Simulation method, electronic device and storage medium for mask pattern correction

Abstract

Embodiments of the present disclosure relate to a simulation method, an electronic device and a storage medium for mask pattern correction. The method comprises: determining a first signal value for a layout, wherein the first signal value comprises a signal value corresponding to a long-range effect of a measurement point in a first region of the layout, and the first region is a region of the layout affected by the long-range effect of a process; determining a second signal value for the layout, wherein the second signal value is a signal value corresponding to a short-range effect of the process of a measurement point of the layout; determining an integrated signal value based on the first signal value and the second signal value; and determining a simulation pattern of the layout based on the integrated signal value. Embodiments of the present disclosure can compensate for the long-range effect, obtain more accurate simulation results, and improve the accuracy of mask pattern correction.

Description

Technical Field

[0001] The embodiments of this disclosure primarily relate to the field of computational lithography, and more specifically, to simulation methods, electronic devices, storage media, and computer program products for mask pattern correction. Background Technology

[0002] Computational lithography has been a key technology driving the miniaturization of integrated circuit patterns since the 1990s. Its core lies in improving the effective resolution and process window of the imaging system from a software perspective through modeling, simulation, and algorithm optimization, given the fixed physical limits of hardware such as lithography machines. This technology allows for breakthroughs in achievable exposure pattern sizes beyond hardware limitations such as optical diffraction, thus becoming a crucial technology supporting the continuous evolution of advanced semiconductor processes, such as 28nm and below.

[0003] However, with the continuous development of advanced semiconductor processes, computer lithography technology is required to have more accurate models and simulation methods. Summary of the Invention

[0004] According to an example embodiment of this disclosure, a simulation scheme is provided to at least partially overcome the above or other potential defects.

[0005] In a first aspect of this disclosure, a simulation method for mask pattern correction is provided. The method includes: determining a first signal value for a layout, wherein the first signal value includes signal values ​​corresponding to long-range effects at measurement points in a first region of the layout, the first region being a region in the layout affected by long-range effects of a process; determining a second signal value for the layout, wherein the second signal value is a signal value corresponding to short-range effects of a process at measurement points in the layout; determining an integrated signal value based on the first and second signal values; and determining a simulation pattern of the layout based on the integrated signal value.

[0006] In a second aspect of this disclosure, an electronic device is provided. The electronic device includes a processor and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: determining a first signal value in a first region, the first region being the area of ​​effect of long-range effects in a layout; determining a second signal value for the layout; determining an integrated signal value based on the first and second signal values; and determining a simulated pattern of the layout based on the integrated signal value.

[0007] In some embodiments, determining the first signal value includes: performing a first density grid calculation based on the long-range effect to obtain a calculation result of the first density grid, wherein the grid spacing of the first density grid is greater than a first distance threshold; and obtaining the first signal value based on the calculation result.

[0008] In some embodiments, performing a first density grid calculation to obtain the calculation result of the first density grid includes: dividing a first region by grid spacing to obtain multiple grid points, the multiple grid points forming a first density grid; determining the grid point signal value of each grid point; and using each grid point signal value as the calculation result of the first density grid.

[0009] In some embodiments, obtaining a first signal value based on the calculation results includes: determining the coordinates of a simulated location point in a first region; determining the grid point closest to the coordinates; using the closest grid point as the center grid point, determining multiple neighboring grid points, wherein the neighboring grid points are grid points whose distance from the center grid point is less than a second distance threshold; and obtaining the first signal value based on the grid point signal value of the center grid point and the grid point signal values ​​of the multiple neighboring grid points.

[0010] In some embodiments, obtaining a first signal value based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points includes: performing interpolation calculation based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points to obtain the first signal value.

[0011] In some embodiments, determining a second signal value for a layout includes performing a convolution operation on at least one second region in the layout based on at least one second convolution kernel to determine the second signal value, wherein the at least one second convolution kernel includes a short-range effect convolution kernel, and the area of ​​each second region is smaller than the area of ​​the first region.

[0012] In some embodiments, performing a convolution operation based on at least one second convolution kernel includes performing a convolution operation based on at least one second convolution kernel and the weight coefficients of at least one second convolution kernel.

[0013] In some embodiments, performing a convolution operation based on at least one second convolution kernel includes: determining a second signal value based on an optical imaging kernel, at least one second convolution kernel, weight coefficients of at least one second convolution kernel, and a layout.

[0014] In some embodiments, determining the integrated signal value based on the first signal value and the second signal value includes: determining the sum of the first signal value and the second signal value as the integrated signal value.

[0015] In some embodiments, determining the simulated pattern of a layout based on an integrated signal value includes: determining that there is no pattern at the simulated position point of the simulated pattern in response to an integrated signal value at a simulated position point in the layout being greater than a signal threshold; and determining that there is a pattern at the simulated position point of the simulated pattern in response to an integrated signal value at a simulated position point being less than or equal to a signal threshold.

