Methods, apparatus, equipment and readable storage media for surface roughness detection

Abstract

This disclosure provides a method, apparatus, device, and readable storage medium for detecting surface roughness, relating to the field of semiconductor technology. The method for detecting the surface roughness of a semiconductor thin film includes: determining the area ratio of a first region on the thin film surface based on thin film pattern process parameters, the first region comprising multiple uniformly distributed patterns; locating the multiple patterns based on overall surface roughness data and the area ratio; and detecting at least one of the roughness of the first region and the roughness of a second region based on the positioning result, the second region comprising areas outside the first region on the thin film surface. Through the technical solution of this disclosure, the location of multiple patterns in the first region can be achieved by using the area ratio of the first region, and further, the roughness of different regions can be output, thereby improving the measurement accuracy and evaluation effectiveness of the surface roughness of the thin film.

Description

Technical Field

[0001] This disclosure relates to the field of semiconductor technology, and in particular to a method for detecting the surface roughness of a semiconductor thin film, a device for detecting the surface roughness of a semiconductor thin film, a detection equipment, and a computer-readable storage medium. Background Technology

[0002] In the semiconductor device manufacturing process, various types of thin films need to be prepared to achieve specific functions. The surface roughness of various thin films has a significant impact on the contact performance between thin films. Therefore, effective roughness measurement and evaluation is of great guiding significance in the process of semiconductor electrical optimization of devices.

[0003] Semiconductor device manufacturing processes involve numerous electrically related pattern structures. For example, after CMP (Chemical Mechanical Polishing) or etch-back, pattern regions are formed on the thin film surface, and height differences occur between these pattern regions and other regions. However, current roughness detection methods built into semiconductor equipment can only obtain the overall roughness of the scanned area of ​​the thin film, and cannot separately obtain the roughness of the pattern region and the roughness of other regions. In other words, the roughness of different structural or compositional partitions cannot be output, resulting in the inability to effectively measure and evaluate the roughness of the thin film surface.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method, apparatus, device, and readable storage medium for detecting the surface roughness of semiconductor thin films, which can output the roughness of different regions separately, thereby improving the measurement accuracy and evaluation effectiveness of the surface roughness of the thin film, and thus facilitating the electrical optimization of semiconductors based on the obtained regional roughness of different regions.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a method for detecting the surface roughness of a semiconductor thin film is provided, comprising: determining an area ratio of a first region on the surface of the thin film based on thin film patterning process parameters, the first region comprising a plurality of uniformly distributed patterns; locating the plurality of patterns based on overall surface roughness data of the thin film and the area ratio; and detecting at least one of the roughness of the first region and the roughness of a second region based on the positioning result, the second region comprising a region on the surface of the thin film other than the first region.

[0008] In an exemplary embodiment of this disclosure, locating the plurality of patterns based on the overall roughness data of the thin film surface and the area ratio includes: generating a selection box larger than the pattern based on the area of ​​the pattern and the area ratio; selecting one of the plurality of patterns based on the selection box to obtain a reference pattern; extracting the roughness data of the reference pattern from the overall roughness data as reference roughness data; sorting the reference roughness data based on a height parameter value to obtain a first sorting result; selecting the reference roughness data whose sorting corresponds to the area ratio from the first sorting result; and locating the plurality of patterns based on the selected reference roughness data.

[0009] In one exemplary embodiment of this disclosure, locating the plurality of patterns based on the selected reference roughness data includes: obtaining planar position coordinates in the selected reference roughness data; calculating the mean of the planar position coordinates to obtain reference coordinates; and locating the plurality of patterns based on the reference coordinates.

[0010] In one exemplary embodiment of this disclosure, the positioning of the plurality of patterns based on the reference coordinates includes: determining the center coordinates of each pattern based on the reference coordinates, the size characteristics of the pattern, the arrangement of the plurality of patterns, and the spacing between adjacent patterns.

[0011] In an exemplary embodiment of this disclosure, determining the center coordinates of each pattern based on the reference coordinates, the size characteristics of the pattern, the arrangement of the plurality of patterns, and the spacing between adjacent patterns includes: determining the distribution position of the reference pattern among the plurality of patterns; determining a first set of center coordinates of the patterns in the same row as the reference pattern based on the reference coordinates, the distribution position, the size characteristics, and the lateral spacing; determining a second set of center coordinates of the patterns in adjacent rows of the reference pattern based on the size characteristics, the vertical spacing, and the offset; and determining a third set of center coordinates of the remaining patterns based on the arrangement.

[0012] In an exemplary embodiment of this disclosure, locating the plurality of patterns based on the overall roughness data of the thin film surface and the area ratio includes: sorting the overall roughness data of the thin film surface based on the height parameter value to obtain a second sorting result; extracting pattern roughness distribution data belonging to the first region from the second sorting result based on the area ratio; and locating the plurality of patterns based on the pattern roughness distribution data.

