A modeling method of a photoresist development rate model and related products

Abstract

The application relates to a modeling method of a photoresist developing rate model and related products. The modeling method comprises the following steps: calculating a pattern density map in a preset sampling window in a pre-design calculation range according to a mask image in the pre-design calculation range; obtaining a developing rate adjustment model according to the pattern density map; and adjusting a basic photoresist developing rate model according to the developing rate adjustment model to obtain an adjusted photoresist developing rate model. The modeling method can reflect developing load in a developing process and improve the simulation accuracy of the photoresist developing rate model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor technology, and in particular, to a modeling method of a photoresist development rate model, a computer readable storage medium, a computer program product and a computer device. BACKGROUND

[0002] A photolithography process is a complex production process, mainly including the steps of spin coating, soft baking, exposure, post-exposure baking, development, etc., for transferring the pattern of a mask to the surface of a silicon wafer coated with photoresist. These process steps can be described by mathematical formulas with models of physical optics, photochemical reaction, physical-chemical reaction, etc., so as to realize the modeling of strict photolithography simulation. Exemplarily, for the exposure process of a chemically amplified resist, the optical model is usually used to simulate the light intensity distribution in the photoresist, and then the photochemical reaction is used to simulate the concentration distribution of photo-acid, base and other substances in the photoresist after exposure. For the post-baking process, the physical diffusion model, acid-catalyzed deprotection reaction, acid-base neutralization reaction, etc. are usually used to obtain the concentration distribution of acid, base and inhibitor after post-baking.

[0003] The development step for forming a three-dimensional photoresist profile is one of the most critical steps in the photolithography process, and whether the development process can be accurately controlled in the manufacturing process directly affects the yield of the chip. In the related art, the photoresist three-dimensional profile after development is usually simulated by using a development rate model based on the concentration of dissolution inhibitor, such as Mack development model, Notch model, Inverse Mack development model, etc.

[0004] However, the above basic photoresist development rate model only considers the relationship between the concentration of a single point of dissolution inhibitor and the development rate, and ignores the influence of the change of the concentration of the surrounding dissolution inhibitor on the development rate of the point. With the development of photolithography technology, the critical dimension of the pattern on the wafer is continuously reduced, and the pattern density effect (PDE) caused by development load (for example, the developer is locally depleted in the highly exposed area, thereby affecting the development rate of the surrounding exposed area) is also more significant. The above basic photoresist development rate model cannot reflect the development load, resulting in that the simulation result cannot accurately reflect the measured result, and the simulation accuracy is poor. SUMMARY

[0005] In view of the above problems, the present application is proposed in order to provide a modeling method of a photoresist development rate model, a computer readable storage medium, a computer program product and a computer device which overcome the above problems or at least partially solve the above problems.

[0006] An object of the present application is to provide a modeling method of a photoresist development rate model, which adjusts an existing basic photoresist development rate model to be able to consider the influence of the concentration of substances around a single point on the development rate of the single point in a development process, at least partially reflects development load, and thus improves the simulation accuracy of the photoresist development rate model.

[0007] Specifically, according to one aspect of the present application, the present application provides a modeling method of a photoresist development rate model, comprising:

[0008] calculating a pattern density map in a preset sampling window in a pre-design calculation range according to a mask image in the pre-design calculation range of a mask pattern;

[0009] obtaining a development rate adjustment model according to the pattern density map;

[0010] adjusting an obtained basic photoresist development rate model by using the development rate adjustment model to obtain an adjusted photoresist development rate model.

[0011] Optionally, in the step of adjusting the obtained basic photoresist development rate model by using the development rate adjustment model to obtain the adjusted photoresist development rate model, the following formula is used for adjustment:

[0012] R ′ (M)=R(M)*R(PD)

[0013] wherein R ′ (M) represents the change relationship of the development rate in the adjusted photoresist development rate model with the concentration of a dissolution inhibitor, R(M) represents the change relationship of the development rate in the basic photoresist development rate model with the concentration of the dissolution inhibitor, M represents the concentration of the dissolution inhibitor in the photoresist, R(PD) represents the change relationship of a development rate adjustment factor in the development rate adjustment model with a pattern density, and PD represents the pattern density at a certain place in the pattern density map.

