Mask process correction method of curve mask

Abstract

The embodiment of the invention provides a mask process correction method of a curve mask, electronic equipment and a computer readable storage medium. The mask process correction method comprises the following steps: dividing a curve segment of a curve mask into a plurality of sections and determining a plurality of simulation sampling points respectively corresponding to the plurality of sections; based on the position data of the corresponding simulation sampling point in the plurality of simulation sampling points and based on simulation of the mask manufacturing process, determining an estimated position of a section where the corresponding simulation sampling point is located; and adjusting the position of the section where the corresponding simulation sampling point is located based on the deviation between the estimated position and the expected position of the section where the corresponding simulation sampling point is located.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to semiconductor manufacturing technology, and more particularly, to a mask process correction method for a curved mask, an electronic device, and a computer-readable storage medium. Background Art

[0002] Photolithography technology transfers the circuit pattern on the mask to the surface of the wafer through optical-chemical reactions and a series of etching steps, thereby forming an integrated circuit or chip with the desired function. As an additional means of photolithography, computational lithography technology mainly reduces or eliminates the image distortion caused by physical limitations such as the proximity effect during the photolithography process through mathematical modeling, simulation, and algorithm optimization. Computational lithography technology has been an important driving force for the continued development of graphic miniaturization technology in recent years. It can break through the hardware limitations of the minimum exposure size by improving software technologies such as resolution while keeping the hardware environment of existing equipment such as photolithography machines unchanged, thereby greatly promoting the development of advanced semiconductor processes.

[0003] Optical Proximity Correction (OPC) is a core branch of computational lithography, and OPC is often used to refer to computational lithography. Mask Process Correction (MPC) is an important application of OPC in mask manufacturing. MPC is dedicated to the mask manufacturing process and corrects errors in mask manufacturing processes such as electron beam exposure. Currently, MPC is basically handled using the same traditional methods and processes as OPC. However, in some cases, the traditional methods and processes of OPC cannot be applied to MPC. Summary of the invention

[0004] Based on the above problems, according to example embodiments of the present disclosure, a method of designing a mask, an electronic device, and a computer-readable storage medium are provided.

[0005] In a first aspect of the present disclosure, a method for designing a mask is provided, comprising: dividing a curve segment of a curve mask into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments; determining an estimated position of the segment where the corresponding simulation sampling point is located based on position data of a corresponding simulation sampling point among the plurality of simulation sampling points and a simulation of a mask manufacturing process; and adjusting the position of the segment where the corresponding simulation sampling point is located based on a deviation between the estimated position and an expected position of the segment where the corresponding simulation sampling point is located.

[0006] In a second aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory coupled to the processor, the memory having instructions stored therein, which, when executed by the processor, cause the device to perform actions, the actions comprising: dividing a curve segment of a curve mask into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments; determining an estimated position of the segment where the corresponding simulation sampling point is located based on position data of a corresponding simulation sampling point among the plurality of simulation sampling points and based on a simulation of a mask manufacturing process; and adjusting the position of the segment where the corresponding simulation sampling point is located based on a deviation between the estimated position and an expected position of the segment where the corresponding simulation sampling point is located.

[0007] In some embodiments of the present disclosure, the mask process correction method further includes: obtaining a curve parameter equation representing the curve segment.

[0008] In some embodiments of the present disclosure, dividing a curve segment of a mask curve into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments includes: evenly dividing the value range of a parameter of a curve parameter equation into a plurality of sub-ranges, the plurality of sub-ranges respectively corresponding to the plurality of segments; and for each of the plurality of sub-ranges, determining a simulation sampling point of a segment corresponding to the corresponding sub-range based on an intermediate value of the corresponding sub-range.

[0009] In some embodiments of the present disclosure, determining the estimated position of the segment where the corresponding simulation sampling point is located based on position data of the corresponding simulation sampling point among multiple simulation sampling points and based on a simulation of the mask manufacturing process includes: determining the normal of the curve segment at the corresponding simulation sampling point based on the position data of the corresponding simulation sampling point; and determining the estimated position of the segment where the corresponding simulation sampling point is located using a simulation model based on the determined normal.

