Method, device and storage medium for OPC modeling

Abstract

According to an exemplary embodiment of the present disclosure, there are provided a method, an apparatus, and a storage medium for optical proximity correction (OPC) modeling. The method includes: initializing a plurality of kernel functions for an OPC model, the plurality of kernel functions including at least a first type of kernel function and a second type of kernel function, the first type of kernel function indicating an optical proximity effect that decays as the distance increases, the second type of kernel function indicating an optical proximity effect that changes abruptly as the distance changes, the distance indicating the spacing between a target point and a neighboring point on a target layout; calibrating the plurality of kernel functions; and constructing an OPC model based on the calibrated plurality of kernel functions. In this way, an OPC model applicable to a contact lithography machine can be constructed, so that the distortion in the lithography process can be compensated, and the finally formed pattern can be closer to the design requirements.

Description

Technical Field

[0001] Embodiments of the present disclosure mainly relate to the field of integrated circuit technology, and more specifically, to methods, devices, and storage media for OPC modeling. Background Art

[0002] Lithography is a key process in chip manufacturing for transferring circuit patterns onto silicon wafers. As chip manufacturing processes continue to shrink, the problem of pattern distortion in lithography becomes increasingly prominent. The Optical Proximity Correction (OPC) model has emerged. It models and analyzes the phenomena in the lithography process and corrects and compensates the original design patterns to improve the accuracy and quality of lithography patterns and ensure the yield of chip manufacturing. The OPC model is an essential part of advanced lithography technology. Summary of the Invention

[0003] In a first aspect of the present disclosure, a method for OPC modeling is provided. The method includes: initializing a plurality of kernel functions for an Optical Proximity Correction (OPC) model, the plurality of kernel functions including at least a first category of kernel functions and a second category of kernel functions, the first category of kernel functions indicating an optical proximity effect that decays as the distance increases, the second category of kernel functions indicating an optical proximity effect that changes abruptly as the distance changes, the distance indicating the spacing between a target point and a neighboring point on a target layout; calibrating the plurality of kernel functions; and constructing an OPC model based on the calibrated plurality of kernel functions.

[0004] In a second aspect of the present disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor. The memory has instructions stored therein that, when executed by the processor, cause the electronic device to perform the method according to the first aspect of the present disclosure.

[0005] In a third aspect of the present disclosure, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. The computer program, when executed by a processor, implements the method according to the first aspect of the present disclosure.

[0006] It will be understood from the following description that, according to an embodiment of the present disclosure, a plurality of kernel functions for an OPC model are first initialized. The plurality of kernel functions at least include a first type of kernel function and a second type of kernel function. The first type of kernel function indicates an optical proximity effect that decays with an increase in distance, and the second type of kernel function indicates an optical proximity effect that changes abruptly with distance. The distance indicates the spacing between a target point and a neighboring point on a target layout. Further, the plurality of kernel functions are calibrated. Finally, an OPC model is constructed based on the calibrated plurality of kernel functions. In this way, an OPC model applicable to a contact lithography machine can be constructed, and while simplifying the computational complexity of the OPC model and reducing the computational time, the correction efficiency is improved.

[0007] It should be understood that the content described in the present invention content section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it used 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

[0008] With reference to the accompanying drawings and the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0009] Figure 1 A schematic diagram showing an example environment in which the embodiments of the present disclosure can be implemented;

[0010] Figure 2 A schematic diagram showing the principle of a contact lithography machine;

[0011] Figure 3A A schematic diagram showing a first type of kernel function according to some embodiments of the present disclosure;

[0012] Figure 3B A schematic diagram showing a second type of kernel function according to some embodiments of the present disclosure;

[0013] Figure 4 A flowchart showing a process for OPC modeling according to some embodiments of the present disclosure; and

[0014] Figure 5 A block diagram showing an electronic device in which one or more embodiments of the present disclosure can be implemented. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0016] In the description of the embodiments of the present disclosure, the term "including" and its similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "at least partially based 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. There may also be other explicit and implicit definitions hereinafter.

[0017] Various exemplary implementations of the solution will be described in detail below with reference to the accompanying drawings.

[0018] First, refer to Figure 1 , which shows a schematic diagram of an exemplary environment 100 in which embodiments of the present disclosure can be implemented. As Figure 1 shown, the exemplary environment 100 generally may include an electronic device 110.

