Photoresist model optimization and construction method and photoetching model construction method

Abstract

The invention discloses a photoresist model optimization and construction method and a photoetching model construction method.The photoresist model optimization method comprises the steps that after iterative optimization of a photoresist threshold model is completed, the coefficient of imaging light intensity curvature in first model parameter data obtained after iterative optimization is reduced to obtain second model parameter data; and iteratively optimizing the second model parameter data again. Compared with a traditional photoresist threshold model which is established only through one-time iterative optimization, the corrected photoresist threshold model obtained through the method can accurately predict the light leakage phenomenon of special patterns in the shape like the Chinese character'hui 'on the wafer on the premise that the model precision is kept, and the situation that the model is excessively corrected is avoided. And meanwhile, only data of the rectangular-ambulatory-plane graph is needed during verification and optimization, so that the data acquisition difficulty is low. It is verified that the photoresist threshold model can more accurately predict the patterns which are represented by the rectangular-ambulatory-plane pattern and have many corners and high density.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor manufacturing technology, and in particular to a photoresist model optimization and construction method and a photolithography model construction method. Background Art

[0002] Integrated circuit (chip) manufacturing is to transfer the graphics on the design layout to the 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 graphics on the layout on the silicon wafer, which is a key process in integrated circuit manufacturing.

[0003] The photolithography process can be simulated using optical models and photoresist models, and then expressed with the help of mathematical formulas, so that the results of the photolithography process can be predicted and corrected through simulation, reducing the cost of trial and error. Light is diffracted when it is irradiated on the mask, and the corresponding order of diffracted light is collected by the projection lens and converged on the surface of the photoresist. This imaging process is an optical process that can be simulated by an optical model; the image projected on the photoresist stimulates a photochemical reaction, and the development reaction of the local photoresist after baking is a photoresist chemical reaction process that can be simulated by a photoresist model. By using calculation and simulation methods to build optical models and photoresist models in the photolithography process, it is possible to simulate the morphology of the wafer after exposure. There are 8 main parameters of the photoresist model, which require a large amount of data fitting to determine, and some special graphics that do not participate in the model iteration are needed to verify the reliability of the model in order to obtain a high-precision model.

[0004] When the optical model and the photoresist model are actually used to calculate and simulate the PSM mask (phase shift mask) to correct the pattern on the mask, for the simulation of the special graphics of the U-shaped type, there may be a problem that there is no light leakage on the actual wafer, but there is light leakage during the model simulation. In response to the above problem, the applicant has consulted a large amount of relevant information in the technical field, but has not found any relevant literature recording the existence of the above problem, and no relevant literature recording how to avoid the above problem. Therefore, the model accuracy of the currently used photoresist model needs to be further improved. Summary of the invention

[0005] The embodiments of the present invention provide a photoresist model optimization and construction method and a photolithography model construction method to solve the problem in the prior art that in a scenario using a PSM mask, a photoresist threshold model constructed has light leakage during simulation, while there is no light leakage on the actual wafer, so as to improve the model accuracy and reliability.

[0006] According to a first aspect of the present invention, a photoresist model optimization method is provided, comprising: Acquire a photoresist threshold model that has completed iterative optimization, reduce the imaging light intensity curvature coefficient in the model parameter data of the acquired photoresist threshold model, and obtain second model parameter data; The second model parameter data is iteratively optimized to obtain third model parameter data, and a revised photoresist threshold model is generated according to the third model parameter data.

[0007] According to a second aspect of the present invention, a method for constructing a photoresist model is provided, comprising: Constructing the initial photoresist threshold model and photoresist threshold model optimization platform; Iteratively optimizing 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, 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 surface; Reducing the imaging light intensity curvature coefficient in the first model parameter data to obtain second model parameter data; Iteratively optimizing 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 surface; A photoresist threshold model is formed according to the third model parameter data.

[0008] According to a third aspect of the present invention, a method for constructing a lithography model is provided, wherein a lithography model is formed based on an iteratively optimized optical model and a corrected photoresist threshold model, wherein the corrected photoresist threshold model is a photoresist threshold model corrected by the photoresist threshold model correction method described in the first aspect, or a photoresist threshold model constructed by the photoresist threshold model construction method described in the second aspect.

