SRAF(Sub-Resolution Assist Feature) generating method, and mask manufacturing method comprising the generating method

Abstract

The technical concept of the present disclosure provides a method for generating an SRAF characterized by comprising: a step of performing Inverse Lithography Technology (ILT) on a main feature; a step of generating a point cloud map including a plurality of points on the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating a Sub-Resolution Assist Feature (SRAF) based on the point cloud map; and a step of generating the SRAF based on the seed.

Description

Technology Field

[0001] The technical concept of the present disclosure relates to a method for manufacturing a mask, and in particular to a method for manufacturing a mask including a method for generating SRAF. Background Technology

[0002] In semiconductor manufacturing processes, a photolithography process using a mask can be performed to form a pattern on a semiconductor substrate, such as a wafer. Simply defined, a mask can be described as a pattern transfer material in which a pattern shape made of an opaque material is formed on a transparent substrate. To briefly explain the mask manufacturing process, first, the required circuit is designed and a layout for the circuit is designed, and then mask design data obtained through Optical Proximity Correction (OPC) is transferred as Mask Tape-Out (MTO) design data. Subsequently, Mask Data Preparation (MDP) is performed based on the MTO design data, and a mask can be manufactured by performing front-end processes (Front End Of Line: FEOL), such as photolithography, and back-end processes (Back End Of Line: BEOL), such as defect inspection. The problem to be solved

[0003] The problem that the technical concept of the present disclosure aims to solve is to provide a method for producing SRAF with increased consistency and a method for manufacturing a mask including said method.

[0004] Furthermore, the problems that the technical concept of the present disclosure aims to solve are not limited to those mentioned above, and other problems can be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0005] To solve the aforementioned problem, the technical concept of the present disclosure provides a method for generating an SRAF characterized by comprising: a step of performing Inverse Lithography Technology (ILT) on a main feature; a step of generating a point cloud map including a plurality of points on the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating a Sub-Resolution Assist Feature (SRAF) based on the point cloud map; and a step of generating the SRAF based on the seed.

[0006] To solve the above-mentioned problem, the technical concept of the present disclosure provides a method for generating an SRAF characterized by comprising: a step of dividing a full shot layout into a plurality of patches; a step of performing ILT on a main feature of at least one of the plurality of patches; a step of generating a point cloud map including a plurality of points for the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating an SRAF based on the point cloud map; and a step of generating the SRAF based on the seed.

[0007] To solve the above-mentioned problem, the technical concept of the present disclosure provides a mask manufacturing method characterized by comprising: a step of performing ILT on a main feature; a step of generating a point cloud map including a plurality of points on the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating SRAF based on the point cloud map; a step of generating the SRAF based on the seed; a step of performing MRC on the SRAF; a step of determining whether there is a defect in the performance of the MRC; a step of transmitting a layout image including the main feature and the SRAF as MTO (Mask Tape-Out) design data if there is no defect; a step of preparing mask data based on the MTO design data; and a step of exposing a substrate for a mask based on the mask data. Effects of the invention

[0008] According to the technical concept of the present disclosure, after generating a point cloud map, the method may include symmetrizing the point cloud map and continuousizing the point cloud map. Accordingly, an SRAF with increased consistency may be generated. Additionally, a method for generating an SRAF with increased consistency and a mask manufacturing method including the generation method may be provided, including symmetrizing the point cloud map between different patches.

[0009] According to the technical concept of the present disclosure, it may include symmetricalizing a point cloud map using a hash value. Accordingly, a D2 symmetrical and / or D4 symmetrical point cloud map can be obtained, and a method for generating an SRAF with increased consistency and a method for manufacturing a mask including said method may be provided.

[0010] In addition, the SRAF generation method of the present disclosure divides the patch and performs ILT by considering a unique design, thereby reducing the number of ILT operations and reducing the Turn Around Time (TAT) of the entire process. Accordingly, an SRAF generation method with increased efficiency and a mask manufacturing method including said generation method can be provided.

[0011] The effects obtainable from the exemplary embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art to which the exemplary embodiments of the present disclosure belong from the description below. That is, unintended effects resulting from the implementation of the exemplary embodiments of the present disclosure can also be derived by those skilled in the art from the exemplary embodiments of the present disclosure. Brief explanation of the drawing