[0010] In some embodiments of the present disclosure, determining the normal of a curve segment at a corresponding simulation sampling point based on position data of the corresponding simulation sampling point includes: determining the tangent slope of the curve segment at the corresponding simulation sampling point; and determining the normal of the curve segment at the corresponding simulation sampling point based on the position data and the tangent slope of the corresponding simulation sampling point.

[0011] In some embodiments of the present disclosure, based on the determined normal, using a simulation model to determine the estimated position of the segment where the corresponding simulation sampling point is located includes: using the simulation model to determine the signal strength of the simulation signal that changes along the normal; and based on comparing the signal strength of the simulation signal with a predetermined threshold, determining the estimated position of the segment where the corresponding simulation sampling point is located.

[0012] In some embodiments of the present disclosure, adjusting the position of the segment where the corresponding simulation sampling point is located based on the deviation between the estimated position and the expected position of the segment where the corresponding simulation sampling point is located includes: in response to the deviation between the estimated position and the expected position, moving the segment where the corresponding simulation sampling point is located along the normal and in a direction to reduce the deviation.

[0013] In some embodiments of the present disclosure, the moving distance of the segment where the corresponding simulation sampling point is located is calculated based on the slope of the simulation signal.

[0014] In some embodiments of the present disclosure, the curve segment includes a Bezier curve.

[0015] In a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0016] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A schematic diagram showing a mask design pattern using straight lines.

[0018] Figure 2 A schematic diagram showing a mask design pattern using curved lines is shown.

[0019] Figure 3 A schematic diagram showing an example scenario in which embodiments of the present disclosure can be implemented is shown.

[0020] Figure 4 A schematic flowchart of a mask process correction method for a curved mask according to an embodiment of the present disclosure is shown.

[0021] Figure 5 A schematic flowchart of a process of segmenting a curve segment and determining simulation sampling points according to an embodiment of the present disclosure is shown.

[0022] Figure 6 A schematic flowchart of a process for determining an estimated position of a segment according to an embodiment of the present disclosure is shown.

[0023] Figure 7A schematic diagram showing a partial segment of a curve segment and its tangent and normal lines according to an embodiment of the present disclosure is shown.

[0024] Figure 8 A schematic diagram showing simulation signals of a simulation model according to an embodiment of the present disclosure is shown.

[0025] Fig. 9 A schematic block diagram of an example device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0026] The following will describe the embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0027] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0028] As mentioned above, the current MPC processing method basically refers to the traditional OPC processing method for layout. The traditional OPC processing method is for masks with straight line graphics. However, in order to cope with more advanced semiconductor processes, some masks are manufactured using curved graphics. In the case of curved graphics used in mask manufacturing, problems will arise when using traditional OPC methods and processes to correct the mask manufacturing process. In addition, when applying computational lithography technology to correct chip design layouts, thousands or even tens of thousands of central processing unit (CPU) core computing resources are often required, and some advanced technology nodes may require more computing resources, so it is expected that the algorithm will be optimized to improve efficiency and reduce costs.

[0029] Figure 1 FIG. 1 is a schematic diagram of a mask design pattern 100 using straight lines. Figure 1As shown, the mask design pattern 100 includes a plurality of straight line segments, such as the straight line segment 110. When the OPC is used to correct the process deviation in the photolithography process, the two end points of the straight line segment 110 can be located, and the simulation sampling points representing the straight line segment 110 can be set on the straight line segment 110 according to the preset rules. These preset rules mainly consider the proximity effect in the photolithography process or the wafer manufacturing process. This proximity effect is the deviation between the actual pattern and the mask design caused by diffraction and interference after the light passes through the mask. After the simulation sampling points are selected for the straight line segment 110, the model can be used to simulate and calculate or estimate the position deviation of the straight line segment 110 caused by the proximity effect in the photolithography process and other process problems, thereby correcting the design position of the straight line segment 110 according to the position deviation. Other straight line segments can be corrected in the same way to obtain a corrected mask design pattern.

[0030] The mask manufacturing process also has process deviations caused by the proximity effect. The proximity effect in mask manufacturing is the pattern edge distortion caused by electron scattering during electron beam exposure or the pattern deviation caused by the optical diffraction of the laser. For the mask design pattern 100 composed of straight line segments, when correcting the process deviation in the mask manufacturing process, the same process and method as OPC are usually used for processing. In other words, the above correction method for the lithography process can be directly used for the manufacturing process correction of the straight mask. For example, in the process of MPC, the same preset rules as OPC can be used to set simulation sampling points on the straight line segments. Although this affects the efficiency of MPC to a certain extent, it can still effectively complete the manufacturing process correction of the mask.