[0014] Optionally, the step of obtaining the development rate adjustment model according to the pattern density map comprises:

[0015] transforming the pattern density map by using a preset transformation function;

[0016] obtaining the development rate adjustment model according to the transformed pattern density map.

[0017] Optionally, the step of transforming the pattern density map by using a preset transformation function comprises:

[0018] acquire a plurality of preset parameters, the plurality of preset parameters including a preset maximum correction rate, a preset minimum correction rate, and a preset pattern density threshold, and generate the preset transformation function;

[0019] transform each place in the pattern density map by the preset transformation function to obtain a transformed pattern density map.

[0020] Optionally, the preset transformation function has the following form:

[0021]

[0022] wherein R(PD) represents a relationship between a developing rate adjustment factor in the developing rate adjustment model and a pattern density, PD represents a pattern density at a certain place in the pattern density map, Rmax represents a preset maximum correction rate, Rmin represents a preset minimum correction rate, slope represents a preset slope parameter, and density_threshold represents a preset pattern density threshold.

[0023] Optionally, after the basic photoresist developing rate model is adjusted by the developing rate adjustment model to obtain an adjusted photoresist developing rate model, the modeling method further includes:

[0024] simulate by using the adjusted photoresist developing rate model to obtain a simulated three-dimensional profile after photoresist development;

[0025] update each of the preset parameters according to the simulated three-dimensional profile and an acquired measured three-dimensional profile after photoresist development;

[0026] update the developing rate adjustment model and the adjusted photoresist developing rate model according to the updated each of the preset parameters.

[0027] Optionally, the calculating the pattern density map in a preset sampling window in the pre-design calculation range according to the mask images in the pre-design calculation range of the mask pattern includes:

[0028] processing the mask images in the pre-design calculation range by using an acquired preset image smoothing algorithm, and obtaining the pattern density map in the preset sampling window.

[0029] Optionally, the calculating the pattern density map in a preset sampling window in the pre-design calculation range according to the mask images in the pre-design calculation range of the mask pattern includes:

[0030] The mask image in the preset sampling window is processed by using the mask image in the preset calculation range and a preset image smoothing algorithm, and the pattern density map in the preset sampling window is obtained.

[0031] Optionally, the preset image smoothing algorithm is a preset image filter.

[0032] Optionally, the preset calculation range is larger than the preset sampling window, so that when the preset image filter is used to perform convolution calculation on any sampling point of the mask image in the preset sampling window, the filter window of the preset image filter is in the preset calculation range.

[0033] According to another aspect of the present application, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the modeling method of the photoresist development rate model.

[0034] According to still another aspect of the present application, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the modeling method of the photoresist development rate model.

[0035] According to yet another aspect of the present application, there is also provided a computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to implement the steps of the modeling method of the photoresist development rate model.

[0036] In the modeling method of the photoresist development rate model of the present application, the mask image can reflect the concentration of the substance (e.g. the concentration of the dissolution inhibitor) related to development at each single point in the photoresist, and the pattern density map can reflect the concentration of the substance at each single point and its neighborhood in the photoresist. The existing basic photoresist development rate model is adjusted by the development rate adjustment model calculated from the pattern density map, so that the adjusted photoresist development rate model takes into account the influence of the concentration of the surrounding substance on the development rate of the single point during the development process, at least partially reflects the development load, and thus improves the simulation accuracy of the development rate model.

[0037] Further, in the modeling method of the present application, the pattern density map is transformed by a preset transformation function, so that the transformed pattern density map further accurately reflects the degree of influence of the concentration of the neighborhood of each single point in the photoresist on the development rate of the single point, further accurately reflects the development load, and improves the simulation accuracy of the development rate model.

