Method and device for OPC modeling and storage medium

Abstract

An example embodiment in accordance with the present disclosure provides a method, apparatus, and storage medium for optical proximity correction (OPC) modeling. The method comprises: initializing a plurality of kernel functions for an OPC model, the plurality of kernel functions comprising at least a first category of kernel functions and a second category of kernel functions, the first category of kernel functions indicating optical proximity effects that attenuate as distance increases, the second category of kernel functions indicating optical proximity effects that mutate as distance increases, the distance indicates the distance between a target point and an adjacent point on the target layout; calibrating the plurality of kernel functions; and constructing an OPC model based on the plurality of calibrated kernel functions. In this way, the OPC model suitable for the contact type photoetching machine can be constructed, so that distortion in the photoetching process can be compensated, and finally formed patterns are closer to design requirements.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of integrated circuit technology, and more particularly, to a method, device, and storage medium for OPC modeling. Background Art

[0002] Photolithography is a key process in chip manufacturing, which is used to transfer circuit patterns onto silicon wafers. As chip manufacturing processes continue to shrink, the problem of image distortion in photolithography becomes more and more prominent. The Optical Proximity Correction (OPC) model came into being. It models and analyzes phenomena in the photolithography process and corrects and compensates the original design graphics to improve the accuracy and quality of the photolithography graphics and ensure the yield rate of chip manufacturing. The OPC model is an indispensable and important part of advanced photolithography 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 multiple kernel functions for an optical proximity correction (OPC) model, the multiple kernel functions at least including 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 with increasing distance, the second category of kernel functions indicating an optical proximity effect that changes suddenly with distance, the distance indicating the spacing between a target point and a neighboring point on a target layout; calibrating the multiple kernel functions; and constructing an OPC model based on the calibrated multiple 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, and when the instructions are executed by the processor, the electronic device executes 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. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0006] It will be understood from the following description that according to an embodiment of the present disclosure, multiple kernel functions for the 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 spacing between a target point and a neighboring point on the 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 calculation complexity of the OPC model is simplified, and the calculation time is reduced while the correction efficiency is improved.

[0007] It should be understood that the contents described in the summary of the present invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A schematic diagram showing an example environment in which various embodiments of the present disclosure can be implemented; Figure 2 A schematic diagram showing the principle of a contact lithography machine; Figure 3A A schematic diagram showing a kernel function of a first category according to some embodiments of the present disclosure; Figure 3B A schematic diagram showing a kernel function of the second category according to some embodiments of the present disclosure; Figure 4 A flowchart illustrating a process for OPC modeling according to some embodiments of the present disclosure; and Figure 5 A block diagram of an electronic device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0009] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0010] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "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.

[0011] Various example implementations of the scheme will be described in detail below with reference to the accompanying drawings.

[0012] See first Figure 1 , which shows a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. Figure 1 As shown, example environment 100 generally may include an electronic device 110 .

[0013] In some embodiments, the electronic device 110 may interact with other devices (not shown). 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 corresponding operation results 130 to other devices. In some embodiments, the operation results may be corrected layout data.

[0014] In the example environment 100, the electronic device 110 can be any type of device with computing capabilities, including a terminal device or a server device. The terminal device can 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 / camcorder, 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 can include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0015] 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.

[0016] As briefly mentioned above, the OPC model is an indispensable and important part of advanced lithography technology. Lithography technology aims to break through the hardware limitations of the minimum exposure size by improving software technologies such as resolution while keeping the hardware environment of existing lithography equipment unchanged, which has greatly promoted the development of advanced semiconductor processes.

[0017] In the photolithography process, due to physical phenomena such as diffraction and interference of light, there will be differences between the graphics formed by actual exposure and the designed graphics on the mask. Especially when the feature size continues to shrink, this difference will be more obvious. The OPC model accurately models the physical process of light propagation and imaging during the photolithography process, analyzes the law and degree of graphic distortion, and then corrects the graphics on the mask. For example, for some long and thin line graphics, problems such as uneven line edges and inconsistent widths may occur after photolithography. The OPC model can appropriately deform the line graphics on the mask or add auxiliary graphics based on the calculation results to compensate for the optical distortion in the photolithography process, so that the graphics finally formed on the silicon wafer are closer to the design requirements.