[0013] In an exemplary embodiment of this disclosure, locating the plurality of patterns based on the pattern roughness distribution data includes: performing a clustering operation on the pattern roughness distribution data based on the number of patterns, the size characteristics of the patterns, and the spacing between adjacent patterns to obtain a plurality of initial clusters, such that each initial cluster corresponds to a pattern; performing a calibration and optimization operation on the initial clusters based on the arrangement of the plurality of patterns, the size characteristics, and the shape characteristics of the patterns to obtain optimized clusters; and determining the center coordinates of the pattern based on the cluster centers of the optimized clusters.

[0014] In one exemplary embodiment of this disclosure, the method further includes: determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern; extracting the edge roughness data of the pattern region from the overall roughness data as roughness data to be checked; and performing a check operation on the center coordinates based on the roughness data to be checked.

[0015] In one exemplary embodiment of this disclosure, determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern includes: if the shape of the pattern is rectangular, determining the pattern region based on coordinates.

[0016] Wherein, the coordinate system is (x0, y0) are the center coordinates of the initial position pattern, m is the row number, n is the pattern number in the row, a is the length of the rectangle, b is the width of the rectangle, c is the horizontal spacing, d is the vertical spacing, and e is the horizontal offset of the initial pattern in each row relative to the initial position pattern.

[0017] In one exemplary embodiment of this disclosure, determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern includes: if the shape of the pattern is elliptical, determining the pattern region based on a relational expression.

[0018] Wherein, the relation is (x0, y0) are the center coordinates of the initial position pattern, m is the row number, n is the pattern number in the row, e is the lateral offset of the initial pattern in each row relative to the initial position pattern, f is the semi-major axis of the ellipse, g is the semi-minor axis of the ellipse, h is the longitudinal distance between the centers of two patterns in adjacent rows, and i is the distance between the centers of two adjacent patterns in the same row.

[0019] In an exemplary embodiment of this disclosure, the step of detecting the roughness of the first region based on the positioning result includes: using the center coordinates as the positioning point, performing a delineation operation with a first frame to delineate roughness distribution data within multiple first frames, which serve as a first set of roughness data. The first frames have the same shape as the pattern, and the area of ​​the first frames is smaller than the area of ​​the pattern. The mean value of the first set of roughness data is determined as the roughness of the first region.

[0020] In an exemplary embodiment of this disclosure, the step of detecting the roughness of the second region based on the positioning result includes: using the center coordinates as the positioning point, performing a delineation operation with a second frame to delineate the roughness distribution data outside the second frame, which serves as a second set of roughness data. The second frame has the same shape as the pattern, and the area of ​​the second frame is larger than the area of ​​the pattern. The mean value of the second set of roughness data is determined as the roughness of the second region.

[0021] In one exemplary embodiment of this disclosure, determining the area percentage of the first region on the surface of the thin film based on thin film patterning process parameters includes: the process parameters include the pattern shape, pattern size, and number of patterns formed on the thin film; and the area percentage is determined based on the total area of ​​the thin film, the pattern shape, the pattern size, and the number of patterns.

[0022] According to another aspect of this disclosure, a semiconductor thin film surface roughness detection device is provided, comprising: a determining module, configured to determine the area ratio of a first region on the thin film surface based on thin film pattern process parameters, the first region comprising a plurality of uniformly distributed patterns; a positioning module, configured to position the plurality of patterns based on overall surface roughness data of the thin film and the area ratio; and a detection module, configured to detect at least one of the roughness of the first region and the roughness of a second region based on the positioning result, the second region comprising a region on the thin film surface other than the first region.

[0023] According to another aspect of this disclosure, a detection apparatus is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method for detecting the surface roughness of a semiconductor thin film as described in any one of the first aspects of the technical solutions above by executing the executable instructions.

[0024] According to another aspect of this disclosure, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for detecting the surface roughness of a semiconductor thin film as described in the above embodiments.

[0025] The semiconductor thin film surface roughness detection scheme provided by the embodiments of this disclosure calculates the area ratio of a first region comprising multiple patterns on the semiconductor thin film surface, locates the multiple patterns in the first region based on the overall roughness and area ratio of different regions of the detected thin film surface, and outputs the roughness of different regions separately based on the obtained location results, thereby improving the measurement accuracy and evaluation effectiveness of the surface roughness of the thin film, and thus facilitating the electrical optimization of the semiconductor based on the obtained partitioned roughness of different regions.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to an embodiment of the present disclosure is shown;

[0029] Figure 2 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to another embodiment of this disclosure is shown;

[0030] Figure 3 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to another embodiment of the present disclosure is shown;

[0031] Figure 4 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to yet another embodiment of this disclosure is shown;

[0032] Figure 5A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to yet another embodiment of this disclosure is shown;

[0033] Figure 6 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to yet another embodiment of this disclosure is shown;

[0034] Figure 7 A schematic diagram of a thin film surface pattern provided in one embodiment of the present disclosure is shown;