[0009] According to a fourth aspect of the present invention, there is provided a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the photoresist model optimization method described in the first aspect, the photoresist model construction method described in the second aspect, or the photoresist model construction method described in the third aspect.

[0010] The method of the present invention is to reduce the coefficient of the imaging light intensity curvature parameter in the parameter data of the obtained photoresist threshold model after the photoresist threshold model is optimized for the first time in modeling the photoresist threshold model, and perform the second iteration optimization again to improve the precision of the obtained photoresist threshold model. The obtained photoresist threshold model can take into account the characteristics of the phase shift mask when simulating, so that the model can effectively avoid the light leakage phenomenon of simulation. Compared with the traditional photoresist threshold model established by only performing one iteration optimization, the modified photoresist threshold model obtained by the method of the present invention can more accurately predict the light leakage phenomenon of the special graphics of the round-shaped type on the wafer under the premise of maintaining the model accuracy, so as to avoid the situation that the model simulation is incorrect and light leakage occurs. At the same time, the more accurate photoresist threshold model constructed by the present invention can only need to use the data of the round-shaped graphics to verify the optimization effect, without the need for other more complicated special graphics data to verify, and its data acquisition difficulty is low. It has been verified that the modified photoresist threshold model established by the present invention can more accurately predict the simulation conditions of graphics of different sizes represented by U-shaped graphics with many and dense corners. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of a process for constructing a photoresist model according to an embodiment of the present invention; Figure 2 A schematic diagram of a model of CM1 used when constructing a photoresist model in a photoresist model construction method according to an embodiment of the present invention; Figure 3 A schematic flow chart of a method for constructing an optical model for forming an initial photoresist threshold model in a method for constructing a photoresist model in one embodiment of the present invention; Figure 4 The parameters and simulation results used in iterative optimization of the photoresist threshold model obtained by conventional modeling; Figure 5 The parameters and simulation results used in iterative optimization of the photoresist threshold model modeled by the method of the present invention; Figure 6 It is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0013] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0014] In the description of the present invention, it should be understood that if the terms "center", "middle", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which 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, and therefore cannot be understood as a limitation on the present invention. The features defined as "first" and "second" are used to distinguish the feature names, rather than having special meanings. In addition, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0015] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0016] It should also be noted that, in this article, the terms "include" and "comprise" include not only those elements, but also other elements not explicitly listed, or also include elements inherent to such processes, methods, articles or equipment. In the absence of further restrictions, the elements defined by the sentence "include..." do not exclude the existence of other identical elements in the process, method, article or equipment that includes the elements. The terms used in this article are generally commonly used by those skilled in the art. If there is any inconsistency with the commonly used terms, the terms in this article shall prevail.

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0019] The present invention provides a photoresist model optimization method, which is mainly aimed at the prior art, when simulating the photolithography process of the PSM mask for the special graphics of the U-shaped type, the model has a light leakage phenomenon, but there is no light leakage phenomenon on the actual wafer. The rare situation. 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 the model. When modeling, the obtained photoresist threshold model that has been iteratively optimized can first form an initial photoresist threshold model, and then iteratively optimize it according to the traditional method. When the photoresist threshold model completes the 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. 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 the first model parameter data. Afterwards, 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, i.e., the parameter data of the modified photoresist threshold model, so that the modified photoresist threshold model can be formed according to the third model parameter data. Specifically, when the coefficient of the imaging light intensity curvature parameter of the first model parameter data is reduced, it has been verified that reducing the coefficient of the imaging light intensity curvature parameter to 50%-60% of the original value can effectively avoid the above-mentioned rare situation that the model has light leakage during the simulation of the Chinese-shaped graphics, 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 value, the obtained photoresist threshold model still has light leakage during the simulation. When the coefficient of the imaging light intensity curvature parameter is reduced to more than 60% of the original value, it will affect the accuracy of the obtained photoresist threshold model in predicting other graphics. In practical applications, it is preferred to reduce the imaging light intensity curvature parameter to 50% of the original value. When iteratively optimizing the second model parameter data, the iterative optimization process can be consistent with the relevant process steps of iteratively optimizing the initial photoresist threshold model. For details, please refer to the relevant description in the prior art, and this part of the content will not be described in detail here.