[0012] FIG. 1 is a flowchart illustrating a method for generating a Sub-Resolution Assist Feature (SRAF) according to one embodiment of the present disclosure. FIG. 2 is a drawing illustrating a part of the layout of a full shot design including a plurality of patches according to one embodiment of the present disclosure. Figure 3 is an enlarged view of the first patch of Figure 2. FIG. 4 is a drawing for explaining a method for finding a unique shape segment according to one embodiment of the present disclosure. FIG. 5 is a drawing for explaining a method for finding a unique shape point according to one embodiment of the present disclosure. FIG. 6 is a flowchart illustrating a method for generating a point cloud map according to one embodiment of the present disclosure. FIGS. 7 to 10 are drawings illustrating the generation of a point cloud map according to one embodiment of the present disclosure. FIG. 11 is a drawing showing a point cloud map according to one embodiment of the present disclosure. FIG. 12 is a diagram showing a combination of point cloud maps of multiple main features. FIG. 13 is a flowchart illustrating the process of symmetrizing a point cloud map according to one embodiment of the present disclosure. FIG. 14 is a drawing showing the arrangement of unique shape points of a main feature according to one embodiment of the present disclosure. FIG. 15 is a diagram showing the relative coordinates of all points in a point cloud map, with each unique shape point of a main feature according to one embodiment of the present disclosure as a reference point. FIG. 16 is a diagram showing the loading of an ILT image onto a point cloud map according to one embodiment of the present disclosure. Figure 17 is a drawing showing an ILT image corresponding to area A of Figure 16. FIG. 18 is a drawing showing a point cloud map and an ILT image corresponding to area A of FIG. 16. Figure 19 is a diagram showing the values ​​inside the points of the point cloud map corresponding to area A of Figure 16. FIG. 20 is a drawing showing a continuous point cloud map according to one embodiment of the present disclosure. Figure 21 is a diagram showing the location of a seed extracted from a point cloud map containing two main features. Figure 22 is a drawing showing an SRAF generated based on the seed of Figure 21. FIG. 23 is a flowchart illustrating a method for manufacturing a mask according to one embodiment of the present disclosure. Specific details for implementing the invention

[0013] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Identical components in the drawings are denoted by the same reference numerals, and redundant descriptions thereof are omitted. In the drawings below, the thickness or size of each layer is exaggerated for convenience and clarity of explanation, and accordingly, may differ somewhat from the actual shape and proportions.

[0014] Terms indicating spatial position, such as "bottom," "below," "lower," "top," "upper," etc., used herein are intended solely for ease of understanding and are intended to describe the relative positional relationships between elements or features depicted in the drawings; they do not limit the technical scope of the invention in any sense. Terms regarding relative spatial position are intended to encompass variations in the orientation of the semiconductor device in addition to the directions disclosed in the drawings. That is, the semiconductor device may be oriented in various directions during use (or manufacturing), and even in such cases, the terms regarding position used herein will be readily understood by those skilled in the art.

[0015] FIG. 1 is a flowchart illustrating a method for generating a Sub-Resolution Assist Feature (SRAF) according to one embodiment of the present disclosure.

[0016] Referring to FIG. 1, a full shot layout can be divided into a plurality of patches containing one or more main features (Fm) (S110). Here, the main feature (Fm) may correspond to a target pattern to be formed on a substrate such as a wafer. Such a target pattern can be formed by transferring a pattern on a mask onto the substrate through an exposure process. Accordingly, first, a layout for a pattern on a mask corresponding to the target pattern, i.e., a mask layout, can be designed. For reference, due to the characteristics of the exposure process, the shape of the target pattern and the shape of the pattern on the mask may generally be different. Also, since the pattern on the mask is projected down and transferred onto the substrate, the pattern on the mask may have a larger size than the target pattern.

[0017] Meanwhile, as patterns become finer, an Optical Proximity Effect (OPE) caused by the influence between neighboring patterns occurs during the exposure process. To overcome this, Optical Proximity Correction (OPC) can be performed to suppress the occurrence of OPE by correcting the mask layout. OPC is broadly divided into two types: one is rule-based OPC, and the other is simulation-based or model-based OPC. Model-based OPC can be advantageous in terms of time and cost because it utilizes only the measurement results of representative patterns without the need to measure all of a large number of test patterns. Meanwhile, OPC may include not only modifications to the mask layout but also, in a broad sense, methods for adding sub-lithographic features called serifs on the corners of the pattern, or methods for adding SRAFs such as scattering bars. Accordingly, the method for generating curved SRAFs in this embodiment may be included in OPC. Meanwhile, SRAF is an auxiliary pattern introduced to solve the problem of deviation caused by OPC due to different diffraction patterns in each region in terms of optical properties when patterns within a chip are formed in high-density and low-density regions, and such SRAF is not a pattern actually formed on the wafer.

[0018] Inverse Lithography Technology (ILT) is one of the most important techniques for curved masks and is a type of Optical Process Control (OPC). Conventional OPC operates by correcting distortion caused by diffraction by finely dividing the edges of a pattern and moving them up, down, left, and right, or by inserting square auxiliary features based on rules. Meanwhile, the image transferred from the photomask to the wafer surface can be calculated by mathematically representing the optical system. This is called the forward function, and ILT is a technique for calculating the inverse function of this forward function.

