Modeling method of photoresist development rate model and related product

Abstract

The invention relates to a modeling method of a photoresist development rate model and a related product. The modeling method comprises the following steps: calculating a graph density map in a preset sampling window in a preset calculation range according to a mask image in the preset calculation range of an obtained mask plate graph; calculating according to the graph density map to obtain a development rate adjustment model; and adjusting the obtained basic photoresist development rate model by using the development rate adjustment model to obtain an adjusted photoresist development rate model. According to the modeling method, the developing load in the developing process can be reflected, and the simulation accuracy of the photoresist developing rate model is improved.

Description

Technical Field

[0001] The present invention relates to the 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 Art

[0002] The photolithography process is a complex production process, which mainly includes spin coating, soft baking, exposure, post-exposure baking, and development steps, which are used to transfer the pattern of the mask to the surface of the silicon wafer coated with photoresist. These process can be described by physical optics, photochemical reaction, physical and chemical reaction models with the help of mathematical formulas, so as to achieve strict photolithography simulation modeling. For example, for the exposure process of chemical amplification glue, the light intensity distribution in the photoresist is usually simulated by an optical model, and then the concentration distribution of photoacids, alkalis and other substances in the photoresist after exposure is obtained by photochemical reaction simulation. For the post-baking process, physical diffusion models, acid-catalyzed de-simulation protection reactions, acid-base neutralization reactions, etc. are usually used to obtain the acid, alkali, and inhibitor concentration distribution after post-baking.

[0003] The development step of forming a three-dimensional photoresist profile is one of the most critical steps in the photolithography process. Whether the development process can be accurately controlled during the manufacturing process directly affects the yield of the chip. In related technologies, a development rate model based on the concentration of a dissolution inhibitor is usually used to simulate the three-dimensional profile of the developed photoresist, such as the Mack development model, the Notch model, and the Inverse Mack development model.

[0004] However, the above basic photoresist development rate model only considers the relationship between the concentration of the dissolution inhibitor at a single point and the development rate, while ignoring the effect of the change in the concentration of the surrounding dissolution inhibitor on the development rate of the point. With the development of photolithography technology, the critical dimensions of wafer patterns continue to decrease, and the pattern density effect (Pattern Density Effect) caused by the development load (for example, the developer is locally depleted in the highly exposed area, which in turn affects the development rate of the surrounding exposed areas) is also more significant. The above basic photoresist development rate model cannot reflect the development load, resulting in the simulation results not being able to accurately reflect the measured results, and the simulation accuracy is poor. Summary of the invention

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

[0006] An object of the present invention is to provide a modeling method for a photoresist development rate model, to adjust the existing basic photoresist development rate model so as to take into account the influence of the material concentration around a single point on the development rate of the single point during the development process, to at least partially reflect the development load, and thus improve the simulation accuracy of the photoresist development rate model.

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

[0008] Calculating a pattern density map within a preset sampling window within the preset calculation range according to a mask image within a preset calculation range of the acquired mask layout;

[0009] Calculate and obtain a development rate adjustment model according to the graphic density map;

[0010] The acquired basic photoresist developing rate model is adjusted using the developing rate adjustment model to obtain an adjusted photoresist developing rate model.

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

[0012] R ′ (M) = R(M)*R(PD)

[0013] Where R ′ (M) represents the relationship between the development rate and the concentration of the dissolution inhibitor in the adjusted photoresist development rate model, R(M) represents the relationship between the development rate and the concentration of the dissolution inhibitor in the basic photoresist development rate model, 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 graphic density, PD represents the graphic density at a certain point in the graphic density map.

[0014] Optionally, the step of calculating and obtaining a development rate adjustment model according to the graphic density map includes:

[0015] Transforming the graphic density map by a preset transformation function;

[0016] The development rate adjustment model is obtained according to the transformed graphic density map.

[0017] Optionally, the transforming the graphic density map by using a preset transformation function includes:

[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 graphic density threshold, and generate the preset transformation function;

[0019] The preset transformation function is used to transform each location in the graphic density map to obtain the transformed graphic density map.

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

[0021]

[0022] Wherein R(PD) represents the relationship between the development rate adjustment factor in the development rate adjustment model and the graphic density, PD represents the graphic density at a certain point in the graphic 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 graphic density threshold.

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

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

[0025] updating each of the preset parameters according to the simulated three-dimensional profile and the obtained measured three-dimensional profile after the photoresist is developed;

[0026] According to the updated preset parameters, the developing rate adjustment model is updated, and the adjusted photoresist developing rate model is updated.