[0038] The above and other objects, advantages and features of the present application will become more apparent from the following detailed description of specific embodiments thereof, when taken in conjunction with the accompanying drawings. Attached Figure Description

[0039] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0040] Figure 1 This is a schematic flowchart of a modeling method according to an embodiment of the present invention;

[0041] Figure 2 This is a schematic flowchart of a modeling method for obtaining a development rate adjustment model according to an embodiment of the present invention;

[0042] Figure 3 This is a schematic flowchart illustrating the transformation of a graphic density distribution map according to a modeling method of an embodiment of the present invention.

[0043] Figure 4 This is a schematic diagram of the process of updating various preset parameters in a modeling method according to an embodiment of the present invention;

[0044] Figure 5 This is a schematic diagram of the curve of the development rate adjustment factor changing with the image density in the development rate adjustment model of the modeling method according to an embodiment of the present invention.

[0045] Figure 6a This is a distribution diagram of the deviation values ​​of key dimensions of the pattern as a function of pattern density in the simulation results and measured results of the basic photoresist development rate model of the modeling method according to an embodiment of the present invention.

[0046] Figure 6b This is a distribution diagram of the deviation values ​​of key dimensions of the pattern as a function of pattern density in the simulation results and measured results of the adjusted photoresist development rate model according to an embodiment of the present invention.

[0047] Figure 7 This is a schematic diagram of a computer program product according to an embodiment of the present invention;

[0048] Figure 8 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and

[0049] Figure 9 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0050] The modeling method of the photoresist development rate model of the embodiment aims to adjust the existing basic photoresist development rate model to be able to consider the influence of the concentration of substances around a single point on the development rate of the single point in the development process, at least partially reflect the development load, and thus improve the simulation accuracy of the development rate model.

[0051] Figure 1 The flowchart of the modeling method according to one embodiment of the present application can generally include the following steps:

[0052] S100, calculating a pattern density map in a preset sampling window in a pre-design calculation range according to a mask image in the pre-design calculation range of the mask pattern;

[0053] S200, calculating a development rate adjustment model according to the pattern density map;

[0054] S300, adjusting the acquired basic photoresist development rate model by using the development rate adjustment model to obtain an adjusted photoresist development rate model.

[0055] The modeling method of the embodiment of the present application can be applied to simulation methods, simulation programs, simulation software (such as PangenSim) and the like related to the photoresist development rate model.

[0056] The mask pattern is an electronic layout of the layout design of the mask by an electronic design automation (EDA) tool (such as PanGen). After the design is completed, the design data will be converted into a digital file format (such as a GDSII file). The mask pattern can be a positive mask or a negative mask, and the photoresist can be a positive photoresist or a negative photoresist, which is not limited here.

[0057] The mask pattern can be sampled or converted to obtain a mask image. The mask image can be a discrete pixel array image to facilitate image processing. Illustratively, the mask image can be a grayscale image, and the pixel value of each point in the grayscale image can be normalized in the interval [0, 1], and the pixel value can represent an exposure-related quantity (for example, the pixel value of the opaque part in the mask pattern can be 0, the pixel value of the transparent part can be 1, and the pixel value of the transparent and opaque boundary can be a value in the interval (0, 1)). The pixel value of each point in the mask image can reflect the exposure-related quantity of the photoresist at the single point in the exposure process, thereby reflecting the development-related substance concentration (such as the concentration of the dissolution inhibitor) of the photoresist at the single point.

[0058] In the embodiment, the shape, size and position of the preset sampling window and the preset calculation range can be set as required. The shape of the preset sampling window can be square, rectangular or other shapes. The size of the preset calculation range can be equal to or greater than the preset sampling window. In actual use, the target area of the mask pattern can be sampled to model the photoresist development rate model of the target area, or the entire mask pattern can be sampled to model the photoresist development rate model of the entire mask pattern. For example, the size of the preset calculation range and the preset sampling window can be set according to the chip manufacturing process or computing resource conditions (generally, the length and / or width of the preset calculation range can be several microns, and the length and / or width of the preset sampling window can be several hundred nanometers to several microns).