[0018] Traditionally, lithography machines that require OPC models for correction usually include stepper lithography machines or scanner lithography machines. This type of lithography machine usually uses projection lithography, that is, the pattern on the mask is reduced and projected onto the photoresist through a lens, and its line width ratio is generally 4:1 (for example, the pattern on the mask will be reduced by 4 times and then projected onto the photoresist). The other type of contact aligner lithography machine usually involves mask feature sizes that are usually larger, and the optical proximity effect is not obvious, so the OPC model is not required to correct or compensate for the distortion in the lithography process.

[0019] Figure 2 FIG. 2 is a schematic diagram showing the principle of a contact lithography machine 200 . Figure 2 The mask structure of the lithography machine 200 generally includes a mask substrate 210 and a mask 220 for supporting the entire mask layer. The mask 220 includes, for example, a mask chrome layer, which is used to form a shielding area and determine the photoresist pattern after exposure. The photoresist 230 is the 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 accurately process the structure required for the chip through the photolithography process. As the material that ultimately carries the photolithography pattern, the thickness of the wafer substrate 240 can be regarded as infinite.

[0020] The contact lithography machine 200 usually adopts a proximity exposure exposure method, that is, the spacing between the mask 220 and the photoresist 230 is 0-20 µm. This means that the line width of the pattern on the mask 220 will be copied 1:1 (or close to 1:1) to the photoresist 230. Since the contact lithography machine processes a larger feature size (such as 2µm line width), the impact of the optical proximity effect is not significant, so OPC correction is usually not required.

[0021] However, with the increase in industrial demand, the minimum feature size of contact lithography machines may be reduced to 0.2µm or less, which is close to the wavelength of the light source used. This leads to an increasingly significant optical proximity effect. However, since the traditional OPC model for projection lithography machines is calculated based on a mask scaling ratio of 4:1 or 5:1, the optical processing part of the existing OPC model (such as optical transfer function, aberration compensation, etc.) is not applicable to contact lithography machines. In addition, projection lithography machines usually use monochromatic light sources (such as 248nm or 193nm). Contact lithography machines usually use multi-wavelength mixed light sources (such as 365nm, 405nm, 436nm) or broad-spectrum light sources, and their proximity effects are more complex than monochromatic light sources, resulting in unexpected deformation of some features in the layout. Therefore, the correction method based on the traditional OPC model is not suitable for lithography machines such as contact lithography machines where the line width ratio between the mask and the photoresist is close to 1:1.

[0022] In some solutions, in order to adapt to contact lithography machines, a large number of computing modules may be used to correct the optical proximity effect. However, this method has a large amount of calculations and high computing resource requirements, resulting in additional costs, making the OPC model unable to be applied to production on a large scale.

[0023] To this end, an embodiment of the present disclosure proposes a scheme 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 calculation complexity of the OPC model can be simplified, and the correction efficiency can be improved while reducing the calculation time.

[0024] In the following description, the embodiments will be described with reference to constructing an OPC model for the lithography process of a contact lithography machine. Of course, it is 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 scenarios of other lithography systems (e.g., nanoimprint lithography) that may have the same or similar line width ratio as the contact lithography machine.

[0025] Various example implementations of the solution are described in detail below in conjunction with the accompanying drawings. In some embodiments, the above OPC modeling process can be performed as follows: Figure 1 The electronic device 110 shown in FIG. Figure 1 to explain in detail.

[0026] The simulation signal of the traditional OPC model can be expressed by the following formula: simulation_signal=[optical+C1*Kernel1+C2*Kernel2+…+C m *Kernel m ] @layout(1) Among them, simulation_signal represents the analog 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 weight 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.

[0027] In the traditional optical proximity correction method, the lithography machine usually relies on a lens system to reduce the pattern on the mask and project it onto the photoresist. Since the light is refracted, diffracted, and interfered by the lens, it is usually necessary to explicitly establish an optical imaging model to calculate how the mask pattern forms the final image during the lithography process through the Hopkins equation or other optical modeling methods.

[0028] In contrast, for contact lithography (or other lithography systems with the same or similar line width ratio as contact lithography), the spacing 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 are usually not present, and there are only local effects such as local diffraction (proximity effect) instead of optical deformation propagated over long distances. Therefore, in some embodiments, instead of directly calculating the optical part, multiple kernel functions may be used to characterize the optical proximity effect, thereby establishing an OPC model.