[0027] Optionally, the step of calculating the pattern density map within a preset sampling window within the preset calculation range according to the acquired mask image within the preset calculation range of the mask layout includes:

[0028] The acquired preset image smoothing algorithm is used to process the mask image within the preset calculation range, and the graphic density map within the preset sampling window is obtained.

[0029] Optionally, the step of calculating the pattern density map within a preset sampling window within the preset calculation range according to the acquired mask image within the preset calculation range of the mask layout includes:

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

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

[0032] Optionally, the size of 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 within the preset sampling window, the filtering window of the preset image filter is within the preset calculation range.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the modeling method of the photoresist development rate model are implemented.

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

[0035] According to another aspect of the present invention, there is provided a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned modeling method of the photoresist development rate model.

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

[0037] Furthermore, in the modeling method of the present invention, the graphic density map is transformed by a preset transformation function, so that the transformed graphic density map further accurately reflects the degree of influence of the material concentration in the neighborhood of each single point in the photoresist on the development rate of the single point, thereby further accurately reflecting the development load and improving the simulation accuracy of the development rate model.

[0038] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0040] Figure 1 is a flow chart of a modeling method according to an embodiment of the present invention;

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

[0042] Figure 3 is a schematic diagram of a process of transforming a graphic density distribution map according to a modeling method of an embodiment of the present invention;

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

[0044] Figure 5 is a curve schematic diagram showing the variation of the development rate adjustment factor with the pattern density in the development rate adjustment model according to the modeling method of one embodiment of the present invention;

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

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

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

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

[0049] Fig. 9 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The purpose of the modeling method of the photoresist development rate model of this embodiment is to adjust the existing basic photoresist development rate model so as to consider the influence of the material concentration around the single point on the development rate of the single point during the development process, at least partially reflect the development load, and thus improve the simulation accuracy of the development rate model.

[0051] Figure 1 FIG. 1 is a flow chart of a modeling method according to an embodiment of the present invention. The method may generally include:

[0052] S100, calculating a pattern density map (Pattern Density Map) within a preset sampling window within the preset calculation range according to a mask image (Mask Image) within a preset calculation range of the acquired mask layout;

[0053] S200, obtaining a development rate adjustment model by calculation according to the graphic density map;

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

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

[0056] The mask layout is an electronic layout for mask layout design through electronic design automation (EDA) tools (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 layout 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 image can be obtained by sampling or converting the mask layout. The mask image can be a discrete pixel array diagram to facilitate image processing. Exemplarily, the mask image can be a grayscale image, and the pixel value of each point in the grayscale image can be normalized to be in the interval [0, 1], and the pixel value can represent a quantity related to exposure (for example, the pixel value of the opaque part in the mask layout can be 0, the pixel value of the transparent part can be 1, and the pixel value at 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 amount of the photoresist at the single point in the exposure process, thereby reflecting the concentration of the development-related substance in the photoresist at the single point (for example, the concentration of the dissolution inhibitor).

[0058] In this embodiment, the shape, size and position of the preset sampling window and the preset calculation range can be set as needed. The shape of the preset sampling window can generally be square, rectangular, or even other shapes. The size of the preset calculation range can be equal to the preset sampling window, or it can be larger than the preset sampling window. In actual use, a certain target area of ​​the mask layout can be sampled, and then the photoresist development rate model of the target area can be modeled, or the entire mask layout can be sampled, and then the photoresist development rate model of the entire mask layout can be modeled. Exemplarily, the size of the preset calculation range and the preset sampling window can be set according to the chip process technology or computing power resources (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 hundreds of nanometers to several microns).

[0059] In this embodiment, the mask image can be calculated by a variety of methods to obtain a graphic density map within a preset sampling window, such as an improved image smoothing algorithm, a sliding window method, a deep learning method, etc., which are not limited here. The pixel value of each single point in the graphic density map contains the information of the pixel value of the single point and its neighborhood, thereby reflecting the concentration of development-related substances at each single point and its neighborhood in the photoresist. A development rate adjustment model can be obtained based on the graphic density map.

[0060] The basic photoresist development rate model may be Mack development model, Notch model, InverseMack development model, etc. The basic photoresist development rate model may be established based on the R(M) function, which may 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 present invention adjusts the existing basic photoresist development rate model through the development rate adjustment model obtained by calculating the graphic density map, so that the adjusted photoresist development rate model takes into account the influence of the concentration of surrounding substances on the development rate of a single point during 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 present invention, the step of calculating a pattern density map within a preset sampling window within a preset calculation range based on a mask image within a preset calculation range of an acquired mask layout includes:

[0063] The acquired preset image smoothing algorithm is used to process the mask image within the preset calculation range, and a graphic density map within the preset sampling window is obtained.