[0016] In a third aspect of this disclosure, a computer-readable storage medium is provided. The medium stores machine-executable instructions that, when executed by a processor, cause the processor to implement the method according to a first aspect of this disclosure.

[0017] In a fourth aspect of this disclosure, a computer program product is provided. The computer program product includes computer-executable instructions that, when executed by a processor, cause the processor to implement the method according to a first aspect of this disclosure.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0020] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0021] Figure 2 A flowchart of a simulation method for mask image correction according to some embodiments of the present disclosure is shown;

[0022] Figure 3 A schematic diagram of a long-range effect convolution kernel is shown;

[0023] Figure 4A A schematic diagram of simulation location points and grid points according to some embodiments of this disclosure is shown;

[0024] Figure 4B A schematic diagram of neighborhood grid points according to some embodiments of the present disclosure is shown;

[0025] Figure 5 A block diagram of a computing device according to some embodiments of the present disclosure is shown. Detailed Implementation

[0026] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0027] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "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. Other explicit and implicit definitions may also be included below.

[0028] As semiconductor technology advances, design dimensions are shrinking. Due to light diffraction and interference phenomena, there is optical distortion between the actual wafer image formed on the silicon wafer by exposing a photomask and the circuit layout on the photomask (referred to as the photomask layout). For example, the wafer image may exhibit phenomena such as narrower line widths, shrinkage of narrow lines and short points, and rounded corners.

[0029] Optical proximity correction (OPC) is widely used in semiconductor manufacturing to reduce the difference between the wafer image and the target pattern. OPC compensates for the image by modifying the mask pattern, so that the obtained wafer image is closer to the target pattern.

[0030] In semiconductor manufacturing processes, nonlinear effects during masking can lead to electron beam blurring, photoresist diffusion, and lateral etching deviations. Mask process correction (MPC) is a key application of OPC in mask manufacturing. To overcome these defects, MPC technology is applied to reduce the impact of these nonlinear effects, thereby aligning the circuit layout on the mask as closely as possible with the OPC-corrected mask. Figure 1 To.

[0031] When manufacturing masks at advanced nodes, the effects of long-range effects need to be considered in order to obtain more accurate simulation models.

[0032] For example, long-range effects in electron beam lithography can include energy deposition effects with an impact range of several micrometers caused by backscattered electrons. Backscattered electrons are incident electrons that, after penetrating deep into the material during the masking process, undergo large-angle elastic scattering with atomic nuclei, and may even be reflected back to the surface. Long-range effects can also include stray light effects. Stray light effects refer to light rays that do not follow the expected path during the masking process, but instead reach unexpected areas due to reflection, scattering from the surface of optical elements, or inhomogeneity of the material's refractive index, potentially damaging areas that were originally sharp and clear. Long-range effects make the mask pattern manufacturing result strongly correlated with the surrounding environment and are a major influencing factor on mask accuracy control at advanced nodes.

[0033] For example, extreme ultraviolet (EUV) lithography uses OPC to address imaging distortion caused by factors such as the physical properties of light diffraction, the chemical reactions of the photoresist, and process fluctuations during the lithography process. Because EUV lithography uses an extremely short light source wavelength of only 13.5 nm, even minute fluctuations are amplified, directly affecting image fidelity. Therefore, EUV lithography is highly sensitive to long-range effects, thus placing higher precision requirements on EUV OPC.

[0034] However, current MPC and EUV OPC simulation models do not consider the effects of long-range effects. The effective range of existing simulation models is relatively small, failing to reflect the impact of long-range effects and resulting in low model accuracy. Furthermore, using existing models for long-range effect simulations would be extremely slow, reducing simulation efficiency.

[0035] In view of this, this disclosure provides a simulation method for mask pattern correction, which can be used, for example, in MPC models to correct mask patterns; it can also be used in EUV OPC to obtain a corrected mask image based on the correction of optical proximity effects. The method includes: determining a first signal value for the layout, wherein the first signal value includes signal values ​​of measurement points in a first region of the layout corresponding to long-range effects, the first region being a region in the layout affected by long-range effects of the process; determining a second signal value for the layout, wherein the second signal value is a signal value of measurement points in the layout corresponding to short-range effects of the process; determining an integrated signal value based on the first and second signal values; and determining a simulation pattern of the layout based on the integrated signal value. Embodiments of this disclosure can consider the influence of long-range effects in mask pattern correction simulation, compensate for long-range effects, and simultaneously integrate the influence of short-range effects, thereby obtaining more accurate simulation results, achieving more accurate full-range process compensation, and improving the accuracy of mask pattern correction.

[0036] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0037] First refer to Figure 1 This illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. For example... Figure 1 As shown, the example environment 100 includes a computing device 110 and a client 120.