[0029] 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.

[0030] In some embodiments, the first type of kernel function indicates an optical proximity effect that decays with increasing distance. The distance refers to the distance between the target point and the neighboring point on the target layout. This type of kernel function is used to describe a continuously changing optical proximity effect, that is, as the distance between the target point and the neighboring point increases, the optical effect gradually weakens.

[0031] In some embodiments, the kernel function of the first category is used to calculate the first degree of influence of the analog signal of the target point by the neighboring point based on the optical proximity effect that decays with increasing distance, and the decay speed of the first degree of influence is controlled based on the first parameter. The analog signal of the target point will be affected by the neighboring point, but this influence will gradually weaken as the distance of the neighboring point increases. The kernel function of the first category is used to calculate this attenuation relationship and output the first degree of influence (i.e., the optical influence of the neighboring point on the target point).

[0032] In the photolithography process, the point spread function of the optical system is usually close to Gaussian distribution. Therefore, a two-dimensional Gaussian kernel function can be used to fit the influence range of the optical proximity effect. The imaging of the target point is affected by multiple neighboring points, and the influence of each neighboring point on the target point can be weighted by Gaussian distribution to obtain a more accurate simulation signal.

[0033] As an example, Figure 3A FIG. 3 is a schematic diagram of 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 (eg, called Loading Kernel) based on the distance and the first parameter, which may be represented by the following formula: (2) Among them, σ is the first parameter of the kernel function of the first category, that is, the standard deviation. The smaller σ is, the smaller the influence 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 the neighboring point). As r increases, the optical proximity effect gradually decays, that is, the farther the neighboring point is, the smaller the influence on the target point is.

[0034] In some embodiments, the second type of kernel function indicates an optical proximity effect that changes suddenly with distance. The distance refers to the distance between the target point and the neighboring point on the target layout. This type of kernel function is used to describe an optical proximity effect with clear boundaries. For example, in some complex lithography processes, certain structures on the mask will have a significant effect on the target point within a certain distance, and the effect will completely disappear beyond this distance.

[0035] In some embodiments, the kernel function of the second category is used to calculate the second degree of influence of the simulated signal of the target point by the neighboring point based on the optical proximity effect that mutates with distance. The second degree of influence is uniformly 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 distance range. The degree of influence is reduced to 0 outside this range, that is, the influence range is a clear threshold boundary.

[0036] As an example, Figure 3B FIG. 3 is a schematic diagram of a kernel function 300B of the second category according to some embodiments of the present disclosure. Figure 3B As shown, the kernel function is in the shape of a truncated frustum (or pie), which can be expressed by the following formula: (3) Among them, 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 map to the target point. In the area of ​​r≤d, the kernel function value is 1, indicating that the target point is uniformly affected by the neighboring points within this range. After r>d, the kernel function value suddenly changes to 0, indicating that the neighboring points no longer affect the target point at all.

[0037] Further, 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 a plurality of 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.

[0038] In some embodiments, the electronic device 110 may obtain a first initial value of a first parameter of a kernel function of the first category. The first parameter (e.g., standard deviation σ) may be used to control the speed at which the optical proximity effect decays with distance. For example, a smaller σ value corresponds to a smaller kernel function with a smaller influence range, and the proximity effect decays rapidly. A larger σ value corresponds to a larger kernel function with a larger influence range, and the proximity effect decays more slowly.

[0039] In some embodiments, the electronic device 110 may obtain a second initial value of a second parameter of the kernel function of the second category, where the second parameter indicates a location where the mutation occurs (e.g., an impact range radius d). 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.

[0040] In some embodiments, the multiple kernel functions include a first number of kernel functions of a first category and a second number of kernel functions of a second category, 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 category 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 category is 1 (e.g., Disk_Kernel), and its corresponding second parameter is d. By using two kernel functions of the first category with different scales and one kernel function of the second category to simulate different optical proximity effects respectively, the calculation overhead of the OPC model can be reduced, thereby improving the efficiency of OPC correction.