[0031] Figure 2 FIG. 2 is a schematic diagram of a mask design pattern 200 using a curve. Figure 2 As shown, the mask design pattern 200 includes a curved segment 210. At present, in order to cope with more advanced processes, it is expected to be able to manufacture a mask with a curved pattern. However, when a curved mask such as the mask design pattern 200 is subjected to manufacturing process correction, the OPC technology applicable to a straight mask as described above cannot be applied to the MPC of a curved mask. For example, the rules for setting simulation sampling points in OPC are all for straight patterns and straight segments such as the mask design pattern 100, and therefore cannot be used to select simulation sampling points in a curved pattern. In addition, the proximity effect in mask manufacturing is different from the proximity effect in the photolithography process. An electron beam is used in mask manufacturing, and the wavelength of the electron beam is only a few nanometers, which is much smaller than the wavelength of the light used in the photolithography process. Since the wavelength of the light used in the photolithography process is larger, the setting rules of the simulation sampling points of OPC are more complicated. Therefore, forcing the same rules as OPC to set the simulation sampling points of MPC will also unnecessarily increase the complexity of MPC, resulting in low efficiency.

[0032] The embodiment of the present disclosure provides a mask process correction method for a curved mask. In this method, the curved segment of the curved mask is divided into multiple segments and a simulation sampling point representing the segment is set in each segment, and the position of the segment is estimated by simulation, so as to adjust the position of the segment to eliminate the deviation in the mask manufacturing process. In this way, the difference between the proximity effect in mask manufacturing and the proximity effect in the lithography process is utilized, and the correction of the curved mask can be efficiently realized according to the error of the mask manufacturing process, thereby improving the accuracy of semiconductor manufacturing.

[0033] Figure 3 A schematic diagram of an example scenario 300 in which embodiments of the present disclosure can be implemented is shown. The computing device 320 in the example scenario 300 can be any device with computing capabilities. In one example, the computing device 320 can be any type of fixed computing device, mobile computing device, or portable computing device. For example, the computing device 320 includes but is not limited to a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a multimedia computer, a mobile phone, etc. In another example, all or part of the components of the computing device 320 can be distributed in the cloud. Figure 3 As shown, the computing device 320 is deployed with a model 321, for example, the model 321 can be stored in the memory of the computing device 320. The model 321 can be pre-built and can accurately simulate the influence of mask manufacturing processes such as electron beam (or laser) exposure, development, and etching on the mask pattern.

[0034] In the example scenario 300, the computing device 320 can obtain an initial layout data set 310 for manufacturing a curved mask, and use a model 321 to correct the curved graph of the mask in the initial layout data set 310. The correction process can be one-time or performed multiple times in an iterative manner. Through one or more correction processes, errors caused by process problems such as proximity effects in mask manufacturing can be reduced or eliminated. Thus, the computing device 320 generates or outputs a corrected layout data set 330.

[0035] Figure 4 FIG. 4 is a schematic flow chart of a mask process correction method 400 for a curved mask according to an embodiment of the present disclosure. The method 400 may be performed in Figure 3 The exemplary scenario 300 is implemented and executed by the computing device 320. For the purpose of discussion, Figure 2 and Figure 3 Method 400 is described.

[0036] At box 401, the computing device 320 divides the curve segment 210 of the curve mask into a plurality of segments and determines a plurality of simulation sampling points corresponding to the plurality of segments. Specifically, the curve segment 210 can be divided into a plurality of segments to perform subsequent manufacturing process corrections respectively. The smaller the segment is, the higher the accuracy of the correction is, but the operation time will also increase, resulting in increased costs. Therefore, a trade-off can be made between accuracy and operation time according to actual needs to select the specific size of the segment. After determining each segment, a simulation sampling point representing the segment can be determined on each segment. Compared with the wavelength of light in the photolithography process, the electron beam or laser used in mask manufacturing has a relatively small wavelength. Based on this key difference, the curve segment in the MPC process can be divided into smaller segments, and the points in the segment represent the segment for subsequent corrections. In other words, the simulation signal obtained by each simulation sampling point determines how to correct the segment in which it is located (for example, the direction and size of the correction). Generally speaking, the simulation sampling point can no longer be changed after it is set.