[0019] The OPC process can be briefly explained as follows. First, basic data for OPC is prepared. Next, an OPC model is generated that includes an optical OPC model and an OPC model for the photoresist (PR). Subsequently, OPC-certified layout images or data are obtained through a simulation process using the OPC model. Next, a Mask Rule Check (MRC) is performed on the OPC-certified layout images. Here, MRC refers to checking the width or spacing limits that must be maintained when manufacturing a mask. For example, there may be mask process limits, such as restrictions preventing the pattern width from being smaller than a set minimum width or the spacing between patterns from being smaller than a set minimum spacing. Therefore, performing or verifying MRC refers to the process of checking whether the aforementioned limits are being observed in the mask layout. Through this MRC execution, the final OPC-certified layout images can be obtained. These final OPC-certified layout images can then be delivered to the mask manufacturing team as Mask Tape-Out (MTO) design data for subsequent mask production.

[0020] A patch may include one or more main features (Fm). The entire area occupied by a plurality of target patterns to be formed on a substrate, such as a wafer, may be divided into one or more patches. In one embodiment, the patches may be divided so that the amount of OPC computation can be minimized. The step (S110) of dividing a full shot (FS of FIG. 2) layout into a plurality of patches containing one or more main features (Fm) is described with reference to FIG. 2 and FIG. 3 together.

[0021] FIG. 2 is a drawing showing a part of the layout of a full-shot design including a plurality of patches according to one embodiment of the present disclosure, and FIG. 3 is an enlarged drawing showing the first patch of FIG. 2.

[0022] Referring to FIGS. 2 and 3, a full shot (FS) may be the entire area of ​​a lithography mask that can be transferred to a wafer (or a photoresist formed on the wafer) through a single exposure. Here, lithography is a technique for transferring a pattern of a pre-prepared mask onto a wafer through exposure and development processes. Generally, the pattern formed on the lithography mask has a larger size than the actual pattern to be implemented, so it can be reduced and transferred to the wafer. Since the patterns implemented on the actual wafer are very fine, the precision of pattern formation can be improved by forming a pattern larger than the actual pattern on the lithography mask.

[0023] A full shot (FS) may, for example, have a length of approximately 26 mm in width and approximately 33 mm in height. Here, the design layout on the full shot (FS) may include two-dimensional patterns. For convenience of explanation, the mutually orthogonal directions of the design layout are defined as the first horizontal direction (X direction) and the second horizontal direction (Y direction). Since the patches (pa1 to pa25) have a length of approximately 1 µm in width and height, millions of patches (pa1 to pa25) may be arranged within a single full shot (FS). The patches (pa1 to pa25) may be arranged to form a matrix along the first horizontal direction (X direction) and the second horizontal direction (Y direction). The edges of each patch may be substantially parallel to either the first horizontal direction (X direction) or the second horizontal direction (Y direction).

[0024] Each of the plurality of patches (pa1 to pa25) may contain one or more main features (Fm). In FIG. 3, the first to fifth main features (Fm1, Fm2, Fm3, Fm4, Fm5) are exemplarily shown disposed in the first patch (pa1), but the technical concept of the present disclosure is not limited thereto. For example, each patch may contain four or fewer main features (Fm) or six or more main features (Fm).

[0025] To minimize computation, a full shot (FS) layout may be divided into multiple patches so that the number of patches of the same type is maximized. Here, patches of the same type may be patches in which the shape of the main feature (Fm) within the patch and / or the arrangement of the main feature (Fm) is identical. That is, a full shot (FS) layout may be divided into multiple patches so that patches of different types are minimized.

[0026] Returning to FIG. 1, after the patch is segmented (S110), ILT can be performed (S120). ILT can be performed on all unique shape points and / or all unique shape segments. ILT is performed to generate an ILT image. A method for finding the unique shape segments and unique shape points of the main feature (Fm) is described with reference to FIG. 4 and FIG. 5 together.

[0027] FIG. 4 is a drawing for explaining a method for finding a unique shape segment according to one embodiment of the present disclosure, and FIG. 5 is a drawing for explaining a method for finding a unique shape point according to one embodiment of the present disclosure.

[0028] Referring to FIG. 4, in order to find a unique shape segment, each main feature (Fm) can be divided into multiple segments. Then, a hash value for each of the multiple segments can be generated.

[0029] First, the hash value of a key segment (KSG) among multiple segments can be calculated. The key segment (KSG) may be one of the segments of the main feature (Fm). Specifically, a first query region (QR1) based on the key segment (KSG) may be generated. The first query region (QR1) may be a peripheral region that optically affects the key segment (KSG). The first query region (QR1) may contain the key segment (KSG) and peripheral segments (SSG). Within the first query region (QR1), all segments excluding the key segment (KSG) may be peripheral segments (SSG).