[0019] In some embodiments, the electronic device 110 may interact with other devices (not shown in the figure). For example, the electronic device 110 may receive input information from other devices and output feedback information to other devices. In some embodiments, the input message from other devices may be design layout data 120. The electronic device 110 may perform corresponding mathematical operations on the design layout data and output the corresponding operation result 130 to other devices. In some embodiments, the operation result may be the corrected layout data.

[0020] In the exemplary environment 100, the electronic device 110 may be any type of device with computing capabilities, including a terminal device or a server device. The terminal device may be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / video camera, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. The server device may, for example, include a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0021] It should be understood that the structure and function of the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. Example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings.

[0022] As briefly mentioned above, the OPC model is an essential component of advanced lithography technology. Lithography aims to overcome hardware limitations in minimum exposure dimensions by improving resolution and other software technologies, while maintaining the existing hardware environment of lithography equipment. This has significantly advanced the development of advanced semiconductor processes.

[0023] During the photolithography process, due to physical phenomena such as light diffraction and interference, there will be differences between the actual pattern formed by exposure and the designed pattern on the mask. This difference becomes more pronounced as feature sizes continue to shrink. The OPC model accurately models the physical processes of light propagation and imaging during the photolithography process, analyzes the patterns and degree of pattern distortion, and then corrects the pattern on the mask. For example, some long and thin line patterns may have uneven line edges and inconsistent widths after photolithography. Based on the calculation results, the OPC model can appropriately deform the line pattern on the mask or add auxiliary patterns to compensate for the optical distortion during the photolithography process, so that the final pattern formed on the silicon wafer is closer to the design requirements.

[0024] Traditionally, lithography machines requiring OPC model correction typically include steppers or scanners. These machines typically utilize projection lithography, where the pattern on the mask is reduced and projected onto the photoresist through a lens. The linewidth ratio is typically 4:1 (for example, the pattern on the mask is reduced by a factor of 4 before being projected onto the photoresist). Contact aligners, on the other hand, typically utilize larger mask feature sizes, where optical proximity effects are less pronounced, and therefore do not require OPC models to correct or compensate for distortion during the lithography process.

[0025] Figure 2 Schematic diagram showing the principle of a contact lithography machine 200. Figure 2 The mask structure of the lithography machine 200 typically includes a mask substrate 210 and a mask 220, which supports the entire mask layer. Mask 220, for example, includes a chrome layer, which forms a shielding area and determines the photoresist pattern after exposure. Photoresist 230 is a core material in the photolithography process. Its function is to form a temporary protective layer on the surface of the wafer substrate 240, and to enable the precise processing of the chip structure required by the photolithography process. As the material that ultimately carries the photolithography pattern, the thickness of the wafer substrate 240 can be considered infinite.

[0026] Contact lithography machines 200 typically use proximity exposure, meaning the spacing between mask 220 and photoresist 230 is 0-20 µm. This means the line width of the pattern on mask 220 is replicated 1:1 (or nearly 1:1) on photoresist 230. Because contact lithography machines process larger feature sizes (e.g., 2 µm line width), the optical proximity effect is not significant, and therefore OPC correction is generally not required.

[0027] However, as industrial demand increases, the minimum feature size of contact lithography systems may shrink to 0.2µm or smaller, approaching the wavelength of the light source used. This leads to an increasingly significant optical proximity effect. However, because traditional OPC models for projection lithography systems are calculated based on a mask scaling ratio of 4:1 or 5:1, the optical processing components of existing OPC models (such as optical transfer functions and aberration compensation) are not applicable to contact lithography systems. Furthermore, projection lithography systems typically use a monochromatic light source (such as 248nm or 193nm). Contact lithography systems typically use multi-wavelength mixed light sources (such as 365nm, 405nm, 436nm) or broad-spectrum light sources. The proximity effect is more complex than that of monochromatic light sources, resulting in unexpected deformation of certain features in the layout. Therefore, correction methods based on traditional OPC models are not applicable to lithography systems such as contact lithography systems, where the line width ratio between the mask and photoresist approaches 1:1.

[0028] In some solutions, to adapt to contact lithography machines, a large number of computing modules may be used to correct for the optical proximity effect. However, this approach is computationally intensive and requires high computing resources, resulting in additional costs and making the OPC model unsuitable for large-scale production application.