[0064] Image smoothing algorithm is a commonly used operation in image processing, which is used to reduce noise or details in the image and make the image look smoother and more continuous. In this embodiment, through appropriate design and processing, the preset image smoothing algorithm can extract certain features of the mask image, thereby indirectly reflecting 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 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 within the entire preset calculation range may be smoothed to obtain a graphic density map within the preset calculation range, and then the graphic density map within the preset sampling window may be extracted.

[0066] In some embodiments of the modeling method of the present invention, the step of calculating a pattern density map within a preset sampling window within a preset calculation range based on a mask image within a preset calculation range of an acquired mask layout includes:

[0067] The mask image in the preset sampling window is processed by using the mask image in the preset 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 image smoothing is performed only on the mask image within the preset sampling window. When the preset calculation range is much larger than the preset sampling window, this solution can greatly save computing power and quickly obtain the graphic density map within the preset sampling window.

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

[0070] Image filter is a tool for processing and analyzing images, which achieves enhancement, denoising, feature extraction and other purposes by operating pixel values. Image filters are usually based on convolution operations and can quickly process images in local areas. This makes them computationally efficient in practical applications and can focus on local features of images (such as local density), which is not directly achievable by many other complex algorithms. In particular, compared to other images, mask images usually have lower complexity. When image filters are applied, fast processing can be achieved, which greatly shortens the calculation time and improves 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-mentioned image filter. Those skilled in the art can select and use them as needed, which can be included in the inventive spirit of the present invention and will not be repeated here.

[0071] Taking the preset image filter as an example of a mean filter, the preset image filter can extract the pixel value features of each single point and its neighborhood in the preset sampling window, and after the mean calculation, the result is mapped into the pattern density map as the pattern density of the single point. In this way, the pattern density at each point in the pattern density map can reflect the average concentration of the development-related substances at each single point and its surroundings 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 invention, the size of the preset calculation range is larger than the preset sampling window, so that when a preset image filter is used to perform convolution calculation on any sampling point of the mask image within the preset sampling window, the filtering window of the preset image filter is within the preset calculation range.

[0073] It should be understood that when the preset calculation range is the same size as the preset sampling window, or when a boundary of the preset calculation range is too close to the boundary of the preset sampling window, when the preset image filter is used to perform convolution calculation on the pixel points near the boundary of the preset sampling window, there may be a lack of sufficient neighborhood pixels, resulting in the calculation result of the preset image filter not being able to reflect the characteristics 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 preset calculation range, the concentration of the development-related substances in the entire neighborhood can be reflected when the pixel points near the boundary of the preset sampling window are convoluted, thereby improving the accuracy of modeling and improving the accuracy of simulation.

[0074] Exemplarily, when the preset calculation range and the preset sampling window are both rectangular, the preset calculation range can be set to be centered on the center of the preset sampling window, and the window width (or height) of the preset calculation range can be set to be greater than 2 times the window width (or height) of the preset sampling window. In this way, during the convolution calculation, the characteristics of the entire neighborhood of the pixel points near the boundary of the preset sampling window can be fully considered. Furthermore, the filter window (or height) of the preset image filter can also be set to be less than 1 / 2 of the window width (or height) of the preset calculation range. By limiting the size of the filter window of the preset image filter, it is possible to prevent the features of the area far from the single point in the mask image from being mapped to the graphic density of the single point, so that the graphic density of the single point is adapted to the actual degree of 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 modeling efficiency.

[0075] In some embodiments of the modeling method of the present invention, Figure 2 As shown, the developing rate adjustment model is obtained by calculating the graphic density map, including:

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

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

[0078] The goal of the transformation function is to map input data from one space or distribution to another space or distribution, which is usually used to improve model performance or make data more consistent with certain assumptions. In this embodiment, the preset transformation function is used to transform or weight the density of each part of the graphic density map to more accurately reflect the degree of development load, thereby improving the simulation accuracy.

[0079] Exemplarily, the preset transformation function may be a Sigmoid function, a double Gaussian function, a Hard Sigmoid function, a standardized transformation function, a Min-Max transformation function, etc., or it may be an improved form of the above functions. Technical personnel in this field may select and use it as needed, which will not be repeated here.

[0080] Exemplarily, the development rate adjustment model may be established based on the R(PD) function, which may describe the relationship between the development rate adjustment factor in the development rate adjustment model and the graphic density, where PD represents the graphic density at a certain point in the graphic density map.

