Mask process correction method for curved mask

Abstract

Embodiments of the present disclosure provide a mask process correction method for a curved mask, an electronic device, and a computer-readable storage medium. The mask process correction method includes: dividing a curved segment of the curved mask into multiple segments and determining multiple simulation sampling points corresponding to the multiple segments; determining an estimated position of the segment where the corresponding simulation sampling point is located based on position data of the corresponding simulation sampling point among the multiple simulation sampling points and based on a simulation of the 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 the expected position of the segment 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 transfers the circuit pattern on a mask to the wafer surface through optical-chemical reactions and a series of etching steps, thereby forming an integrated circuit or chip with the desired functionality. Computational lithography, as an additional tool for photolithography, primarily uses mathematical modeling, simulation, and algorithm optimization to reduce or eliminate pattern distortion caused by physical limitations such as the proximity effect during the photolithography process. Computational lithography has been a key driver of the continued advancement of pattern miniaturization technology in recent years. By leveraging software technologies such as resolution enhancement, while maintaining the hardware environment of existing lithography equipment, computational lithography can achieve minimum exposure dimensions that exceed hardware limitations, significantly advancing the development of advanced semiconductor processes.

[0003] Optical Proximity Correction (OPC) is a core branch of computational lithography and is often referred to as OPC. Mask Process Correction (MPC) is a key application of OPC in mask manufacturing. MPC is specifically used in mask manufacturing and corrects errors in mask manufacturing processes such as electron beam lithography. Currently, MPC is handled using essentially the same traditional methods and processes as OPC. However, in some cases, traditional OPC methods and processes are not applicable 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 curved segment of a curved mask into a plurality of segments and determining a plurality of simulation sampling points 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 the corresponding simulation sampling point among the plurality of simulation sampling points and a simulation of a mask manufacturing process; and adjusting a 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 corresponding to the plurality of segments, respectively; 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 a 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 multiple segments and determining multiple simulation sampling points corresponding to the multiple segments respectively includes: evenly dividing the value range of the parameter of the curve parameter equation into multiple sub-ranges, the multiple sub-ranges corresponding to the multiple segments respectively; and for each sub-range in the multiple sub-ranges, determining the simulation sampling points of the segment corresponding to the corresponding sub-range based on the middle value of the corresponding sub-range.

[0009] In some embodiments of the present disclosure, determining an estimated position of a segment where a corresponding simulation sampling point is located based on position data of a corresponding simulation sampling point among a plurality of simulation sampling points and based on a simulation of a mask manufacturing process includes: determining a normal of a 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 the curve segment at the corresponding simulation sampling point based on the 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 section 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 readily 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:

[0018] Figure 1 A schematic diagram showing a mask design pattern using straight lines.

[0019] Figure 2 A schematic diagram showing a mask design pattern using curved lines is shown.

[0020] Figure 3 A schematic diagram illustrating an example scenario in which embodiments of the present disclosure can be implemented is shown.

[0021] 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.

[0022] Figure 5 A schematic flowchart of a process of dividing curve segments and determining simulation sampling points according to an embodiment of the present disclosure is shown.

[0023] 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.

[0024] 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.

[0025] Figure 8 A schematic diagram showing simulation signals of a simulation model according to an embodiment of the present disclosure is shown.

[0026] Figure 9 A schematic block diagram of an example device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0027] The following describes 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. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0028] 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.

[0029] As mentioned above, current MPC processing methods largely mirror traditional OPC layout processing. Traditional OPC processing methods are used for masks with straight line patterns. However, to accommodate more advanced semiconductor processes, some masks are manufactured using curved patterns. When curved patterns are used in mask manufacturing, using traditional OPC methods and processes to correct the mask manufacturing process can present problems. Furthermore, applying computational lithography to correct chip design layouts often requires the computing resources of thousands or even tens of thousands of central processing unit (CPU) cores, and some advanced technology nodes may require even more computing resources. Therefore, algorithm optimization is desired to improve efficiency and reduce costs.