[0035] Figure 8 A schematic diagram of a thin film surface pattern provided in another embodiment of this disclosure is shown;

[0036] Figure 9 A schematic diagram of a lateral cross-section of a thin film provided in one embodiment of the present disclosure is shown;

[0037] Figure 10 A flowchart of a method for detecting the surface roughness of a semiconductor thin film according to yet another embodiment of this disclosure is shown;

[0038] Figure 11 A schematic block diagram of a semiconductor thin film surface roughness detection device provided in one embodiment of the present disclosure is shown;

[0039] Figure 12 This is a schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0040] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0041] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0042] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and steps, nor do they necessarily need to be performed in the described order. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the actual situation. The terms "a," "one," and "the above" are used to indicate the presence of one or more elements / components / etc. The terms "comprising," "including," and "having" are used to indicate an open-ended inclusion and mean that additional elements / components / etc. may exist besides those listed.

[0043] Reference Figure 1 The embodiments of this disclosure first provide a method for detecting the surface roughness of a semiconductor thin film, including:

[0044] Step S102: Determine the area ratio of the first region on the thin film surface based on the thin film pattern process parameters. The first region includes multiple uniformly distributed patterns.

[0045] like Figure 2 As shown, the first region 202 includes multiple patterns, and the second region 204 is the region outside the first region. For example, the pattern in the first region can be SiN, and the pattern in the second region can be a polysilicon poly layer.

[0046] In addition, as a setting method for evenly distributing multiple patterns, such as Figure 2 As shown, the horizontal arrangement positions of alternating rows are the same, and there is a fixed deviation between the horizontal arrangement positions of adjacent rows.

[0047] Specifically, suppose the area of ​​the first region is 'a', the area of ​​the second region is 'b', and the area ratio is 1 / 2.

[0048] Step S104: Position multiple patterns based on the overall roughness data and area ratio of the thin film surface.

[0049] The pattern can be a circle, ellipse, triangle, square, rectangle, parallelogram, rhombus, regular polygon, trapezoid, etc.

[0050] In addition, the overall roughness data of the thin film surface includes the measurement location points on the thin film and the roughness values ​​measured at those locations.

[0051] The thin film surface is divided into different regions (e.g., a first region and a second region) based on structural function or thin film composition, and there are height differences between the different regions. Multiple patterns are located based on the overall roughness data and area ratio of the thin film surface. This can enable the location of multiple clusters based on the differential distribution of roughness data and the distribution ratio (area ratio).

[0052] Step S106: Detect at least one of the roughness of the first region and the roughness of the second region based on the positioning result, wherein the second region includes the region outside the first region on the film surface.

[0053] One approach is to calculate the roughness of only one region based on the positioning results of multiple patterns, or to calculate the roughness of different regions separately based on the positioning results of multiple patterns.

[0054] In this embodiment, by calculating the area ratio of a first region comprising multiple patterns on the surface of a semiconductor thin film, the multiple patterns in the first region are located based on the overall roughness and area ratio of different regions of the detected thin film surface. Based on the obtained location results, the roughness of different regions is output separately, thereby improving the measurement accuracy and evaluation effectiveness of the roughness of the thin film surface. This is beneficial for optimizing the electrical properties of the semiconductor based on the obtained partitioned roughness of different regions.

[0055] like Figure 3 As shown, in an exemplary embodiment of this disclosure, step S104, which involves locating multiple patterns based on the overall roughness data and area ratio of the thin film surface, includes the following specific implementation:

[0056] Step S302: Generate a selection box larger than the pattern based on the area and area ratio of the pattern.

[0057] Let the area of ​​the pattern be S, and the area percentage be... The area of ​​the generated selection box is

[0058] In addition, the shape of the selection box only needs to be able to enclose the pattern within the box, such as an oval, rectangle, trapezoid, etc. As a preferred box shape, the same shape as the pattern is used as the selection box.

[0059] Step S304: Select one of the multiple patterns based on the selection box to obtain the reference pattern.

[0060] Specifically, the pattern selection operation needs to ensure that the selected pattern is within the selection box, and that the reference pattern includes the first part of the pattern within the selection box and the second part outside the pattern.

[0061] As a preferred implementation, assuming that multiple patterns are all within the first quadrant of the Cartesian coordinate system, the pattern closest to the origin can be selected as the reference pattern. Figure 2 The image shows reference pattern 206 obtained by using a rectangle as a selection box.

[0062] Step S306: Extract the roughness data of the reference pattern from the overall roughness data and use it as the reference roughness data.

[0063] Extracting roughness data from the reference pattern from the overall roughness data involves extracting the overall roughness data divided within the reference pattern, specifically including the roughness data of the first part mentioned above and the roughness data of the second part mentioned above.

[0064] Step S308: Sort the reference roughness data based on the height parameter value to obtain the first sorting result.

[0065] The first sorting result is obtained by sorting the roughness data in the reference pattern, that is, by sorting the roughness data of the first part and the roughness data of the second part.