[0059] In the embodiment, the pattern density map in the preset sampling window can be obtained by calculating the mask image by various methods, such as improved image smoothing algorithm, sliding window method, deep learning method, etc., which are not limited here. The pixel value of each single point in the pattern density map contains the information of the pixel value of the single point and its neighborhood, so as to reflect the development-related substance concentration of each single point and its neighborhood in the photoresist. The development rate adjustment model can be obtained based on the pattern density map.

[0060] The basic photoresist development rate model can be Mack development model, Notch model, Inverse Mack development model, etc. The basic photoresist development rate model can be established based on the R(M) function, which can describe the relationship between the development rate and the concentration of the dissolution inhibitor, where M represents the concentration of the dissolution inhibitor in the photoresist.

[0061] The modeling method of the photoresist development rate model of the application adjusts the existing basic photoresist development rate model by the development rate adjustment model obtained by calculating the pattern density map, so that the adjusted photoresist development rate model considers the influence of the surrounding substance concentration on the development rate of a single point in the development process, at least partially reflects the development load, thereby improving the simulation accuracy of the development rate model.

[0062] In some embodiments of the modeling method of the application, the pattern density map in the preset sampling window in the preset calculation range is calculated according to the acquired mask image in the preset calculation range of the mask pattern, including:

[0063] The mask image in the preset calculation range is processed using the acquired preset image smoothing algorithm, and the pattern density map in the preset sampling window is obtained.

[0064] The image smoothing algorithm is a commonly used operation in image processing, which is used to reduce noise or details in the image, so that the image looks smoother and more continuous. In this embodiment, through proper design and processing, the preset image smoothing algorithm can extract some features of the mask image, and then indirectly reflect the graphic density of the mask image. Compared with other graphic density calculation methods, the image smoothing algorithm can quickly and effectively calculate the features of a single point and the features of each pixel point in the neighborhood of the single point, and obtain the graphic density of the single point. In this embodiment, the preset image smoothing algorithm can be a linear image filter, a nonlinear image filter, a convolutional neural network algorithm, an adaptive smoothing method, etc.

[0065] In this embodiment, the mask image in the entire pre-design calculation range can be first subjected to image smoothing processing to obtain a graphic density map in the pre-design calculation range, and then the graphic density map in the preset sampling window is extracted.

[0066] In some embodiments of the modeling method of the present application, the graphic density map in the preset sampling window in the pre-design calculation range is calculated according to the mask image in the pre-design calculation range of the acquired mask plate image, comprising:

[0067] The mask image in the preset sampling window is processed using the mask image in the pre-design calculation range and the acquired preset image smoothing algorithm, and a graphic density map in the preset sampling window is obtained.

[0068] The main difference between this embodiment and the previous embodiment is that only the mask image in the preset sampling window is subjected to image smoothing processing. When the pre-design calculation range is much larger than the preset sampling window, this scheme can greatly save computing power and quickly obtain the graphic density map in the preset sampling window.

[0069] In some embodiments of the modeling method of the present application, the preset image smoothing algorithm is a preset image filter.

[0070] An image filter is a tool for processing and analyzing images, which operates on pixel values to achieve the purposes of enhancement, denoising, feature extraction, etc. Image filters are usually based on convolution operations and can quickly process images in local regions. This makes them computationally efficient in practical applications and can focus on local features of images (such as local density), which many other complex algorithms cannot directly achieve. In particular, compared with other images, mask images usually have lower complexity, and when an image filter is applied, fast processing can be achieved, greatly shortening the calculation time and improving the modeling efficiency. In this embodiment, the preset image filter can be a mean filter, a median filter, a Gaussian filter, a non-local mean filter, etc., and can also be an improved image filter based on the above image filters. Those skilled in the art can select and use as needed, which can be covered within the spirit of the present application, and will not be repeated here.