[0041] In some embodiments, an empirical value range can be predetermined for the parameters and weights of each kernel function. For example, the predetermined range of σ1 and σ2 is 0 ≤ σ1 ≤ σ2 ≤ 0.365. The predetermined range of d is 0 < d ≤ 0.365. The predetermined range can 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.

[0042] The multiple kernel functions are respectively used to simulate the effects of different characteristics. To ensure the reasonable contribution of each kernel function, the electronic device 110 needs to assign reasonable weights to them so as to finally be weighted and combined into a reasonable model. In some embodiments, the electronic device 110 obtains the initial values of the corresponding weights of the multiple kernel functions. The initial weight of each kernel function is within the predetermined range corresponding to the kernel function. Further, the electronic device 110 can initialize the corresponding weights of the multiple kernel functions by normalizing the initial values of the corresponding weights of the multiple kernel functions.

[0043] 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 neither too large nor too small. For example, the weights of the kernel functions Loading_Kernel1 and Loading_Kernel2 of the first category are c1 and c2 respectively, and the weight of the kernel function Disk_Kernel of the second category is c3. In addition, the predetermined range of c1 and c2 is 0 ≤ c1 ≤ 0.45, 0 ≤ c2 ≤ 0.45. c3 is obtained through a normalization operation, such as c3 = 1 - c1 - c2. After normalization, the contributions of all kernel functions are within a controllable range, ensuring that the model will not be unbalanced due to improper weight setting of different kernel functions.

[0044] 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 the corresponding image signals of each test pattern in the test layout, where the image signals indicate the shapes of each test pattern; 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, calibrate at least one of the corresponding parameters and corresponding weights of the plurality of kernel functions.

[0045] As an example, the test layout can be a reference layout for calibrating the OPC process, including test patterns of different shapes. Through the OPC model, the electronic device 110 can calculate the corresponding image signal of each test pattern. By comparing the obtained image signal with the exposure result on the actual photoresist, the electronic device 110 can adjust the parameters of the kernel function (e.g., σ1, σ2, d) to make the simulation result more accurate. Alternatively or additionally, the 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 result on the actual photoresist. By only using two types of kernel functions less than the threshold number and introducing a normalization condition, the electronic device 110 can reduce the number of calibration parameters, thereby further reducing the amount of calculation.

[0046] 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 completing the calibration, the electronic device 110 can use the parameters and weights of the multiple calibrated kernel functions to construct the final OPC model for OPC correction calculation.

[0047] Alternatively or additionally, the electronic device 110 may construct an OPC model based on the weight of the optical signal and the corresponding weights of the calibrated multiple kernel functions, wherein the weight of the optical signal is less than the corresponding weights of the multiple kernel functions. For example, the electronic device 110 may not use the complete Hopkins formula to calculate the optical signal, and set a very small weight to the optical signal calculation part (for example, the weight of the optical signal is 0 or close to 0), thereby retaining certain optical information while maintaining computational efficiency.

[0048] In some embodiments, the electronic device 110 may acquire corresponding image signals of each graphic in the target layout, the image signals indicating the shapes 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 the analog signal generated according to the OPC model.

[0049] The target layout is the circuit pattern corresponding to the chip, which is used to make the photolithography mask. The layout may include polygons, lines, contact holes and other graphics. The image signal reflects the basic information of these graphics, such as position (X, Y coordinates), size (CD, critical dimension), shape characteristics, etc. The electronic device 110 can use the constructed OPC model to perform convolution calculation on the target layout, thereby obtaining the analog signal of each graphic on the photoresist.

[0050] For example, the simulation signal of the OPC model can be expressed by the following formula: simulation_signal=[ C1*Loading_Kernel1+C2* Loading_Kernel2+C3*Disk_Kernel] @ layout (4) 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 weights of each kernel function, layout represents the corresponding image signal of each graphic in the mask, * represents a product symbol, and @ represents a convolution symbol. The electronic device 110 can determine the first signal component based on the kernel functions Loading Kernel1 and Loading Kernel2 of the first category. Based on the kernel function Disk_Kernel of the second category, determine the second signal component. Then, based on the first signal component, the second signal component, and the corresponding weights C of the multiple kernel functions, determine the simulation signal simulation_signal.

