CN107144210A - Method for measuring line width and roughness of electron microscopic image - Google Patents
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Abstract
本发明属于扫描电子显微测量技术领域，公开了一种电子显微图像线条宽度和粗糙度的测量方法包括：获得待测线条结构的扫描电子显微图像；截取第一区域；沿线条方向平均化处理，获得线条边缘像素分布曲线；根据所述线条边缘像素分布曲线，确定第一边界区域；局域像素分析，获得边界分布；根据所述边界分布，计算待测线条的宽度和粗糙度，提取待测线条宽度和粗糙度数值。本发明解决了现有技术中测量线条宽度和粗糙度工作量较大、存在人为干预造成的测量误差且只能分析有限个数据点的问题，达到了提高测量的准确性和可靠性，节省工程师实际量测时间和成本的技术效果。
The invention belongs to the technical field of scanning electron microscopic measurement, and discloses a method for measuring the line width and roughness of an electron microscopic image, which includes: obtaining a scanning electron microscopic image of the line structure to be measured; intercepting the first region; According to the pixel distribution curve of the line edge, the first boundary area is determined; the local pixel analysis is obtained to obtain the boundary distribution; according to the boundary distribution, the width and the roughness of the line to be measured are calculated, Extract the line width and roughness values to be tested. The invention solves the problems in the prior art that the workload of measuring line width and roughness is relatively large, there are measurement errors caused by human intervention, and only a limited number of data points can be analyzed, so as to improve the accuracy and reliability of measurement and save engineers The technical effect of practical measurement of time and cost.
Description
技术领域technical field
本发明涉及扫描电子显微测量技术领域，尤其涉及一种电子显微图像线条宽度和粗糙度的测量方法。The invention relates to the technical field of scanning electron microscopic measurement, in particular to a method for measuring line width and roughness of an electron microscopic image.
背景技术Background technique
在微电子、光电子、MEMS等领域，精确测量线条宽度和粗糙度是一项非常重要的应用。特别是对于某些情形，微纳器件结构包含非常严重的背景电子束强度分布，例如前层图层存在图形对电子束成像有较大影响，或切片之后对截面进行电子束成像及评估沿高度方向线条宽度时背景电子束分布受到高度位置的严重影响等，这些现象均使得在量测线条宽度时存在严重缺陷。In the fields of microelectronics, optoelectronics, MEMS, etc., accurate measurement of line width and roughness is a very important application. Especially for some cases, the micro-nano device structure contains a very serious background electron beam intensity distribution, for example, the existence of patterns in the front layer has a great influence on the electron beam imaging, or the electron beam imaging of the section after slicing and the evaluation along the height The distribution of the background electron beam is seriously affected by the height position when the width of the line is measured in the direction of the line. These phenomena all make there are serious defects in the measurement of the line width.
现有的技术在处理这类问题时，通常需要工程师指定量测位置避开背景图形的影响，或不同区域采用独立的参数值，这些方法带来了较大的工作量，并且只能分析有限个数据点，且存在人为干预造成的测量误差。When existing technologies deal with such problems, engineers usually need to specify measurement positions to avoid the influence of background graphics, or use independent parameter values for different areas. These methods bring a large workload and can only analyze limited data points, and there are measurement errors caused by human intervention.
发明内容Contents of the invention
本申请实施例通过提供一种电子显微图像线条宽度和粗糙度的测量方法，解决了现有技术中测量线条宽度和粗糙度工作量较大、存在人为干预造成的测量误差且只能分析有限个数据点的问题。The embodiment of the present application provides a method for measuring the line width and roughness of electron microscopic images, which solves the problem of large workload in measuring line width and roughness in the prior art, measurement errors caused by human intervention, and limited analysis. data point problem.
本申请实施例提供一种电子显微图像线条宽度和粗糙度的测量方法，包括：获得待测线条结构的扫描电子显微图像；An embodiment of the present application provides a method for measuring the line width and roughness of an electron microscopic image, including: obtaining a scanning electron microscopic image of the line structure to be measured;
截取第一区域；Intercept the first area;
对所述第一区域内的图像进行沿线条方向平均化处理，获得线条边缘像素分布曲线；performing averaging processing along the line direction on the image in the first region to obtain a line edge pixel distribution curve;
根据所述线条边缘像素分布曲线，确定第一边界区域；determining a first boundary area according to the pixel distribution curve at the edge of the line;
对所述第一边界区域内的图像进行局域像素分析，获得边界分布；performing local pixel analysis on the image in the first boundary area to obtain boundary distribution;
根据所述边界分布，计算所述待测线条的宽度和粗糙度，提取所述待测线条的宽度数值和粗糙度数值。According to the boundary distribution, the width and roughness of the line to be tested are calculated, and the width and roughness values of the line to be tested are extracted.
优选的，在所述获得待测线条结构的扫描电子显微图像之后，还包括：确定横向和纵向的每一个像素所代表的实际物理长度。Preferably, after obtaining the scanning electron microscopic image of the line structure to be tested, the method further includes: determining the actual physical length represented by each pixel in the horizontal and vertical directions.
