

[0074] And, when the sharpness of all large units R X / R Y Corresponding standard sharpness level BSL X / BSL Y When the absolute values ​​of the differences are all less than or equal to the corresponding preset values ​​spec1 / spec2, the subsequent judgment process can be continued.

[0075] Continue to refer Figure 4As shown, each row constitutes a small unit, and the maximum sharpness R in each small unit is obtained. MAX-X , minimum sharpness R MIN-X , average sharpness R AVE-X .exist Figure 3 In the example, M small units in the row direction can be divided accordingly, each small unit contains N equal parts, and the maximum sharpness R is obtained according to the sharpness of each equal part in each small unit. MAX-X , minimum sharpness R MIN-X , average sharpness R AVE-X .

[0076] Next, the maximum sharpness R of each small unit MAX-X , minimum sharpness R MIN-X and the average sharpness R AVE-X The corresponding standard maximum value BSL MAX-X , Standard minimum value BSL MIN-X and the standard mean BSL AVE-X When any comparison result of any small unit does not meet the requirements, it is judged that the corresponding boundary area is out of focus, and the detection image is correspondingly out of focus.

[0077] Specifically, it can be based on the maximum sharpness R of each small unit. MAX-X , minimum sharpness R MIN-X and the average sharpness R AVE-X The corresponding standard maximum value BSL MAX-X , Standard minimum value BSL MIN-X and the standard mean BSL AVE-X Whether the absolute value of the difference between them exceeds the corresponding preset value. When the absolute value of the difference is greater than the corresponding preset value, it means that the comparison result does not meet the requirements; conversely, when the absolute value of the difference is less than or equal to the corresponding preset value, it means that the comparison result meets the requirements.

[0078] In a further solution, the difference R between the maximum sharpness and the minimum sharpness in each small unit can be calculated. MAX-X -R MIN-X , and judge whether there is a defocus phenomenon in the boundary area. Specifically, when the difference between the maximum sharpness and the minimum sharpness in each small unit R MAX-X -R MIN-X When it is greater than the corresponding preset value, it means that the comparison result does not meet the requirements and there is a defocus phenomenon in the boundary area; on the contrary, when the difference between the maximum sharpness and the minimum sharpness in each small unit R MAX-X -R MIN-X When it is less than or equal to the corresponding preset value, it means that the comparison result meets the requirements and there is no defocusing phenomenon in the boundary area.

[0079] That is, in the specific example, when any comparison result of any small unit does not meet the following requirements, it is judged that the boundary area is out of focus, and the detected image is correspondingly out of focus; when the comparison results of all small units meet the following requirements, the subsequent judgment process can continue to be executed.

[0080] │R MAX-X -BSL MAX-X │≤MAX spec1

[0081] │R MIN-X -BSL MIN-X │≤Min spec1

[0082] │R AVE-X -BSL AVE-X │≤AVE spec1

[0083] R MAX-X -R MIN-X ≤range spec1

[0084] Continue to refer Figure 4 As shown, each column constitutes a small unit, and the maximum sharpness R in each small unit is obtained. MAX-Y , minimum sharpness R MIN-Y , average sharpness R AVE-Y .exist Figure 3 In the example, N small units in the column direction can be divided accordingly, each small unit contains M equal parts, and the maximum sharpness R is obtained according to the sharpness of each equal part in each small unit. MAX-Y , minimum sharpness R MIN-Y , average sharpness R AVE-Y .

[0085] Next, the maximum sharpness R of each small unit MAX-Y , minimum sharpness R MIN-Y , average sharpness R AVE-Y The corresponding standard maximum value BSL MAX-Y , Standard minimum value BSL MIN-Y and the standard mean BSL AVE-Y A comparison is performed, and when any comparison result of any small unit does not meet the requirements, it is determined that the boundary area is out of focus, and the detection image is correspondingly out of focus.

[0086] Specifically, it can be based on the maximum sharpness R of each small unit. MAX-Y , minimum sharpness R MIN-Y , average sharpness R AVE-Y The corresponding standard maximum value BSLMAX-Y , Standard minimum value BSL MIN-Y and the standard mean BSL AVE-Y Whether the absolute value of the difference between them exceeds the corresponding preset value. When the absolute value of the difference is greater than the corresponding preset value, it means that the comparison result does not meet the requirements; conversely, when the absolute value of the difference is less than or equal to the corresponding preset value, it means that the comparison result meets the requirements.

[0087] In a further solution, the difference R between the maximum sharpness and the minimum sharpness in each small unit can be calculated. MAX-Y -R MIN-Y , and judge whether there is a defocus phenomenon in the boundary area. Specifically, when the difference between the maximum sharpness and the minimum sharpness in each small unit R MAX-Y -R MIN-Y When it is greater than the corresponding preset value, it means that the comparison result does not meet the requirements and the corresponding boundary area is out of focus; on the contrary, when the difference between the maximum sharpness and the minimum sharpness in each small unit is R MAX-Y -R MIN-Y When it is less than or equal to the corresponding preset value, it means that the comparison result meets the requirements and there is no defocus phenomenon in the corresponding boundary area.

