Photoresist Model Optimization and Construction Method and Lithography Model Construction Method

Abstract

The present invention discloses a method for optimizing and constructing a photoresist model and a method for constructing a lithography model. The photoresist model optimization method includes, after the photoresist threshold model finishes iterative optimization, reducing the coefficient of the imaging light intensity curvature in the first model parameter data obtained after the iterative optimization to obtain second model parameter data, and then performing iterative optimization on the second model parameter data again. Compared with the photoresist threshold model established by traditional methods with only one iterative optimization, the corrected photoresist threshold model obtained by the method of the present invention can accurately predict the light leakage phenomenon of special patterns of the jogged type on the wafer while maintaining the model accuracy, avoiding the situation of over-correction of the model. At the same time, only the data of the jogged pattern is required for verification and optimization, and the difficulty of data acquisition is low. It has been verified that the photoresist threshold model of the present invention can more accurately predict such patterns with many corners and a high density represented by the jogged pattern.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular, to a method for optimizing and constructing a photoresist model and a method for constructing a lithography model. Background Art

[0002] Integrated circuit (chip) manufacturing transfers the patterns on the design layout to a silicon wafer through semiconductor manufacturing processes such as oxidation, lithography, etching, diffusion, epitaxial growth, thin film deposition, and electroplating according to the design requirements. Among them, lithography technology is responsible for accurately realizing the patterns on the layout on the silicon wafer and is a key process in integrated circuit manufacturing.

[0003] The lithography process can adopt an optical model and a photoresist model, and then use mathematical formulas to express the simulation, so that the results of the lithography process can be predicted and corrected through simulation, reducing the trial-and-error cost. Light diffracts when it shines on the mask, and the diffracted light of the corresponding order is collected by the projection lens and converges on the surface of the photoresist. This imaging process is an optical process and can be simulated by the optical model; the image projected on the photoresist stimulates a photochemical reaction, and the development reaction occurs in the local photoresist after baking. This is the photoresist chemical reaction process and can be simulated by the photoresist model. By using the method of calculation and simulation to build the optical model and the photoresist model in the lithography process, the morphology of the wafer after exposure can be simulated. Among them, there are 8 main parameters of the photoresist model, which need to be determined by a large amount of data fitting, and some special patterns that do not participate in the model iteration need to be used to verify the reliability of the model before a high-precision model can be obtained.

[0004] When actually using the optical model and the photoresist model to perform calculation and simulation on a PSM mask (phase shift mask) to correct the patterns on the mask, for the simulation of special patterns of the nested type, there may be a problem that there is no light leakage phenomenon on the actual wafer, but there is a light leakage phenomenon in the model simulation. In view of the above problems, the applicant has consulted a large number of relevant materials in this technical field, but has not found any relevant literature documenting the above problems, nor any relevant literature documenting how to avoid the above problems. Therefore, the model accuracy of the currently used photoresist model needs to be further improved. Summary of the Invention

[0005] An embodiment of the present invention provides a method for optimizing and constructing a photoresist model and a method for constructing a lithography model to solve the problem that in the scenario of using a PSM mask in the prior art, there is a light leakage phenomenon in the simulation of the constructed photoresist threshold model, while there is no light leakage phenomenon on the actual wafer, so as to improve the model accuracy and reliability.

[0006] According to a first aspect of the present invention, there is provided a method for optimizing a photoresist model, including:

[0007] Obtain a photoresist threshold model that has completed iterative optimization, and reduce the imaging light intensity curvature coefficient in the model parameter data of the obtained photoresist threshold model to obtain second model parameter data;

[0008] Perform iterative optimization on the second model parameter data to obtain third model parameter data, and generate a corrected photoresist threshold model according to the third model parameter data.

