Patent Publication Number: US-11662665-B2

Title: Lithography method using multiscale simulation, and method of manufacturing semiconductor device and exposure equipment based on the lithography method

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims benefit of priority to Korean Patent Application No. 10-2021-0056221 filed on Apr. 30, 2021 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. 
     BACKGROUND 
     The present disclosure relates to a lithography method, and more particularly, to a lithography method using a simulation, and a method of manufacturing a semiconductor device and exposure equipment based on the lithography method. 
     Recently, as the width of semiconductor circuit lines becomes finer, a light source having a shorter wavelength is required for photolithography processes. For example, extreme ultraviolet (EUV) is commonly used as an exposure light source. Due to absorption characteristics of EUV, a reflective EUV mask is commonly used in an EUV exposure process. In addition, illumination optics for transmitting EUV to the EUV mask and projection optics for projecting EUV reflected from the EUV mask to an object to be exposed may each include a plurality of mirrors. As a level of difficulty of the exposure process gradually increases, the significance of development of the EUV resist used in a lithography process is also increasing. 
     SUMMARY 
     Example embodiments provide a lithography method of selecting an optimal resist, and a method of manufacturing a semiconductor device and exposure equipment based on the lithography method. 
     According to some example embodiments, a lithography method may include: estimating a shape of a virtual resist pattern for a selected resist based on a multiscale simulation; forming a test resist pattern by performing an exposure process on a layer formed of the selected resist; determining whether an error range between the test resist pattern and the virtual resist pattern is in an allowable range; and forming a resist pattern on a patterning object using the selected resist when the error range is in the allowable range. The estimating of the virtual resist pattern may include: selecting a material composition for the selected resist; modeling a unit lattice cell of the selected resist using a molecular scale simulation; calculating a dissociation energy curve of a photo-acid generator (PAG) for the material composition, a reaction rate constant of an acid-base neutralization reaction, and a reaction rate constant of deprotection of a molecular chain included in the material composition using a quantum scale simulation; simulating the acid-base neutralization reaction, acid/base diffusion, and deprotection of the molecular chain using a continuum scale simulation; calculating solubility of the molecular chain after the deprotection; forming the virtual resist pattern by stabilizing a pattern formed after a soluble molecular chain is removed from the unit lattice cell; estimating the shape of the virtual resist pattern; and calculating a numerical value for the shape of the virtual resist pattern. 
     According to some example embodiments, a method of manufacturing a semiconductor device may include: estimating a shape of a virtual resist pattern for a selected resist based on a multiscale simulation; forming a test resist pattern by performing an exposure process on a layer formed of the selected resist; determining whether an error range between the test resist pattern and the virtual resist pattern is in an allowable range; forming a resist pattern on a patterning object using the selected resist pattern when the error range between the test resist pattern and the virtual resist pattern is in the allowable range; forming a pattern by etching the patterning object using the resist pattern as an etching mask; and performing a subsequent semiconductor process after the pattern is formed. The estimating of the virtual resist pattern may include: selecting a material composition for the selected resist; modeling a unit lattice cell of the selected resist using a molecular scale simulation; calculating a dissociation energy curve of a photo-acid generator (PAG) for the material composition, a reaction rate constant of an acid-base neutralization reaction, and a reaction rate constant of deprotection of a molecular chain included in the material composition using a quantum scale simulation; simulating an acid-base neutralization reaction, acid/base diffusion, and deprotection of the molecular chain using a continuum scale simulation; calculating solubility of the molecular chain after the deprotection; forming the virtual resist pattern by stabilizing a pattern, formed after a soluble molecular chain is removed from the unit lattice cell; estimating the shape of the virtual resist pattern; and calculating a numerical value for the shape of the virtual resist pattern. 
     According to some example embodiments, exposure equipment may include: a simulation device including processing circuitry configured to estimate a shape of a virtual resist pattern of a selected resist by performing a quantum scale simulation based on a dissociation energy curve of a photo-acid generator (PAG) of the selected resist, a reaction rate constant of an acid-base neutralization reaction of the selected resist, and a reaction rate constant of deprotection of a molecular chain included in the selected resist, performing a molecular scale simulation based on a unit lattice cell of the selected resist, the unit lattice modeled, at a molecular level, based on a mixture including a molecular chain, the PAG, and a quencher, and performing a continuum scale simulation of an acid-base neutralization reaction, acid/base diffusion, and deprotection reaction; an exposure device configured to perform an exposure process to form a test resist pattern based on a result from the simulation device; and a measurement device configured to measure the test resist pattern and to compare the test resist pattern with the virtual resist pattern. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The above and other aspects, features, and advantages of the present disclosure will be more clearly understood from the following detailed description, taken in conjunction with the accompanying drawings. 