[0066] The height parameter value is specifically the longitudinal height (or depth) value measured at different measurement points on the film surface.

[0067] Specifically, if the combined pattern is higher than the second region, the sorting method is to arrange them from high to low according to the height parameter value; if the combined pattern is lower than the second region, the sorting method is to arrange them from low to high according to the height parameter value.

[0068] Step S310: Select the reference roughness data corresponding to the sorting and area ratio from the first sorting results.

[0069] Specifically, selecting the reference roughness data corresponding to the sorting and area percentage involves choosing the reference roughness data within the top area percentage range from the first sorting result. For example, if the area percentage is... Then select the one that appears first in the first sorting result. The reference roughness data obtained by taking the height parameter value and the corresponding position coordinates is the roughness data of the first part mentioned above, which is the roughness data of the pattern within the selection box.

[0070] Step S312: Position multiple patterns based on the selected reference roughness data.

[0071] Specifically, since the selected reference roughness data is the roughness data of the pattern within the selection box, the center coordinates of the pattern within the selection box can be determined based on the selected reference roughness data, and then the positions of other patterns can be further determined based on these center coordinates.

[0072] In this embodiment, as a way to locate multiple patterns, a reference pattern and reference roughness data within the pattern are selected, and the reference roughness data are sorted based on the height parameter value. Then, the corresponding reference roughness data is selected from the sorting results based on the area ratio. The reference coordinates, i.e. the center coordinates of the pattern within the selection box, are obtained by calculating the position of the selected reference roughness data. Then, the positions of other patterns are further determined based on the reference coordinates, thus realizing the positioning operation of multiple patterns.

[0073] In one exemplary embodiment of this disclosure, step S210, which involves locating multiple patterns based on selected reference roughness data, includes: obtaining planar position coordinates from the selected reference roughness data; calculating the average of the planar position coordinates to obtain reference coordinates; and locating the multiple patterns based on the reference coordinates.

[0074] The reference coordinates are the center coordinates of the pattern within the selection box.

[0075] In this embodiment, since the selected reference roughness data can be considered to be evenly distributed around the center point of the reference image, the mean values ​​of the x and y coordinates of the planar position coordinates are calculated to obtain the corresponding mean values ​​of the x and y directions, which are the center coordinates of the reference image. These mean values ​​are then used as reference coordinates to further combine with parameters such as the spacing between adjacent patterns and the pattern size to determine the center coordinates of other patterns. This enables the positioning of multiple patterns and ensures the positioning accuracy of the patterns.

[0076] In one exemplary embodiment of this disclosure, positioning multiple patterns based on reference coordinates includes: determining the center coordinates of each pattern based on the reference coordinates, the size characteristics of the patterns, the arrangement of the multiple patterns, and the spacing between adjacent patterns.

[0077] like Figure 4 As shown, in an exemplary embodiment of this disclosure, a specific implementation of determining the center coordinates of each pattern based on reference coordinates, the size characteristics of the pattern, the arrangement of multiple patterns, and the spacing between adjacent patterns includes:

[0078] Step S402: Determine the distribution position of the reference pattern in multiple patterns.

[0079] Step S404: Determine the first set of center coordinates of the pattern that is in the same row as the reference pattern based on the reference coordinates, distribution position, size characteristics and lateral spacing.

[0080] Specifically, in Figure 2In the coordinate system shown, after determining the position of the pattern in reference pattern 206, the center coordinates of other patterns that are in the same row as the reference pattern can be determined based on the reference coordinates (x0, y0), distribution position, size characteristics and lateral spacing of the pattern.

[0081] Step S406: Determine the second set of center coordinates of the pattern of adjacent rows of the reference pattern based on the size characteristics, longitudinal spacing and offset.

[0082] Step S408: Determine the third set of center coordinates of the remaining patterns based on the arrangement.

[0083] In this embodiment, given the reference coordinates (i.e., the center coordinates of the pattern within the selection box), for adjacent patterns in the same row as the reference pattern, the center coordinates of the adjacent patterns can be determined based on the horizontal spacing and horizontal dimensions. For adjacent patterns in different rows from the reference pattern, if there is a horizontal offset, such as... Figure 2 As shown, the center coordinates of adjacent images can be determined based on the vertical spacing, vertical dimension, and horizontal offset. Then, the coordinates are extended sequentially along the horizontal and vertical directions until the center coordinates of all patterns are determined, so as to ensure the reliability of the positioning of multiple patterns.

[0084] like Figure 5 As shown, in an exemplary embodiment of this disclosure, step S104, another implementation of locating multiple patterns based on the overall roughness data and area ratio of the thin film surface, includes:

[0085] Step S502: Sort the overall roughness data of the film surface based on the height parameter value to obtain the second sorting result.

[0086] The second sorting result is the sorting result of all roughnesses on the film surface.

[0087] Step S504: Extract the pattern roughness distribution data belonging to the first region from the second sorting result based on the area ratio.

[0088] Step S506: Locate multiple patterns based on pattern roughness distribution data.