[0051] Furthermore, the 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 means that the pattern shrinks after photolithography, and the pattern on the mask should be widened. This adjustment process is automatically completed using an OPC correction engine or other systems or software suitable for OPC correction. The final mask layout is the mask layout after OPC correction, not the original design layout. This mask layout can be delivered to a mask manufacturer for mask production. In the photolithography process, since the mask layout has been corrected, the pattern on the photoresist can correctly match the design target.

[0052] In summary, the OPC model for contact lithography constructed in the above manner greatly improves the production accuracy of contact lithography. Only a few kernel functions are used to effectively reduce the amount of calibration and simulation calculations, so that the OPC calculation time can be greatly shortened while still maintaining sufficient simulation accuracy. This optimized design improves the OPC processing efficiency, reduces the burden of lithography calculations, and makes the overall process more efficient and controllable. In addition, since only the kernel function selection and number of parameters of the OPC model are optimized, the overall framework is not changed. The simulation signal calculated by the OPC model can be directly integrated into the existing OPC software, thereby improving the applicability of OPC correction and reducing costs.

[0053] Figure 4 FIG. 4 is a flow chart showing 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 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.

[0054] like Figure 4 As shown, in box 410, the electronic device 110 initializes multiple kernel functions for the optical proximity correction OPC model, 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 spacing between the target point and the neighboring point on the target layout. In box 420, the electronic device 110 calibrates the multiple kernel functions. In box 430, the electronic device 110 constructs the OPC model based on the calibrated multiple kernel functions.

[0055] In some embodiments, constructing the OPC model includes: constructing the 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.

[0056] In some embodiments, the weight of the optical signal is zero.

[0057] In some embodiments, initializing multiple kernel functions for an optical proximity correction (OPC) model includes: obtaining a first initial value of a first parameter of a first category of kernel functions, the first parameter being used to control the speed of attenuation, and the first initial value being within a first predetermined range; and obtaining a second initial value of a second parameter of a second category of kernel functions, the second parameter indicating a location where a mutation occurs, and the second initial value being within a second predetermined range.

[0058] In some embodiments, initializing multiple kernel functions for an optical proximity correction (OPC) model further includes: obtaining initial values ​​of corresponding weights of the multiple kernel functions, the initial weight of each kernel function being within a predetermined range corresponding to the kernel function; and determining the corresponding weights of the multiple kernel functions by normalizing the initial values ​​of the corresponding weights of the multiple kernel functions.

[0059] In some embodiments, calibrating multiple kernel functions and corresponding weights of the multiple kernel functions includes: using an OPC model to determine corresponding image signals of each test pattern in the test layout, the image signals indicating the shapes of each test pattern; 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 corresponding parameters of the multiple kernel functions and at least one of the corresponding weights of the multiple kernel functions.

[0060] In some embodiments, the plurality of kernel functions includes a first number of kernel functions of a first category and a second number of kernel functions of a second category, and a sum of the first number and the second number is less than a threshold.

[0061] In some embodiments, the kernel function of the first category is used to calculate a first degree of influence of a simulated signal of a target point by neighboring points based on an optical proximity effect that decays with increasing distance, and a decay speed of the first degree of influence is controlled based on a first parameter.

[0062] In some embodiments, the kernel function of the first category comprises a two-dimensional Gaussian distribution function based on the distance and a first parameter, and the first parameter comprises a standard deviation of the two-dimensional Gaussian distribution function.

[0063] In some embodiments, the kernel function of the second category is used to calculate the second degree of influence of the analog signal of the target point by the neighboring points based on the optical proximity effect that mutates with distance, and the second degree of influence is uniformly distributed within the position where the mutation occurs indicated by the second parameter.

[0064] In some embodiments, the weight of the second influence degree of the neighboring point within the position where the mutation occurs on the target point is 1, and the weight of the second influence degree of the neighboring point outside the position where the mutation occurs on the target point is 0.

[0065] In some embodiments, process 400 also includes: acquiring corresponding image signals of each graphic in the target layout, the image signals indicating the shapes of each graphic in the target layout; determining a simulation 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 simulation signal.

[0066] In some embodiments, determining the analog signal generated by the OPC model includes: determining a first signal component based on a first category of kernel functions; determining a second signal component based on a second category of kernel functions; and determining the analog signal based on the first signal component, the second signal component, and corresponding weights of multiple kernel functions.