[0020] Figure 1 The schematic diagram of the process of the photoresist model construction method according to one embodiment of the present invention is shown schematically. Figure 1 As shown, the photoresist model construction method of the present invention comprises the following steps: Step S11: constructing an initial photoresist threshold model and a photoresist threshold model optimization platform; Step S12: iteratively optimizing 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, 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 surface; Step S13: reducing the imaging light intensity curvature coefficient in the first model parameter data to obtain second model parameter data; Step S14: iteratively optimizing 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 surface; Step S15: forming a photoresist threshold model according to the third model parameter data.

[0021] 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 the optical model. The photoresist threshold model optimization platform is used to simulate the mathematical approximate expression of the photolithography reaction, and its specific construction method can also be constructed according to the relevant instructions in the prior art. The collected CDU measurement data (CDU, critical dimension uniformity, key line width uniformity) is the actual data obtained by actual experimental measurement of the photoresist, and is used as a benchmark for iterative optimization of various parameters of the photoresist threshold model. Specifically, before constructing the optical model and the photoresist threshold model, it is necessary to collect wafer data first. When collecting wafer data, first select the graphics required for modeling on a mask with multiple types of patterns, and the images required for modeling include the graphics required for building the optical model and the graphics required for building the photoresist model. Then, collect the FEM (exposure focal length-exposure energy matrix) graphic measurement data on the wafer required for the optical model to determine the optimal focal length and energy. Finally, under the exposure conditions determined according to the optimal focal length and energy, the CDU graphic measurement data on the wafer required by the photoresist model is collected.

[0022] For example, when constructing the photoresist threshold model, the CM1 (compact resist model) model building process is used to form an initial photoresist threshold model. Figure 2 As shown, this process is a physical and chemical process, which refers to the physical and chemical reaction that occurs during the photoresist exposure process. The model expression of CM1 is as follows:

[0023] in, , C i It refers to the coefficient of each term in the formula; b Refers to the light intensity threshold of the photoacid / developer imaging surface; k : 0 refers to the imaging surface, 1 refers to the imaging light intensity slope, and 2 refers to the imaging factor; n : 1 refers to the light intensity distribution, 2 refers to the maximum light intensity; s : refers to the diffusion length of photoacid / developer; p : The weight of the item involved in the iteration; G Refers to Gaussian convolution.

[0024] The above model expression uses mathematical terms to express various parameters involved in the photoresist exposure-development process.

[0025] After that, a photoresist threshold model optimization platform is constructed. Specifically, in the present invention, the constructed photoresist threshold model optimization platform preferably adopts Siemens' Calibre tool software platform, that is, the photoresist threshold model optimization platform can be implemented by using Siemens' Calibre tool software 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 the industry's leading design rule checking (DRC), layout and principle Figure 1 It is one of the tools for LVS and DFM. In the photoresist threshold model optimization platform, modelform22 is used for model iteration optimization. Modelform22 expands the above-mentioned initial photoresist threshold model mathematical formula again, and uses 8 parameters M0-M7 to perform mathematical iteration optimization on the photoresist threshold model, where M0 refers to the photoresist imaging light intensity distribution; M1 and M2 refer to the short-range diffusion and long-range diffusion of photoacid; M3 refers to the developer diffusion; M4 refers to the light intensity slope of photoresist imaging; M5 and M6 refer to the maximum light intensity and minimum light intensity of photoresist imaging distribution, respectively; M7 refers to the photoresist imaging light intensity curvature; MCOEF0-7 is the above-mentioned mathematical formula. C i coefficient.