[0030] A hash value can be calculated based on the key segment (KSG) and surrounding segment (SSG) within the first query area (QR1). In one embodiment, the hash value can be calculated based on the length of the key segment (KSG), the length of the surrounding segment (SSG), and / or the distance between the key segment (KSG) and the surrounding segment (SSG). That is, a hash value can be calculated based on the characteristics of the key segment (KSG) itself and the surrounding condition.

[0031] Referring to FIG. 5, in order to find unique shape points, each main feature (Fm) can be divided into multiple points. Then, a hash value can be generated for each of the multiple points.

[0032] First, hash values ​​of multiple points can be calculated. Specifically, a second query area (QR2) based on a key point (KP) can be generated. The key point (KP) may be one of the points of the main feature (Fm). The second query area (QR2) may be a surrounding area that optically affects the key point (KP). The second query area (QR2) may contain the key point (KP) and surrounding points (SP). Within the second query area (QR2), all points excluding the key point (KP) may be surrounding points (SP).

[0033] A hash value can be calculated based on a key point (KP) and a surrounding point (SP) within a second query area (QR2). In one embodiment, a hash value can be calculated based on an array of key points (KP) and surrounding points (SP). In one embodiment, a hash value can be calculated based on the distance between the key point (KP) and the surrounding point (SP). That is, a hash value can be calculated based on the characteristics of the key point (KP) itself and surrounding conditions.

[0034] Here, a hash value refers to the output value produced by a hash function. A hash function can convert the characteristics of a segment (or point) into a unique value. That is, segments (or points) with the same hash value may be segments (or points) possessing the same geometric feature. A hash value is output for all segments (or points), and segments (or points) having different hash values ​​(i.e., different geometric features) can be selected as unique-type segments (or unique-type points).

[0035] Returning to Fig. 1, after ILT is performed (S120), a point cloud map (PCM) for the main feature (Fm) can be generated (S130). The point cloud map (PCM) may be a set of data points in space.

[0036] A point cloud map (PCM) may include multiple points (PT). In one embodiment, the point cloud map (PCM) may have an elliptical shape. In another embodiment, the point cloud map (PCM) may have a rectangular shape. However, the shape of the point cloud map (PCM) is not limited to this, and it is understood that the point cloud map (PCM) may have various shapes. In one embodiment, the shape of the point cloud map may be selected based on the shape of the SRAF.

[0037] A method for generating a point cloud map (PCM) is explained with reference to FIGS. 6 to 12.

[0038] FIG. 6 is a flowchart illustrating a method for generating a point cloud map according to an embodiment of the present disclosure, and FIGS. 7 to 10 are drawings illustrating the generation of a point cloud map according to an embodiment of the present disclosure. FIG. 11 is a drawing illustrating a point cloud map according to an embodiment of the present disclosure. In addition, FIG. 12 is a drawing illustrating a combination of point cloud maps of a plurality of main features.

[0039] Referring to FIGS. 6 to 12, the step (S130) of generating a point cloud map (PCM) for a plurality of main features (Fm) may include the step (S132) of first forming a point cloud map (PCM) for each of a single main feature (Fm) and the step (S134) of combining the point cloud maps (PCM) for each main feature (Fm).

[0040] In one embodiment, the point cloud map (PCM) may be generated based on a main feature (Fm). In one embodiment, the point cloud map (PCM) may be generated based on rules. For example, the point cloud map (PCM) may be generated by adjusting the distance from the main feature (Fm) based on rules. For example, the point cloud map (PCM) may be generated based on a pre-selected distance from the main feature (Fm) and a pre-selected distance between points (PT) of the point cloud map (PCM).

[0041] In one embodiment, the point cloud map (PCM) may be formed outside the main feature (Fm). For example, the space defined by the point cloud map (PCM) may be larger than the space defined by the main feature (Fm).

[0042] First, to generate a point cloud map (PCM) for a single main feature (Fm), the main feature (Fm) can be subdivided into multiple subdivided edges (Pe). The rules for subdividing the edges of the main feature (Fm) into multiple subdivided edges (Pe) can be defined in various ways. For example, in FIG. 7, the edges of the main feature (Fm) can be subdivided at predetermined intervals to generate multiple subdivided edges (Pe). The black dots may correspond to subdivision points for edge subdivision.

[0043] After subdividing the main feature (Fm) into multiple subdivided edges (Pe), first and second rectangular position polygons (PP1, PP2) are generated at a distance to generate a point cloud map (PCM) for each of the multiple subdivided edges (Pe). In FIG. 8, two types of position polygons are exemplified, but one or more than three types of position polygons may be generated corresponding to one main feature (Fm).