[0038] In some embodiments, the computing device 110 can interact with the client 120. For example, the computing device 110 can receive input messages from the client 120 and output feedback messages to the client 120. In some embodiments, the input messages from the client 120 may be data related to mask pattern correction. The computing device 110 can perform corresponding mathematical operations on the data related to mask pattern correction. The computing device 110 can output the corresponding operation results to the client 120.

[0039] In some embodiments, the computing device 110 may include, but is not limited to, a personal computer, a server computer, a handheld or laptop device, a mobile device (such as a mobile phone, a personal digital assistant PDA, a media player, etc.), a consumer electronics product, a minicomputer, a mainframe computer, cloud computing resources, etc.

[0040] It should be understood that the description of the structure and functionality of example environment 100 for illustrative purposes only is not intended to limit the scope of the topics described herein. The topics described herein may be implemented in different structures and / or functionalities.

[0041] The technical solutions described above are for illustrative purposes only and are not intended to limit this disclosure. It should be understood that the example environment 100 can also have many other implementation methods. To more clearly explain the principles of the solutions disclosed herein, reference will be made below. Figure 2 Let's describe it in more detail.

[0042] Figure 2 A flowchart of a simulation method 200 for mask pattern correction according to some embodiments of the present disclosure is shown.

[0043] At box 202, the first signal value is determined for the layout.

[0044] The first signal value may include the signal value of the measurement point in the first region of the layout corresponding to the long-range effect, and the first signal value can be used to determine the simulation pattern.

[0045] Measurement points can include key points in a predefined mask pattern or specific locations that affect shape accuracy. After simulation, the signal values ​​of these measurement points can be acquired first to quickly determine whether the shape and accuracy of the mask image meet the requirements.

[0046] The first region can be the area in the layout affected by long-range effects of the manufacturing process. The layout can be the original design layout of the chip. The first region can be obtained by calibrating the simulation model by comparing the simulation results with the actual mask pattern. For example, the area affected by long-range effects can be 10 μm or larger, and correspondingly, the range of the first region can be 10 μm or larger.

[0047] In some embodiments, the layout may include one or more first regions, and the first signal value may include the signal values ​​of all the first regions in the layout. The simulation method provided in the embodiments of this disclosure can be executed in each first region. It should be understood that the number of first regions should not be considered a limitation of this disclosure.

[0048] In some embodiments, the first signal value can be represented in matrix form. Each element in the matrix corresponds to a measurement point in the first region, that is, a measurement point of the layout falling within the first region. Therefore, in the first signal value matrix, the matrix elements corresponding to the measurement points in the first region may be meaningful signal values, i.e., capable of reflecting the influence of long-range effects. However, when considering the influence of long-range effects, for regions other than the first region, the matrix elements corresponding to the measurement points may not have any practical meaning; for example, the value of such matrix element can be set to 0.

[0049] In some embodiments, the first signal value can be represented by the following formula:

[0050] Signal_L represents the first signal value. Kernel L A convolutional kernel representing a long-range effect, which can capture long-range effects. C L The asterisk (*) represents the weighting coefficients of the convolution kernel for long-range effects. The asterisk between the long-range effect convolution kernel and its weighting coefficients indicates the product. "Layout" refers to the chip's original design layout. The layout can consist of polygons and is represented by a binary matrix. This is the convolution symbol. The layout range for the convolution operation is determined by the effective range of the long-range effect convolution kernel.

[0051] Figure 3 The diagram shows a schematic of a long-range effect convolution kernel. For example, long-range effect convolution kernels can be constructed based on validated process parameters. The influence region of long-range effect convolution kernels is relatively large, typically exceeding 20 μm.

[0052] It should be noted that the number of convolutional kernels for long-range effects in this disclosure can be one or more, and their weighting coefficients can also be one or more. The number of convolutional kernels for long-range effects should not be considered a limitation of this disclosure.

[0053] The above embodiments, by constructing a convolution kernel for long-range effects and performing convolution operations with the layout, can obtain the signal values ​​of the regions affected by long-range effects, thereby improving the accuracy of simulation results.

[0054] It should be noted that, Figure 3The example shown is a relatively simple and regular Gaussian convolution kernel. Due to the interaction distance and physical mechanism, actual long-range effect convolution kernels may be more complex, with a larger influence area, requiring a larger layout area for simulation. For example, when the influence area of ​​a long-range effect convolution kernel is large, the layout area needs to reach 20 μm or more.

[0055] In existing simulation models, the effective range of the convolution kernel is usually on the order of 1 μm. When performing convolution operations, the same layout range as the convolution kernel can be used.

[0056] Therefore, for convolution kernels with long-range effects that have a large range of action and complex mechanisms, since the kernel layout range is more than 20 times that of other convolution kernels, a large amount of computing resources will be required, resulting in MPC simulations consuming a lot of computing resources and having extremely low running efficiency.