         FIG.  1    is a schematic flowchart illustrating a lithography method using a multiscale simulation according to some example embodiments. 
         FIG.  2    is a conceptual diagram for describing the lithography method of  FIG.  1    in term of a comparison between experimental data and a simulation result. 
         FIG.  3 A  is a flowchart illustrating an operation of predicting a shape of a virtual resist pattern of  FIG.  1   , and  FIG.  3 B  is a flowchart illustrating an operation of forming a resist pattern of  FIG.  1   . 
         FIGS.  4 A and  4 B  are a flowchart illustrating a correlation between simulations used in an operation of predicting a shape of a virtual resist pattern of  FIG.  3 B  and a graph illustrating a comparison between scales treated in the simulations, respectively. 
         FIGS.  5 A and  5 B  are conceptual diagrams of a unit lattice cell of a resist. 
         FIGS.  6 A to  6 F  are conceptual diagrams illustrating acid/base diffusion, acid-base neutralization reaction, and deprotection. 
         FIGS.  7 A and  7 B  are views illustrating a comparison between a result of a multiscale simulation of a 2-component system and a result of a multiscale simulation of a 3-component system. 
         FIGS.  8 A and  8 B  are views illustrating LER obtained by a multiscale simulation of a 2-component system and LER obtained by a multiscale simulation of a 3-component system, respectively. 
         FIG.  9    is a flowchart illustrating a method of manufacturing a semiconductor device based on the lithography method of  FIG.  1    according to some example embodiments. 
         FIG.  10    is a block diagram of exposure equipment based on the lithography method of  FIG.  1    according to some example embodiments. 
         FIG.  11 A  is a block diagram illustrating a simulation device in the exposure equipment of  FIG.  10   , and  FIG.  11 B  is a block diagram illustrating an exposure device in the exposure equipment of  FIG.  10   . 
         FIGS.  12 A to  12 C  are schematic cross-sectional views illustrating a method of manufacturing a semiconductor device according to an example embodiment. 
         FIG.  13    is a schematic cross-sectional view illustrating an extreme ultraviolet (EUV) photomask for manufacturing a semiconductor device according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, example embodiments will be described with reference to the accompanying drawings. 
       FIG.  1    is a schematic flowchart illustrating a lithography method using a multiscale simulation according to some example embodiments. 
     Referring to  FIG.  1   , a lithography method using multiscale simulation according to the example embodiment (hereinafter simply referred to as “lithography method”) may start with operation S 110  of estimating a shape of a virtual resist pattern based on a multiscale simulation. The multiscale simulation may be a simulation in which simulations of different scales are organically integrated with each other. For example, in the multiscale simulation, a quantum scale simulation, a molecular scale simulation, and/or a continuum scale simulation may be organically integrated with each other. In the multiscale simulation, a result of the quantum scale simulation may be used for the molecular scale simulation and/or the continuum scale simulation, a result of the molecular scale simulation may be used for the continuum scale simulation, and a result of the continuum scale simulation may be used for the molecular scale simulation and accordingly, the simulations may be organically connected to each other. 
     The quantum scale simulation may be a simulation and/or modeling of a chemical reaction accompanying a change in electronic structure. For example, the quantum scale simulation may include simulations of dissociation energy of a photo-acid generator (PAG) by a secondary electron, a reaction rate constant of an acid-base neutralization reaction, a reaction rate constant of deprotection, and/or the like. The molecular scale simulation may be a simulation on an atomic and/or molecular level. For example, the molecular scale simulation may include a simulation for modeling a unit lattice cell on the molecular level, a simulation for detecting a combination structure between a polymer chain and a PAG in a unit lattice cell, and/or a simulation for detecting a protection group position, an acid concentration profile, and/or a quencher concentration distribution. 