[0088] That is, in the specific example, when any comparison result of any small unit does not meet the following requirements, it is judged that the boundary area is out of focus, and the corresponding detected image is out of focus; when the comparison results of all small units meet the following requirements, it can be judged that the corresponding boundary area is not out of focus.

[0089] │R MAX-Y -BSL MAX-Y │≤MAX spec2

[0090] │R MIN-Y -BSL MIN-Y │≤Min spec2

[0091] │R AVE-Y -BSL AVE-Y │≤AVE spec2

[0092] R MAX-Y -R MIN-Y ≤range spec2

[0093] exist Figure 4In the image defocus detection method shown, the sharpness comparison of large units is first performed (i.e., multi-row comparison and multi-column comparison). When the sharpness comparison result of the large unit does not meet the requirements, it can be directly judged that the corresponding boundary area is out of focus, and the detected image is correspondingly out of focus without continuing the subsequent judgment process; when the sharpness comparison result of the large unit meets the requirements, the subsequent more refined sharpness comparison of small units (i.e., single-row comparison and single-column comparison) can be continued, so as to obtain a more accurate judgment result. It should be recognized that in actual applications, the order of multi-row comparison and multi-column comparison can be adjusted arbitrarily or performed simultaneously, which is not limited here; and the order of single-row comparison and single-column comparison can also be adjusted arbitrarily or performed simultaneously, which is not limited here.

[0094] In addition, for a pattern to be tested having at least two boundaries (eg Figure 2 The stripe pattern in the pattern to be tested has two boundaries), then each boundary of the pattern to be tested can be subjected to the following test, for example Figure 4 When any boundary is judged as being out of focus, it indicates that the detection image is out of focus; when all boundaries are judged as being not out of focus, it indicates that the detection image is not out of focus. At this time, the real boundary of the pattern to be detected can be accurately identified based on the current detection image.

[0095] Based on the above-mentioned image defocus detection method, the present disclosure also provides a key dimension measurement method, which specifically includes: using the above-mentioned image defocus detection method to determine whether the current detection image has a defocus phenomenon; if so, reacquire the detection image, and determine whether the reacquired detection image has a defocus phenomenon, until the acquired detection image has no defocus phenomenon; then, the key dimension measurement can be performed based on the detection image without the defocus phenomenon. In this embodiment, before measuring the key dimension, it is first determined whether the acquired detection image has a defocus phenomenon, so as to ensure that the measurement of the key dimension is based on the detection image without defocus, so as to ensure the accuracy of the measurement result.

[0096] It should be noted that, although the present invention has been disclosed as a preferred embodiment, the above embodiment is not intended to limit the present invention. For any technician familiar with the art, without departing from the scope of the technical solution of the present invention, the technical content disclosed above can be used to make many possible changes and modifications to the technical solution of the present invention, or modified into equivalent embodiments of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still belongs to the scope of protection of the technical solution of the present invention.

[0097] It must be noted that as used herein and in the appended claims, the singular forms "a," "an," and "an" include plural references unless the context clearly indicates otherwise. For example, reference to "a step" or "a device" means reference to one or more steps or devices and may include secondary steps as well as secondary devices. All conjunctions used should be interpreted in the broadest sense. Also, the word "or" should be interpreted as having the definition of a logical "or" rather than a logical "exclusive or" unless the context clearly indicates otherwise.

Claims

1. A method for detecting image defocus, characterized in that: include: Acquire a detection image containing a pattern to be detected, and cut out a boundary area that at least covers a boundary of the pattern to be detected; Dividing the boundary area into a plurality of equal parts, and obtaining the sharpness of each equal part according to the grayscale values ​​of a plurality of pixels in each equal part; as well as, Whether the detection image is out of focus is determined based on the sharpness of the equal parts.

2. The method for detecting image defocus according to claim 1, characterized in that: The method of judging whether the detection image has a defocus phenomenon according to the sharpness of the equal parts includes: Combining each adjacent plurality of equal parts into a unit; and, Whether the detection image is out of focus is determined based on the sharpness of the multiple equal parts in each unit.

3. The method for detecting image defocus as claimed in claim 2, characterized in that: The method of judging whether the detection image has a defocus phenomenon according to the sharpness of the equal parts includes: Step 1: combine each adjacent P equal parts to form a large unit; Step 2: judging whether the sharpness of each large unit meets the requirement according to the sharpness of multiple equal parts in each large unit; when the sharpness of any large unit does not meet the requirement, it is judged that the detection image is out of focus; when the sharpness of all large units meets the requirement, executing step 3; Step 3: Combine each adjacent Q equal parts to form a small unit, where P is greater than Q, and both P and Q are positive integers greater than or equal to 2; Step 4: judging whether the sharpness of each small unit meets the requirement according to the sharpness of multiple equal parts in each small unit; when the sharpness of any small unit does not meet the requirement, it is judged that the detection image is out of focus.