[0009] According to a second aspect of the present invention, there is provided a method for constructing a photoresist model, including:

[0010] Construct an initial photoresist threshold model and a photoresist threshold model optimization platform;

[0011] Perform iterative optimization on the model parameter data of the initial photoresist threshold model according to the pre-collected CDU measurement data and the photoresist threshold model optimization platform to obtain first model parameter data after iterative optimization, wherein the goal of the iterative optimization is to fit the model parameter data of the initial photoresist threshold model to find the best photoresist imaging plane;

[0012] Reduce the imaging light intensity curvature coefficient in the first model parameter data to obtain second model parameter data;

[0013] Perform iterative optimization on the second model parameter data according to the CDU measurement data and the photoresist threshold model optimization platform to obtain third model parameter data, wherein the goal of the iterative optimization is to fit the second model parameter data to find the best photoresist imaging plane;

[0014] Form a photoresist threshold model according to the third model parameter data.

[0015] According to a third aspect of the present invention, there is provided a method for constructing a lithography model, and a lithography model is formed according to the iteratively optimized optical model and the corrected photoresist threshold model, wherein the corrected photoresist threshold model is a photoresist threshold model corrected by using the photoresist threshold model correction method described in the first aspect above, or a photoresist threshold model constructed by using the photoresist threshold model construction method described in the second aspect above.

[0016] According to a fourth aspect of the present invention, there is provided a storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the photoresist model optimization method described in the first aspect above, the photoresist model construction method described in the second aspect above, or the lithography model construction method described in the third aspect above are implemented.

[0017] The method of the present invention, when modeling the photoresist threshold model, after the first iterative optimization of the photoresist threshold model, reduces the coefficient of the imaging light intensity curvature parameter in the parameter data of the obtained photoresist threshold model, and performs the second iterative optimization again to improve the accuracy of the obtained photoresist threshold model. The obtained photoresist threshold model can take into account the characteristics of the phase-shifting mask during simulation, thereby effectively avoiding the light leakage phenomenon simulated by the model. Compared with the traditional photoresist threshold model established by only one iterative optimization, the corrected photoresist threshold model obtained by the method of the present invention can more accurately predict the light leakage phenomenon of special patterns of the square-within-a-square type on the wafer while maintaining the model accuracy, thus avoiding the incorrect model simulation and light leakage. At the same time, the more accurate photoresist threshold model constructed by the present invention can be verified and optimized only by using the data of the square-within-a-square pattern, without using the data of other more complex special patterns for verification, and the difficulty of obtaining data is low. It is verified that the corrected photoresist threshold model established by the present invention can more accurately predict the simulation of this type of pattern with many and dense corners represented by the square-within-a-square pattern of different sizes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 Schematic flow chart of the method for constructing a photoresist model according to an embodiment of the present invention;

[0020] Figure 2 Schematic diagram of the CM1 model used for constructing the photoresist model in the method for constructing a photoresist model according to an embodiment of the present invention;

[0021] Figure 3 Schematic flow chart of the method for constructing an optical model for forming an initial photoresist threshold model in the method for constructing a photoresist model according to an embodiment of the present invention;

[0022] Figure 4 Parameters and simulation results used in the iterative optimization of the photoresist threshold model obtained by the conventional method of modeling;

[0023] Figure 5 Parameters and simulation results used in the iterative optimization of the photoresist threshold model obtained by the method of the present invention;

[0024] Figure 6Structural schematic diagram of an embodiment of the electronic device of the present invention. Detailed implementation manners

[0025] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0026] In the description of the present invention, it should be understood that if terms such as "center", "middle part", "longitudinal direction", "transverse direction", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial direction", "radial direction", "circumferential direction", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, so it cannot be understood as a limitation to the present invention. The features defined with "first" and "second" are used to distinguish the feature names and do not have special meanings. In addition, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0027] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "mounted", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0028] It also should be noted that in this article, the terms "comprise" and "include" not only include those elements, but also other elements not explicitly listed, or elements inherent to this process, method, article or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the existence of other identical elements in the process, method, article or device comprising the said elements. The terms used in this article are generally the commonly used terms by those skilled in the art. If they are inconsistent with the commonly used terms, the terms in this article shall prevail.

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] The present invention will be further described in detail below with reference to the accompanying drawings.