[0071] Taking the mean filter as an example, the preset image filter can extract the pixel value features of each single point and its neighborhood in the preset sampling window, perform mean calculation, and map the result as the pattern density of the single point to the pattern density map. In this way, the pattern density at each place in the pattern density map can reflect the average concentration of the development-related substance around each single point in the mask image, thereby reflecting the development load problem in the development process.

[0072] In some embodiments of the modeling method of the present application, the size of the pre-design calculation range is greater than the size of the preset sampling window, so that when the preset image filter is used to perform convolution calculation on any sampling point in the mask image in the preset sampling window, the filter window of the preset image filter is always in the pre-design calculation range.

[0073] It should be understood that when the size of the pre-design calculation range is the same as that of the preset sampling window, or when the distance between the boundary of the pre-design calculation range and the boundary of the preset sampling window is too close, the convolution calculation using the preset image filter on the pixel points near the boundary of the preset sampling window may lack sufficient neighborhood pixels, resulting in that the calculation result of the preset image filter cannot reflect the features of the entire neighborhood of the pixel points near the boundary of the preset sampling window. In this embodiment, by expanding the size of the pre-design calculation range, the convolution calculation on the pixel points near the boundary of the preset sampling window can also reflect the concentration of the development-related substance in the entire neighborhood thereof, thereby improving the accuracy of modeling and improving the simulation accuracy.

[0074] For example, when the pre-design calculation range and the preset sampling window are both rectangular, the pre-design calculation range can be set to have the center of the preset sampling window as the center, and the window width (or height) of the pre-design calculation range can be set to be greater than twice the window width (or height) of the preset sampling window. In this way, the features of the entire neighborhood of the pixel points near the boundary of the preset sampling window can be fully considered in the convolution calculation. Further, the filter window (or height) of the preset image filter can be set to be less than 1 / 2 of the window width (or height) of the pre-design calculation range. By limiting the size of the filter window of the preset image filter, it can be prevented that the features of the region far away from the single point in the mask image are mapped to the pattern density of the single point, so that the pattern density of the single point is adapted to the actual development load, thereby improving the simulation accuracy. On the other hand, limiting the size of the filter window of the preset image filter can also save computing resources and improve the modeling efficiency.

[0075] In some embodiments of the modeling method of the present application, as shown in Figure 2 the method further comprises:

[0076] S211, transforming the graph density map by a preset transformation function;

[0077] S213, obtaining a developing rate adjustment model according to the transformed graph density map.

[0078] The target of the transformation function is to map the input data from one space or distribution to another space or distribution, which is usually used to improve the model performance or make the data more consistent with certain assumptions. In the embodiment, the preset transformation function is used to transform or weight the density at each place of the graph density map, so as to more accurately reflect the degree of developing load and improve the simulation accuracy.

[0079] Exemplarily, the preset transformation function can be a Sigmoid function, a double Gaussian function, a Hard Sigmoid function, a normalized transformation function, a Min-Max transformation function, etc., or an improved form of the above functions, which can be selected and used by those skilled in the art as needed, and will not be described here.

[0080] Exemplarily, the developing rate adjustment model can be established based on a R(PD) function, which can describe the relationship between the developing rate adjustment factor in the developing rate adjustment model and the graph density. PD represents the graph density at a certain place in the graph density map.

[0081] In some embodiments of the modeling method of the present application, as shown in Figure 3 The transformation of the graph density map by the preset transformation function comprises:

[0082] S221, obtaining a plurality of preset parameters, the plurality of preset parameters comprising a preset maximum correction rate, a preset minimum correction rate and a preset graph density threshold, and generating a preset transformation function;

[0083] S223, transforming each place in the graph density map by the preset transformation function to obtain a transformed graph density map.

[0084] The following will be described taking the improved form of the Sigmoid function as the preset transformation function as an example. The preset transformation function can have the following form:

[0085]

[0086] wherein R(PD) represents the relationship between the developing rate adjustment factor in the developing rate adjustment model and the graph density, PD represents the graph density at a certain place in the graph density map, Rmax represents the preset maximum correction rate, Rmin represents the preset minimum correction rate, slope represents the preset slope parameter, and density_threshold represents the preset graph density threshold.