[0081] In some embodiments of the modeling method of the present invention, Figure 3 As shown, the transformation of the graphic density map by a preset transformation function includes:

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

[0083] S223, transforming each location in the graphic density map using a preset transformation function to obtain a transformed graphic density map.

[0084] The following description is made by taking the preset transformation function as an improved form based on the Sigmoid function as an example. The preset transformation function may have the following form:

[0085]

[0086] Wherein R(PD) represents the relationship between the development rate adjustment factor in the development rate adjustment model and the graphic density, PD represents the graphic density at a certain point in the graphic 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 graphic density threshold.

[0087] The Sigmoid function can map any input real number to the interval [0,1] and present a single S-shaped curve, so that its 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 correct the basic photoresist development rate model, using the preset transformation function based on the Sigmoid function to transform the pattern density map and then adjust the basic photoresist development rate model can better reduce the error between the simulation results and the measured results 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 shows the curve of the development rate adjustment factor in the development rate adjustment model changing with the pattern density when Rmax=1, Rmin=0, slope=-20, and density_threshold=0.5.

[0089] In the present embodiment, the Sigmoid function is improved by using parameters such as a preset maximum correction rate, a preset minimum correction rate, a preset slope parameter, and a preset graphic density threshold, so that the adjustment range of the development rate adjustment model can be constrained on the one hand to avoid excessive adjustment. On the other hand, by correcting the above parameters, the development rate adjustment model can be conveniently corrected so that the development rate adjustment model more accurately reflects the development load. In particular, for different chip process technologies, different mask types, different photoresist types, and different developer types, targeted preset maximum correction rates, preset minimum correction rates, preset slope parameters, preset graphic density thresholds, and other parameters can be set so that the development rate adjustment model more accurately reflects the development load in the corresponding development process, thereby improving the accuracy of the model simulation.

[0090] In some embodiments of the modeling method of the present invention, in the step of adjusting the acquired basic photoresist development rate model using the development rate adjustment model to obtain the adjusted photoresist development rate model, the following formula can be used for adjustment:

[0091] R ′ (M) = R(M)*R(PD)

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

[0093] The pattern density effect will cause the deviation of the wafer's pattern critical dimension (CD). Please refer to Figure 6a and Figure 6b , Figure 6a The simulation results of the basic photoresist development rate model and the measured results show the distribution of the deviation value of the key size of the photoresist three-dimensional profile with the pattern density. Figure 6b The figure shows the distribution of the deviation values ​​of the key dimensions of the graphics in the simulation results and the measured results of the improved photoresist development rate model with the density of the graphics. The horizontal axis in the figure is the graphics density, and the vertical axis is the deviation value of the key dimensions of the graphics in the three-dimensional profile of the photoresist in the simulation results and the measured results, in nanometers. By comparing the two figures, it can be concluded that the deviation value of the simulation results and the measured results of the improved photoresist development rate model is closer to the 0 axis position in the deviation value, and the simulation accuracy is significantly improved.

[0094] In some embodiments of the modeling method of the present invention, Figure 4 As shown, after the acquired basic photoresist development rate model is adjusted by using the development rate adjustment model to obtain the adjusted photoresist development rate model, the modeling method further includes:

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

[0096] S513, updating each preset parameter according to the simulated three-dimensional profile and the obtained measured three-dimensional profile after the photoresist is developed;

[0097] S515, updating the development rate adjustment model according to the updated preset parameters, and updating the adjusted photoresist development rate model.

[0098] In this embodiment, the key size of the graphics in the simulated three-dimensional profile after the photoresist is developed can be obtained, and the key size of the graphics in the measured three-dimensional profile can be obtained. By comparing the deviation between the two, if the deviation is large, the development rate adjustment model can be updated by adjusting preset parameters such as the preset maximum correction rate, the preset minimum correction rate, and the preset graphic density threshold, and the updated adjusted photoresist development rate model, and then simulated again, and the deviation value of the key size of the graphics in the simulated three-dimensional profile and the measured three-dimensional profile is continuously reduced until the preset conditions are met.

[0099] It should be understood that in actual use, the adjusted photoresist development rate model based on the same set of preset parameters can be used to simulate different areas in the mask layout, and then a comprehensive judgment is made based on the deviation values ​​of the key dimensions of the graphics in the simulated three-dimensional contours and the measured three-dimensional contours of each area to update the preset parameters such as the preset maximum correction rate, the preset minimum correction rate, and the preset graphic density threshold.