[0030] Figure 1 FIG. 1 shows a schematic diagram of a mask design pattern 100 using straight lines. Figure 1As shown, the mask design pattern 100 includes multiple straight line segments, such as straight line segment 110. When using OPC to correct process deviations during the photolithography process, the two endpoints of straight line segment 110 can be located, and simulation sampling points representing straight line segment 110 can be set on straight line segment 110 according to preset rules. These preset rules primarily take into account the proximity effect during the photolithography process or wafer manufacturing process. This proximity effect is the deviation between the actual pattern and the mask design caused by diffraction and interference after light passes through the mask. After selecting simulation sampling points for straight line segment 110, a model can be used to simulate and calculate or estimate the position deviation of straight line segment 110 caused by the proximity effect during the photolithography process and other process issues. The designed position of straight line segment 110 can then be corrected based on the position deviation. Other straight line segments can be corrected in the same manner to obtain a corrected mask design pattern.

[0031] The mask manufacturing process also has process deviations caused by the proximity effect. The proximity effect in mask manufacturing is the distortion of the pattern edge 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 adopted. In other words, the correction method for the lithography process can be directly used for the manufacturing process correction of the straight mask. For example, in the MPC process, 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 mask manufacturing process correction.

[0032] Figure 2 FIG. 2 shows a schematic diagram of a mask design pattern 200 using a curve. Figure 2 As shown, mask design pattern 200 includes curved segments 210. Currently, in order to cope with more advanced processes, it is desirable to manufacture masks with curved patterns. However, when performing manufacturing process corrections on curved masks such as mask design pattern 200, the OPC technology described above, which is applicable to straight masks, cannot be applied to MPC for curved masks. For example, the rules for setting simulation sampling points in OPC are all specific to straight patterns and straight line segments such as mask design pattern 100, and therefore cannot be used to select simulation sampling points in curved patterns. Furthermore, the proximity effect in mask manufacturing differs from the proximity effect in the photolithography process. Mask manufacturing utilizes an electron beam, which has a wavelength of only a few nanometers, which is much smaller than the wavelength of light used in the photolithography process. Due to the longer wavelength of light used in the photolithography process, the rules for setting simulation sampling points in OPC are more complex. Therefore, forcing the use of the same rules as in OPC to set simulation sampling points in MPC would unnecessarily increase the complexity of MPC and lead to inefficiencies.

[0033] Embodiments of the present disclosure provide a mask process correction method for curved masks. This method divides the curved segment of the curved mask into multiple segments, sets simulation sampling points representing each segment, and uses simulation to estimate the segment positions, thereby adjusting the segment positions to eliminate deviations in the mask manufacturing process. This method leverages the differences between the proximity effect in mask manufacturing and the proximity effect in the photolithography process, efficiently correcting curved masks for errors in the mask manufacturing process and improving semiconductor manufacturing accuracy.

[0034] Figure 3 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, computing device 320 is deployed with model 321, for example, model 321 can be stored in a memory of computing device 320. Model 321 can be pre-built and can accurately simulate the impact of mask manufacturing processes such as electron beam (or laser) exposure, development, and etching on mask patterns.

[0035] In example scenario 300, computing device 320 can obtain an initial layout dataset 310 for manufacturing a curved mask and use model 321 to correct the curved geometry of the mask in initial layout dataset 310. This correction process can be a one-time process or performed multiple times in an iterative manner. Through one or more correction processes, errors caused by process issues such as proximity effects in mask manufacturing can be reduced or eliminated. Computing device 320 then generates or outputs a corrected layout dataset 330.

[0036] 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. Figure 3 300 and executed by computing device 320. For the purpose of discussion, Figure 2 and Figure 3 Method 400 will be described.

[0037] At block 401, the computing device 320 divides the curved mask segment 210 into multiple segments and determines multiple simulation sampling points corresponding to each of the segments. Specifically, the curved mask segment 210 can be segmented into multiple segments for subsequent manufacturing process corrections. Smaller segments increase the accuracy of corrections, but also increase computation time and cost. Therefore, the specific size of the segments can be selected based on a trade-off between accuracy and computation time. After determining the segments, simulation sampling points representing each segment can be determined. Compared to the wavelength of light used in photolithography, the electron beam or laser used in mask manufacturing has a relatively small wavelength. Based on this key difference, the curved mask segment in the MPC process can be divided into smaller segments, with points within each segment representing the segment for subsequent corrections. In other words, the simulation signal obtained at each simulation sampling point determines how the correction to the segment in which it resides (e.g., the direction and magnitude of the correction) is performed. Generally speaking, once a simulation sampling point is set, it can be permanently retained.