[0031] The present invention provides a method for optimizing a photoresist model, which mainly aims at a rare situation in the prior art that may occur during the simulation of the lithography process of a PSM mask for special patterns of the loop type, where light leakage occurs in the model but there is no light leakage on the actual wafer. Specifically, in the method of the present invention, it is necessary to first obtain a photoresist threshold model that has been iteratively optimized, and further correct it based on this model. When obtaining the photoresist threshold model that has been iteratively optimized, during modeling, an initial photoresist threshold model can be first constructed, and then it is iteratively optimized according to the traditional method. When the photoresist threshold model is completed with iterative optimization, the iteratively optimized photoresist threshold model can be obtained, and then the corresponding parameter data of the iteratively optimized photoresist threshold model can be obtained. Here, the iteratively optimized photoresist threshold model is referred to as the first model, and the corresponding parameter data of the iteratively optimized photoresist threshold model is referred to as the first model parameter data. After that, the coefficient of the imaging light intensity curvature parameter (imagecurvature) in the first model parameter data is reduced to form the second model parameter data, and the second model parameter data is iteratively optimized again to obtain the third model parameter data, that is, the parameter data of the corrected photoresist threshold model. Thus, according to the third model parameter data, the corrected photoresist threshold model can be formed. Specifically, when reducing the coefficient of the imaging light intensity curvature parameter of the first model parameter data, after verification, reducing the coefficient of the imaging light intensity curvature parameter to 50%-60% of the original can effectively avoid the above-mentioned rare situation that for loop-type patterns during simulation, light leakage occurs in the model but there is no light leakage on the actual wafer. When the coefficient of the imaging light intensity curvature parameter is reduced to less than 50% of the original, light leakage still occurs in the photoresist threshold model during simulation. When the coefficient of the imaging light intensity curvature parameter is reduced to more than 60% of the original, it will affect the prediction accuracy of the obtained photoresist threshold model for other patterns. In practical applications, preferably, the imaging light intensity curvature parameter is reduced to 50% of the original. When iteratively optimizing the second model parameter data, the iterative optimization process can be the same as the relevant process steps for iteratively optimizing the initial photoresist threshold model. Specifically, reference can be made to the relevant descriptions in the prior art, and this part of the content will not be elaborated in detail here.

[0032] Figure 1 Schematically shows a flowchart of a method for constructing a photoresist model according to an embodiment of the present invention. Referring to Figure 1 as shown, the method for constructing a photoresist model of the present invention includes the following steps:

[0033] Step S11: Construct an initial photoresist threshold model and a photoresist threshold model optimization platform;

[0034] Step S12: Iteratively optimize the parameter data of the initial photoresist threshold model according to the pre-collected CDU measurement data and the photoresist threshold model optimization platform to obtain the first model parameter data after iterative optimization, where the goal of the iterative optimization is to fit the model parameter data of the initial photoresist threshold model to find the best photoresist imaging plane;

[0035] Step S13: Reduce the imaging light intensity curvature coefficient in the first model parameter data to obtain the second model parameter data;

[0036] Step S14: Iteratively optimize the second model parameter data according to the CDU measurement data and the photoresist threshold model optimization platform to obtain the third model parameter data, where the goal of the iterative optimization is to fit the second model parameter data to find the best photoresist imaging plane;

[0037] Step S15: Form a photoresist threshold model according to the third model parameter data.

[0038] Step S11 is a step of obtaining or constructing a model or data for forming a photoresist threshold model. Among them, the initial photoresist threshold model is constructed based on an optical model. The photoresist threshold model optimization platform is used to simulate the mathematical approximation expression of the photolithography reaction, and its specific construction method can also be constructed according to the relevant descriptions in the prior art. The collected CDU measurement data (CDU, critical dimension uniformity) is the actual data obtained by actual measurement of the photoresist, and is used as a benchmark when iteratively optimizing the parameters of the photoresist threshold model. Specifically, before constructing the optical model and the photoresist threshold model, it is necessary to first collect wafer data. When collecting wafer data, first select the patterns required for model building on a reticle with various types of patterns. These patterns required for model building include the patterns required for building the optical model and the patterns required for building the photoresist model. Then, collect the FEM (exposure focal length - exposure energy matrix) pattern measurement data on the wafer required for the optical model to determine the best focal length and energy. Finally, under the exposure conditions of the determined best focal length and energy, collect the CDU pattern measurement data on the wafer required for the photoresist model.