[0044] After that, after the first and second position polygons (PP1, PP2) are created, the first and second position polygons (PP1, PP2) are rounded to create a plurality of curve axes. In FIG. 9, curve axes (CA1, CA2) can be created by rounding both the first and second position polygons (PP1, PP2) in the first horizontal direction (X direction). In one embodiment, the curve axes (CA1, CA2) can be created by defining the lines of the curve axes (CA1, CA2) based on the segments of the first and second position polygons (PP1, PP2). For example, the curve axes (CA1, CA2) can be created such that the lines of the curve axes (CA1, CA2) form the corresponding ellipse by making specific points of the line segment satisfy the elliptic equation.

[0045] After the first and second curve axes (CA1, CA2) are created, curve points (CP1, CP2) are created on the lines of the curve axes (CA1, CA2). The curve points (CP1, CP2) can be created in various ways on the lines of the curve axes (CA1, CA2) according to a predetermined rule. For reference, the points on the first and second position polygons (PP1, PP2) in FIG. 8 and the points on the curve axes (CA1, CA2) in FIG. 9 may be points corresponding to the subdivision points for edge division in the main feature (Fm) mentioned earlier.

[0046] At least some of the points on the first and second position polygons (PP1, PP2) and points on the curve axes (CA1, CA2) can be combined to generate the first and second point cloud maps (PCM1, PCM2). In one embodiment, the vertex points of the first and second position polygons (PP1, PP2) may not be included in the point cloud map (PCM).

[0047] In another embodiment, a rectangular point cloud map (PCM) can be generated by adding points on the line segments of the first and second position polygons (PP1, PP2) of FIG. 9.

[0048] Referring to FIG. 12, the point cloud maps of each of the first to fifth main features (Fm1, Fm2, Fm3, Fm4, Fm5) can be combined. The point cloud map (PCM) of each main feature (Fm) can be combined in a Voronoi diagram manner. A Voronoi diagram can divide a plane (or space) based on a generating point. A Voronoi diagram can divide a plane (or space) into multiple polygons. Here, the generating point may be the center point of each main feature (Fm). For example, the center point of the first main feature (Fm1) may be called the first generating point, and the center point of the second main feature (Fm2) may be called the second generating point. When the first point cloud map of the first main feature (Fm1) and the second point cloud map of the second main feature (Fm2) are combined, a boundary can be formed on a line at the same distance from each of the first generation point and the second generation point.

[0049] Returning to Fig. 1, after the point cloud map (PCM) is generated (S130), the point cloud map (PCM) can be symmetrical (S140). The process of symmetricalizing the point cloud map (PCM) is explained with reference to Fig. 13.

[0050] FIG. 13 is a flowchart illustrating the process of symmetrizing a point cloud map according to one embodiment of the present disclosure. It will be explained together with reference to FIG. 11.

[0051] Referring to FIG. 13, first, relative coordinates for all points (PT) of the point cloud map (PCM) can be calculated based on the unique shape point of the main feature (Fm) (S142). Here, the unique shape point can be calculated based on the hash value described in FIG. 4 and FIG. 5. The step of calculating relative coordinates for all points (PT) of the point cloud map (PCM) (S142) is explained with reference to FIG. 14 and FIG. 15 together.

[0052] FIG. 14 is a diagram showing the arrangement of unique shape points of a main feature according to one embodiment of the present disclosure. FIG. 15 is a diagram showing the relative coordinates of all points of a point cloud map using each unique shape point of a main feature according to one embodiment of the present disclosure as a reference point. This will be explained together with reference to FIG. 11.

[0053] Referring to FIG. 14, as described above, points on the main feature (Fm) can be selected as three types of unique shape points by considering geometric features. For example, in the main feature (Fm) of FIG. 14, a unique shape point located at a vertex of the main feature (Fm) can be called a first unique shape point (USP1), a unique shape point on an edge extended in the first horizontal direction (X direction) can be called a second unique shape point (USP2), and a unique shape point on an edge extended in the second horizontal direction (Y direction) can be called a third unique shape point (USP3).

[0054] Referring to FIG. 15, relative coordinates for all points (PT) of the point cloud map (PCM) can be calculated based on each of the first to third unique shape points (USP1, USP2, USP3). That is, relative coordinates for all points (PT) of the point cloud map (PCM) can be calculated using each unique shape point of the main feature (Fm) as a reference point. The relative coordinates may be the value obtained by subtracting the coordinates of the unique shape points of the main feature (Fm) from the absolute coordinates of all points (PT) of the point cloud map (PCM).

[0055] Returning to FIG. 13, after the relative coordinates for all points (PT) of the point cloud map (PCM) are calculated (S142), an ILT image can be loaded into the point cloud map (PCM) (S144). The step of loading the ILT image (S144) is explained with reference to FIG. 16 through 19.