[0089] In this embodiment, as another way to locate multiple patterns, all roughness data of the thin film surface are sorted to obtain a second sorting result, and the pattern roughness distribution data of the first region is extracted based on the area ratio. Then, the multiple patterns are located based on the extracted pattern roughness distribution data, thus realizing the location of multiple patterns based on the distribution characteristics of pattern roughness data.

[0090] like Figure 6As shown, in an exemplary embodiment of this disclosure, step S306, which involves locating multiple patterns based on pattern roughness distribution data, includes:

[0091] Step S602: Based on the number of patterns, the size characteristics of the patterns, and the spacing between adjacent patterns, clustering operation is performed on the pattern roughness distribution data to obtain multiple initial clusters, so that each initial cluster corresponds to a pattern.

[0092] The number of clusters is determined based on the number of patterns. Multiple initial center points are selected as initial cluster centers based on the distribution of the pattern coarse distribution data, and clustering operations are performed to obtain multiple initial clusters. Since the number of clusters is the same as the number of patterns, the distribution of the initial clusters can reflect the distribution of the patterns.

[0093] Step S604: Based on the arrangement, size and shape features of multiple patterns, the initial cluster is calibrated and optimized to obtain an optimized cluster.

[0094] Specifically, the pattern region defined by the pattern is determined based on the size and shape features of the pattern. The initial cluster is then optimized and updated in combination with the pattern arrangement to ensure that the position coordinates in the optimized pattern roughness distribution data are all within the corresponding pattern region.

[0095] Step S606: Determine the center coordinates of the pattern based on the cluster centers of the optimized clusters.

[0096] In this embodiment, initial clustering and optimized clustering operations are performed sequentially based on pattern roughness distribution data to obtain corresponding cluster centers based on the clustering results. Multiple position coordinates are then determined based on the cluster centers, and these multiple position coordinates are used as the center coordinates of multiple patterns. This method helps to ensure the accuracy of locating multiple patterns.

[0097] In one exemplary embodiment of this disclosure, the method further includes: determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern; extracting the edge roughness data of the pattern region from the overall roughness data as roughness data to be checked; and performing a check operation on the center coordinates based on the roughness data to be checked.

[0098] In this embodiment, to further ensure the accuracy of pattern positioning, the obtained center coordinates can be further checked based on the roughness data within the determined pattern area. Specifically, a closed annular band including the pattern border can be generated, and the roughness data within the annular band can be obtained as edge roughness data. Based on the relative relationship with the border, the accuracy of the center coordinates can be verified. Furthermore, when data that should belong to the pattern is not within the pattern area, or when the pattern area includes data that should not belong to the pattern, adjustments can be made in a timely manner to ensure that the obtained center coordinates have high accuracy.

[0099] like Figure 7 As shown, in an exemplary embodiment of this disclosure, determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern includes: if the shape of the pattern is rectangular, determining the pattern region based on a coordinate system, wherein the coordinate system is:

[0100]

[0101] (x0, y0) are the center coordinates of the initial position pattern, m is the row number, n is the pattern number in the row, a is the length of the rectangle, b is the width of the rectangle, c is the horizontal spacing, d is the vertical spacing, and e is the horizontal offset of the initial pattern in each row relative to the initial position pattern.

[0102] like Figure 7 As shown, assuming the center coordinates of the first rectangle in the lower left corner are (x0, y0), the bottom row is considered the first row, and counting upwards, the coordinates of the rectangular area can be determined by the pattern features and the dimensions such as the pattern spacing, including a, b, c, d, and e. For odd-numbered rows, e = 0.

[0103] Specifically, for the first row, the coordinate region of the nth rectangle from left to right is:

[0104]

[0105] For the second row, the coordinate region of the nth rectangle from left to right is:

[0106]

[0107] For the third row, the coordinate region of the nth rectangle from left to right is:

[0108]

[0109] For the fourth row, the coordinate region of the nth rectangle from left to right is:

[0110]

[0111] ...

[0112] For the m-th row, the coordinate region of the n-th rectangle from left to right is:

[0113]

[0114] like Figure 8 As shown, in an exemplary embodiment of this disclosure, determining the pattern region of each pattern based on the center coordinates and the size characteristics of the pattern includes: if the shape of the pattern is elliptical, determining the pattern region based on a relation, wherein the relation is:

[0115]

[0116] Where (x0, y0) are the center coordinates of the initial position pattern, m is the row number, n is the pattern number in the row, e is the lateral offset of the initial pattern in each row relative to the initial position pattern, f is the semi-major axis of the ellipse, g is the semi-minor axis of the ellipse, h is the longitudinal distance between the centers of two patterns in adjacent rows, and i is the distance between the centers of two adjacent patterns in the same row.

[0117] Those skilled in the art will understand that, for circles or ellipses, the spacing can be the horizontal or vertical distance between the centers.