[0087] The sigmoid function can map any real number input to the interval [0, 1], and presents a single S-shaped curve, so that the output smoothly transitions from 0 to 1. The preset transformation function based on the sigmoid function can better reflect the relationship between the pattern density and the actual development load. In practice, compared with directly using the pattern density map to modify the basic photoresist development rate model, using the preset transformation function based on the sigmoid function to transform the pattern density map and then adjusting the basic photoresist development rate model can better reduce the error between the simulation result and the actual measurement result, and improve the simulation accuracy.

[0088] Please refer to Figure 5 , the horizontal axis is the pattern density, and the vertical axis is the development rate adjustment factor. The figure illustrates the change curve of the development rate adjustment factor in the development rate adjustment model with the pattern density when Rmax=1, Rmin=0, slope=-20, and density_threshold=0.5.

[0089] In this embodiment, the sigmoid function is improved by using preset maximum correction rate, preset minimum correction rate, preset slope parameter, and preset pattern density threshold, which can constrain the adjustment range of the development rate adjustment model and avoid over-adjustment. On the other hand, by modifying the above parameters, the development rate adjustment model can be easily modified, so that the development rate adjustment model can more accurately reflect the development load. In particular, for different chip process technologies, different mask types, different photoresist types, and different developers, the preset maximum correction rate, preset minimum correction rate, preset slope parameter, and preset pattern density threshold can be set to be targeted, so that the development rate adjustment model can more accurately reflect the development load in the corresponding development process, and improve the accuracy of model simulation.

[0090] In some embodiments of the modeling method of the present application, the step of adjusting the obtained basic photoresist development rate model using the development rate adjustment model to obtain an adjusted photoresist development rate model can be adjusted using the following formula:

[0091] R ′ (M)=R(M)*R(PD)

[0092] Wherein R ′ (M) represents the relationship between the development rate in the adjusted photoresist development rate model and the concentration of the dissolution inhibitor, R(M) represents the relationship between the development rate in the basic photoresist development rate model and the concentration of the dissolution inhibitor, M represents the concentration of the dissolution inhibitor in the photoresist, R(PD) represents the relationship between the development rate adjustment factor in the development rate adjustment model and the pattern density, and PD represents the pattern density at a certain position in the pattern density map.

[0093] The pattern density effect can cause the deviation of the critical dimension (CD) of the pattern of the wafer. Figure 6a and Figure 6b , Figure 6a a distribution diagram of the deviation value of the critical dimension of the pattern in the three-dimensional profile of the photoresist in the simulation result and the measured result of the basic photoresist developing rate model with the pattern density is shown, Figure 6b a distribution diagram of the deviation value of the critical dimension of the pattern in the simulation result and the measured result of the improved photoresist developing rate model with the pattern density is shown. In the diagram, the horizontal axis is the pattern density, and the vertical axis is the deviation value of the critical dimension of the pattern in the three-dimensional profile of the photoresist in the simulation result and the measured result, in nanometers. By comparing the two diagrams, it can be obtained that the deviation value of the simulation result and the measured result of the improved photoresist developing rate model is closer to the 0-axis position of the deviation value, and the simulation accuracy is obviously improved.

[0094] In some embodiments of the modeling method of the present application, as shown in Figure 4 after the basic photoresist developing rate model is adjusted by using the developing rate adjustment model to obtain the adjusted photoresist developing rate model, the modeling method further comprises:

[0095] S511, simulation is performed by using the adjusted photoresist developing rate model to obtain a simulated three-dimensional profile after photoresist development;

[0096] S513, each preset parameter is updated according to the simulated three-dimensional profile and the measured three-dimensional profile after photoresist development that has been obtained;

[0097] S515, the developing rate adjustment model is updated according to the updated each preset parameter, and the adjusted photoresist developing rate model is updated.