[0057] In some embodiments of this disclosure, determining the first signal value may include: performing a first density grid calculation based on long-range effects to obtain a calculation result of the first density grid, wherein the grid spacing of the first density grid is greater than a first distance threshold; and obtaining the first signal value based on the calculation result. It should be understood that the embodiments of this disclosure are not limited thereto, and other calculation methods capable of characterizing long-range effects and obtaining corresponding signal values ​​are also applicable to the process of determining the first signal value in the embodiments of this disclosure.

[0058] The above embodiments, based on the characteristics of long-range effects, perform calculations using a first-density grid. The grid spacing of the first-density grid can be set to be greater than a first distance threshold, thereby reducing the number of grid points, lowering the grid density, and improving simulation efficiency. Since the influence area of ​​long-range effects is relatively large and the signal changes slowly, the required simulation grid points do not need to be as dense as those in MPC and OPC models. Performing calculations using the first-density grid can obtain the first signal value of the long-range effect while ensuring simulation efficiency.

[0059] The first distance threshold can be determined based on empirical values ​​or process specifications. For example, the first distance threshold can be set to 1.5 μm. For example, for a 5 mm × 5 mm chip, the grid spacing can be set to 2 μm. The first density grid calculation is performed using a grid with a spacing of 2 μm to obtain the calculation result of the first density grid. The calculation result of the first density grid can be represented in matrix form.

[0060] In comparison, current OPC models typically use simulation grids on the order of 1 / 16 μm or 1 / 32 μm. Therefore, the simulation method provided in this embodiment reduces the number of simulation grids, thus lowering the computational load. Performing the first-density grid calculation balances the characteristics of long-range effects with simulation efficiency, yielding signal values ​​corresponding to the long-range effects.

[0061] In some embodiments, performing a first density grid calculation to obtain the calculation result of the first density grid may include: dividing a first region by grid spacing to obtain multiple grid points, the multiple grid points forming a first density grid; determining the grid point signal value of each grid point; and using each grid point signal value as the calculation result of the first density grid.

[0062] The first region affected by the long-range effects of the process can be divided in the layout according to the grid spacing, and the resulting multiple grid points form the first density grid. After determining the grid signal value of each grid point, the grid signal values ​​of all grid points can be used as the calculation result of the first density grid.

[0063] In some embodiments, the calculation result of the first density grid can be referred to as the signal matrix. This signal matrix can be represented as the following matrix:

[0064] in ... (s is a positive integer, r is a positive integer) represent the grid signal value of each grid point.

[0065] In some embodiments, obtaining a first signal value based on the calculation results may include: determining the coordinates of a simulated location point in a first region; determining the grid point closest to the coordinates; using the nearest grid point as the center grid point, determining multiple neighboring grid points, where neighboring grid points are grid points whose distance from the center grid point is less than a second distance threshold; and obtaining the first signal value based on the grid point signal value of the center grid point and the grid point signal values ​​of the multiple neighboring grid points. (Refer to the following...) Figure 4A and Figure 4B This will be described further.

[0066] Figure 4A This is a schematic diagram of the grid of simulated location points according to some embodiments of the present disclosure. Figure 4B This is a schematic diagram of neighborhood grid points according to some embodiments of the present disclosure. The following is in conjunction with... Figure 4A and Figure 4B The process of determining the data point used for the first signal value is illustrated using a simulated location point as an example. It should be understood that the same process can be performed on other simulated location points. Figure 4A and Figure 4B The same operation is shown at a simulated location point.

[0067] In some embodiments, a coordinate system can be established, and the grid point closest to the simulation location point can be determined based on the coordinates of the simulation location point and the coordinates of each grid point. The simulation location point is the location point in the design layout that needs to be simulated. The simulation location point may include measurement points, or more than measurement points.

[0068] like Figure 4AAs shown, the pentagram represents the simulation location point 410, and the hollow circle represents a portion of multiple grid points in the first region obtained earlier. For example, multiple grid points may include... Figure 4A The grid point 420-p shown is a positive integer. The cross represents the grid point 420-n (where n is a positive integer and n≤p) that is closest to the simulation location point.

[0069] The nearest grid point 420-n to the simulation location is selected as the center grid point, and multiple neighboring grid points are determined. For example, neighboring grid points can be grid points whose distance to the nearest grid point 420-n is less than a second distance threshold. The second distance threshold can be determined based on simulation accuracy requirements and experience. When higher simulation accuracy is required, the value of the second distance threshold can be increased to select more grid points for determining the first signal value. Figure 4B As shown, the solid circles represent neighborhood grid points 420-k (where k is a positive integer, k≤p and k≠n). For example, when the grid spacing is 2μm, the second distance threshold can be 2.83μm, n can be 8, and the neighborhood grid points include 8 grid points, i.e. Figure 4B A solid circle in the middle.