[0039] Exemplarily, when constructing the photoresist threshold model, the CM1 (compact resist model) model building process is adopted to form the initial photoresist threshold model. The CM1 model refers to Figure 2 As shown, this process is a physicochemical process, which refers to the physical and chemical reactions that occur during the photoresist exposure process. The model expression of CM1 is as follows:

[0040]

[0041] Among them, , C i refers to the coefficient of each term in the formula; b refers to the threshold of the light intensity on the imaging surface of the photoacid / developer; k : 0 refers to the imaging surface, 1 refers to the slope of the imaging light intensity, and 2 refers to the imaging factor; n : 1 refers to the light intensity distribution, and 2 refers to the maximum light intensity; s : refers to the diffusion length of the photoacid / developer; p : the weight of the terms participating in the iteration; G refers to Gaussian convolution.

[0042] The above model expression uses mathematical terms to represent the various parameters involved in the photoresist exposure - development process.

[0043] After that, a photoresist threshold model optimization platform is constructed. Specifically, in the present invention, the constructed photoresist threshold model optimization platform preferably uses the Calibre tool software platform of Siemens. That is to say, the photoresist threshold model optimization platform can be implemented as using the Calibre tool of Siemens to optimize the initial photoresist threshold model. Calibre is a comprehensive physical verification system developed by Siemens EDA, mainly used for the verification of integrated circuit (IC) design. Calibre is widely regarded as one of the leading design rule check (DRC), layout versus schematic Figure 1 consistency check (LVS), and design for manufacturability (DFM) tools. In the photoresist threshold model optimization platform, modelform22 is specifically used for model iterative optimization. Modelform22 expands the above-mentioned initial photoresist threshold model mathematical formula again, and uses 8 parameters of M0 - M7 to perform mathematical iterative optimization on the photoresist threshold model. Among them, M0 refers to the photoresist imaging light intensity distribution; M1 and M2 refer to the short-range diffusion and long-range diffusion of the photoacid; M3 refers to the developer diffusion; M4 refers to the slope of the imaging light intensity of the photoresist; M5 and M6 respectively refer to the maximum light intensity and the minimum light intensity of the photoresist imaging distribution; M7 refers to the curvature of the photoresist imaging light intensity; MCOEF0 - 7 are the C i coefficients.

[0044] After obtaining the initial photoresist threshold model, the photoresist threshold model optimization platform, and the CDU data, first perform step S12. According to the CDU data and the photoresist threshold model optimization platform, perform multiple iterative optimizations on the data in the initial photoresist threshold model to find the optimal solution and output a photoresist threshold model, that is, find the best photoresist imaging surface. After the iterative optimization, the parameter data of the photoresist threshold model after iterative optimization can be obtained, and here it is referred to as the first model parameter data.

[0045] Then perform step S13, reduce the imaging light intensity curvature coefficient (i.e., parameter M7) in the first model parameter data to obtain the second model parameter data. Specifically, the imaging light intensity curvature coefficient can be reduced to 50%-60% of the original, and in this embodiment, the imaging light intensity curvature coefficient can be reduced to 50% of the original.

[0046] Then perform step S14, and again perform iterative optimization on the second model parameter data according to the CDU data and the photoresist threshold model optimization platform. After this iterative optimization, the third model parameter data is obtained.

[0047] Finally, perform step S15, and form a photoresist threshold model according to the obtained third model parameter data, that is, complete the construction of the photoresist threshold model.

[0048] In some possible implementation manners, when constructing the initial photoresist threshold model in step S11, it can be formed by outputting the optical model after iterative optimization. Figure 3 Schematically shows a flowchart of the construction method of the optical model for forming the initial photoresist threshold model in the photoresist model construction method according to an embodiment of the present invention. Refer to Figure 3 As shown, the construction method of this optical model can be specifically implemented as including the following steps:

[0049] Step S21: Establish an initial optical model optimization platform using the properties of the chemical materials on the wafer and the properties of the exposure optical system;

[0050] Step S22: Set the initial focus plane and establish an initial optical model;

[0051] Step S23: According to the FEM measurement data collected in advance and the optical model optimization platform, perform iterative optimization on the parameter data of the initial optical model to obtain the first optical model parameter data after iterative optimization. Among them, the goal of the iterative optimization is to fit the model parameter data of the initial optical model to find the best focus plane;

[0052] Step S24: Form an optical model after iterative optimization according to the first optical model parameter data.