[0118] Specifically, such as Figure 8 As shown, assuming the center coordinates of the first rectangle in the lower left corner are (x0, y0), the bottom row is considered the first row, and counting upwards, the coordinates of the elliptical region can be determined by the pattern features and the dimensions such as the pattern spacing, including e, f, g, h and i, where e = 0 for odd-numbered rows.

[0119] For the first row, the coordinates of the nth ellipse from left to right satisfy:

[0120]

[0121] For the second row, the coordinates of the nth ellipse from left to right satisfy:

[0122]

[0123] For the third row, the coordinates of the nth ellipse from left to right satisfy:

[0124]

[0125] For the fourth row, the coordinates of the nth ellipse from left to right satisfy:

[0126]

[0127] ...

[0128] For the m-th row, the coordinates of the n-th ellipse from left to right satisfy:

[0129]

[0130] In an exemplary embodiment of this disclosure, detecting the roughness of a first region based on the positioning result includes: using the center coordinates as the positioning point, performing a delineation operation with a first frame to delineate roughness distribution data within multiple first frames, which serve as a first set of roughness data. The first frames have the same shape as the pattern, and the area of ​​the first frames is smaller than the area of ​​the pattern. The mean value of the first set of roughness data is determined as the roughness of the first region.

[0131] In this embodiment, by using a first frame smaller than the pattern shape to perform the delineation operation, it is ensured that all roughness data belonging to the second region can be excluded from the first frame, thereby ensuring the accuracy of the roughness of the first region.

[0132] In an exemplary embodiment of this disclosure, detecting the roughness of the second region based on the positioning result includes: using the center coordinates as the positioning point, performing a delineation operation with a second frame to delineate the roughness distribution data outside the second frame, which serves as the second set of roughness data. The second frame has the same shape as the pattern, and the area of ​​the second frame is larger than the area of ​​the pattern. The mean value of the second set of roughness data is determined as the roughness of the second region.

[0133] In this embodiment, by using a second frame larger than the pattern shape to perform the delineation operation, it is ensured that all roughness data belonging to the first region can be delineated within the second frame. In this way, when using the roughness data within the second frame for calculation, the accuracy of the roughness of the second region can be guaranteed.

[0134] In one exemplary embodiment of this disclosure, determining the area percentage of a first region on the surface of a thin film based on thin film patterning process parameters includes: the process parameters include the pattern shape, pattern size, and number of patterns formed on the thin film; and the area percentage is determined based on the total area of ​​the thin film, the pattern shape, the pattern size, and the number of patterns.

[0135] In this embodiment, since the thin film pattern is formed based on process requirements, parameters such as pattern shape, pattern size, number of patterns, and spacing are all known. Based on these parameters, when the pattern position is unknown, the area ratio between the area of ​​the first region and the area of ​​the second region can be determined, so as to infer the position information of multiple patterns based on the area ratio.

[0136] like Figure 8 and Figure 9As shown, 802 corresponds to the first region, and 804 corresponds to the second region. Assuming the multiple patterns in the first region are silicon nitride (SiN) layers, and the second region is a polysilicon (poly) layer, and assuming the center coordinates of the pattern SiN[1,1](Line1,Row 1) are (x0, y0), f = 26.5 nm, and g = 26 nm, then the coordinates (x, y) within the region of the pattern SiN[1,1] must satisfy:

[0137] (x-x0) 2 / 26.5 2 +(y-y0) 2 / 26 2 <1

[0138] The center coordinates of patterns SiN[1,2] and SiN[1,3] can be determined based on the lateral spacing i of (x0, y0).

[0139] The center coordinates of pattern SiN[2,1] can be determined based on the longitudinal spacing h and relative offset e between pattern SiN[1,1].

[0140] Furthermore, after extracting the roughness data within the first frame or the roughness data outside the second frame, the roughness of the first region or the roughness of the second region can be calculated based on the following formula (3).

[0141]

[0142] Where n is the amount of data, The height value of the roughness measurement reference surface.

[0143] like Figure 10 As shown, a method for detecting the surface roughness of a semiconductor thin film according to a specific embodiment of the present disclosure includes:

[0144] Step S1002: Obtain the shape and size data of the pattern and the spacing data of the distribution.

[0145] Step S1004: Based on the shape and size data of the patterns and the spacing data of their distribution, determine the area ratio of multiple patterns relative to the film surface and the area of ​​a single pattern.

[0146] Where, assuming the area of ​​the first region is 'a' and the area of ​​the second region is 'b', then the area ratio is .

[0147] Step S1006: Select a pattern using a selection box generated based on the pattern area and area ratio to obtain a reference pattern.

[0148] Step S1008: Extract the roughness data from the reference pattern to obtain reference roughness data. Sort the height parameter values ​​according to the empirical height difference between the first region and the second region. Extract the height parameter values ​​that are sorted within the above area proportion range and determine their corresponding position coordinates.

[0149] Step S1010: Calculate the average of these position coordinates to determine the center coordinates of the selected pattern.