[0070] After determining the central grid point and neighboring grid points, the first signal value of simulation location point 410 can be obtained based on the grid point signal values ​​of the central grid point and the neighboring grid points. For other simulation location points, the same operation as for simulation location point 410 can be performed to obtain the first signal value of the first region.

[0071] In the above embodiments, considering that the simulation location points in the layout may not necessarily be located exactly on the grid points obtained by dividing the first region according to the grid spacing, the first signal value of the simulation location point is determined by the first signal values ​​of the grid points closest to the simulation location point and their neighboring grid points, thereby obtaining the first signal value of the first region. Since the first signal value of each simulation location point is obtained based on multiple surrounding grid points, the accuracy of the simulation can be guaranteed.

[0072] In some embodiments, obtaining a first signal value based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points may include: performing interpolation calculation based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points to obtain the first signal value.

[0073] Interpolation is a method for estimating the value of unknown data points using known discrete data points. The implementation described above uses a finite number of neighborhood grid points within a second distance threshold to calculate the first signal value of the simulated location point, which is a type of local interpolation. This local interpolation method can adapt to the uneven distribution of simulated location points, ensuring a certain level of calculation accuracy, and it is computationally inexpensive and very fast.

[0074] For example, according to Figure 4A and Figure 4B After obtaining the grid signal values ​​of the central grid point and multiple neighboring grid points, the first signal value can be obtained by performing interpolation calculations.

[0075] The first signal value obtained by interpolation in the above embodiments can be understood as an approximation of the first signal value, which can be represented as Signal_L'. Due to the large range of influence of long-range effects but the slow signal change, Signal_L' can achieve the accuracy requirement of the first signal value Signal_L, therefore, Signal_L' can be used to represent the first signal value.

[0076] In some embodiments, obtaining a first signal value based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points may include: performing an average calculation based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points to obtain the first signal value.

[0077] Mean calculation is a commonly used data aggregation method in statistics. In the above embodiment, the mean calculation is performed using the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points. This can be understood as adding the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points, and then dividing by the total number of the central grid point and multiple neighboring grid points to obtain a signal value that represents the average level of this set of grid signal values. This signal value is used as the first signal value.

[0078] Although mean calculation is susceptible to extreme values ​​in the data, the above embodiment can control the number of neighborhood grid points used to obtain the first signal value through a second distance threshold. For example, based on practical experience or needs, a second threshold distance can be set to select neighborhood grid points within a smaller range, ensuring that the first signal value is within a reasonable range.

[0079] In some embodiments, obtaining a first signal value based on the grid signal value of the central grid point and the grid signal values ​​of multiple neighboring grid points may include: determining the weight of the grid signal value of the central grid point and the weight of the grid signal value of each neighboring grid point respectively, and performing a weighted average calculation to obtain the first signal value, wherein the weight of the signal value of the central grid point is greater than the weight of the grid signal values ​​of the neighboring grid points.

[0080] A weighted average is a method of calculating an average that considers the importance or influence of each data point, with the importance of each data point reflected by its weight. Unlike the mean algorithm, the weighted average assumes that each data point in the dataset contributes differently to the final result.

[0081] In the above embodiments, considering that the central grid point is the closest grid point to the simulation location, and given that the long-range effect changes slowly over short distances, the grid point signal value of the central grid point is likely to be closest to the signal value of the simulation location. Therefore, the weight of the grid point signal value is increased compared to the weight of the grid point signal values ​​of neighboring grid points, so that the first signal value of the simulation location more closely reflects the actual impact of the long-range effect.

[0082] In some embodiments, the first signal value can also be obtained using methods such as least squares method or trapezoidal quadrature method. This disclosure does not limit the method used to obtain the first signal value.

[0083] Back Figure 2 At box 204, the second signal value is determined for the layout.

[0084] In some embodiments, the second signal value is the signal value of the measurement point of the layout corresponding to the short-range effect of the process. The second signal value can be used to determine the simulation pattern.

[0085] Short-range effects can be understood as proximity effects that contrast with long-range effects in terms of scale of action and physical mechanism. In contrast to long-range effects, the effective region of short-range effects is mostly below 1 μm, and generally less than 1.5 μm.

[0086] In some embodiments, the second signal value can be represented in matrix form. Each element in the matrix corresponds to a measurement point on the layout.

[0087] In some embodiments, determining a second signal value for a layout may include performing a convolution operation on at least one second region in the layout based on at least one second convolution kernel to determine the second signal value, wherein the at least one second convolution kernel includes a short-range effect convolution kernel, and the area of ​​each second region is smaller than the area of ​​the first region. It should be understood that the embodiments of this disclosure are not limited thereto, and other methods for determining the second signal that can accurately characterize short-range process effects may also be applied to this invention.

[0088] The layout includes at least one second region, which is a region in the layout affected by the short-range effect of the process. The area of ​​each second region is smaller than the area of ​​the first region. For example, the area of ​​each second region is less than 1.5 μm.