[0053] Step S21 is the step of establishing an optical model optimization platform. When establishing the optical model optimization platform, an initial optical model optimization platform can be established by using the properties of various chemical materials on the wafer and the properties of the exposure optical system. The properties of these chemical materials and the exposure optical system include information such as photoresist and substrate parameters (thickness, refractive index, extinction coefficient) and optical system parameters (numerical aperture, illumination parameters).

[0054] Step S22 is the step of establishing an initial optical model. When establishing the initial optical model, an initial focusing platform can be set up to establish the initial optical model.

[0055] In step S23, according to the pre-collected FEM measurement data (FEM, Focus Energy Matrix, exposure focal length - exposure energy matrix), the collected FEM measurement data is the actual data obtained from actual optical experiments and is used as a benchmark for iterative optimization of various parameters of the optical model.

[0056] After that, step S24 is executed. The FEM measurement data and the parameter data of each item in the initial optical model are put into the optical model optimization platform constructed in step S21 for fitting iterative optimization to find the best focusing plane, so as to obtain the first optical model parameter data after iterative optimization. Finally, step S25 is executed to form the optical model after iterative optimization according to the first optical model parameter data.

[0057] Exemplarily, when constructing the optical model, the CTR (constant threshold model) model building process is adopted. This process is a physical process, and its principle is the iteration of mathematical formulas, that is, Intensity(x,y)=Threshold (where Threshold is a constant), that is, a relationship between the light intensity of the optical imaging plane composed of x and y and the threshold, so as to form an initial optical model. The specific process of CRT modeling can be implemented with reference to relevant existing technologies and will not be elaborated here.

[0058] The constructed optical model optimization platform is similar to the constructed photoresist threshold model, and can also be implemented using Siemens' Calibre tool software. Specifically, the various parameters of the optical model (including exposure wavelength, numerical aperture, ambient refractive index, light source information, film layer structure information on the wafer, etc.) can be used to perform model iteration optimization, and the above parameters are input into the software model of the Calibre tool. After multiple iterations of optimization, according to the FEM measurement data collected on the wafer, an optical imaging graph of a graph under different energies and focal lengths is output, and an optical imaging plane is obtained. At this point, the optical model is built. It is understandable that in this embodiment, the optical model optimization platform used can also be implemented using models of other software capable of iterative optimization in addition to Siemens' Calibre tool software, and the present invention is not limited to this.

[0059] Figure 4 and Figure 5 The comparison between the parameters and simulation results used in iterative optimization of the photoresist threshold model obtained by conventional modeling and the parameters and simulation results used in iterative optimization of the photoresist threshold model obtained by the method of the present invention is schematically shown. Figure 4 The parameters and simulation results used in the iterative optimization of the photoresist threshold model obtained by conventional modeling show that the value of the M7 parameter is 7.504075e-01. In the simulation results, light leakage occurs in the middle of the U-shaped figure, thereby exposing the figure in the middle where there is no figure, making the simulation result inaccurate. Figure 5 The parameters and simulation results of the photoresist threshold model obtained by the method of the present invention during iterative optimization, the value of its M7 parameter is 3.7504075e-01, which is 50% of the value of the M7 parameter used by the photoresist threshold model in the conventional method during iterative optimization, and the simulation results show that the light leakage phenomenon in the middle of the Chinese-shaped figure no longer exists. In fact, the photoresist threshold model obtained after correction by the method of the present invention can not only effectively improve the light leakage phenomenon of the PSM mask in the Chinese-shaped figure during simulation, but also effectively avoid the light leakage phenomenon of the PSM mask in special figures such as the Chinese-shaped figure with more and denser corners during simulation.