[0038] At block 402, computing device 320 determines an estimated position of a segment where the corresponding simulated sampling point is located based on the position data of the corresponding simulated sampling point from among the plurality of simulated sampling points and based on a simulation of the mask manufacturing process. Specifically, computing device 320 may, for example, utilize model 321 to simulate the mask manufacturing process, thereby estimating the position of the segment where the corresponding simulated sampling point is located after manufacturing.

[0039] At block 403, based on the deviation between the estimated position and the expected position of the segment where the corresponding simulated sampling point is located, the computing device 320 adjusts the position of the segment where the corresponding simulated 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 is consistent with the expected position. Furthermore, the computing device 320 can correct the positions of other segments of the curved segment 210 in the same manner to eliminate deviations caused by the manufacturing process, thereby obtaining the desired mask pattern. Traditional OPC methods and processes do not consider the case where the mask has a curved pattern. In addition, traditional OPC considers the proximity effect of light when setting sampling points. For example, more sampling points are required near corners and on short straight segments, and straight segments or other line segments are not segmented. However, research has shown that the proximity effect in mask manufacturing differs from the proximity effect in photolithography, and the wavelength of the electron beam or laser used in mask manufacturing is much smaller than that of light in photolithography. Therefore, when setting simulation sampling points for MPC of curved graphics, there's no need to adhere to the sampling point setting rules in OPC. Based on this, the disclosed embodiments segment the curved segments when performing manufacturing process corrections on curved masks and set simulation sampling points representing these smaller segments. This approach effectively implements MPC for curved masks without relying on traditional OPC techniques.

[0040] 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 expressed by parameters. 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:

[0041] (1)

[0042] 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 parameter variablet The point on the curve segment 210 determined by the change, n represents the order of the Bezier curve, and j Indicates 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 can 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 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.

[0043] 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 Figure 4 This is implemented at block 401 of .

[0044] At block 501, the computing device 320 evenly divides the value range of the parameter of the curve parametric 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 parametric equation, the curve segment 210 can be divided by evenly dividing the value range of the parameter of the curve parametric equation. For example, equation (1) above is a vector representation of a Bezier curve, which can be converted into a scalar coordinate representation, so that the position of each point on the curve segment 210 as a Bezier curve can be represented by the following equation:

[0045] ,in , (2)

[0046] 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 ts and t e Represents 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 quickly and easily.

[0047] At block 502, for each of the plurality of sub-ranges, the computing device 320 determines, based on the median value of the corresponding sub-range, a simulation sampling point for the segment corresponding to the corresponding sub-range. That is, after determining the sub-ranges of the parameter's value range, the simulation sampling point for the segment can be determined by simply calculating the median value of the sub-range. As an example, for the Bezier curve represented by equation (2), the median value of each sub-range can be determined by the following equation:

[0048] (3)

[0049] in m Indicates parameter variables t The number of sub-ranges after the value range of is divided or the number of segments of the curve segment, and t i Indicates the 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 positioned at the midpoint of each segment, so that other points in the segment are not too far away from the simulation sampling point. This makes the simulation sampling point representative of the entire segment in MPC, thereby simplifying the calculation and maximizing the correction accuracy of the segment.

[0050] It is understood that in some cases, the simulation sampling point can also be set at a location other than the segment midpoint. For example, the simulation sampling point can be set within a certain range near the segment midpoint. For example, if the segment length is defined as L, then the simulation sampling point can also be set on a portion of the segment within a range of 0.05L, 0.1L, or 0.2L from the segment midpoint. However, setting the simulation sampling point at the segment midpoint is more preferred because the simulation sampling point needs to represent the segment to perform manufacturing process corrections, and a simulation sampling point located at the midpoint can maximize correction accuracy.

[0051] Figure 6 A schematic flow chart of a process 600 for 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 This is implemented at block 402 of .