[0067] In some embodiments, the OPC model is applicable to a contact lithography machine.

[0068] 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 shown. It should be understood that Figure 5 The electronic device 500 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein.

[0069] like Figure 5 As shown, the electronic device 500 is in the form of a general electronic device. The components of the electronic device 500 may include, but are not limited to, one or more processors 510 or processing units, a memory 520, a storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing unit may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 520. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0070] The electronic device 500 typically includes a plurality of computer storage media. Such media may 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 may be a volatile memory (e.g., a register, a cache, a random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 may be a removable or non-removable medium, and may include a machine-readable medium, such as a flash drive, a disk, or any other medium, which may be capable of being used to store information and / or data (e.g., training data for training) and may be accessed within the electronic device 500.

[0071] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from 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. The memory 520 may include a computer program product 525 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0072] The communication unit 540 enables communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0073] The input device 550 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 560 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 may also communicate with one or more external devices (not shown) through the communication unit 540 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 500, or communicate with any device (e.g., a network card, a modem, etc.) that allows the electronic device 500 to communicate with one or more other electronic devices. Such communication may be performed via an input / output (I / O) interface (not shown).

[0074] 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, wherein the one or more computer instructions are executed by a processor to implement the method described above.

[0075] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0076] 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 device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0077] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0078] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0079] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the marketplace, or to enable other persons of ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. A method for OPC modeling, characterized in that: include: Initializing a plurality of kernel functions for an optical proximity correction (OPC) model, the plurality of kernel functions comprising 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 with increasing distance, the second category of kernel functions indicating an optical proximity effect that changes suddenly with distance, the distance indicating a spacing between a target point and a neighboring point on a target layout; calibrating the plurality of kernel functions; as well as The OPC model is constructed based on the calibrated plurality of kernel functions.

2. The method for OPC modeling according to claim 1, characterized in that: Constructing the OPC model includes: The OPC model is constructed based on a weight of an optical signal and corresponding weights of the plurality of calibrated kernel functions, the weight of the optical signal being smaller than the corresponding weights of the plurality of kernel functions.

3. The method for OPC modeling according to claim 2, characterized in that: The optical signal has a weight of zero.

4. The method for OPC modeling according to claim 1, characterized in that: Initialization of multiple kernel functions for the optical proximity correction (OPC) model includes: Obtaining a first initial value of a first parameter of the kernel function of the first category, where the first parameter is used to control the decay speed, and the first initial value is within a first predetermined range; and A second initial value of a second parameter of the kernel function of the second category is obtained, where the second parameter indicates a position where the mutation occurs, and the second initial value is within a second predetermined range.

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

6. The method for OPC modeling according to claim 1, characterized in that: Calibrating the plurality of kernel functions comprises: Determining corresponding image signals of each test pattern in a test layout using the OPC model, wherein the image signals indicate shapes of the each test pattern; and Based on the corresponding image signals of the respective test patterns in the test layout and the exposure results corresponding to the corresponding image signals, at least one of the corresponding parameters of the plurality of kernel functions and the corresponding weights of the plurality of kernel functions is calibrated.

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

8. The method for OPC modeling according to claim 1, characterized in that: The kernel function of the first category is used to calculate the first degree of influence of the analog signal of the target point by the neighboring point based on the optical proximity effect that decays with increasing distance, and the decay speed of the first degree of influence is controlled based on a first parameter.

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

10. The method for OPC modeling according to claim 1, characterized in that: The kernel function of the second category is used to calculate the second degree of influence of the neighboring point on the analog signal of the target point based on the optical proximity effect that mutates with distance, and the second degree of influence is uniformly distributed within the position 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 the neighboring point within the position where the mutation occurs on the target point is 1, and the weight of the second influence degree of the neighboring point 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 comprises: Acquire corresponding image signals of each graphic in the target layout, where the image signals indicate the shapes of each graphic in the target layout; Determining a simulation signal generated according to the OPC model based on the OPC model and corresponding image signals of each graphic in the target layout; and Based on the simulation signal, optical proximity effect correction is performed on the target layout.

13. The method for OPC modeling according to claim 12, characterized in that: 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; The analog signal is determined based on the first signal component, the second signal component, and 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: include: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 14 when executed by the at least one processing unit.

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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