[0150] Step S1012: Based on the shape and size data of the pattern and the spacing data of the distribution, determine the center coordinates of other patterns.

[0151] Step S1014: Based on the center coordinates and size data of each pattern, assign a first frame to each pattern, and use the average roughness of all the first frames as the average roughness of the first region.

[0152] The first frame is concentric and conforms to the pattern, and the area of ​​the first frame is smaller than the area of ​​the pattern.

[0153] Step S1016: Based on the center coordinates and size data of each pattern, assign a second frame to each pattern, and use the average roughness of all areas outside the second frame as the average roughness of the second region.

[0154] The second frame is concentric and conforms to the pattern, and the area of ​​the second frame is larger than the area of ​​the pattern.

[0155] The following reference Figure 11 This invention relates to a semiconductor thin film surface roughness detection device 1100 according to this embodiment of the invention. Figure 11 The semiconductor thin film surface roughness detection device 1100 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0156] The semiconductor thin film surface roughness detection device 1100 is manifested in the form of a hardware module. The components of the semiconductor thin film surface roughness detection device may include, but are not limited to: a determination module 1102, used to determine the area ratio of a first region on the thin film surface based on thin film pattern process parameters, the first region comprising a plurality of uniformly distributed patterns; a positioning module 1104, used to position the plurality of patterns based on the overall roughness data and area ratio of the thin film surface; and a detection module 1106, used to detect at least one of the roughness of the first region and the roughness of a second region based on the positioning result, the second region comprising the area outside the first region on the thin film surface.

[0157] The following is for reference. Figure 12It shows a schematic diagram of the structure of a computer system 1200 suitable for implementing electronic devices according to embodiments of the present disclosure. Figure 12 The computer system 1200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0158] like Figure 12 As shown, the computer system 1200 includes a central processing unit (CPU) 1201, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1202 or programs loaded from storage section 1208 into random access memory (RAM) 1203. The RAM 1203 also stores various programs and data required for system operation. The CPU 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An input / output (I / O) interface 1209 is also connected to the bus 1204.

[0159] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0160] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the semiconductor thin film surface roughness detection method as described in the above embodiments.

[0161] For example, electronic devices can achieve such Figure 1 As shown: Step S102, determine the area ratio of a first region on the film surface based on the thin film pattern process parameters, the first region including multiple patterns evenly distributed; Step S104, locate the multiple patterns based on the overall roughness data and area ratio of the thin film surface; Step S106, detect at least one of the roughness of the first region and the roughness of the second region based on the positioning result, the second region including the area outside the first region on the thin film surface.

[0162] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this application.

[0163] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0165] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0166] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0167] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0168] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0169] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for detecting surface roughness of a semiconductor thin film, characterized by, The method comprises the following steps: determining the area proportion of a first area on the film surface based on the film pattern process parameters, the first area comprising a plurality of patterns uniformly distributed; positioning the plurality of patterns based on the overall roughness data of the film surface and the area proportion; detecting at least one of the roughness of the first area and the roughness of a second area based on the positioning result, the second area comprising an area outside the first area on the film surface; wherein the positioning of the plurality of patterns based on the overall roughness data of the film surface and the area proportion comprises: generating a selection box larger than the pattern based on the area of the pattern and the area proportion; selecting one of the plurality of patterns based on the selection box to obtain a reference pattern; extracting the roughness data of the reference pattern from the overall roughness data as reference roughness data; sorting the reference roughness data based on the height parameter value to obtain a first sorting result; selecting the reference roughness data corresponding to the area proportion from the first sorting result; positioning the plurality of patterns based on the selected reference roughness data.

2. The method of claim 1, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The positioning of the plurality of patterns based on the selected reference roughness data comprises: obtaining the plane position coordinates in the selected reference roughness data; performing mean value calculation on the plane position coordinates to obtain a reference coordinate; positioning the plurality of patterns based on the reference coordinate.

3. The method of claim 2, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The positioning of the plurality of patterns based on the reference coordinate comprises: determining the center coordinates of each pattern based on the reference coordinate, the size characteristics of the pattern, the arrangement mode of the plurality of patterns, and the spacing between adjacent patterns.

4. The method of claim 3, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The determination of the center coordinates of each pattern based on the reference coordinate, the size characteristics of the pattern, the arrangement mode of the plurality of patterns, and the spacing between adjacent patterns comprises: determining the distribution position of the reference pattern in the plurality of patterns; determining a first group of center coordinates of the patterns in the same row as the reference pattern based on the reference coordinate, the distribution position, the size characteristics, and the lateral spacing; determining a second group of center coordinates of the patterns in the adjacent row of the reference pattern based on the size characteristics, the longitudinal spacing, and the offset; determining a third group of center coordinates of the remaining patterns based on the arrangement mode.

5. The method of claim 1, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The positioning of the plurality of patterns based on the overall roughness data of the film surface and the area proportion comprises: sorting the overall roughness data of the film surface based on the height parameter value to obtain a second sorting result; extracting the pattern roughness distribution data belonging to the first area from the second sorting result based on the area proportion; positioning the plurality of patterns based on the pattern roughness distribution data.