[0029] To this end, an embodiment of the present disclosure proposes a solution for OPC modeling. According to an embodiment of the present disclosure, multiple kernel functions for an optical proximity correction (OPC) model are first initialized, and the multiple kernel functions include at least a first category of kernel functions and a second category of kernel functions. The first category of kernel functions indicates an optical proximity effect that decays with increasing distance, and the second category of kernel functions indicates an optical proximity effect that changes suddenly with distance, and the distance indicates the distance between a target point and a neighboring point on a target layout. Further, the multiple kernel functions are calibrated. Finally, an OPC model is constructed based on the calibrated multiple kernel functions. In this way, an OPC model suitable for a contact lithography machine can be constructed, and the computational complexity of the OPC model can be simplified, the computational time can be reduced, and the correction efficiency can be improved.

[0030] In the following description, embodiments will be described with reference to constructing an OPC model for the lithography process of a contact lithography machine. Of course, it can be understood that the embodiments of the present disclosure are not limited to this application scenario. The OPC model construction process can also be used in other scenarios of lithography systems (such as nanoimprint lithography) that may have the same or similar line width ratios as the contact lithography machine.

[0031] The following further describes various example implementations of this solution in conjunction with the accompanying drawings. In some embodiments, the above OPC modeling process can be executed by an electronic device 110 as shown in Figure 1 . The following will be described in detail in conjunction with Figure 1 .

[0032] The simulation signal of the traditional OPC model can be expressed by the following formula:

[0033] simulation_signal=[optical+C1*Kernel1+C2*Kernel2+…+C m *Kernel m @layout (1)

[0034] Wherein, simulation_signal represents the simulation signal, optical represents the optical signal, Kernel1, Kernel2, Kernel m represents the kernel function, m represents the number of kernel functions, C1, C2, C m represents the weights of each kernel function, layout represents the corresponding image signals of each pattern in the mask, * represents the multiplication symbol, and @ represents the convolution symbol.

[0035] In the traditional optical proximity correction method, the lithography machine usually relies on the lens system to reduce and project the pattern on the mask onto the photoresist. Due to the refraction, diffraction, and interference of light passing through the lens, it is usually necessary to explicitly establish an optical imaging model and calculate how the mask pattern forms the final image during the lithography process through the Hopkins Equation or other optical modeling methods.

[0036] In contrast, for a contact lithography machine (or other lithography systems with the same or similar line width ratios as the contact lithography machine), the distance between the mask and the photoresist is extremely small. Due to the short optical path, large-scale optical diffraction and refraction effects in traditional projection lithography machines usually do not occur, and only local diffraction (proximity effect), a type of local influence, rather than long-distance optical distortion, will occur. Therefore, in some embodiments, instead of directly calculating the optical part, multiple kernel functions can be used to characterize the optical proximity effect, thereby establishing an OPC model.

[0037] In some embodiments, the electronic device 110 may first initialize multiple kernel functions for the optical proximity correction (OPC) model, wherein the multiple kernel functions include at least a first category of kernel functions and a second category of kernel functions.

[0038] In some embodiments, a first type of kernel function describes an optical proximity effect that decays with increasing distance. The distance refers to the distance between a target point and a neighboring point on the target layout. This type of kernel function is used to describe a continuously varying optical proximity effect, i.e., the optical effect gradually decreases as the distance between the target point and the neighboring point increases.

[0039] In some embodiments, a first type of kernel function is used to calculate a first degree of influence of a target point's analog signal on a neighboring point based on an optical proximity effect that decays with increasing distance. The decay rate of this first degree of influence is controlled by a first parameter. The target point's analog signal is affected by the neighboring point, but this influence gradually decreases as the distance from the neighboring point increases. The first type of kernel function is used to calculate this decay relationship and output a first degree of influence (i.e., the optical influence of the neighboring point on the target point).

[0040] In the photolithography process, the point spread function of an optical system typically approximates a Gaussian distribution. Therefore, a two-dimensional Gaussian kernel function can be used to approximate the range of influence of the optical proximity effect. The imaging of a target point is influenced by multiple neighboring points, and the influence of each neighboring point on the target point can be weighted using a Gaussian distribution, resulting in a more accurate simulation signal.