优选的，所述第一区域为不包含以下任意一种或多种的区域：标尺、标注和非关注区域。Preferably, the first area is an area that does not contain any one or more of the following: scales, labels, and non-focused areas.
优选的，所述根据所述线条边缘像素分布曲线，确定第一边界区域，包括：Preferably, the determining the first boundary area according to the line edge pixel distribution curve includes:
选择像素第一极值范围内的区域作为基本边界区域；Select the area within the first extremum range of the pixel as the basic boundary area;
向所述基本边界区域外拓展第一宽度，所述基本边界区域和所述第一宽度构成所述第一边界区域。A first width is extended outside the basic boundary area, and the basic boundary area and the first width form the first boundary area.
优选的，所述像素第一极值范围为像素极值对应宽度的20％～80％。Preferably, the range of the first extreme value of the pixel is 20%-80% of the width corresponding to the extreme value of the pixel.
优选的，在所述根据所述线条边缘像素分布曲线，确定第一边界区域之后，还包括：Preferably, after determining the first boundary area according to the line edge pixel distribution curve, the method further includes:
对所述线条边缘像素分布曲线求导，获得斜率绝对值的最大值，与所述第一边界区域进行交集检查，排除干扰区域。Deriving the pixel distribution curve at the edge of the line to obtain the maximum value of the absolute value of the slope, and performing an intersection check with the first boundary area to exclude the interference area.
优选的，所述对所述第一边界区域内的图像进行局域像素分析，获得边界分布，包括：Preferably, performing local pixel analysis on the image in the first boundary area to obtain boundary distribution includes:
选择边界局域区域范围；Select the border local area range;
筛选或去除不合理像素点；Filter or remove unreasonable pixels;
以平均化局域边界的极大值、极小值为依据计算边界位置。The boundary position is calculated based on the maximum and minimum values of the averaged local boundaries.
优选的，所述边界局域区域范围在沿线条方向上覆盖至少一个像素区域，所述边界局域区域范围在沿线条方向的最大像素个数不超过背景有用信息包含像素的二分之一。Preferably, the boundary local area range covers at least one pixel area along the line direction, and the maximum number of pixels in the boundary local area area range along the line direction does not exceed half of the pixels containing useful background information.
优选的，所述筛选或去除不合理像素点，包括：Preferably, the screening or removal of unreasonable pixels includes:
沿线条分布方向平均化局域像素值，或加入筛选算法分析孤立的像素值是否明显为独立噪声，或采用最佳曲线拟合的方法进行数据处理，以筛选或去除不合理像素点。Average the local pixel values along the line distribution direction, or add a screening algorithm to analyze whether the isolated pixel values are obviously independent noise, or use the best curve fitting method for data processing to filter or remove unreasonable pixels.
优选的，所述计算线条宽度和粗糙度的方法包括：标准离散分析法，或功率谱密度分析法。Preferably, the method for calculating the line width and roughness includes: a standard discrete analysis method, or a power spectral density analysis method.
本申请实施例中提供的一个或多个技术方案，至少具有如下技术效果或优点：One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:
在本申请实施例中，采用边界区域和局域范围相结合的方法，前者通过沿线条方向平均化处理，获得线条边缘像素分布曲线，然后确定第一边界区域，从而有效界定了边界范围；后者通过局域像素分析，获得边界分布，从而通过设置合理的局域范围有效降低了背景像素影响，使边界定位更加准确。本发明方法能够有效降低现有方法在确定电子束成像图形时的局限性，提高测量的准确性和可靠性，并极大地节省了工程师实际量测的时间和成本。In the embodiment of the present application, the method of combining the boundary area and the local area is adopted. The former obtains the line edge pixel distribution curve by averaging along the line direction, and then determines the first boundary area, thereby effectively defining the boundary area; the latter The author obtains the boundary distribution through local pixel analysis, thereby effectively reducing the influence of background pixels by setting a reasonable local range, and making the boundary positioning more accurate. The method of the invention can effectively reduce the limitation of the existing method when determining the electron beam imaging pattern, improve the accuracy and reliability of the measurement, and greatly save the time and cost of the engineer's actual measurement.
附图说明Description of drawings
为了更清楚地说明本实施例中的技术方案，下面将对实施例描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一个实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the technical solution in this embodiment more clearly, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are an embodiment of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.
图1为本发明实施例提供的一种电子显微图像线条宽度和粗糙度的测量方法的流程图；Fig. 1 is a flow chart of a method for measuring line width and roughness of an electron microscopic image provided by an embodiment of the present invention;
图2为本发明实施例一中的SEM图像及其沿线条方向的SEM灰度平均值分布曲线。FIG. 2 is the SEM image and its SEM gray average distribution curve along the line direction in Embodiment 1 of the present invention.
图3为本发明实施例一采用原始的固定像素阈值测量方法获得的线条边缘分布曲线，及采用本发明方法获得的线条边缘分布曲线。3 is a line edge distribution curve obtained by using the original fixed pixel threshold measurement method according to Embodiment 1 of the present invention, and a line edge distribution curve obtained by using the method of the present invention.