     The continuum scale simulation may be a simulation for and/or modeling of physical and/or chemical phenomena accompanying a continuous change in material properties in time and space. For example, the continuum scale simulation may include a simulation for acid/base diffusion, a simulation for an acid-base neutralization reaction, and/or a simulation for a deprotection reaction. The term “continuum” refers to a material having characteristics maintained overall even when the material is infinitely divided into small elements. In such a continuum concept, it may be ignored that the material is not continuous, is formed of atoms, and/or has a non-uniform microstructure. 
     In some embodiments, during the operation S 110 , the virtual resist pattern may be formed while changing model parameters for resist using the multiscale simulation, and/or a shape of a corresponding virtual resist pattern may be estimated. In operation S 110 , the simulation may be repeated until the shape of the virtual resist pattern reaches a shape of a required object resist pattern. 
     Example embodiments of operation S 110  will be described in more detail with reference to  FIG.  3 A . 
     The lithography method according to the present embodiment may include all types of lithography processes including an exposure process. For example, the lithography method according to the present embodiment may include an extreme ultraviolet (EUV) lithography method, an ArF-Immersion lithography method, an ArF lithography method, a KrF lithography method, an electron beam lithography method, an ion-beam lithography method, and/or a neutron beam lithography method. Hereinafter, for ease of description, the EUV lithography method will be mainly described. 
     After estimating the shape of the virtual resist pattern, in operation S 120 , a resist is selected based on a simulation result, and an exposure process is performed on a layer, formed of the selected resist, to form a test resist pattern. The resist may be, for example, an EUV resist and the exposure process may be performed using EUV. However, the resist is not limited to an EUV resist and the exposure process is not limited to an exposure process performed using EUV. A process of forming the test resist pattern may be the same as a subsequent process of forming a resist pattern on an object to be patterned (hereinafter referred to as a “patterning object), except that the resist pattern is formed on a test substrate. 
     After forming the test resist pattern, the test resist pattern is compared with the virtual resist pattern and a determination is made as to whether an error is in an allowable range, in operation S 130 . The test resist pattern may be compared with the virtual resist pattern by comparing various values with each other. For example, a critical dimension (CD), line edge roughness (LER), line width roughness (LWR), and local CD uniformity (LCDU) may be compared with each other. The allowable range may be arbitrarily set by a user in consideration of a process error in an actual lithography process. For example, an error of 10% or less may be set as the allowable range. 
     When the error is in the allowable range (YES), the lithography method proceeds to operation S 140  in which the resist pattern may be formed on the patterning object using the resist. The exposure process may be, for example, an EUV exposure process, and the patterning object (see W of  FIG.  11 B ) may be an object on which a pattern is to be formed later using a resist pattern. For example, the patterning object W may be and/or include a wafer and/or a mask for manufacturing a plurality of semiconductor devices. 
     Example embodiments of the operation S 140  will be described in more detail with reference to  FIG.  3 B . 
     When the error is outside of the allowable range (NO), the lithography method proceeds to operation S 150  in which the model parameters of the multiscale simulation are changed. The model parameters may be model parameters used for each simulation in the multiscale simulation. For example, the model parameters may be calculation formulas used in each simulation or parameters included in the calculation formulas. As described above, changing the model parameters may result in different result values of the multiscale simulation for the same resist material composition. 
     After changing the model parameters, the lithography method proceeds to operation S 110  of estimating of the shape of the virtual resist pattern, and subsequent operations are performed. 
     In the lithography method according to the present embodiment, through the multiscale simulation in which the quantum scale, the molecular scale, and/or the continuum scale are integrated, physical phenomena on a multilevel, such as acid activation, acid/base diffusion, an acid-base neutralization reaction and/or a change in concentration of the acid caused by the neutralization reaction, deprotection, a change in solubility of a polymer chain, and/or control of acid diffusion by base in unexposed regions, may be simulated. Therefore, in the lithography method according to the present example embodiments, selection of a structure or a material composition of the resist to patterning of the resist (for example, resist manufacturing or selection→exposure→post-exposure bake (PEB)→developing) may all be performed together and may significantly reduce development time and costs of lithography resist. In addition, the lithography method according to the present example embodiments may overcome limitations of conventional single-scale simulations by significantly improving the consistency for estimations of resist pattern shapes (e.g., as compared with the single scale simulations). 