[0041] As an example, Figure 3A FIG. 3 is a schematic diagram showing a kernel function 300A of the first category according to some embodiments of the present disclosure. Figure 3A As shown, the kernel function of the first category may include a two-dimensional Gaussian distribution function based on distance and a first parameter (for example, called a Loading Kernel), which can be expressed by the following formula:

[0042] (2)

[0043] Here, σ is the first parameter of the kernel function of the first category, also known as the standard deviation. The smaller σ is, the smaller the range of the optical proximity effect is, and the faster it decays. r is the distance from other points in the layout to the target point (for example, the Euclidean distance between the target point and its neighboring points). As r increases, the optical proximity effect gradually decays, meaning that the farther the neighboring points are, the less influence they have on the target point.

[0044] In some embodiments, the kernel function of the second category indicates the optical proximity effect that mutates with distance. This distance refers to the distance between a target point and an adjacent point on the target layout. This type of kernel function is used to describe the optical proximity effect with a clear boundary. For example, in some complex lithography processes, certain structures on the mask will have a significant impact on the target point within a specific distance, and the impact will completely disappear after exceeding this distance.

[0045] In some embodiments, the kernel function of the second category is used to calculate the degree of the second influence of adjacent points on the analog signal of the target point based on the optical proximity effect that mutates with distance. The degree of the second influence is evenly distributed within the position where the mutation occurs indicated by the second parameter, that is, the degree of influence of the optical proximity effect simulated by the kernel function of the second category is uniform within a certain specific distance range. And the degree of influence drops to 0 outside this range, that is, the influence range is a clear threshold boundary.

[0046] As an example, Figure 3B FIG. shows a schematic diagram of the kernel function 300B of the second category according to some embodiments of the present disclosure. As Figure 3B shown, the kernel function presents a truncated frustum shape (or pie shape), which can be expressed by the following formula:

[0047] (3)

[0048] where d is the second parameter of the kernel function of the second category, that is, the boundary distance of the influence range, and r is the distance from other points in the layout to the target point. In the region where r ≤ d, the kernel function value is 1, indicating that the target point is evenly affected by adjacent points within this range. After r > d, the kernel function value suddenly becomes 0, indicating that adjacent points no longer affect the target point at all.

[0049] Furthermore, the electronic device 110 can determine the parameters and weights of each kernel function. In the OPC modeling process, the selection and parameter setting of the kernel function are crucial. After determining multiple kernel functions of a predetermined category, the electronic device 110 can further determine the initial values of the parameters and weights of each kernel function to accurately simulate the optical proximity effect and optimize the fitting ability of the model.

[0050] In some embodiments, the electronic device 110 can obtain the first initial value of the first parameter of the kernel function of the first category. The first parameter (such as the standard deviation σ) can be used to control the speed at which the optical proximity effect decays with distance. For example, a smaller σ value corresponds to a kernel function with a small influence range, and the proximity effect decays rapidly. A larger σ value corresponds to a kernel function with a large influence range, and the proximity effect decays more slowly.

[0051] In some embodiments, the electronic device 110 may obtain a second initial value of a second parameter of a second type of kernel function, where the second parameter indicates a position where a mutation occurs (e.g., the radius d of the influence range). In some embodiments, the electronic device 110 obtains initial values of the first parameter and the second parameter from a predetermined range. For example, the first initial value is within a first predetermined range, and the second initial value is within a second predetermined range.

[0052] In some embodiments, the multiple kernel functions include a first number of kernel functions of a first type and a second number of kernel functions of a second type, and the sum of the first number and the second number is less than a threshold. For example, the number of kernel functions of the first type is 2 (e.g., Loading_Kernel1 and Loading_Kernel2), and their corresponding first parameters are σ1 and σ2 respectively. The number of kernel functions of the second type is 1 (e.g., Disk_Kernel), and its corresponding second parameter is d. By using two kernel functions of the first type with different scales and one kernel function of the second type to simulate different optical proximity effects respectively, the calculation overhead of the OPC model can be reduced, thereby improving the efficiency of OPC correction.