图4为本发明实施例一采用原始方法获得的线条宽度粗糙度功率谱密度分布曲线，及采用本方法获得的线条宽度粗糙度功率谱密度分布曲线。FIG. 4 is the power spectral density distribution curve of the line width roughness obtained by the original method and the power spectral density distribution curve of the line width roughness obtained by the present method in Embodiment 1 of the present invention.
图5为本发明实施例二中的包含两层结构的SEM俯视图像及其沿X和Y方向的灰度平均值分布曲线。FIG. 5 is an SEM top view image including a two-layer structure in Example 2 of the present invention and its average gray level distribution curves along the X and Y directions.
图6为本发明实施例二采用原始的固定像素阈值测量方法获得的线条边缘分布曲线，及采用本发明方法获得的线条边缘分布曲线。6 is a line edge distribution curve obtained by using the original fixed pixel threshold measurement method in Embodiment 2 of the present invention, and a line edge distribution curve obtained by using the method of the present invention.
图7为本发明实施例二采用原始方法获得的线条宽度粗糙度功率谱密度分布曲线，及采用本方法获得的线条宽度粗糙度功率谱密度分布曲线。Fig. 7 is the power spectral density distribution curve of the line width roughness obtained by the original method in the second embodiment of the present invention, and the power spectral density distribution curve of the line width roughness obtained by the present method.
具体实施方式detailed description
本申请实施例通过提供一种电子显微图像线条宽度和粗糙度的测量方法，解决了现有技术中测量线条宽度和粗糙度工作量较大、存在人为干预造成的测量误差且只能分析有限个数据点的问题。The embodiment of the present application provides a method for measuring the line width and roughness of electron microscopic images, which solves the problem of large workload in measuring line width and roughness in the prior art, measurement errors caused by human intervention, and limited analysis. data point problem.
本申请实施例的技术方案为解决上述技术问题，总体思路如下：The technical solution of the embodiment of the present application is to solve the above-mentioned technical problems, and the general idea is as follows:
一种电子显微图像线条宽度和粗糙度的测量方法，包括：A method for measuring line width and roughness of an electron microscopic image, comprising:
获得待测线条结构的扫描电子显微图像；obtaining a scanning electron microscopic image of the line structure to be tested;
截取第一区域；Intercept the first area;
对所述第一区域内的图像进行沿线条方向平均化处理，获得线条边缘像素分布曲线；performing averaging processing along the line direction on the image in the first region to obtain a line edge pixel distribution curve;
根据所述线条边缘像素分布曲线，确定第一边界区域；determining a first boundary area according to the pixel distribution curve at the edge of the line;
对所述第一边界区域内的图像进行局域像素分析，获得边界分布；performing local pixel analysis on the image in the first boundary area to obtain boundary distribution;
根据所述边界分布，计算所述待测线条的宽度和粗糙度，提取所述待测线条的宽度数值和粗糙度数值。According to the boundary distribution, the width and roughness of the line to be tested are calculated, and the width and roughness values of the line to be tested are extracted.
通过采用边界区域和局域范围相结合的方法，前者通过沿线条方向平均化处理，获得线条边缘像素分布曲线，然后确定第一边界区域，从而有效界定了边界范围；后者通过局域像素分析，获得边界分布，从而通过设置合理的局域范围有效降低了背景像素影响，使边界定位更加准确。本发明方法能够有效降低现有方法在确定电子束成像图形时的局限性，提高测量的准确性和可靠性，并极大地节省了工程师实际量测的时间和成本。By using the method of combining the boundary area and the local area, the former obtains the line edge pixel distribution curve by averaging along the line direction, and then determines the first boundary area, thereby effectively defining the boundary area; the latter uses local pixel analysis , to obtain the boundary distribution, thereby effectively reducing the influence of background pixels by setting a reasonable local range, and making the boundary positioning more accurate. The method of the invention can effectively reduce the limitation of the existing method when determining the electron beam imaging pattern, improve the accuracy and reliability of the measurement, and greatly save the time and cost of the engineer's actual measurement.
为了更好的理解上述技术方案，下面将结合说明书附图以及具体的实施方式对上述技术方案进行详细的说明。In order to better understand the above-mentioned technical solution, the above-mentioned technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.
实施例一：Embodiment one:
本实施例提供了一种电子显微图像线条宽度和粗糙度的测量方法，如图1所示，包括：This embodiment provides a method for measuring the line width and roughness of an electron microscopic image, as shown in Figure 1, including:
步骤10：获得待测线条结构的扫描电子显微图像。Step 10: Obtain a scanning electron microscopic image of the line structure to be tested.