[0056] FIG. 16 is a diagram illustrating the loading of an ILT image onto a point cloud map according to an embodiment of the present disclosure. FIG. 17 is a diagram illustrating an ILT image corresponding to area A of FIG. 16, and FIG. 18 is a diagram illustrating a point cloud map and an ILT image corresponding to area A of FIG. 16. FIG. 19 is a diagram corresponding to area A of FIG. 16. This is a diagram showing the values ​​inside the points of a point cloud map. In FIGS. 17 and 18, values ​​are displayed for each pixel of the ILT image, and in FIG. 19, values ​​are displayed for each point (PT) of the point cloud map (PCM). This will be explained with reference to FIG. 11.

[0057] Referring to FIGS. 16 through 19, loading an ILT image onto a point cloud map (PCM) may include placing a corresponding location ILT image on the point cloud map (PCM). The ILT image may have a value per pixel. In one embodiment, the value per pixel of the ILT image may be the light intensity for each pixel. In another embodiment, the value per pixel of the ILT image may be the light transmittance for each pixel. In one embodiment, each pixel of the ILT image may have a value corresponding to a grayscale level. In one embodiment, the value per pixel within the ILT image may be normalized. For example, each pixel within the ILT image may have a value of 0 to 1.

[0058] The database unit (DBU) of an ILT image and the DBU of a point cloud map (PCM) may differ. That is, the minimum length unit of each pixel in an ILT image may differ from the minimum length unit of a point (PT) in a point cloud map (PCM). For example, the DBU of an ILT image may be larger than the DBU of a point cloud map (PCM).

[0059] Therefore, the position of each pixel in the ILT image and each point (PT) in the point cloud map (PCM) may be misaligned. In this case, the value of each pixel in the ILT image is interpolated to each point (PT) in the point cloud map (PCM) so that every point (PT) in the point cloud map (PCM) can have a value.

[0060] Subsequently, a representative value for a point (PT) in the point cloud map (PCM) can be selected so that points having the same relative coordinates based on the unique shape point of the main feature (Fm) have the same value (S146). That is, points having the same relative coordinates from unique shape points having the same hash value in the point cloud map (PCM) can all be changed to have the same value (e.g., representative value). Each of the plurality of points may have a representative value. In one embodiment, the maximum value, minimum value, and / or average value of each point having the same relative coordinates based on the unique shape point of the same type of main feature (Fm) may be selected as the representative value of the point.

[0061] The above step S146 may be performed within the same patch (intra-pach) and / or between different patches of the same type (inter-patch). Here, different patches of the same type may refer to patches in which the shape of the main feature (Fm) and / or the arrangement of the main feature (Fm) within the reference patch is identical. In one embodiment, step S146 may be performed in different patches of the same type after being performed within the same patch.

[0062] As the above symmetry is performed, the point cloud map (PCM) may be D2 symmetric and / or D4 symmetric. D2 symmetry may mean that there are 2 equal parts that can divide an object by rotation, and D4 symmetry may mean that there are 4 equal parts that can divide an object by rotation. For example, a rectangle may be an example of D2 symmetry, and a square may be an example of D4 symmetry.

[0063] More specifically, if there are n equal parts (n is a natural number) into which an object can be divided by rotation, it can be represented by Dn symmetry. In the case of Dn symmetry, the object rad ( When rotated by ) amount, it can have the same shape as the original shape of the target object.

[0064] Returning to Fig. 1, after the point cloud map (PCM) is symmetrical (S140), the point cloud map (PCM) can be continuous (S150). The process of continuousizing the point cloud map (PCM) is explained with reference to Fig. 20.

[0065] FIG. 20 is a drawing showing a continuous point cloud map according to one embodiment of the present disclosure.

[0066] Referring to FIG. 20, a continuous point cloud map (PCM) is illustrated. In one embodiment, the discontinuous point cloud map (PCM) can be continuousized using a Delaunay triangulation method. An example of continuousization using the Delaunay triangulation method is illustrated in FIG. 20. Delaunay triangulation can provide a continuous space from a discrete space. For example, providing a continuous space from a discrete space can be called spatial interpolation.

[0067] A point cloud map (PCM) may contain values ​​of discontinuous points within a plane (or higher-dimensional space). For example, as described in FIGS. 16 through 19, the values ​​of the space where a point (PT) is located may be defined, but the values ​​of the space where a point (PT) is not located (e.g., between adjacent points (PT)) may not be defined.

[0068] Delaunay triangulation can calculate values ​​for the space (discontinuous points) between points (PT) based on the coordinates of each point (PT) in a point cloud map (PCM) and the values ​​of each point (PT) (e.g., light intensity at each point (PT)).

[0069] The Delaunay triangulation method can create a triangle mesh from a set of points in a plane (or higher-dimensional space) (here, points (PT) of a point cloud map (PCM)). The triangle mesh can satisfy the conditions that i) the minimum angle of the triangle is maximized and / or ii) no other point is contained within the circumcircle of each triangle. Values ​​of regions other than the vertices of the triangle can be calculated by performing interpolation based on the values ​​of the vertices of the triangle formed through the above process. That is, values ​​(e.g., light intensity between points (PT) of a point cloud map (PCM)) can be provided in a continuous space by performing interpolation based on the values ​​of the vertices of the triangle formed through the above process.