[0037] At block 402, based on the position data of the corresponding simulation sampling point among the plurality of simulation sampling points and based on the simulation of the mask manufacturing process, the computing device 320 determines the estimated position of the segment where the corresponding simulation sampling point is located. Specifically, the computing device 320 may, for example, simulate the mask manufacturing process using the model 321 to estimate the position of the segment where the corresponding simulation sampling point is located after being manufactured.

[0038] At box 403, based on the deviation between the estimated position and the expected position of the segment where the corresponding simulation sampling point is located, the computing device 320 adjusts the position of the segment where the corresponding simulation sampling point is located. Specifically, if the estimated position does not match the expected position, the segment position data in the design data can be adjusted to move the segment so that the estimated position or the actual position can be consistent with the expected position. Further, the computing device 320 can correct the positions of other segments of the curve segment 210 based on the same method to eliminate the deviation caused by the manufacturing process, thereby obtaining the desired mask pattern. The method and process of traditional OPC have not considered the situation where the mask has a curve pattern. In addition, when setting the sampling points, the traditional OPC must consider the proximity effect of light, for example, it is necessary to set more sampling points near the corners and on the shorter straight line segments, and will not cut the straight line segments or other line segments. However, studies have shown that the proximity effect in the mask manufacturing process is different from the proximity effect in the photolithography process, and the wavelength of the electron beam or laser used in the mask manufacturing process is much smaller than the wavelength of light in the photolithography process. Therefore, when setting simulation sampling points for the MPC of the curve graph, it is not necessary to stick to the sampling point setting rules in the OPC. Based on this, the embodiment of the present disclosure divides the curve segment when performing manufacturing process correction for the curve mask and sets simulation sampling points representing the segment on the smaller segment after division. In this way, the MPC of the curve mask can be effectively implemented without relying on the traditional OPC technology.

[0039] In some embodiments of the present disclosure, the method 400 may further include the computing device 320 acquiring a curve parameter equation representing the curve segment 210. Specifically, the computing device 320 may acquire design data related to the curve segment 210 of the mask. For example, the acquired design data may be data related to the curve segment 210 in the initial layout data set 310. The computing device 320 may store the design data in its own memory, and / or the computing device 320 may load the acquired design data into its own processor for processing. The design data of the curve segment 210 of the mask may be stored and transmitted in the form of a certain function curve. The curve segment 210 in the form of such a function curve may be stored using several fixed points and may be described by a parameter variable. In one embodiment, the curve segment 210 includes a Bezier curve. In the case where the curve segment 210 is a Bezier curve, the curve segment 210 may be represented, for example, by the following equation: (1) in t represents the parameter variable, P j represents information for determining a fixed point or a control point of the curve segment 210, B(t) Indicates the variable tThe point on the curve segment 210 determined by the change, n represents the degree of the Bezier curve, and j represents the index of a fixed point or control point, where P j and B(t) are vectors used to represent points. In a Bezier curve, fixed points or control points are used to define the shape of the Bezier curve and may include the starting point, end point, and intermediate points used to adjust the shape of the curve as direction control points. t The range of can be an interval greater than or equal to 0 and less than or equal to 1, where t Take 0 as the starting point of this curve, and t When 1 is taken, it is the end point of this curve. The curve segment 210 of the curve mask may include a Bezier curve or be formed by connecting multiple Bezier curves, wherein each Bezier curve may be a complete curve from the starting point to the end point, or a part thereof.

[0040] Figure 5 A schematic flowchart of a process 500 of segmenting a curve segment 210 and determining simulation sampling points according to an embodiment of the present disclosure is shown. Figure 5 The process 500 shown may be performed at Figure 4 The block 401 of is implemented.