[0060] The method of the present invention, when modeling the photoresist threshold model, after the first iterative optimization of the photoresist threshold model, reduces the coefficient of the imaging light intensity curvature parameter in the parameter data of the obtained photoresist threshold model, and performs a second iterative optimization again to improve the accuracy of the obtained photoresist threshold model. The obtained photoresist threshold model can take into account the characteristics of the phase-shifting mask during simulation, thereby effectively avoiding the light leakage phenomenon simulated by the model. Compared with the photoresist threshold model established by only one iterative optimization in the traditional method, the corrected photoresist threshold model obtained by the method of the present invention can more accurately predict the light leakage phenomenon of special patterns of the square frame type on the wafer while maintaining the model accuracy, thus avoiding the incorrect model simulation and the occurrence of light leakage. At the same time, the more accurate photoresist threshold model constructed by the present invention can be verified and optimized only by using the data of the square frame pattern, without the need for data of other more complex special patterns for verification, and the difficulty of obtaining data is low. It is verified that the corrected photoresist threshold model established by the present invention can more accurately predict the simulation of patterns with many and dense corners represented by the square frame pattern of different sizes.

[0061] In some embodiments, the embodiment of the present invention provides a method for constructing a lithography model, which is set to form a lithography model according to the iteratively optimized optical model and the corrected photoresist threshold model. Among them, the corrected photoresist threshold model is the optimized photoresist threshold model obtained by adopting any one of the above embodiments.

[0062] In some embodiments, the embodiment of the present invention provides a non-volatile computer-readable storage medium, in which one or more programs including execution instructions are stored, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) for executing the photoresist model optimization and construction method and the lithography model construction method of any one of the above embodiments of the present invention.

[0063] In some embodiments, the embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is made to execute the photoresist model optimization and construction method and the lithography model construction method of any one of the above embodiments.

[0064] In some embodiments, the embodiments of the present invention further provide an electronic device, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the photoresist model optimization and construction method and the lithography model construction method of any of the above embodiments.

[0065] In some embodiments, the embodiments of the present invention further provide a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the photoresist model optimization and construction method and the lithography model construction method of any of the above embodiments.

[0066] Figure 6 FIG. is a schematic hardware structure diagram of an electronic device for executing the photoresist model optimization and construction method and the lithography model construction method provided by another embodiment of the present invention, as Figure 6 shown, the device includes:

[0067] One or more processors 610 and a memory 620, Figure 6 Taking one processor 610 as an example.

[0068] The device for executing the photoresist model optimization and construction method and the lithography model construction method may further include: an input device 630 and an output device 640.

[0069] The processor 610, the memory 620, the input device 630, and the output device 640 may be connected through a bus or other means, Figure 6 Taking the connection through a bus as an example.

[0070] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules corresponding to the photoresist model optimization and construction method and the lithography model construction method in the embodiments of the present invention. The processor 610 runs the non-volatile software programs, instructions, and modules stored in the memory 620, thereby executing various functional applications and data processing of the server, that is, implementing the photoresist model optimization and construction method and the lithography model construction method in the above method embodiments.

[0071] The memory 620 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the photoresist model optimization and construction method and the photolithography model construction method. In addition, the memory 620 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 620 may optionally include a memory remotely disposed relative to the processor 610, and these remote memories may be connected to the electronic device through a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0072] The input device 630 may receive input digital or character information and generate signals related to user settings and function controls of the image processing device. The output device 640 may include a display device such as a display screen.

[0073] The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform the photoresist model optimization and construction method and the photolithography model construction method in any of the above method embodiments.

[0074] The above product may execute the method provided in the embodiments of the present invention and has corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment may be referred to the method provided in the embodiments of the present invention.

[0075] The electronic device in the embodiments of the present invention exists in various forms, including but not limited to:

[0076] (1) Mobile communication devices: Such devices are characterized by having mobile communication functions and mainly aim to provide voice and data communication. Such terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones, etc.

[0077] (2) Ultra-mobile personal computer devices: Such devices belong to the category of personal computers, have computing and processing functions, and generally also have the characteristic of mobile Internet access. Such terminals include: PDAs, MIDs, and UMPC devices, etc., such as iPad.