[0070] Returning to FIG. 1, after continuousizing the point cloud map (PCM) (S150), a seed for forming the SRAF can be extracted based on the continuousized point cloud map (PCM) (S160). The seed may be a reference point for determining the arrangement of the SRAF. In one embodiment, the seed may be extracted by differentiating the continuousized point cloud map (PCM). In one embodiment, a point where the second derivative of the continuousized point cloud map (PCM) is zero may be extracted as the seed. During the process of extracting the seed, constraints on the seed (e.g., the width of the seed) may be considered.

[0071] After that, an SRAF can be generated based on the seed (S170). The SRAF can be generated based on the position of the seed and by considering the SRAF generation conditions. In one embodiment, the SRAF can be generated based on the position of the seed and by considering the width of the SRAF, the spacing of the SRAF, the area of ​​the SRAF, the length of the SRAF, and / or the angle of the SRAF. In one embodiment, the SRAF can have a curvilinear shape. That is, the dimensions of the SRAF can be determined based on the position of the seed.

[0072] Later, if the generation conditions for SRAF are changed (for example, if the MRC for SRAF is changed), SRAF can be easily generated based on the seed extracted in step S160.

[0073] Examples of the step of extracting seeds (S160) and the step of generating SRAF (S170) are shown in FIGS. 21 and FIGS. 22.

[0074] FIG. 21 is a diagram showing the location of a seed extracted from a point cloud map containing two main features. FIG. 22 is a diagram showing an SRAF generated based on the seed of FIG. 21. FIG. 21 and FIG. 22 illustrate, by way of example, the case where two main features (Fm) are placed.

[0075] Referring to FIGS. 21 and 22, as described above, the location where a seed is to be placed in a symmetric and continuous point cloud can be extracted. Based on the extracted seed, an SRAF can be generated. As described above, the SRAF can be generated by applying SRAF constraints to the extracted seed.

[0076] Returning to FIG. 1, after generating the SRAF (S170), an MRC for the SRAF may be performed (S175). Step S175 may include verifying the SRAF length, SRAF width, SRAF area, the distance between the SRAF and an adjacent pattern, and / or the angle of the SRAF. In another embodiment, the MRC for the SRAF may be omitted.

[0077] The SRAF generation method of the present disclosure may include generating a point cloud map (PCM), then symmetrizing the point cloud map (PCM) and continuing the point cloud map (PCM). Thus, an SRAF with increased consistency may be generated. Additionally, an SRAF with increased consistency may be generated by including symmetrizing the point cloud map (PCM) between different patches. When manufacturing a mask and / or wafer with an SRAF with increased consistency, mask process variation may be reduced and / or wafer critical dimension (CD) variation may be reduced.

[0078] Additionally, the SRAF generation method of the present disclosure may include symmetricing a point cloud map (PCM) using a hash value. Accordingly, a D2 symmetric and / or D4 symmetric point cloud map (PCM) can be obtained, and an SRAF with increased consistency can be generated.

[0079] In addition, the SRAF generation method of the present disclosure divides the patch and performs ILT by considering a unique design, thereby reducing the number of ILT operations and reducing the Turn Around Time (TAT) of the entire process. Therefore, the efficiency of the SRAF generation method can be increased.

[0080] FIG. 23 is a flowchart illustrating a method for manufacturing a mask according to one embodiment of the present disclosure. It will be explained together with reference to FIG. 1.

[0081] Referring to FIG. 23, a mask manufacturing method including the SRAF generation method of the present disclosure (hereinafter simply referred to as the "mask manufacturing method") sequentially performs the step of dividing a patch (S110) to the step of performing MRC (S175). The steps of dividing a patch (S110) to performing MRC (S175) are as described in the description section regarding the SRAF generation method of FIG. 1.

[0082] After performing MRC (S175), it is determined whether there is a defect (S180). In other words, it is determined whether there are any violations of the MRC conditions in the generated SRAF from the result of performing MRC. If there is a defect (Yes), the process proceeds to the step of generating the SRAF (S170) and changes the shape of the SRAF to satisfy the MRC conditions. For example, the SRAF length, SRAF width, SRAF area, separation distance from the SRAF and adjacent patterns, and / or the angle of the SRAF are changed to satisfy the MRC conditions. Afterwards, the process proceeds to the step of performing MRC again (S175).

[0083] If there are no defects (No), a layout image including the main feature (Fm) and SRAF is transmitted to the mask production team as MTO design data (S185). Generally, MTO may refer to the final mask data obtained through the OPC method being handed over to the mask production team to request mask production. Such MTO design data may have a graphic data format used in Electronic Design Automation (EDA) software, etc. For example, MTO design data may have a data format such as GDS II (Graphic Data System II) or OASIS (Open Artwork System Interchange Standard).