所述待测线条结构可以是经过光刻工艺之后所获得的光刻图形。所述光刻工艺是在光刻胶上形成图形，该图形进一步用于刻蚀的掩膜层。所述待侧线条结构也可以是通过刻蚀工艺对待刻蚀层进行刻蚀之后获得中间图形或目标图形，待刻蚀层可以为栅材料层、衬底、介质材料层或金属层等任何需要刻蚀的材料层，刻蚀工艺可以为湿法刻蚀或光刻刻蚀。所述待测线条结构可以是经过切片之后的线条图形。所述待测线条结构还可以是进行其他的工艺之后的需要测量线条宽度或者粗糙度的图形，在此不作限制，并不再一一列举。The line structure to be tested may be a photolithography pattern obtained after a photolithography process. The photolithography process is to form a pattern on the photoresist, and the pattern is further used as a mask layer for etching. The structure of the side lines to be etched can also be obtained by etching the layer to be etched to obtain an intermediate pattern or target pattern, and the layer to be etched can be any required material layer, substrate, dielectric material layer or metal layer, etc. For the material layer to be etched, the etching process can be wet etching or photolithography etching. The line structure to be tested may be a sliced line pattern. The line structure to be measured may also be a pattern that needs to measure line width or roughness after other processes, which is not limited here and will not be listed one by one.
特别的，待测线条结构的扫描电子显微图像的电子束成像之后的背景像素分布不均匀，或背景包含其他图形。例如，电子束对当前层结构成像的同时，能反应前层结构图形，从而造成电子束成像存在前层图形成像干扰；或电子束对切片结构进行成像，由于不同位置的电子束运行过程受到的约束不同而导致的电子束成像像素不均匀，故采用传统的测量方法量测线条宽度或者粗糙度时会存在严重缺陷。In particular, the background pixel distribution of the scanning electron microscopic image of the line structure to be tested is not uniform after the electron beam imaging, or the background contains other graphics. For example, while the electron beam is imaging the current layer structure, it can reflect the structure pattern of the previous layer, thus causing the interference of the image of the previous layer image in the electron beam imaging; The electron beam imaging pixels are uneven due to different constraints, so there will be serious defects when using traditional measurement methods to measure line width or roughness.
本实施例中，待测线条结构为深刻蚀线条结构的切片图，如图2所示，沿线条方向，坐标位置从1增加到100时，均值像素明显增大。In this embodiment, the line structure to be measured is a slice diagram of a deeply etched line structure. As shown in FIG. 2 , when the coordinate position increases from 1 to 100 along the line direction, the average pixel increases significantly.
此外，在所述获得待测线条结构的扫描电子显微图像之后，确定横向和纵向的每一个像素所代表的实际物理长度。In addition, after the scanning electron microscopic image of the line structure to be tested is obtained, the actual physical length represented by each pixel in the horizontal and vertical directions is determined.
本实施例中，确定的横向和纵向每一个像素代表的物理长度均为1nm。一般的，横向和纵向的像素所代表的实际物理长度相同，但是针对特殊情况可以存在不同。In this embodiment, the determined physical length represented by each pixel in the horizontal and vertical directions is both 1 nm. Generally, the actual physical lengths represented by horizontal and vertical pixels are the same, but may be different for special cases.
步骤20：截取第一区域。Step 20: Capture the first area.
所述第一区域为不包含以下任意一种或多种的区域：标尺、标注和非关注区域。The first area is an area that does not contain any one or more of the following: a ruler, a label, and a non-focused area.
一般的，可以使用其他工具对原始图片进行最佳区域选择处理，也可以在该步骤根据实际需求灵活截取合适区域。本实施例所示的图片均经过了合理区间截取，去除标尺、标注等干扰区域。Generally, other tools can be used to select the best area of the original image, or a suitable area can be flexibly intercepted according to actual needs in this step. The pictures shown in this embodiment have been intercepted in reasonable intervals to remove interference areas such as scales and labels.
根据实际情形可以选择对所述第一区域内的图像使用或不使用去噪算法；如选择使用去燥算法，可以使用但不限于高斯去噪算法，去除随机噪声的影响。本实施例没有使用去噪算法。According to the actual situation, you can choose to use or not use the denoising algorithm for the image in the first region; if you choose to use the denoising algorithm, you can use but not limited to the Gaussian denoising algorithm to remove the influence of random noise. This embodiment does not use a denoising algorithm.
步骤30：对所述第一区域内的图像进行沿线条方向平均化处理，获得线条边缘像素分布曲线。Step 30: Perform averaging processing along the line direction on the image in the first area to obtain a line edge pixel distribution curve.
所述沿线条方向平均化处理，指沿线条方向累加所有像素值，再除以像素点个数，以获取平均后的线条像素分布曲线。该步骤的作用是去除测量过程中产生的信号噪声，获取光滑的边界分布，从而为下一步骤确定基本边界区域提供准确依据。特别的，为评估前层/背景像素分布的影响，往往对底层像素值沿垂直于线条方向进行平均化处理，获取背景像素分布曲线，如本实施例中的图2所示。The averaging process along the line direction refers to accumulating all pixel values along the line direction, and then dividing by the number of pixel points to obtain an averaged line pixel distribution curve. The function of this step is to remove the signal noise generated in the measurement process and obtain a smooth boundary distribution, so as to provide an accurate basis for determining the basic boundary area in the next step. In particular, in order to evaluate the influence of the front layer/background pixel distribution, the bottom layer pixel values are often averaged along the direction perpendicular to the line to obtain the background pixel distribution curve, as shown in FIG. 2 in this embodiment.