[0098] In the embodiment, the critical dimension of the pattern in the simulated three-dimensional profile after photoresist development can be obtained, and the critical dimension of the pattern in the measured three-dimensional profile can be obtained. By comparing the deviation of the two, if the deviation is large, the developing rate adjustment model can be updated by adjusting the preset maximum correction rate, the preset minimum correction rate, the preset pattern density threshold and other preset parameters, and the adjusted photoresist developing rate model is updated, and then simulation is performed again, so as to continuously reduce the deviation value of the critical dimension of the pattern in the simulated three-dimensional profile and the measured three-dimensional profile until the preset condition is met.

[0099] It should be understood that in actual use, the different regions in the reticle pattern can be simulated by using the adjusted photoresist development rate model based on the same set of preset parameters, and then the deviations of the pattern critical dimensions in the simulated three-dimensional profile and the measured three-dimensional profile of each region are comprehensively judged to update the preset maximum correction rate, the preset minimum correction rate, the preset pattern density threshold and other preset parameters.

[0100] The flowchart provided by the embodiment is not intended to indicate that the operations of the method should be performed in any particular order, or that all of the operations included in the method should be included in every implementation of the method. In addition, the method can include additional operations. Additional changes can be made to the above-described method within the scope of the technical ideas provided by the embodiment method.

[0101] It should be understood that in some embodiments, the parts can be realized by hardware, software, firmware or a combination thereof. In the above-described implementation, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0102] The embodiment of the application further provides a computer program product 10, a computer readable storage medium 20 and a computer device 30. Figure 7 is a schematic diagram of the computer program product 10 according to an embodiment of the application, Figure 8 is a schematic diagram of the computer readable storage medium 20 according to an embodiment of the application, Figure 9 is a schematic diagram of the computer device 30 according to an embodiment of the application. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, realizes the steps of any of the modeling methods described above. The computer readable storage medium 20 has the computer program 11 stored thereon, and the computer program 11, when executed by the processor 32, realizes the steps of the modeling method of any of the embodiments described above. The computer device 30 can include a memory 31, a processor 32 and a computer program 11 stored on the memory 31 and running on the processor 32.

[0103] The computer program 11 for performing the operations of the present application can be assembly code, instruction set architecture (ISA) code, machine code, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, all of which can be transformed by an implementation of an interpreter, compiler, code builder, or assembler, etc. into machine code or object code suitable for execution by the machine 12. The computer program 11 can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0104] For the purposes of this description of the embodiments, the computer program product 10 is a product of authorship that includes the computer program 11.

[0105] For the purposes of this description of the embodiments, the computer readable storage medium 20 is a tangible device that can retain and store computer program instructions 11 for use by or in connection with an instruction execution system, apparatus, or device. More specifically, the computer readable storage medium 20 can be, without limitation, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, and / or a mechanism of information storage such as a

[0106] The computer device 30 can be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smart phone. In some examples, the computer device 30 can be a cloud computing node. The computer device 30 can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The computer device 30 can be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0107] The computer device 30 can include a processor 32 adapted to execute stored instructions, a memory 31 that provides temporary storage for operations of the instructions during operation. The processor 32 can be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 31 can include random access memory (RAM), read only memory, flash memory, or any other suitable memory system.

[0108] The computer device 30 can also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows for input and output of data with external devices that can be connected to the computer device. The network adapter / interface can provide for communication between the computer device and a network, typically illustrated as a communication network.

[0109] To this end, it will be appreciated that, although specific embodiments of the application have been described herein for purposes of illustration, various modifications can be made in the details of the application without departing from the spirit and scope thereof. Therefore, it is to be understood that the scope of the application is not to be limited to the specific examples disclosed and that modifications or variations are possible so long as the spirit or principles of the application remain unaltered.