[0084] Subsequently, Mask Data Preparation (MDP) is performed based on MTO design data (S190). Mask data preparation may include, for example, i) format conversion, ii) augmentation of a barcode for mechanical reading, a standard mask pattern for inspection, a job deck, etc., called fracturing, and iii) verification in automatic and manual ways. Here, a job deck may refer to creating a text file containing a series of instructions such as batch information of multiple mask files, a reference dose, and exposure speed or method.

[0085] After preparing the mask data, the mask substrate is exposed using the mask data, i.e., the E-beam data (S195). Here, exposure may refer, for example, to E-beam writing. Here, E-beam writing can be performed using a gray writing method, for example, with a Multi-Beam Mask Writer (MBMW). Additionally, E-beam writing may be performed using a Variable Shape Beam (VSB) writer.

[0086] After the photolithography process, a series of processes can be performed to complete the mask. These processes may include, for example, development, etching, and cleaning. Additionally, the series of processes for mask manufacturing may include metrology, defect inspection, or defect repair processes. Furthermore, a pellicle application process may be included. Here, the pellicle application process refers to the process of attaching a pellicle to the mask surface to protect it from subsequent contamination during shipping and throughout its service life, once it is confirmed through final cleaning and inspection that there are no contaminants or chemical stains.

[0087] The mask manufacturing method of the present embodiment may include the method for generating the SRAF of FIG. 1 described above. Accordingly, optimal OPC layout images for masks are generated, and based on the optimal OPC layout images, masks including corresponding patterns can be manufactured accurately with high reliability.

[0088] Up to this point, the present disclosure has been described with reference to the embodiments illustrated in the drawings, but this is merely illustrative, and those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of the present disclosure should be determined by the technical spirit of the appended claims.

Claims

Claim 1 A method for generating an SRAF characterized by comprising: a step of performing Inverse Lithography Technology (ILT) on a main feature; a step of generating a point cloud map including a plurality of points on the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating a Sub-Resolution Assist Feature (SRAF) based on the point cloud map; and a step of generating the SRAF based on the seed. Claim 2 A method for generating an SRAF according to claim 1, wherein the step of symmetrizing the point cloud map is performed based on at least one of a unique shape segment and a unique shape point of the main feature. Claim 3 A method for generating an SRAF according to claim 2, wherein the step of generating the point cloud map comprises: a step of generating a point cloud map for each of the single main features; and a step of combining the point cloud maps for each of the main features. Claim 4 A method for generating an SRAF according to claim 1, wherein the step of generating the SRAF is characterized by selecting at least one of the spacing distance of the SRAF, the area of ​​the SRAF, the length of the SRAF, and the angle of the SRAF by considering the Mask Rule Check (MRC) on the seed. Claim 5 A method for generating an SRAF characterized by comprising: a step of dividing a full shot layout into a plurality of patches; a step of performing ILT on a main feature of at least one of the plurality of patches; a step of generating a point cloud map including a plurality of points for the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating an SRAF based on the point cloud map; and a step of generating the SRAF based on the seed. Claim 6 In claim 5, the step of symmetrizing the point cloud map based on the ILT image is, A step of calculating relative coordinates for all points of the above point cloud map; A step of loading the above ILT image onto the above point cloud map; and A method for generating an SRAF characterized by including a step of selecting the value of a point of the above point cloud map. Claim 7 A method for generating an SRAF according to claim 6, wherein the step of calculating relative coordinates for all points of the point cloud map is performed by subtracting the coordinates of the unique shape point of the main feature from the absolute coordinates of all points of the point cloud map based on the unique shape point of the main feature. Claim 8 In claim 5, the step of selecting the value of a point of the point cloud map is characterized by calculating the value of the point by interpolating the value of the loaded ILT image. Claim 9 In claim 5, the step of selecting the value of a point in the point cloud map is characterized by selecting a representative value of a point in the point cloud map such that points having the same relative coordinates based on the same unique shape point of the main feature have the same value. Claim 10 A mask manufacturing method characterized by comprising: a step of performing ILT on a main feature; a step of generating a point cloud map including a plurality of points on the main feature; a step of symmetrizing the point cloud map based on the ILT image; a step of serializing the point cloud map; a step of extracting a seed for generating SRAF based on the point cloud map; a step of generating the SRAF based on the seed; a step of performing MRC on the SRAF; a step of determining whether there is a defect in the performance of MRC; a step of transmitting a layout image including the main feature and the SRAF as MTO (Mask Tape-Out) design data if there is no defect; a step of preparing mask data based on the MTO design data; and a step of exposing a substrate for a mask based on the mask data.

Images