[0100] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0101] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0102] The embodiment of the present invention 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 a computer program product 10 according to an embodiment of the present invention, Figure 8 is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention, Fig. 9 is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which implements the steps of any of the above-mentioned modeling methods when executed by a processor 32. The computer-readable storage medium 20 stores the above-mentioned computer program 11, which implements the steps of the modeling method of any of the above-mentioned embodiments when executed by the processor 32. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.

[0103] The computer program 11 for performing the operation of the present invention may be an assembly instruction, an instruction set architecture (ISA) instruction, a machine instruction, a machine-related instruction, a microcode, a firmware instruction, a state setting data, a configuration data of an integrated circuit, or a source code or an object code written in any combination of one or more programming languages ​​and process programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, in order to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit.

[0104] In the description of this embodiment, the computer program product 10 is a related product including the computer program 11 .

[0105] For the purpose of the description of the present embodiment, the computer readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, which can be any device that can contain, store, communicate, propagate or transmit the computer program 11 for use with an instruction execution system, device or apparatus or in conjunction with these instruction execution systems, devices or apparatuses. More specific examples (a non-exhaustive list) of the computer readable storage medium 20 include the following: a portable computer disk, 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 disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the above.

[0106] The computer device 30 may 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 may be a cloud computing node. The computer device 30 may be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, a program module may include routines, programs, target programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer device 30 may be implemented in a distributed cloud computing environment where remote processing devices linked via a communication network perform tasks. In a distributed cloud computing environment, program modules may be located on a local or remote computing system storage medium including a storage device.

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

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

[0109] At this point, those skilled in the art should recognize that, although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A modeling method for a photoresist development rate model, characterized in that: include: Calculating a pattern density map within a preset sampling window within the preset calculation range according to a mask image within a preset calculation range of the acquired mask layout; Calculate and obtain a development rate adjustment model according to the graphic density map; The acquired basic photoresist developing rate model is adjusted using the developing rate adjustment model to obtain an adjusted photoresist developing rate model.

2. The modeling method according to claim 1, characterized in that: In the step of using the development rate adjustment model to adjust the acquired basic photoresist development rate model to obtain the adjusted photoresist development rate model, the following formula is used for adjustment: R ′ (M)=R(M)*R(PD) Where R ′ (M) represents the relationship between the development rate and the concentration of the dissolution inhibitor in the adjusted photoresist development rate model, R(M) represents the relationship between the development rate and the concentration of the dissolution inhibitor in the basic photoresist development rate model, 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 graphic density, PD represents the graphic density at a certain point in the graphic density map.

3. The modeling method according to claim 1, characterized in that: The method of calculating and obtaining the development rate adjustment model according to the graphic density map includes: Transforming the graphic density map by a preset transformation function; The development rate adjustment model is obtained according to the transformed graphic density map.

4. The modeling method according to claim 3, characterized in that: The transforming of the graphic density map by a preset transformation function includes: 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 graphic density threshold, and generate the preset transformation function; The preset transformation function is used to transform each location in the graphic density map to obtain the transformed graphic density map.

5. The modeling method according to claim 4, characterized in that: The preset transformation function has the following form: Wherein R(PD) represents the relationship between the development rate adjustment factor in the development rate adjustment model and the graphic density, PD represents the graphic density at a certain point in the graphic 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 graphic density threshold.

6. The modeling method according to claim 4, characterized in that: After the acquired basic photoresist development rate model is adjusted by using the development rate adjustment model to obtain the adjusted photoresist development rate model, the modeling method further includes: Performing simulation using the adjusted photoresist development rate model to obtain a simulated three-dimensional profile after photoresist development; updating each of the preset parameters according to the simulated three-dimensional profile and the obtained measured three-dimensional profile after the photoresist is developed; According to the updated preset parameters, the developing rate adjustment model is updated, and the adjusted photoresist developing rate model is updated.

7. The modeling method according to claim 1, characterized in that: The step of calculating the pattern density map within a preset sampling window within the preset calculation range according to the mask image within the preset calculation range of the acquired mask layout includes: Using the acquired preset image smoothing algorithm, the mask image within the preset calculation range is processed to obtain the graphic density map within the preset sampling window; or The mask image in the preset sampling window is processed by using the mask image in the preset calculation range and the acquired preset image smoothing algorithm, and the graphic density map in the preset sampling window is obtained.

8. The modeling method according to claim 7, characterized in that: The preset image smoothing algorithm is a preset image filter.

9. The modeling method according to claim 8, characterized in that: The size of 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 within the preset sampling window, the filter window of the preset image filter is within the preset calculation range.

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

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for modeling a photoresist development rate model according to any one of claims 1 to 9 are implemented.

12. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the modeling method of the photoresist development rate model according to any one of claims 1 to 9.

Images