步骤40：根据所述线条边缘像素分布曲线，确定第一边界区域。Step 40: Determine a first boundary area according to the pixel distribution curve of the line edge.
所述根据所述线条边缘像素分布曲线，确定第一边界区域，包括：The determining the first boundary area according to the pixel distribution curve of the line edge includes:
选择像素第一极值范围内的区域作为基本边界区域；Select the area within the first extremum range of the pixel as the basic boundary area;
向所述基本边界区域外拓展第一宽度，所述基本边界区域和所述第一宽度构成所述第一边界区域。A first width is extended outside the basic boundary area, and the basic boundary area and the first width form the first boundary area.
例如，对于线条扭曲的情形，沿线条平均处理的方法所获得的像素极值范围往往不能涵盖特殊扭曲线条，因此需要向外适当拓展，即向所述基本边界区域外拓展第一宽度。For example, in the case of distorted lines, the pixel extremum range obtained by the method of averaging along the lines often cannot cover the special distorted lines, so it needs to be appropriately extended outward, that is, to expand the first width outside the basic boundary area.
其中，所述像素第一极值范围优选为像素极值对应宽度的20％～80％。Wherein, the range of the first extreme value of the pixel is preferably 20% to 80% of the width corresponding to the extreme value of the pixel.
在所述根据所述线条边缘像素分布曲线，确定第一边界区域之后，还可以对所述线条边缘像素分布曲线求导，获得斜率绝对值的最大值，与所述第一边界区域进行交集检查，排除干扰区域，以确定准确的边界范围。After the first boundary area is determined according to the line edge pixel distribution curve, the line edge pixel distribution curve may also be derived to obtain the maximum value of the absolute value of the slope, and the intersection check with the first boundary area may be performed. , to exclude the interference area to determine the accurate boundary range.
步骤50：对所述第一边界区域内的图像进行局域像素分析，获得边界分布。Step 50: Perform local pixel analysis on the image in the first boundary area to obtain boundary distribution.
所述对所述第一边界区域内的图像进行局域像素分析，获得边界分布，包括：The performing local pixel analysis on the image in the first border area to obtain the border distribution includes:
选择边界局域区域范围；Select the border local area range;
筛选或去除不合理像素点；Filter or remove unreasonable pixels;
以平均化局域边界的极大值、极小值为依据计算边界位置。The boundary position is calculated based on the maximum and minimum values of the averaged local boundaries.
通过局域像素分析可以降低背景像素影响，获得较准确的边界阈值分布。Through local pixel analysis, the influence of background pixels can be reduced, and a more accurate boundary threshold distribution can be obtained.
其中，边界局域区域范围的合理选择方法通常需要兼顾背景像素分布情况。Among them, the reasonable selection method of the boundary local area usually needs to take into account the distribution of background pixels.
较少的边界局域范围可以有效减小平滑处理对最佳边界寻找的影响，但是却增加了噪声影响；较多的边界局域范围可以有效抑制噪声影响，但同时加强了背景像素的影响，使实际边界位置偏离真实位置。A smaller boundary local area can effectively reduce the influence of smoothing processing on finding the optimal boundary, but it increases the influence of noise; a larger boundary local area can effectively suppress the influence of noise, but at the same time strengthen the influence of background pixels, Make the actual boundary position deviate from the real position.
一般的，所述边界局域区域范围在沿线条方向上覆盖至少一个像素区域，所述边界局域区域范围在沿线条方向的最大像素个数不超过背景有用信息包含像素的二分之一。Generally, the range of the boundary local area covers at least one pixel area in the direction along the line, and the maximum number of pixels in the range of the boundary local area in the direction of the line does not exceed half of the pixels containing useful background information.
可以通过沿线条分布方向平均化局域像素值，或加入筛选算法分析孤立的像素值是否明显为独立噪声，或采用最佳曲线拟合的方法进行数据处理，以筛选或去除不合理像素点。Unreasonable pixels can be screened or removed by averaging the local pixel values along the line distribution direction, or adding a screening algorithm to analyze whether the isolated pixel values are obviously independent noise, or using the best curve fitting method for data processing.
以平均化局域边界的极大值、极小值为依据计算边界位置。其中，计算最佳边界位置时，采用局域边界平均化之后的极大值和极小值，边界阈值为固定像素百分制阈值。The boundary position is calculated based on the maximum and minimum values of the averaged local boundaries. Wherein, when calculating the optimal boundary position, the maximum and minimum values after local boundary averaging are used, and the boundary threshold is a fixed pixel percentage threshold.