[0089] It should be understood that the second region can overlap with the first region, indicating that this region is affected by both long-range and short-range effects. Similarly, the second region can also partially overlap with the first region, or the first and second regions can not intersect.

[0090] At least one second convolution kernel, corresponding to at least one second region, can be introduced into the simulation model to perform convolution in at least one second region to determine the second signal value.

[0091] Since the effective range of the second convolution kernel due to short-range effects is on the order of 1 μm and will not exceed 1.5 μm, the convolution operation in the simulation is performed using the maximum effective range of the convolution kernel for all second convolution kernels, i.e., a layout range of less than 1.5 μm. With a smaller effective range of the convolution kernel, layout processing is simpler, ensuring computational efficiency.

[0092] In some embodiments, performing a convolution operation based on at least one second convolution kernel may include performing a convolution operation based on at least one second convolution kernel and the weight coefficients of at least one second convolution kernel.

[0093] Since the functions of each second convolutional kernel are not necessarily the same, a weighting coefficient for each convolutional kernel is considered in the convolution operation to ensure the accuracy of the simulation. The weighting coefficients of the convolutional kernels can be determined by measuring the actual manufactured mask pattern and calibrating the simulation model, so that the simulation results closely approximate the actual measured values.

[0094] In some embodiments, performing a convolution operation based on at least one second convolution kernel may include: determining a second signal value based on an optical imaging kernel, at least one second convolution kernel, weighting coefficients of at least one second convolution kernel, and a layout.

[0095] For example, the second signal value in the above embodiment can be represented by the following formula:

[0096] Signal_S represents the second signal value. `optical` represents the optical imaging kernel, describing the fundamental optical effects of light diffraction and transmission through a mask; it is the most crucial effect in the photolithography process. Kernel1, Kernel2…Kernel m(m is a positive integer) represent the first, second, and m-th second convolutional kernels, respectively. Each second convolutional kernel is used to quantify a specific physical distortion effect. For example, in the MPC model, the second convolutional kernel can characterize forward scattering during electron beam exposure, microscopic loading effects during mask etching, etc. C1, C2…C m These represent the weight coefficients corresponding to at least one second convolution kernel. The asterisk between the second convolution kernel and the weight coefficients indicates the product. "Layout" refers to the chip's original design layout. The layout can consist of polygons and is represented by a binary matrix. This is the convolution symbol.

[0097] Convolution can be understood as performing a specific multiplication and addition operation on the layout value covered by each kernel and the kernel value at each position in the layout matrix, obtaining the total effect value of the influence of the surrounding graphics on that point. Convolution can reflect that the final shape of a point is not only determined by itself, but is also affected by all graphics within a certain range.

[0098] The convolution operation in the above embodiment integrates the effects of the optical imaging kernel and multiple second convolution kernels. Therefore, the obtained second signal value can more comprehensively reflect the influence of the short-range effect.

[0099] It should be noted that, physically speaking, the influence of the aforementioned optical imaging kernel is long-range. However, current MPC simulation practices have shown that, for example, in mature lithography processes, the weight of the optical imaging kernel decays to a negligible level within a few micrometers. Therefore, modeling and simulating within the effective range of short-range effects can achieve the simulation accuracy of the optical imaging kernel. For applications such as EUV lithography, when long-range effects need to be considered, the simulation method provided in the embodiments of this disclosure can be used to determine the signal value of the long-range effect.

[0100] Back Figure 2 At box 206, the integrated signal value is determined based on the first signal value and the second signal value.

[0101] In some embodiments, determining the integrated signal value based on the first signal value and the second signal value may include: determining the sum of the first signal value and the second signal value as the integrated signal value.

[0102] For example, the integrated signal value can be represented by the following formula:

[0103] Where simulation_signal represents the integrated signal value, Signal_S represents the second signal value, and Signal_L represents the first signal value.

[0104] The integrated signal value can characterize the cumulative effect of the full range of spatial effects at any location on the layout under current process conditions.

[0105] In some embodiments, both the first signal value and the second signal value can be represented in matrix form, and therefore the integrated signal value can also be represented in matrix form. For example, the first signal value and the second signal value are dimensionless constants based on normalization, and the integrated signal value is also a dimensionless constant.

[0106] For each simulation location point, the first signal value and the second signal value can be summed separately, and their values ​​can be used as the corresponding matrix elements.

[0107] For example, for a given simulation location, the integrated signal value obtained by adding the matrix elements of the corresponding first signal value and the matrix elements of the corresponding second signal value can represent the cumulative result of long-range and short-range effects on that simulation location. For instance, setting the matrix elements to 0 indicates that there are no long-range or short-range effects at the corresponding simulation location. At a certain simulation location, if the matrix element of the first signal value is 0 and the matrix element of the second signal value is a non-zero constant, then the integrated signal value of that simulation location is determined only by the second signal value. If both the matrix elements of the first and second signal values ​​corresponding to that simulation location are 0, then the integrated signal value of that simulation location is also 0, indicating that the simulation location is not affected by long-range or short-range effects.