6. The method of claim 5, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The positioning of the plurality of patterns based on the pattern roughness distribution data comprises: clustering the pattern roughness distribution data based on a number of the patterns, size features of the patterns, and a spacing between adjacent patterns, to obtain a plurality of initial clustering clusters, each of the initial clustering clusters corresponding to a pattern; calibration optimizing the initial clustering clusters based on an arrangement of the plurality of patterns, the size features, and shape features of the patterns, to obtain optimized clustering clusters; determining a center coordinate of the pattern based on a clustering center of the optimized clustering clusters.

7. The method of claim 3 or 6, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface roughness meter. Further comprising: determining a pattern area of each pattern based on the center coordinate and the size features of the pattern; extracting edge roughness data of the pattern area from the overall roughness data as to-be-inspected roughness data; inspecting the center coordinate based on the to-be-inspected roughness data.

8. The method of claim 7, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The determination of the pattern area of each pattern based on the center coordinate and the size features of the pattern comprises: if the shape of the pattern is rectangular, determining the pattern area based on a coordinate formula, Wherein, the coordinate formula is ( ×c+ , (), , ) represents the center coordinates of the initial position pattern, m is the row number, n is the pattern number in the row, a is the length of the rectangle, b is the width of the rectangle, c is the horizontal spacing, d is the vertical spacing, and e is the horizontal offset of the initial pattern in each row relative to the initial position pattern.

9. The method of claim 7, wherein the step of detecting the surface roughness of the semiconductor thin film is performed by using a surface profiler. The determination of the pattern area of each pattern based on the center coordinate and the size features of the pattern comprises: if the shape of the pattern is elliptical, determining the pattern area based on a relational formula, wherein the relationship is + =1, (1) , ) is the center coordinate of the initial position pattern, m is the row number, n is the sequence number of the pattern in the row, e is the lateral offset of the initial pattern of each row relative to the initial position pattern, f is the semi-major axis of the ellipse, g is the semi-minor axis of the ellipse, h is the longitudinal distance between the centers of two patterns in adjacent rows, and i is the distance between the centers of two patterns adjacent in the same row.

10. The method of claim 3 or 6, wherein the semiconductor thin film is a silicon thin film. The detection of the roughness of the first area based on the positioning result comprises: taking the center coordinate as a positioning point, and using a first frame to perform a circumscribing operation to circumscribe roughness distribution data within a plurality of the first frames as a first group of roughness data, the first frame being the same shape as the pattern, and the area of the first frame being smaller than the area of the pattern; determining the mean value of the first group of roughness data as the roughness of the first area.

11. The method of claim 3 or 6, wherein the semiconductor thin film is a silicon thin film. The detection of the roughness of the second area based on the positioning result comprises: taking the center coordinate as a positioning point, and using a second frame to perform a circumscribing operation to circumscribe roughness distribution data outside the second frame as a second group of roughness data, the second frame being the same shape as the pattern, and the area of the second frame being larger than the area of the pattern; determining the mean value of the second group of roughness data as the roughness of the second area.

12. The method of claim 1 to 6, wherein The determination of the area ratio of the first area on the film surface based on the film pattern process parameters comprises: The process parameters include the pattern shape, pattern size, and pattern number formed on the film, and the area ratio is determined based on the total area of the film, the pattern shape, the pattern size, and the pattern number.

13. A device for detecting surface roughness of a semiconductor thin film, characterized by comprising: a light source; a light receiving element; and a light path length adjusting means for adjusting a light path length between the light source and the light receiving element. Comprising: a determination module configured to determine an area ratio of a first area on a film surface based on film pattern process parameters, the first area including a plurality of patterns uniformly distributed; a positioning module configured to position the plurality of patterns based on overall roughness data of the film surface and the area ratio; a detection module configured to detect at least one of roughness of the first area and roughness of a second area based on a positioning result, the second area including an area outside the first area on the film surface; The positioning of the plurality of patterns based on the overall roughness data of the film surface and the area ratio comprises: generating a bounding box larger than the pattern based on the area of the pattern and the area ratio; performing a selection operation on one of the plurality of patterns based on the bounding box to obtain a reference pattern; extracting roughness data of the reference pattern from the overall roughness data as reference roughness data; sorting the reference roughness data based on height parameter values to obtain a first sorting result; selecting the reference roughness data corresponding to the area ratio in sorting from the first sorting result; positioning the plurality of patterns based on the selected reference roughness data.

14. A device for detecting surface roughness of a semiconductor thin film, characterized by comprising: comprise: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the executable instructions to perform the semiconductor thin film surface roughness detection method of any one of claims 1-12.

15. A computer readable medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the semiconductor thin film surface roughness detection method of any one of claims 1-12. The program is executed by the processor to implement the semiconductor thin film surface roughness detection method of any one of claims 1-12.

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