[0078] (3) Portable entertainment devices: Such devices can display and play multimedia content. Such devices include: audio and video players (such as iPod), handheld game consoles, e-books, and smart toys and portable in-vehicle navigation devices.

[0079] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but due to the need to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, manageability, etc.

[0080] (5) Other electronic devices with data interaction functions.

[0081] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware.

[0083] Based on such an understanding, the above technical solution, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A method for optimizing a photoresist model, characterized in that The photoresist threshold model obtained by applying the method can avoid the situation of incorrect model simulation and light leakage, and it can be used in the graphic simulation scenario, so that when calculating and simulating the PSM mask, there will be no problem that there is no light leakage on the actual wafer but light leakage occurs in model simulation. The method includes: Obtain the photoresist threshold model that has completed iterative optimization, and use the corresponding parameter data of the photoresist threshold model that has completed iterative optimization as the first model parameter data; Reduce the imaging light intensity curvature coefficient in the first model parameter data to obtain the second model parameter data; Perform iterative optimization on the second model parameter data to obtain the third model parameter data, and generate a corrected photoresist threshold model according to the third model parameter data.

2. The photoresist model optimization method according to claim 1, wherein Reducing the imaging light intensity curvature coefficient includes setting the coefficient of the imaging light intensity curvature to 50%-60% of the original value.

3. The photoresist model optimization method according to claim 1, wherein Reducing the imaging light intensity curvature coefficient includes setting the coefficient of the imaging light intensity curvature to 50% of the original value.

4. A method for constructing a photoresist model, characterized in that, The photoresist threshold model obtained by applying the method can avoid the situation of incorrect model simulation and light leakage, and it can be used in the graphic simulation scenario, so that when calculating and simulating the PSM mask, there will be no problem that there is no light leakage on the actual wafer but light leakage occurs in model simulation. The method includes: Construct an initial photoresist threshold model and a photoresist threshold model optimization platform; Iteratively optimize the model parameter data of the initial photoresist threshold model according to the pre-collected CDU measurement data and the photoresist threshold model optimization platform to obtain the first model parameter data after iterative optimization. Among them, the goal of the iterative optimization is to fit the model parameter data of the initial photoresist threshold model to find the best photoresist imaging surface; Reduce the imaging light intensity curvature coefficient in the first model parameter data to obtain the second model parameter data; Iteratively optimize the second model parameter data according to the CDU measurement data and the photoresist threshold model optimization platform to obtain the third model parameter data. Among them, the goal of the iterative optimization is to fit the second model parameter data to find the best photoresist imaging surface; Form a photoresist threshold model according to the third model parameter data.

5. The method for constructing a photoresist model according to claim 4, wherein The imaging light intensity curvature coefficient in the second model parameter data is reduced to 50%-60% of the imaging light intensity curvature coefficient in the first model parameter data.

6. The method for constructing a photoresist model according to claim 4, wherein Reducing the imaging light intensity curvature coefficient includes setting the coefficient of the imaging light intensity curvature to 50% of the original value.

7. The method for constructing a photoresist model according to claim 4, wherein The construction of the initial photoresist threshold model is realized by forming the output of the obtained iteratively optimized optical model into the initial photoresist threshold model.

8. The method for constructing a photoresist model according to claim 7, wherein The iteratively optimized optical model is obtained by the following method: Establish an initial optical model optimization platform using the properties of the chemical materials on the wafer and the properties of the exposure optical system; Set the initial focus plane and establish an initial optical model; Iteratively optimize the model parameter data of the initial optical model according to the pre-collected FEM measurement data and the optical model optimization platform to obtain the first optical model parameter data after iterative optimization. Among them, the goal of the iterative optimization is to fit the model parameter data of the initial optical model to find the best focus plane; Form an optical model after iterative optimization according to the first optical model parameter data.

9. A method for constructing a lithography model, characterized in that, Form a lithography model according to the optical model after iterative optimization and the modified photoresist threshold model. The modified photoresist threshold model is a photoresist threshold model obtained after being modified by the photoresist model optimization method described in any one of claims 1 to 3 above, or a photoresist threshold model constructed by the photoresist model construction method described in any one of claims 4 to 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 9.

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