[0108] In some embodiments, after model calibration and normalization, the range of the integrated signal value may include positive, zero, or negative numbers. For example, the range of the integrated signal value may be [-1, 1].

[0109] In some embodiments, such as for certain regions, if model calibration reveals that the region is significantly more affected by long-range effects than by short-range effects, then the weights of the first signal value and the second signal value can be set based on model calibration, and the first signal value and the second signal value can be weighted and summed to obtain the integrated signal value.

[0110] In the above embodiments, the integrated signal values ​​include both long-range and short-range effect signal values, i.e., the signal values ​​of long-range and short-range effects are summed. By simultaneously superimposing the contributions of both long-range and short-range physical effects, the actual process can be reflected more completely, avoiding calculation deviations caused by ignoring one type of effect. Therefore, the accuracy and reliability of the simulation can be effectively improved.

[0111] Back Figure 2 At box 208, the simulation pattern of the layout is determined based on the integrated signal value.

[0112] The integrated signal values ​​can be represented as a matrix, where each value corresponds to the predicted intensity of a point in the layout. A threshold can be set and compared with the integrated signal values ​​to predict whether that point will be successfully manufactured. In this way, the predicted simulation pattern is rendered based on the integrated signal values.

[0113] In some embodiments, determining the simulated pattern of a layout based on the integrated signal value may include: determining that there is no pattern at the simulated location of the simulated pattern in response to the integrated signal value of the simulated location being greater than a signal threshold; and determining that there is a pattern at the simulated location of the simulated pattern in response to the integrated signal value of the simulated location being less than or equal to the signal threshold. It should be understood that the embodiments of this disclosure are not limited thereto, and other determination methods, such as multi-level threshold determination, may be used to determine the pattern according to actual process conditions and simulation accuracy requirements.

[0114] For example, the integrated signal value can be a dimensionless constant. The signal threshold can be set based on process experience, comparison of simulated values ​​with measured values, and adjustments. The signal threshold can be a reference value determined based on process conditions and model calibration, used to process the integrated signal value to obtain a binarized simulation pattern. For example, the signal threshold can be set to 0.5. For each simulated location point in the layout, when the integrated signal value of the simulated location point in the layout is greater than 0.5, there is no pattern at that simulated location point. When the integrated signal value of the simulated location point in the layout is less than or equal to 0.5, there is a pattern at that simulated location point. By comparing the integrated signal values ​​of all simulated location points in the layout with the signal threshold, the simulation pattern of the layout can be drawn.

[0115] The above embodiments obtain simulated patterns by integrating the comparison of signal values ​​and signal thresholds, making the generation of simulated patterns quantifiable. Furthermore, the signal threshold can be set based on process experience and simulation corrections, enabling optimization for different process nodes or design requirements.

[0116] Embodiments of this disclosure also provide an electronic device. The electronic device includes: a processor; and a memory coupled to the processor, the memory having instructions stored therein, the instructions causing the device to perform actions when executed by the processor, the actions including: determining a first signal value in a first region, the first region being the area of ​​effect of long-range effects in a layout; determining a second signal value for the layout; determining an integrated signal value based on the first and second signal values; and determining a simulated pattern of the layout based on the integrated signal value.

[0117] Embodiments of this disclosure also provide a computer-readable storage medium storing machine-executable instructions that, when executed by a processor, cause the processor to implement the simulation method of the above embodiments.

[0118] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] Embodiments of this disclosure also provide a computer program product, including machine-executable instructions that, when executed by a processor, cause the processor to implement the simulation method of the above embodiments. It should be understood that the embodiments shown in the accompanying drawings are merely illustrative of some embodiments of this disclosure and are not intended to limit the scope of this disclosure. Embodiments of this disclosure may also have various other forms.

[0120] Figure 5 Schematic block diagrams of electronic devices according to some exemplary embodiments of the present disclosure are shown. The electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0121] like Figure 5 As shown, device 500 includes a CPU 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0122] Multiple components in device 500 are connected to I / O interface 505. These components include: input unit 506, such as a keyboard and mouse; output unit 507, such as various types of displays and speakers; storage unit 508, such as a disk and optical disk; and communication unit 509, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0123] The various processes and procedures described above, such as method 200, can be executed by CPU 501. For example, in some embodiments, method 200 can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by CPU 501, one or more steps of method 200 described above can be performed.

[0124] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0125] Computer program products may include computer-readable storage media on which computer-readable program instructions for performing various aspects of this disclosure are loaded. The computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. The computer-readable program instructions may be downloaded from the computer-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device.