[0053] In some embodiments, an empirical value range may be predetermined for the parameters and weights of each kernel function. For example, the predetermined range for σ1 and σ2 is 0 ≤ σ1 ≤ σ2 ≤ 0.365. The predetermined range for d is 0 < d ≤ 0.365. The predetermined range may be an empirical setting for the parameters of each kernel function in the OPC model, so as to ensure that the OPC model operates within a reasonable range.

[0054] The multiple kernel functions are respectively used to simulate the effects of different characteristics. To ensure that the contributions of each kernel function are reasonable, the electronic device 110 needs to assign reasonable weights to them so that they are finally weighted and combined into a reasonable model. In some embodiments, the electronic device 110 obtains initial values of the corresponding weights of the multiple kernel functions. The initial weight of each kernel function is within a predetermined range corresponding to that kernel function. Further, the electronic device 110 may initialize the corresponding weights of the multiple kernel functions by normalizing the initial values of the corresponding weights of the multiple kernel functions.

[0055] As an example, each kernel function has a corresponding initial weight, and these initial weights can be within a predetermined range to ensure that the influence of each kernel function is not too large or too small. For example, the weights of the kernel functions Loading_Kernel1 and Loading_Kernel2 in the first category are c1 and c2 respectively, and the weight of the kernel function Disk_Kernel in the second category is c3. In addition, the predetermined ranges of c1 and c2 are 0≤c1≤0.45 and 0≤c2≤0.45. c3 is obtained by normalization, such as c3=1-c1-c2. After normalization, the contributions of all kernel functions are within a controllable range, ensuring that different kernel functions will not cause model imbalance due to improper weight settings.

[0056] In some embodiments, the electronic device 110 may calibrate the OPC model. For example, the electronic device 110 may use the OPC model to determine corresponding image signals for each test pattern in the test layout, where the image signals indicate the shape of each test pattern; and calibrate at least one of corresponding parameters and corresponding weights of multiple kernel functions based on the corresponding image signals for each test pattern in the test layout and the exposure results corresponding to the corresponding image signals.

[0057] As an example, the test layout can be a reference layout used to calibrate the OPC process, containing test patterns of different shapes. Using the OPC model, electronic device 110 can calculate the corresponding image signal for each test pattern. By comparing the obtained image signal with the exposure results on the actual photoresist, electronic device 110 can adjust the parameters of the kernel function (e.g., σ1, σ2, d) to make the simulation results more accurate. Alternatively or additionally, electronic device 110 can adjust the weights of each kernel function (e.g., C1, C2) based on the comparison between the obtained image signal and the exposure results on the actual photoresist. By only utilizing two types of kernel functions, less than a threshold number, and introducing a normalization condition, electronic device 110 can reduce the number of calibration parameters, thereby further reducing the amount of computation.

[0058] In some embodiments, the electronic device 110 can construct an OPC model based on multiple calibrated kernel functions. The core goal of the calibration process is to find the most appropriate kernel function parameters and weights to improve the accuracy of the analog signal. For example, after calibration, the electronic device 110 can use the parameters and weights of the calibrated kernel functions to construct the final OPC model for OPC correction calculations.

[0059] Alternatively or additionally, the electronic device 110 may construct an OPC model based on a weight of the optical signal and corresponding weights of a plurality of calibrated kernel functions, wherein the weight of the optical signal is less than the corresponding weights of the plurality of kernel functions. For example, the electronic device 110 may not use the full Hopkins formula to calculate the optical signal, and may assign a very small weight to the optical signal calculation portion (e.g., the optical signal weight is 0 or close to 0), thereby retaining certain optical information while maintaining computational efficiency.

[0060] In some embodiments, the electronic device 110 may obtain corresponding image signals of each graphic in the target layout, where the image signals indicate the shape of each graphic in the target layout. Based on the OPC model and the corresponding image signals of each graphic in the target layout, the electronic device 110 may determine an analog signal generated according to the OPC model.

[0061] The target layout is the circuit pattern corresponding to the chip, used to create the photolithography mask. The layout can include polygons, lines, contact holes, and other shapes. The image signal reflects the basic information of these shapes, such as their position (X, Y coordinates), size (CD, critical dimension), and shape characteristics. Electronic device 110 can use the constructed OPC model to perform convolution calculations on the target layout, thereby deriving the analog signal for each shape on the photoresist.