本实施例中，沿线条方向像素分布呈现均匀变化趋势，因此局域范围沿线条方向可以选择较大的像素区域，例如本实施例使用的11个像素作为局域处理区，由此确定的边界分布如图3(b)所示。对比的，原始方法确定的边界分布如图3(a)所示，明显的，原始方法确定的边界在Y方向偏离真实边界，从而造成较大的量测误差。In this embodiment, the pixel distribution along the line direction presents a uniform trend of change, so a larger pixel area can be selected along the line direction for the local area, for example, the 11 pixels used in this embodiment are used as the local processing area, and the boundary determined thereby The distribution is shown in Fig. 3(b). In contrast, the boundary distribution determined by the original method is shown in Figure 3(a). Obviously, the boundary determined by the original method deviates from the real boundary in the Y direction, resulting in a large measurement error.
步骤60：根据所述边界分布，计算所述待测线条的宽度和粗糙度，提取所述待测线条的宽度数值和粗糙度数值。Step 60: Calculate the width and roughness of the line to be tested according to the boundary distribution, and extract the width and roughness values of the line to be tested.
所述计算线条宽度和粗糙度的方法包括：标准离散分析法，或功率谱密度分析法。The method for calculating line width and roughness includes: standard discrete analysis method, or power spectral density analysis method.
本实施例中，采用标准离散方法分别计算了原始方法确定的边界和本发明方法确定的边界的平均宽度，两者分别为31.4(纳米或像素点)和31.2(纳米或像素点)，即本发明方法的使用对平均宽度降低了0.2(纳米或像素点)。In this embodiment, the standard discrete method is used to calculate the average width of the boundary determined by the original method and the boundary determined by the method of the present invention respectively, and the two are respectively 31.4 (nanometer or pixel point) and 31.2 (nanometer or pixel point), that is, this The use of the inventive method reduces the average width by 0.2 (nanometers or pixels).
本实施例中，采用功率谱密度方法分别获得了原始方法和本发明方法得到的线条宽度粗糙度功率谱密度曲线，如图4所示，使用本发明方法可以有效降低线条宽度粗糙度，特别是低频范围的功率谱密度值。宽度粗糙度从原始方法的3.6纳米降低至本发明方法的1.6纳米，极大地提高了测量线条宽度及其均匀性范围的准确性。In this embodiment, the power spectral density curves of the line width roughness obtained by the original method and the method of the present invention were respectively obtained by using the power spectral density method. As shown in Figure 4, the method of the present invention can effectively reduce the line width roughness, especially Power spectral density values for the low frequency range. The width roughness is reduced from 3.6 nanometers in the original method to 1.6 nanometers in the method of the present invention, which greatly improves the accuracy of measuring the line width and its uniformity range.
实施例二：Embodiment two:
实施例二与实施例一在多数步骤中相同，这里仅说明不同之处。Embodiment 2 is the same as Embodiment 1 in most steps, and only the differences are described here.
步骤10：获得待测线条结构的扫描电子显微图像。Step 10: Obtain a scanning electron microscopic image of the line structure to be tested.
本实施例中，待测结构为包含双层结构的俯视图，如图5所示，其中，当前层结构的电子束成像亮度高，呈南北分布(上下分布)；前层结构的电子束成像亮度较低，呈东西分布(左右分布)。由于前层结构的存在，其平均像素分布差高达40个像素值。In this embodiment, the structure to be tested is a top view comprising a double-layer structure, as shown in Figure 5, wherein the electron beam imaging brightness of the current layer structure is high, and is distributed north-south (distributed up and down); the electron beam imaging brightness of the front layer structure Lower, east-west distribution (left-right distribution). Due to the existence of the front layer structure, the average pixel distribution difference is as high as 40 pixel values.
本实施例一个像素代表1纳米，并且未使用去噪算法。In this embodiment, one pixel represents 1 nanometer, and no denoising algorithm is used.
步骤30：对所述第一区域内的图像进行沿线条方向平均化处理，获得线条边缘像素分布曲线。Step 30: Perform averaging processing along the line direction on the image in the first area to obtain a line edge pixel distribution curve.
本实施例中，我们分别获取了测量当前层沿线条方向的边缘像素分布曲线，如图5下图所示，该平均化像素分布曲线有效地界定了当前层线条的基本边界区域；同时为评估前层图层的干扰像素信号强度，沿前层线条方向进行了像素平均化处理，获取了前层干扰信号的像素分布变化曲线，如图5左图所示，该线条分布有助于评估前层图层干扰信号强度，并以此合理选取最佳的局域化像素范围。In this embodiment, we obtained the edge pixel distribution curves measuring the current layer along the line direction respectively, as shown in the lower figure of Figure 5, the averaged pixel distribution curve effectively defines the basic boundary area of the current layer lines; The signal intensity of the interfering pixels in the front layer is averaged along the line direction of the front layer, and the pixel distribution curve of the interference signal in the front layer is obtained, as shown in the left figure of Figure 5. The line distribution is helpful for evaluating the front layer. Layer-by-layer interference signal strength is used to reasonably select the best localized pixel range.
步骤50：对所述第一边界区域内的图像进行局域像素分析，获得边界分布。Step 50: Perform local pixel analysis on the image in the first boundary area to obtain boundary distribution.