[0041] At block 501, the computing device 320 evenly divides the value range of the parameter of the curve parameter equation into a plurality of sub-ranges, each of which corresponds to a plurality of segments. As an example, when the curve segment 210 is represented and stored by the curve parameter equation, the curve segment 210 can be segmented by evenly dividing the value range of the parameter of the curve parameter equation. For example, the equation (1) above is a vector expression of a Bezier curve, which can be converted into a scalar coordinate expression, so that the position of each point on the curve segment 210 as a Bezier curve can be expressed by the following equation: ,in , (2) in P(x,y) represents a point on the curve segment 210 x Axis and y The coordinates of the axes, f(t) Indicates that with t changes with the changes x Axis coordinates, g(t) Indicates that with t changes with the changes y axis coordinates, and t s and t eRespectively represent the parameter variables t As an example, the range of the parameter t in equation (2) can be ( t s ,t e ) is divided into m segments, so that we can determine the corresponding m Sub-range m In this way, a curve segment can be divided into multiple segments easily and quickly.

[0042] At block 502, for each of the plurality of sub-ranges, the computing device 320 determines the simulation sampling point of the segment corresponding to the corresponding sub-range based on the middle value of the corresponding sub-range. That is, after determining the sub-range of the value range of the parameter variable, the simulation sampling point of the segment can be determined by simply calculating the middle value of the sub-range. As an example, for the Bezier curve represented by equation (2), the middle value of each sub-range can be determined by the following equation: (3) in m Indicates the parameter variable t The number of sub-ranges after the range of values ​​is divided or the number of segments of the curve segment, and t i Indicates i The middle value of the sub-range. t i After the determination, the first i In this way, the simulation sampling point can be basically located at the midpoint of each segment, so that other points in the segment will not be too far away from the simulation sampling point, which makes the simulation sampling point represent the entire segment in MPC, thereby simplifying the calculation and maximizing the correction accuracy of the segment.

[0043] It is understandable that in some cases, the simulation sampling point can also be set at other places other than the midpoint of the segment. For example, the simulation sampling point can be set within a certain range near the midpoint of the segment. For example, if the length of the segment is defined as L, the simulation sampling point can also be set on a part of the segment within a range of 0.05L, 0.1L or 0.2L from the midpoint of the segment. However, it is more preferred to set the simulation sampling point at the midpoint of the segment, because the simulation sampling point needs to represent the segment to perform the manufacturing process correction, and the simulation sampling point located at the midpoint can ensure the correction accuracy to the greatest extent.

[0044] Figure 6 A schematic flow chart of a process 600 of determining an estimated position of a segment according to an embodiment of the present disclosure is shown. Figure 6 The process 600 shown may be performed at Figure 4 The block 402 of is implemented.

[0045] At block 601, the computing device 320 determines the normal line of the curve segment 210 at the corresponding simulation sampling point based on the position data of the corresponding simulation sampling point. For example, the normal line equation at the simulation sampling point can be determined by mathematical calculation based on the design data of the curve segment.

[0046] At block 602, based on the determined normal, the computing device 320 uses the simulation model 321 to determine the estimated position of the segment where the corresponding simulation sampling point is located. As an example, using the model 321 that simulates the mask manufacturing process, the estimated position of the mask manufactured according to the design data of each segment of the curve segment 210 can be estimated. Under the influence of errors caused by proximity effects and other process problems, the estimated position of the segment of the curve segment 210 may deviate from the expected position. If it is determined that there is a deviation, the computing device 320 can adjust the position of the segment to eliminate the deviation, and if there is no deviation, it indicates that the design pattern is consistent with the manufactured pattern, and no further correction of the position of the segment is required.

[0047] Figure 7 A schematic diagram of a segment 210-N of a curve segment 210 and its tangent TL and normal NL according to an embodiment of the present disclosure is shown. In some embodiments of the present disclosure, the computing device 320 determines the slope of the tangent TL of the curve segment 210 at the corresponding simulation sampling point SP, and determines the normal NL of the curve segment 210 at the corresponding simulation sampling point SP based on the position data of the corresponding simulation sampling point SP and the slope of the tangent TL. As an example, segment 210-N is one of the multiple segments of the curve segment 210, and point SP is a simulation sampling point set on segment 210-N. In order to determine the normal of segment 210-N at point SP, the slope of the tangent of segment 210-N at point SP can be determined first. For example, taking the curve represented by equation (2) as an example, if the parameter at point SP is t i , then the slope K of the tangent line TL of the curve segment 210 at the point SP can be expressed by the following equation: (4) in g'(t i ) express g(t i ) The derivative of , and f'(t i ) express f(t i )Further, it can be determined that the slope of the normal line NL of the curve segment 210 at the point SP is -1 / K . Thus, the normal line NL can be expressed by the following equation: (5) (6) It is understood that the normal line NL can also be determined directly by equation (6) without calculating the slope of the tangent line TL. f'(t i ) and g'(t i ) It is necessary to judge whether the denominator is zero to avoid the situation where the calculation cannot be completed due to the denominator being zero.