4. The method for detecting image defocus according to claim 1, characterized in that: Dividing the boundary area into a number of equal parts includes: dividing the boundary area into M rows along the first direction and into N columns along the second direction, so as to divide the boundary area into M*N equal parts.

5. The method for detecting image defocus according to claim 4, characterized in that: The method of judging whether the detection image has a defocus phenomenon according to the sharpness of the equal parts includes: Combining at least two adjacent rows in the M rows into a large unit, and / or combining at least two adjacent columns in the N columns into a large unit; Obtaining the sharpness of each large unit according to the average value of the sharpness of a plurality of equal parts within each large unit; and, The sharpness of each large unit is compared with the corresponding standard sharpness. When the absolute value of the difference between the sharpness of any large unit and the corresponding standard sharpness exceeds a preset value, it is determined that the detection image is out of focus.

6. The method for detecting image defocus as claimed in claim 4, characterized in that: The method of judging whether the detection image has a defocus phenomenon according to the sharpness of the equal parts includes: Combining multiple equal parts in each row into one small unit, and / or combining multiple equal parts in each column into one small unit; Obtain the maximum sharpness, minimum sharpness and average sharpness in multiple equal parts of each small unit; The maximum sharpness, minimum sharpness and average sharpness of each small unit are compared with the corresponding standard values ​​respectively. When any comparison result does not meet the requirements, it is determined that the detection image is out of focus.

7. The method for detecting image defocus according to claim 6, characterized in that: The difference between the maximum sharpness and the minimum sharpness of each small unit is also compared with the preset value.

8. The method for detecting image defocus as claimed in claim 4, characterized in that: The method of judging whether the detection image has a defocus phenomenon according to the sharpness of the equal parts includes: Step 1: combine at least two adjacent rows in the M rows into a large unit, and / or combine at least two adjacent columns in the N columns into a large unit; Step 2, obtaining the sharpness of each large unit according to the average value of the sharpness of multiple equal parts in each large unit; Step 3: Compare the sharpness of each large unit with the corresponding standard sharpness. When the absolute value of the difference between the sharpness of any large unit and the corresponding standard sharpness is greater than a preset value, it is determined that the detection image is out of focus; when the absolute value of the difference between the sharpness of all large units and the corresponding standard sharpness is less than or equal to the preset value, execute step 4; Step 4, combining multiple equal parts in each row into a small unit, and / or combining multiple equal parts in each column into a small unit; Step 5, obtaining the maximum sharpness, the minimum sharpness and the average sharpness in multiple equal parts of each small unit; Step six, compare the maximum sharpness, minimum sharpness and average sharpness of each small unit with the corresponding standard value respectively. When any comparison result does not meet the requirements, it is judged that the detection image is out of focus; when all comparison results meet the requirements, it is judged that the corresponding boundary area is not out of focus.

9. The method for detecting image defocus according to claim 1, characterized in that: Methods for obtaining the sharpness of each equal portion according to the grayscale values ​​of multiple pixels in each equal portion include: Sharpness = (maximum pixel gray value - minimum pixel gray value) / (maximum pixel gray value + minimum pixel gray value).

10. The method for detecting image defocus according to claim 1, characterized in that: The method of cutting out a boundary area that at least covers the boundary of the pattern to be tested includes: Acquire scanning information of a first area containing a pattern to be measured, wherein the first area at least covers a boundary area of ​​the pattern to be measured, and the scanning information contains a signal peak corresponding to a pattern boundary; and According to the position of the signal peak in the scanning information, a second region at least covering the signal peak is cut out from the first region, and the second region constitutes the boundary region.

11. The method for detecting image defocus according to any one of claims 1 to 10, characterized in that: The pattern to be tested has at least two boundaries, and each boundary area covering each boundary of the pattern to be tested is sequentially intercepted; Each boundary area is divided into several equal parts in turn, and whether each boundary area is out of focus is judged according to the sharpness of the several equal parts; when any boundary area is out of focus, it is judged that the detection image is out of focus; when all boundary areas are not out of focus, it is judged that the detection image is not out of focus.

12. A method for measuring a critical dimension, characterized in that: include: Adopting the image defocus detection method as described in any one of claims 1 to 11 to determine whether the current detection image has a defocus phenomenon; If so, reacquire the detection image and determine whether the reacquired detection image has a defocusing phenomenon, until the reacquired detection image does not have a defocusing phenomenon; and perform key dimension measurement based on the detection image without a defocusing phenomenon.

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