本实施例中，沿当前层线条方向，前层结构的电子束成像结果具有较强的影响，因此所确定的局域范围应尽可能小，例如本实施例使用的3个像素作为局域处理区，由此确定的边界分布如图6(b)所示。对比的，原始方法确定的边界分布曲线如图6(a)所示，明显的，在与前层结构重叠的区域，原始方法确定的边界受到非常明显的影响，线条边界向两侧扩张；本发明方法确定的边界只有轻微的波动，并未呈现较大变化。In this embodiment, along the line direction of the current layer, the electron beam imaging results of the front layer structure have a strong influence, so the determined local area should be as small as possible, for example, the 3 pixels used in this embodiment are used as local area processing area, the boundary distribution thus determined is shown in Fig. 6(b). In contrast, the boundary distribution curve determined by the original method is shown in Figure 6(a). Obviously, in the area overlapping with the previous layer structure, the boundary determined by the original method is very obviously affected, and the line boundary expands to both sides; this The boundaries determined by the inventive method fluctuate only slightly and do not show major changes.
步骤60：根据所述边界分布，计算所述待测线条的宽度和粗糙度，提取所述待测线条的宽度数值和粗糙度数值。Step 60: Calculate the width and roughness of the line to be tested according to the boundary distribution, and extract the width and roughness values of the line to be tested.
本实施例中，采用标准离散方法分别计算了原始方法确定的边界和本发明方法确定的边界的平均宽度，两者分别为37.6(纳米或像素点)和37.3(纳米或像素点)。本发明方法的使用对平均宽度降低了0.3(纳米或像素点)。In this embodiment, the standard discrete method is used to calculate the average width of the border determined by the original method and the border determined by the method of the present invention, and the two are 37.6 (nm or pixel) and 37.3 (nm or pixel) respectively. The use of the method of the invention reduces the average width by 0.3 (nanometers or pixels).
本实施例中，采用功率谱密度方法分别获得了原始方法和本发明方法得到的线条宽度粗糙度功率谱密度曲线，如图7所示，该图显示，使用本发明方法可以有效降低线条宽度粗糙度，特别是原始方法确定的功率谱密度曲线在0.02nm-1位置存在突出峰值，对应于原始图像存在50nm的周期波动，与图5沿Y方向的灰度分布以及图6(a)的边界分布曲线一致。使用本发明方法确定的边界宽度粗糙度功率谱密度曲线明显降低了中低频范围内的功率谱密度值，即降低了前层结构的干扰。从数值上看，原始方法确定的宽度粗糙度(3σ)为9.0纳米，而本发明方法确定的宽度粗糙度(3σ)降低至6.5纳米。In this embodiment, the power spectral density curves of the line width roughness obtained by the original method and the method of the present invention were respectively obtained by using the power spectral density method, as shown in Figure 7, which shows that the method of the present invention can effectively reduce the roughness of the line width In particular, there is a prominent peak at the position of 0.02nm -1 in the power spectral density curve determined by the original method, corresponding to the periodic fluctuation of 50nm in the original image, which is consistent with the gray level distribution along the Y direction in Figure 5 and the boundary of Figure 6(a) The distribution curves are consistent. The power spectral density curve of the boundary width roughness determined by the method of the present invention obviously reduces the power spectral density value in the middle and low frequency range, that is, the interference of the front layer structure is reduced. Numerically, the width roughness (3σ) determined by the original method is 9.0 nanometers, while the width roughness (3σ) determined by the method of the present invention is reduced to 6.5 nanometers.
特别需要指出的是，本发明的方法不限于精确量测包含背景影响的线条结构宽度和粗糙度的量测，同时可以精确量测线条边缘粗糙度。In particular, it should be pointed out that the method of the present invention is not limited to accurately measuring the width and roughness of the line structure including background effects, but can also accurately measure the line edge roughness.
本发明方法不仅适用于硅基集成电路制造中的先进量测，也适用于光电子器件、硅锗集成电路、三五族集成结构或微机电系统结构中的任意工艺过程中所形成的线条结构的粗糙度量测。The method of the present invention is not only applicable to advanced measurement in the manufacture of silicon-based integrated circuits, but also applicable to the measurement of line structures formed in any process of optoelectronic devices, silicon-germanium integrated circuits, integrated structures of the third and fifth groups, or micro-electromechanical system structures. Roughness measurement.
本发明实施例公开的精确表征电子显微图像线条宽度和粗糙度的测量方法，不限于集成电路器件研发和量产过程中的对关键尺寸粗糙度的精确测量，其他任何具有一维方向特征的器件或结构的光学成像或电子束成像图像均可以采用本发明提供的方法及其延伸方法进行分析和处理。The measurement method for accurately characterizing the line width and roughness of electron microscopic images disclosed in the embodiments of the present invention is not limited to the accurate measurement of the roughness of key dimensions in the process of R&D and mass production of integrated circuit devices, any other one-dimensional direction characteristics Optical imaging or electron beam imaging images of devices or structures can be analyzed and processed using the method provided by the present invention and its extended methods.