[0062] For example, the simulation signal of the OPC model can be expressed by the following formula:

[0063] simulation_signal=[ C1*Loading_Kernel1+

[0064] C2* Loading_Kernel2+C3*Disk_Kernel] @ layout (4)

[0065] Wherein, simulation_signal represents a simulation signal, Loading Kernel1 and Loading Kernel2 represent kernel functions of the first category, Disk_Kernel represents a kernel function of the second category, C1, C2, and C3 represent the weights of each kernel function, layout represents the corresponding image signal of each graphic in the mask, * represents the product symbol, and @ represents the convolution symbol. Electronic device 110 can determine the first signal component based on the kernel functions Loading Kernel1 and Loading Kernel2 of the first category. It can also determine the second signal component based on the kernel function Disk_Kernel of the second category. Then, based on the first signal component, the second signal component, and the corresponding weights C of the multiple kernel functions, the simulation signal simulation_signal is determined.

[0066] Furthermore, electronic device 110 can perform optical proximity effect correction on the target layout based on the analog signal. For example, if the line width of the analog signal is smaller than the target line width, it indicates that the pattern has shrunk after photolithography, and the pattern on the mask should be widened. This adjustment process is automatically performed using an OPC correction engine or other system or software suitable for OPC correction. The resulting mask layout is an OPC-corrected mask layout, not the original design layout. This mask layout can be delivered to a mask manufacturer for mask production. During the photolithography process, since the mask layout has been corrected, the pattern on the photoresist can accurately match the design target.

[0067] In summary, the OPC model for contact lithography machines constructed using the above method greatly improves the production accuracy of contact lithography. The use of only a few kernel functions effectively reduces the amount of calibration and simulation calculations, significantly shortening the OPC calculation time while still maintaining sufficient simulation accuracy. This optimized design improves OPC processing efficiency, reduces the lithography calculation burden, and makes the overall process more efficient and controllable. Furthermore, because only the kernel function selection and number of parameters of the OPC model are optimized, without changing the overall framework, the simulated signals calculated by the OPC model can be directly integrated into existing OPC software, thereby improving the applicability of OPC corrections and reducing costs.

[0068] Figure 4 FIG. 4 is a flow chart illustrating a process 400 for OPC modeling according to some embodiments of the present disclosure. In some embodiments, the process 400 may be performed by: Figure 1 It should be understood that the process 400 may also include additional blocks not shown and / or may omit one (or some) of the blocks shown, and the scope of the present disclosure is not limited in this respect. Figure 1 The process 400 is described in detail.

[0069] like Figure 4 As shown, at block 410, the electronic device 110 initializes multiple kernel functions for an optical proximity correction (OPC) model. The multiple kernel functions include at least a first category of kernel functions and a second category of kernel functions. The first category of kernel functions indicates an optical proximity effect that decays with increasing distance, and the second category of kernel functions indicates an optical proximity effect that abruptly changes with distance. The distance indicates the distance between a target point and a neighboring point on a target layout. At block 420, the electronic device 110 calibrates the multiple kernel functions. At block 430, the electronic device 110 constructs an OPC model based on the calibrated multiple kernel functions.

[0070] In some embodiments, constructing an OPC model includes: constructing an OPC model based on the weights of optical signals and the corresponding weights of a plurality of calibrated kernel functions, where the weights of the optical signals are less than the corresponding weights of the plurality of kernel functions.

[0071] In some embodiments, the weight of the optical signal is 0.

[0072] In some embodiments, initializing a plurality of kernel functions for an optical proximity correction (OPC) model includes: obtaining a first initial value of a first parameter of a first type of kernel function, where the first parameter is used to control the attenuation rate and the first initial value is within a first predetermined range; and obtaining a second initial value of a second parameter of a second type of kernel function, where the second parameter indicates the position where a mutation occurs and the second initial value is within a second predetermined range.

[0073] In some embodiments, initializing a plurality of kernel functions for an optical proximity correction (OPC) model further includes: obtaining initial values of the corresponding weights of the plurality of kernel functions, where the initial weight of each kernel function is within a predetermined range corresponding to that kernel function; and determining the corresponding weights of the plurality of kernel functions by normalizing the initial values of the corresponding weights of the plurality of kernel functions.