Claims

1. A modeling method of a photoresist development rate model, characterized by, The method comprises the following steps: calculating a pattern density map in a preset sampling window within a pre-design calculation range according to a mask image in the pre-design calculation range of a mask pattern map obtained; calculating a developing rate adjustment model according to the pattern density map; adjusting a basic photoresist developing rate model obtained to obtain an adjusted photoresist developing rate model by using the developing rate adjustment model.

2. The modeling method of claim 1, wherein, In the step of adjusting the basic photoresist developing rate model obtained to obtain the adjusted photoresist developing rate model by using the developing rate adjustment model, the following formula is used for adjustment: R ′ (M) = R(M) * R(PD) wherein R ′ (M) represents the relationship between the development rate in the adjusted photoresist development rate model and the concentration of dissolution inhibitor, R(M) represents the relationship between the development rate in the basic photoresist development rate model and the concentration of dissolution inhibitor, M represents the concentration of dissolution inhibitor in the photoresist, R(PD) represents the relationship between the development rate adjustment factor in the development rate adjustment model and the pattern density, and PD represents the pattern density at a certain location in the pattern density map.

3. The modeling method of claim 1, wherein, The step of calculating the developing rate adjustment model according to the pattern density map comprises the following steps: transforming the pattern density map by using a preset transformation function; obtaining the developing rate adjustment model according to the transformed pattern density map.

4. The modeling method of claim 3, wherein, The step of transforming the pattern density map by using a preset transformation function comprises the following steps: obtaining a plurality of preset parameters, the plurality of preset parameters comprising a preset maximum correction rate, a preset minimum correction rate and a preset pattern density threshold, and generating the preset transformation function; transforming each part of the pattern density map by using the preset transformation function to obtain the transformed pattern density map.

5. The modeling method of claim 4, wherein, The preset transformation function has the following form: wherein R(PD) represents a relationship between a developing rate adjustment factor in the developing rate adjustment model and a pattern density, PD represents a pattern density at a certain position in the pattern density map, Rmax represents a preset maximum correction rate, Rmin represents a preset minimum correction rate, slope represents a preset slope parameter, and density_threshold represents a preset pattern density threshold.

6. The modeling method of claim 4, wherein, After the step of adjusting the basic photoresist developing rate model obtained to obtain the adjusted photoresist developing rate model by using the developing rate adjustment model, the modeling method further comprises the following steps: performing simulation by using the adjusted photoresist developing rate model to obtain a simulated three-dimensional profile after photoresist development; updating each preset parameter according to the simulated three-dimensional profile and a measured three-dimensional profile obtained after photoresist development; updating the developing rate adjustment model and the adjusted photoresist developing rate model according to the updated each preset parameter.

7. The modeling method of claim 1, wherein, The step of calculating a pattern density map in a preset sampling window within a pre-design calculation range according to a mask image in the pre-design calculation range of a mask pattern map obtained comprises the following steps: processing the mask image in the pre-design calculation range by using a preset image smoothing algorithm and obtaining the pattern density map in the preset sampling window; or processing the mask image in the preset sampling window by using the mask image in the pre-design calculation range and a preset image smoothing algorithm obtained and obtaining the pattern density map in the preset sampling window.

8. The modeling method according to claim 7, wherein the preset image smoothing algorithm is a preset image filter.

9. The modeling method according to claim 8, wherein The pre-design calculation range is larger than the preset sampling window, so that when the preset image filter is used to perform convolution calculation on any sampling point of the mask image in the preset sampling window, the filter window of the preset image filter is in the pre-design calculation range.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and the computer program, when executed by a processor, implements the steps of the modeling method of the photoresist development rate model according to any one of claims 1 to 9.

11. A computer program product comprising a computer program, characterized in that, A computer program is stored thereon, and the computer program, when executed by a processor, implements the steps of the modeling method of the photoresist development rate model according to any one of claims 1 to 9.

12. A computer apparatus, comprising: A computer program is stored thereon, and the computer program, when executed by a processor, implements the steps of the modeling method of the photoresist development rate model according to any one of claims 1 to 9.

Images