[0048] if f'(t i ) If it is zero, it means that the tangent line TL at the simulation sampling point is perpendicular to the x-axis. At this time, the normal line NL can be expressed as: (7) if g'(t i ) If NL is zero, it means that the normal line NL at the simulation sampling point is perpendicular to the x-axis. At this time, the normal line NL can be expressed as: (8) After determining the normal line NL, the computing device 320 may calculate a simulation signal of the model according to the normal line NL.

[0049] Figure 8 A schematic diagram of a simulation signal SS according to an embodiment of the present disclosure is shown. In some embodiments of the present disclosure, the computing device 320 uses a simulation model 321 to determine the signal strength of the simulation signal SS that changes along the normal, and determines the estimated position of the segment where the corresponding simulation sampling point is located based on the comparison of the signal strength of the simulation signal SS with a predetermined threshold value TH. Specifically, the simulation model 321 can accurately simulate the mask manufacturing process. The simulation signal SS generated by the simulation model 321 reflects the change in the signal intensity of the electron beam or laser near the curve figure. In the concave part of the waveform of the simulation signal SS, the signal intensity of the simulation signal SS weakens due to the obstruction of the curve figure, and gradually becomes stronger in the direction toward the edge of the curve figure. The predetermined threshold value TH can indicate the edge of the curve figure, which corresponds to the segment position of the curve segment 210. Therefore, by comparing the signal strength of the simulation signal SS with the predetermined threshold value TH, the estimated position of the segment of the curve segment 210 can be determined. For example, in Figure 8In FIG. 2 , the intersection position P2 of the simulation signal SS and the predetermined threshold TH is determined as the estimated position of the segment of the curve segment 210. Since the position indicated by the dotted line P1 is the expected position, there is a deviation of ΔP between the estimated position P1 and the expected position P2.

[0050] In some embodiments of the present disclosure, in response to a deviation ΔP between the estimated position and the expected position, the computing device 320 moves the position of the segment where the corresponding simulation sampling point is located along the normal line in a direction to reduce the deviation ΔP. For example, the computing device 320 may move the segment along the normal line in a direction to reduce the deviation ΔP. Figure 7 The normal line NL in the image is moved toward the inside of the curve graph, so that the estimated position P2 is close to the expected position P1. In one embodiment, the moving distance of the segment 210-N can be calculated according to the slope of the simulation signal. For example, the distance to be moved can be calculated according to the difference between the actual signal strength and the predetermined threshold TH and the slope of the simulation signal. As a result, the movement of the segment can accurately eliminate the deviation ΔP, thereby accurately and efficiently correcting the process deviation. In addition, the segment can also be moved multiple times, and the simulation signal is compared with the predetermined threshold after each movement, so as to accurately eliminate the deviation ΔP in an iterative manner. In this way, each segment of the curve segment can be corrected one by one, so as to complete the MPC of the curve mask and provide corrected mask design data.

[0051] Fig. 9 1 shows a schematic block diagram of an example device 900 that can be used to implement an embodiment of the present disclosure. The device 900 can be implemented as Figure 3 The computing device 320 of FIG. 900 can be used to implement Figure 4 Method 400.

[0052] As shown in the figure, the device 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 902 or computer program instructions loaded from a storage unit 908 to a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored, for example, the measurement data mentioned above can be stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0053] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0054] The processing unit 901 performs the method or process described above, method 400. For example, in some embodiments, method 400 may be implemented as a computer software program or computer program product, which is tangibly contained in a machine-readable medium, such as a non-transitory computer-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by CPU 901, one or more steps of method 400 described above may be performed. Alternatively, in other embodiments, CPU 901 may be configured to perform method 400 in any other suitable manner (e.g., by means of firmware).