[0074] In some embodiments, calibrating a plurality of kernel functions and the corresponding weights of the plurality of kernel functions includes: using the OPC model to determine the corresponding image signals of each test pattern in a test layout, where the image signals indicate the shapes of the respective test patterns; and calibrating at least one of the corresponding parameters of the plurality of kernel functions and the corresponding weights of the plurality of kernel functions based on the corresponding image signals of each test pattern in the test layout and the exposure results corresponding to the corresponding image signals.

[0075] In some embodiments, the plurality of kernel functions include a first number of kernel functions of a first type and a second number of kernel functions of a second type, and the sum of the first number and the second number is less than a threshold.

[0076] In some embodiments, the kernel functions of the first type are used to calculate the degree of the first influence of neighboring points on the analog signal of a target point based on the optical proximity effect that decays as the distance increases, and the attenuation rate of the degree of the first influence is controlled based on the first parameter.

[0077] In some embodiments, the kernel functions of the first type include a two-dimensional Gaussian distribution function based on distance and the first parameter, and the first parameter includes the standard deviation of the two-dimensional Gaussian distribution function.

[0078] In some embodiments, the kernel functions of the second type are used to calculate the degree of the second influence of neighboring points on the analog signal of a target point based on the optical proximity effect that mutates with distance, and the degree of the second influence is uniformly distributed within the position where the mutation occurs indicated by the second parameter.

[0079] In some embodiments, the weight of the second influence degree of the neighboring points within the location where the mutation occurs on the target point is 1, and the weight of the second influence degree of the neighboring points outside the location where the mutation occurs on the target point is 0.

[0080] In some embodiments, process 400 further includes: acquiring corresponding image signals of each graphic in the target layout, the image signals indicating the shape of each graphic in the target layout; determining an analog signal generated according to the OPC model based on the OPC model and the corresponding image signals of each graphic in the target layout; and performing optical proximity effect correction on the target layout based on the analog signal.

[0081] In some embodiments, determining the analog signal generated by the OPC model includes: determining a first signal component based on a first category kernel function; determining a second signal component based on a second category kernel function; and determining the analog signal based on the first signal component, the second signal component, and corresponding weights of multiple kernel functions.

[0082] In some embodiments, the OPC model is applicable to contact lithography machines.

[0083] Figure 5 1 is a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. The electronic device 500 may be used to implement, for example, Figure 1 The electronic device 110 is shown. It should be understood that Figure 5 The illustrated electronic device 500 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0084] like Figure 5 As shown, electronic device 500 is in the form of a general electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors 510 or processing units, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing units may be actual or virtual processors and are capable of performing various processes according to programs stored in memory 520. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of electronic device 500.

[0085] The electronic device 500 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 can be a removable or non-removable medium and can include a machine-readable medium such as a flash drive, a disk, or any other medium that can be used to store information and / or data (e.g., training data for training) and can be accessed within the electronic device 500.

[0086] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5 As shown in FIG, a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. Memory 520 may include a computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

[0087] The communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 500 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or other network nodes.

[0088] Input device 550 may be one or more input devices, such as a mouse, keyboard, or trackball. Output device 560 may be one or more output devices, such as a display, speaker, or printer. Electronic device 500 may also communicate with one or more external devices (not shown) via communication unit 540 as needed, such as storage devices, display devices, or one or more devices that allow a user to interact with electronic device 500, or any device that allows electronic device 500 to communicate with one or more other electronic devices (e.g., a network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0089] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, and the one or more computer instructions are executed by a processor to implement the method described above.

[0090] Aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and the combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0091] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that when the instructions are executed by the processing unit of the computer or other programmable data processing apparatus, a device is produced that implements the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which causes a computer, a programmable data processing apparatus, and / or other devices to operate in a specific manner, so that the computer-readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0092] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device, such that a series of operation steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to multiple implementations of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of an instruction, and the module, program segment, or part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or can be implemented by a combination of dedicated hardware and computer instructions.

[0094] The implementations of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is also not limited to the disclosed implementations. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The choice of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. A method for OPC modeling, characterized in that, Including: Initializing a plurality of kernel functions for an optical proximity correction (OPC) model, the plurality of kernel functions including at least a first type of kernel function and a second type of kernel function, the first type of kernel function indicating an optical proximity effect that decays as the distance increases, the second type of kernel function indicating an optical proximity effect that mutates as the distance changes, the distance indicating the spacing between a target point and an adjacent point on a target layout; Calibrating the plurality of kernel functions; And Based on the calibrated plurality of kernel functions, constructing the OPC model.