[0052] At block 601 , the computing device 320 determines the normal 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 equation at the simulation sampling point can be determined by mathematical calculation based on the design data of the curve segment.

[0053] At block 602, based on the determined normals, computing device 320 utilizes simulation model 321 to determine the estimated position of the segment where the corresponding simulated sampling point is located. For example, using model 321, which simulates the mask manufacturing process, the estimated position of a mask manufactured based on the design data for each segment of curve segment 210 can be estimated. Due to errors caused by proximity effects and other process issues, the estimated position of the segments of curve segment 210 may deviate from the expected position. If a deviation is determined, computing device 320 can adjust the position of the segment to eliminate the deviation. If no deviation is found, it indicates that the design pattern and the manufactured pattern are consistent, and no further correction of the position of the segment is required.

[0054] 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, the segment 210-N is one of the multiple segments of the curve segment 210, and the point SP is the simulation sampling point set on the segment 210-N. In order to determine the normal of the segment 210-N at the point SP, the slope of the tangent of the segment 210-N at the point SP can be determined first. For example, taking the curve represented by equation (2) as an example, if the parameter at the point SP is t i , then the slope K of the tangent line TL of the curve segment 210 at point SP can be expressed by the following equation:

[0055] (4)

[0056] 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 point SP is -1 / K . Thus, the normal line NL can be expressed by the following equation:

[0057] (5)

[0058] (6)

[0059] 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 ) The denominator is zero to avoid the situation where the calculation cannot be completed.

[0060] 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. In this case, the normal line NL can be expressed as:

[0061] (7)

[0062] if g'(t i ) If it is zero, it means that the normal line NL at the simulation sampling point is perpendicular to the x-axis. In this case, the normal line NL can be expressed as:

[0063] (8)

[0064] After determining the normal line NL, the computing device 320 may calculate a simulation signal of the model according to the normal line NL.

[0065] Figure 8A 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 the predetermined threshold 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 becomes weaker 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 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 TH, the estimated position of the segment of the curve segment 210 can be determined. For example, in Figure 8 In FIG, 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.

[0066] 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 that reduces the deviation ΔP. For example, the computing device 320 may move the segment along the normal line in a direction that reduces 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 based on the slope of the simulation signal. For example, the distance to be moved can be calculated based on 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, thereby completing the MPC of the curve mask and providing corrected mask design data.

[0067] Figure 9 1 shows a schematic block diagram of an example device 900 that can be used to implement embodiments of the present disclosure. The device 900 can be implemented as Figure 3 The computing device 320. The device 900 can be used to implement Figure 4 Method 400.

[0068] As shown, 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 loaded from a storage unit 908 into a random access memory (RAM) 903. RAM 903 may also store various programs and data required for the operation of device 900, such as the measurement data mentioned above. CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to bus 904.

[0069] Various 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 magnetic 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 via a computer network such as the Internet and / or various telecommunication networks.

[0070] 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 tangibly embodied on 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 onto 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., via firmware).

[0071] Those skilled in the art will appreciate that the various steps of the method disclosed above can be implemented by a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0072] It should be understood that although the detailed description above mentions several devices or sub-devices of a device, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present disclosure, the features and functions of two or more devices described above may be embodied in a single device. Conversely, the features and functions of a single device described above may be further divided and embodied by multiple devices.

[0073] The foregoing description is merely an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that the present disclosure is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present disclosure shall be included within the scope of protection 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 corresponding to the plurality of segments respectively; Determining an estimated position of a section where the corresponding simulation sampling point is located based on position data of the 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: Obtain a curve parameter equation representing the curve segment.

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 corresponding to the plurality of segments respectively 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 respectively correspond to the plurality of sections; 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 determining the estimated position of the segment 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 the mask manufacturing process comprises: determining a normal 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 determining the normal of the curve segment at the corresponding simulation sampling point based on the position data of 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 determining the estimated position of the segment where the corresponding simulation sampling point is located using a simulation model based on the determined normal line comprises: Determining, using the simulation model, a signal strength of a simulation signal that varies along the normal line; as well as Based on the comparison between the signal strength of the simulation signal and a predetermined threshold, an 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 6, 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 segment 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 corresponding to the plurality of segments respectively; Determining an estimated position of the 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.

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