本发明实施例提供的一种电子显微图像线条宽度和粗糙度的测量方法至少包括如下技术效果：A method for measuring line width and roughness of an electron microscopic image provided by an embodiment of the present invention includes at least the following technical effects:
在本申请实施例中，采用边界区域和局域范围相结合的方法，前者通过沿线条方向平均化处理，获得线条边缘像素分布曲线，然后确定第一边界区域，从而有效界定了边界范围；后者通过局域像素分析，获得边界分布，从而通过设置合理的局域范围有效降低了背景像素影响，使边界定位更加准确。本发明方法能够有效降低现有方法在确定电子束成像图形时的局限性，提高测量的准确性和可靠性，并极大地节省了工程师实际量测的时间和成本。In the embodiment of the present application, the method of combining the boundary area and the local area is adopted. The former obtains the line edge pixel distribution curve by averaging along the line direction, and then determines the first boundary area, thereby effectively defining the boundary area; the latter The author obtains the boundary distribution through local pixel analysis, thereby effectively reducing the influence of background pixels by setting a reasonable local range, and making the boundary positioning more accurate. The method of the invention can effectively reduce the limitation of the existing method when determining the electron beam imaging pattern, improve the accuracy and reliability of the measurement, and greatly save the time and cost of the engineer's actual measurement.
最后所应说明的是，以上具体实施方式仅用以说明本发明的技术方案而非限制，尽管参照实例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的精神和范围，其均应涵盖在本发明的权利要求范围当中。Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention without limitation, although the present invention has been described in detail with reference to examples, those of ordinary skill in the art should understand that the technical solutions of the present invention can be carried out Modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present invention shall be covered by the claims of the present invention.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335990A (en) * | 2018-01-31 | 2018-07-27 | 中国科学院微电子研究所 | Method and device for positioning process defects |
| CN110006374A (en) * | 2019-04-28 | 2019-07-12 | 大连理工大学 | A CFRP roughness measurement method for obtaining image datum by multi-dimensional interpolation |
| CN110553581A (en) * | 2018-06-01 | 2019-12-10 | 华邦电子股份有限公司 | critical dimension measuring method and image processing device for measuring critical dimension |
| CN118196101A (en) * | 2024-05-17 | 2024-06-14 | 深圳市旗云智能科技有限公司 | A cable category detection method and detection system based on image processing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060023937A1 (en) * | 2004-07-30 | 2006-02-02 | Ana Tessadro | Method of measuring occluded features for high precision machine vision metrology |
| US9236219B2 (en) * | 2013-03-13 | 2016-01-12 | Macronix International Co., Ltd. | Measurement of line-edge-roughness and line-width-roughness on pre-layered structures |
| CN105590338A (en) * | 2015-12-07 | 2016-05-18 | 中国科学院微电子研究所 | Three-dimensional reconstruction method of scanning electron microscopic image |
| CN106325005A (en) * | 2016-10-12 | 2017-01-11 | 中国科学院微电子研究所 | Method for measuring photoetching process window |
| CN106352820A (en) * | 2016-08-08 | 2017-01-25 | 中国科学院微电子研究所 | Method and system for measuring roughness of wire |
-
2017
- 2017-04-25 CN CN201710279359.0A patent/CN107144210B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060023937A1 (en) * | 2004-07-30 | 2006-02-02 | Ana Tessadro | Method of measuring occluded features for high precision machine vision metrology |
| US9236219B2 (en) * | 2013-03-13 | 2016-01-12 | Macronix International Co., Ltd. | Measurement of line-edge-roughness and line-width-roughness on pre-layered structures |
| CN105590338A (en) * | 2015-12-07 | 2016-05-18 | 中国科学院微电子研究所 | Three-dimensional reconstruction method of scanning electron microscopic image |
| CN106352820A (en) * | 2016-08-08 | 2017-01-25 | 中国科学院微电子研究所 | Method and system for measuring roughness of wire |
| CN106325005A (en) * | 2016-10-12 | 2017-01-11 | 中国科学院微电子研究所 | Method for measuring photoetching process window |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335990A (en) * | 2018-01-31 | 2018-07-27 | 中国科学院微电子研究所 | Method and device for positioning process defects |
| CN108335990B (en) * | 2018-01-31 | 2021-07-27 | 中国科学院微电子研究所 | A method and device for locating process defects |
| CN110553581A (en) * | 2018-06-01 | 2019-12-10 | 华邦电子股份有限公司 | critical dimension measuring method and image processing device for measuring critical dimension |
| CN110553581B (en) * | 2018-06-01 | 2022-01-14 | 华邦电子股份有限公司 | Critical dimension measuring method and image processing device for measuring critical dimension |
| CN110006374A (en) * | 2019-04-28 | 2019-07-12 | 大连理工大学 | A CFRP roughness measurement method for obtaining image datum by multi-dimensional interpolation |
| CN118196101A (en) * | 2024-05-17 | 2024-06-14 | 深圳市旗云智能科技有限公司 | A cable category detection method and detection system based on image processing |
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