2. The method for OPC modeling according to claim 1, wherein Constructing the OPC model includes: Based on the weight of an optical signal and the corresponding weights of the calibrated plurality of kernel functions, constructing the OPC model, the weight of the optical signal being less than the corresponding weights of the plurality of kernel functions.

3. The method for OPC modeling according to claim 2, wherein, The weight of the optical signal is 0.

4. The method for OPC modeling according to claim 1, wherein Initializing a plurality of kernel functions for an optical proximity correction (OPC) model includes: Obtaining a first initial value of a first parameter of the first type of kernel function, the first parameter being used to control the rate of decay, and the first initial value being within a first predetermined range; and Obtaining a second initial value of a second parameter of the second type of kernel function, the second parameter indicating the location where the mutation occurs, and the second initial value being within a second predetermined range.

5. The method for OPC modeling according to claim 1, characterized in that, Initializing a plurality of kernel functions for an optical proximity correction (OPC) model further includes: Obtaining initial values of the corresponding weights of the plurality of kernel functions, the initial value of the corresponding weight of each kernel function being within a predetermined range corresponding to that kernel function; and Determining the corresponding weights of the plurality of kernel functions by normalizing the initial values of the corresponding weights of the plurality of kernel functions.

6. The method for OPC modeling according to claim 1, characterized in that, Calibrating the plurality of kernel functions includes: Using the OPC model to determine the corresponding image signals of each test pattern in a test layout, the image signals indicating the shapes of the respective test patterns; and Based on the corresponding image signals of each test pattern in the test layout and the exposure results corresponding to the corresponding image signals, calibrating at least one of the corresponding parameters of the plurality of kernel functions and the corresponding weights of the plurality of kernel functions.

7. The method for OPC modeling according to claim 1, characterized in that, The plurality of kernel functions includes a first number of the first type of kernel functions and a second number of the second type of kernel functions, the sum of the first number and the second number being less than a threshold.

8. The method for OPC modeling according to claim 1, characterized in that, The first type of kernel function is used to calculate a first degree of influence of the adjacent point on the analog signal of the target point based on the optical proximity effect that decays as the distance increases, and the rate of decay of the first degree of influence is controlled based on the first parameter.

9. The method for OPC modeling according to claim 8, wherein The first type of kernel function includes a two-dimensional Gaussian distribution function based on the distance and the first parameter, and the first parameter includes the standard deviation of the two-dimensional Gaussian distribution function.

10. The method for OPC modeling according to claim 1, characterized in that, The second type of kernel function is used to calculate a second degree of influence of the adjacent point on the analog signal of the target point based on the optical proximity effect that mutates as the distance changes, and the second degree of influence is uniformly distributed within the location where the mutation occurs indicated by the second parameter.

11. The method for OPC modeling according to claim 10, characterized in that, The weight of the second influence degree of adjacent points whose distance is within the position where the mutation occurs on the target point is 1, and the weight of the second influence degree of adjacent points whose distance is outside the position where the mutation occurs on the target point is 0.

12. The method for OPC modeling according to claim 1, characterized in that, The method further includes: obtaining corresponding image signals of each pattern in the target layout, where the image signals indicate the shapes of each pattern in the target layout; determining an analog signal generated according to the OPC model based on the OPC model and the corresponding image signals of each pattern in the target layout; and performing optical proximity effect correction on the target layout based on the analog signal.

13. The method for OPC modeling according to claim 12, wherein Determining the analog signal generated by the OPC model includes: determining a first signal component based on the kernel function of the first category; determining a second signal component based on the kernel function of the second category; determining the analog signal based on the first signal component, the second signal component, and the corresponding weights of the plurality of kernel functions.

14. The method for OPC modeling according to claim 1, wherein the OPC model is applicable to a contact lithography machine.

15. An electronic device, characterized in that, It includes: at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, and the instructions, when executed by the at least one processing unit, cause the electronic device to execute the method according to any one of claims 1 to 14.

16. A computer-readable storage medium, characterized in that, A computer program is stored thereon, characterized in that the computer program can be executed by a processor to implement the method according to any one of claims 1 to 14.

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