[0026] After obtaining the initial photoresist threshold model, the photoresist threshold model optimization platform and the CDU data, step S12 is first performed to perform multiple iterations of optimization on the data in the initial photoresist threshold model according to the CDU data and the photoresist threshold model optimization platform, and find the optimal solution to output a photoresist threshold model, that is, find the best photoresist imaging surface. After iterative optimization, the parameter data of the iteratively optimized photoresist threshold model can be obtained, which is referred to as the first model parameter data.

[0027] Then, step S13 is performed to 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 value. In this embodiment, the imaging light intensity curvature coefficient can be reduced to 50% of the original value.

[0028] Then, step S14 is performed to iteratively optimize the second model parameter data again according to the CDU data and the photoresist threshold model optimization platform. After this iterative optimization, the third model parameter data is obtained.

[0029] Finally, step S15 is executed to form a photoresist threshold model according to the obtained third model parameter data, that is, the construction of the photoresist threshold model is completed.

[0030] In some possible implementations, when constructing the initial photoresist threshold model in step S11, the optical model output after iterative optimization may be used. Figure 3 The schematic diagram of the process flow 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 one embodiment of the present invention is schematically shown. Figure 3 As shown, the method for constructing the optical model can be specifically implemented by comprising the following steps: Step S21: establishing an initial optical model optimization platform using the properties of the chemical materials on the wafer and the properties of the exposure optical system; Step S22: setting an initial focus plane and establishing an initial optical model; Step S23: iteratively optimizing the parameter data of the initial optical model according to the FEM measurement data of the mobile phone in advance and the optical model optimization platform to obtain the first optical model parameter data after iterative optimization, wherein the goal of the iterative optimization is to fit the model parameter data of the initial optical model to find the best focusing plane; Step S24: forming an iteratively optimized optical model according to the first optical model parameter data.

[0031] Step S21 is a step of establishing an optical model optimization platform, wherein, when establishing the optical model optimization platform, the initial optical model optimization platform can be established by utilizing 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 properties of 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).

[0032] Step S22 is a step of establishing an initial optical model. When establishing the initial optical model, an initial focusing platform may be set to establish the initial optical model.

[0033] Step S23, based on the pre-collected FEM measurement data (FEM, Focus Energy Matrix, exposure focal length-exposure energy matrix), the collected FEM measurement data is actual data obtained from actual optical experimental measurements and is a benchmark for iteratively optimizing various parameters of the optical model.

[0034] Then, step S24 is executed to put the FEM measurement data and the various parameter data in the initial optical model 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, and finally step S25 is executed to form the iteratively optimized optical model according to the first optical model parameter data.

[0035] For example, when constructing an optical model, a CTR (constant threshold model) model building process is used. This process is a physical process, and its principle is the iteration of a mathematical formula, 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 value, to form an initial optical model. The specific process of CRT modeling can be implemented with reference to the relevant existing technology, and will not be repeated here.

[0036] 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.

[0037] 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.

[0038] The method of the present invention is to reduce the coefficient of the imaging light intensity curvature parameter in the parameter data of the obtained photoresist threshold model after the photoresist threshold model is optimized for the first time in modeling the photoresist threshold model, and perform the second iteration optimization again to improve the precision of the obtained photoresist threshold model. The obtained photoresist threshold model can take into account the characteristics of the phase shift mask when simulating, so that the model can effectively avoid the light leakage phenomenon of simulation. Compared with the traditional photoresist threshold model established by only performing one iteration optimization, the modified photoresist threshold model obtained by the method of the present invention can more accurately predict the light leakage phenomenon of the special graphics of the round-shaped type on the wafer under the premise of maintaining the model accuracy, so as to avoid the situation that the model simulation is incorrect and light leakage occurs. At the same time, the more accurate photoresist threshold model constructed by the present invention can only need to use the data of the round-shaped graphics to verify the optimization effect, without the need for other more complicated special graphics data to verify, and its data acquisition difficulty is low. It has been verified that the modified photoresist threshold model established by the present invention can more accurately predict the simulation conditions of graphics of different sizes represented by U-shaped graphics with many and dense corners.

[0039] In some embodiments, an embodiment of the present invention provides a method for constructing a photolithography model, which is configured to form a photolithography model based on an iteratively optimized optical model and a modified photoresist threshold model, wherein the modified photoresist threshold model is an optimized photoresist threshold model obtained by using any of the above embodiments.