[0055] Those skilled in the art should understand that the various steps of the method disclosed above can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented with program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any specific combination of hardware and software.

[0056] It should be understood that although several devices or sub-devices of the device are mentioned in the detailed description above, this division is only exemplary and not mandatory. In fact, according to an embodiment of the present disclosure, the features and functions of two or more devices described above can be embodied in one device. Conversely, the features and functions of one device described above can be further divided into multiple devices to be embodied.

[0057] The above description is only an optional embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A mask process correction method for a curved mask, comprising: Dividing the curve segment of the curve mask into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments; Determining an estimated position of a section where the corresponding simulation sampling point is located based on position data of a corresponding simulation sampling point among the plurality of simulation sampling points and a simulation of a mask manufacturing process; as well as Based on the deviation between the estimated position and the expected position of the segment where the corresponding simulation sampling point is located, the position of the segment where the corresponding simulation sampling point is located is adjusted.

2. The mask process correction method for a curved mask according to claim 1, further comprising: A curve parameter equation representing the curve segment is obtained.

3. The mask process correction method for a curved mask according to claim 2, wherein dividing the curved segment of the curved mask into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments comprises: Evenly dividing the value range of the parameter of the curve parameter equation into a plurality of sub-ranges, wherein the plurality of sub-ranges correspond to the plurality of sections respectively; as well as For each sub-range of the plurality of sub-ranges, Based on the middle value of the corresponding sub-range, a simulation sampling point of the segment corresponding to the corresponding sub-range is determined.

4. The mask process correction method for a curved mask according to claim 1, wherein based on the position data of a corresponding simulation sampling point among the plurality of simulation sampling points and based on the simulation of the mask manufacturing process, determining the estimated position of the section where the corresponding simulation sampling point is located comprises: Determining a normal line of the curve segment at the corresponding simulation sampling point based on the position data of the corresponding simulation sampling point; as well as Based on the determined normal line, the estimated position of the segment where the corresponding simulation sampling point is located is determined using the simulation model.

5. The mask process correction method for a curved mask according to claim 4, wherein based on the position data of the corresponding simulation sampling point, determining the normal of the curve segment at the corresponding simulation sampling point comprises: Determining the tangent slope of the curve segment at the corresponding simulation sampling point; as well as A normal line of the curve segment at the corresponding simulation sampling point is determined based on the position data of the corresponding simulation sampling point and the tangent slope.

6. The mask process correction method for a curved mask according to claim 4, wherein based on the determined normal line, using a simulation model to determine the estimated position of the segment where the corresponding simulation sampling point is located comprises: Determine the signal strength of the simulation signal along the normal line using the simulation model; as well as Based on the comparison between the signal strength of the simulation signal and a predetermined threshold, the estimated position of the section where the corresponding simulation sampling point is located is determined.

7. The mask process correction method for a curved mask according to claim 4, wherein adjusting the position of the segment where the corresponding simulation sampling point is located based on the deviation between the estimated position and the expected position of the segment where the corresponding simulation sampling point is located comprises: In response to a deviation between the estimated position and the expected position, the segment where the corresponding simulation sampling point is located is moved along the normal line in a direction to reduce the deviation. 8 . The mask process correction method according to claim 7 , wherein the moving distance of the section where the corresponding simulation sampling point is located is calculated based on the slope of the simulation signal. 9 . The mask process correction method for a curved mask according to claim 1 , wherein the curved segment comprises a Bezier curve.

10. An electronic device comprising: processor; as well as a memory coupled to the processor, the memory having instructions stored therein, the instructions, when executed by the processor, causing the device to perform actions, the actions comprising: Dividing the curve segment of the curve mask into a plurality of segments and determining a plurality of simulation sampling points respectively corresponding to the plurality of segments; Determining an estimated position of a section where the corresponding simulation sampling point is located based on the position data of the corresponding simulation sampling point among the plurality of simulation sampling points and based on a simulation of a mask manufacturing process; and Based on the deviation between the estimated position and the expected position of the segment where the corresponding simulation sampling point is located, the position of the segment where the corresponding simulation sampling point is located is adjusted.

11. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

Images