[0126] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] The various embodiments of this disclosure have been described above. These descriptions are exemplary and represent only optional embodiments of this disclosure, and are not exhaustive, nor are they intended to limit the scope of this disclosure. Although the claims in this application are formulated for specific combinations of features, it should be understood that the scope of this disclosure also includes any novel feature or any novel combination of features, whether express or implied or generalized herein, whether or not it relates to the same scheme in any currently claimed claim. The applicant hereby informs that new claims may be formulated as these features and / or combinations of these features during the examination of this application or in any further applications derived therefrom.

[0128] The terminology used herein is chosen to best explain the principles, practical applications, or technological improvements of the various embodiments, or to enable those skilled in the art to understand the embodiments disclosed herein. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A simulation method for mask pattern correction, comprising: A first signal value is determined for the layout, wherein the first signal value includes the signal value of the measurement point in a first region of the layout corresponding to the long-range effect, and the first region is the region of the layout affected by the long-range effect of the process; A convolution operation is performed on at least one second region in the layout based on at least one second convolution kernel to determine a second signal value, wherein the second signal value is the signal value of the short-range effect of the process corresponding to the measurement point of the layout, the at least one second convolution kernel includes a short-range effect convolution kernel, and the area of ​​each second region is smaller than the area of ​​the first region. Determine the integrated signal value based on the first signal value and the second signal value; as well as The simulation pattern of the layout is determined based on the integrated signal values; The convolution operation based on at least one second convolution kernel includes: The second signal value is determined based on the optical imaging kernel, the at least one second convolution kernel, the weighting coefficients of the at least one second convolution kernel, and the layout.

2. The simulation method according to claim 1, wherein determining the first signal value includes: Based on the long-range effect, a first density grid calculation is performed to obtain the calculation result of the first density grid, wherein the grid spacing of the first density grid is greater than a first distance threshold. as well as Based on the calculation results, the first signal value is obtained.

3. The simulation method according to claim 2, wherein performing the first density grid calculation to obtain the calculation result of the first density grid includes: The first region is divided according to the grid spacing to obtain multiple grid points, and the multiple grid points form the first density grid. Determine the grid signal value for each grid point; as well as The signal values ​​of each grid point are used as the calculation results of the first density grid.

4. The simulation method according to claim 3, wherein obtaining the first signal value based on the calculation result includes: Determine the coordinates of the simulated location points in the first region; Determine the grid point closest to the given coordinates; Using the nearest grid point as the center grid point, multiple neighboring grid points are determined, wherein each neighboring grid point is a grid point whose distance from the center grid point is less than a second distance threshold; and The first signal value is obtained based on the grid signal value of the central grid point and the grid signal values ​​of the plurality of neighboring grid points.

5. The method according to claim 4, wherein obtaining the first signal value based on the grid signal value of the central grid point and the grid signal values ​​of the plurality of neighboring grid points includes: Based on the grid signal value of the central grid point and the grid signal values ​​of the plurality of neighboring grid points, interpolation calculation is performed to obtain the first signal value.

6. The simulation method according to claim 1, wherein determining the integrated signal value based on the first signal value and the second signal value comprises: The sum of the first signal value and the second signal value is determined as the integrated signal value.

7. The simulation method according to claim 1, wherein determining the simulation pattern of the layout based on the integrated signal value comprises: In response to the integrated signal value of the simulated location point in the layout being greater than a signal threshold, it is determined that there is no pattern at the simulated location point of the simulated pattern; as well as In response to the integrated signal value at the simulated location being less than or equal to the signal threshold, it is determined that there is a pattern at the simulated location of the simulated pattern.

8. An electronic device, comprising: processor; as well as A memory coupled to a processor, containing instructions stored therein, which, when executed by the processor, cause the device to perform actions, including: A first signal value is determined for the layout, wherein the first signal value includes the signal value of the measurement point in a first region of the layout corresponding to the long-range effect, and the first region is the region of the layout affected by the long-range effect of the process; A convolution operation is performed on at least one second region in the layout based on at least one second convolution kernel to determine a second signal value, wherein the second signal value is the signal value of the short-range effect of the process corresponding to the measurement point of the layout, the at least one second convolution kernel includes a short-range effect convolution kernel, and the area of ​​each second region is smaller than the area of ​​the first region. Determine the integrated signal value based on the first signal value and the second signal value; and The simulation pattern of the layout is determined based on the integrated signal values; The convolution operation based on at least one second convolution kernel includes: The second signal value is determined based on the optical imaging kernel, the at least one second convolution kernel, the weighting coefficients of the at least one second convolution kernel, and the layout.

9. A computer-readable storage medium storing machine-executable instructions that, when executed by a processor, cause the processor to implement the simulation method according to any one of claims 1-7.

10. A computer program product comprising machine-executable instructions that, when executed by a processor, cause the processor to implement the simulation method according to any one of claims 1-7.

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