[0040] In some embodiments, an embodiment of the present invention provides a non-volatile computer-readable storage medium, in which one or more programs including execution instructions are stored. 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.) to execute the photoresist model optimization and construction method and the lithography model construction method of any of the above embodiments of the present invention.

[0041] In some embodiments, an 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, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the photoresist model optimization and construction method and the lithography model construction method of any one of the above-mentioned embodiments.

[0042] In some embodiments, an embodiment of the present invention also provides an electronic device, comprising: 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 the instructions are executed by the at least one processor so that the at least one processor can execute the photoresist model optimization and construction method and the lithography model construction method of any of the above-mentioned embodiments.

[0043] In some embodiments, an embodiment of the present invention further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the photoresist model optimization and construction method and the photolithography model construction method of any of the above-mentioned embodiments.

[0044] Figure 6 is a schematic diagram of the hardware structure of an electronic device for executing a photoresist model optimization and construction method and a photolithography model construction method provided by another embodiment of the present invention, such as Figure 6 As shown, the device includes: One or more processors 610 and memory 620, Figure 6 A processor 610 is taken as an example.

[0045] The device for executing the photoresist model optimization and construction method and the photolithography model construction method may further include: an input device 630 and an output device 640 .

[0046] The processor 610, the memory 620, the input device 630 and the output device 640 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0047] 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 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 620, that is, realizing the photoresist model optimization and construction method and the lithography model construction method in the above method embodiments.

[0048] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application 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, etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely arranged relative to the processor 610, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0049] The input device 630 may receive input digital or character information and generate signals related to user settings and function control of the image processing device. The output device 640 may include a display device such as a display screen.

[0050] 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 lithography model construction method in any of the above method embodiments.

[0051] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0052] The electronic devices of the embodiments of the present invention exist in various forms, including but not limited to: (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0053] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0054] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0055] (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 because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0056] (5) Other electronic devices with data interaction functions.

[0057] The device embodiments described above are merely illustrative, wherein 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 on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware.

[0059] Based on this understanding, the above technical solution can essentially or the part that contributes to the relevant technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photoresist model optimization method, characterized in that: include: Acquire a photoresist threshold model that has completed iterative optimization, reduce the imaging light intensity curvature coefficient in the model parameter data of the acquired photoresist threshold model, and obtain second model parameter data; The second model parameter data is iteratively optimized to obtain third model parameter data, and a revised photoresist threshold model is generated according to the third model parameter data.

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

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

4. A method for constructing a photoresist model, characterized in that: include: Constructing the initial photoresist threshold model and photoresist threshold model optimization platform; Iteratively optimizing 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, 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 surface; Reducing the imaging light intensity curvature coefficient in the first model parameter data to obtain second model parameter data; Iteratively optimizing 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 surface; A photoresist threshold model is formed according to the third model parameter data.

5. The method for constructing a photoresist model according to claim 4, characterized in that: 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, characterized in that: Reducing the imaging light intensity curvature coefficient includes setting the imaging light intensity curvature coefficient to 50% of the original value.

7. The method for constructing a photoresist model according to claim 4, characterized in that: The construction of the initial photoresist threshold model is implemented by outputting the acquired iteratively optimized optical model as the initial photoresist threshold model.

8. The method for constructing a photoresist model according to claim 7, characterized in that: 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 focusing plane and establish the initial optical model; Iteratively optimizing 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 iteratively optimized first optical model parameter data, wherein the goal of the iterative optimization is to fit the model parameter data of the initial optical model to find the best focusing plane; An iteratively optimized optical model is formed according to the first optical model parameter data.

9. A method for constructing a photolithography model, characterized in that: A photolithography model is formed based on the iteratively optimized optical model and the modified photoresist threshold model, wherein the modified photoresist threshold model is a photoresist threshold model obtained by correction using the photoresist model optimization method described in any one of claims 1 to 3, or a photoresist threshold model constructed using 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, the steps of the method described in any one of claims 1 to 9 are implemented.

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