     For reference, in an interpretation method of simulating a lithography process using an individual scale simulation and estimating a resist pattern based on a Kinetic Monte Carlo (KMC) simulation (e.g., a meso scale simulation) a photoresist polymer chain may be replaced with a lattice model and the movements of acid molecules between respective lattice regions are simulated using a random walk algorithm. However, in the KMC interpretation model, a photochemical reaction such as exposure→PAG dissociation→acid activation may not be precisely simulated. Moreover, since an amorphous polymer chain is replaced with a cubic structure, a pattern shape of several nanometers may not be precisely estimated. 
     In addition, in an interpretation model based on a finite difference method (FDM) simulation (e.g., a continuum scale simulation) a photoresist is replaced with a volume element array, and then chemical reaction progresses in the respective elements are calculated using an Arrhenius equation. However, the FDM interpretation model may suffer from fundamental limitations such as requirement for preceding experimental data to apply a chemical reaction rate constant in the Arrhenius equation and impossibility to simulate a photochemical reaction such as exposure→PAG dissociation→acid activation, the beginning of the chemical reaction. In addition, after the photochemical reaction, a polymer chain may not be removed the pattern shape may not be estimated by a development process, which is significantly problematic. 
     Meanwhile, in the lithography method according to the present embodiment, physical phenomena on a multilevel, such as acid activation, acid/base diffusion, an acid-base neutralization reaction, a change in concentration of acid caused by the neutralization reaction, deprotection, and/or a change in solubility of a polymer chain may be simulated together in a resist including a 3-component system through a multiscale simulation. Thus, the limitations of the conventional single scale simulations may be overcome to significantly improve consistency for the estimation of the resist pattern shape, as compared with the single scale simulations. 
       FIG.  2    is a conceptual diagram for describing the lithography method of  FIG.  1    in term of a comparison between experimental data and a simulation result. The description previously provided with reference to  FIG.  1    will be simplified or omitted. 
     Referring to  FIG.  2   , in the lithography method according to the present example embodiments, in operation S 210 , a resist is selected based on a simulation result. For example, the resist may be selected using a multiscale simulation. For a multiscale simulation, operation S 201  of integrating simulations of different scales through simulation setup and operation S 202  of setting model parameters for a resist to be used in the multiscale simulation may be performed in advance. 
     In operation S 230 , an exposure process is performed using the selected resist to form a resist pattern, and experimental data on the resist pattern is obtained. The experimental data may be, for example, CD measurement values for the resist pattern. However, the experimental data are not limited to CD measurements on the resist patterns. 
     In operation S 250 , the experimental data is compared with data on a virtual resist pattern estimated through the multiscale simulation. When an error (e.g., a difference between the experimental data and simulation results and/or the deviations in the CD measurement values) is in an allowable range (e.g., are within operation tolerances) through the comparison (PASS), the resist selection based on the multiscale simulation is verified as valid, in operation S 270 . The data on the virtual resist pattern may be similar to data on a desired object resist pattern. 
     On the other hand, when the error is outside of the allowable range through the comparison (FAIL), model parameters in the multiscale simulation are changed, and the operation of selecting a resist using the multiscale simulation is performed again. 
     For reference, since most developments of a conventional resist are selected based on an experimental test (for example, resist manufacturing→exposure→PEB→developing→scanning electron microscopy (SEM) measuring), too long a development period and/or too high costs may be required. In addition, in a conventional simulation method, since a large amount of random parameter may be included in a portion in which resist is patterned, a simulation of a simple equation is used, and the simulation is interlocked with a semi-empirical model including an experimental scanning electron microscopy (SEM) image, a development period may be long and/or the consistency of estimation of the shape of the resist pattern is not high. 
     On the other hand, in the lithography method according to the present example embodiments, a structure and concentration of a resist material (for example, a polymer chain, a PAG, a quencher, a surfactant, or the like) may be optimized using a multiscale simulation interlocked between different scales, and characteristics (for example, CD, LER, LWR, LCDU, and the like) of the resist pattern may be calculated after simulation of PEB and/or a development process. Thus, prescreening may be performed before an experimental test, and/or the resist developing time and costs may be significantly reduced by securing consistency for a resist development solution and significantly reducing tests through integration of the experimental test with the multiscale simulation. Furthermore, in the lithography method according to the present example embodiments, use of a random parameter may be significantly reduced and the parameter may be calculated in association with the other scale parameters, based on the multiscale simulation. Thus, reliability of a calculation result may be secured and, distortion of an SEM noise level may be significantly reduced, thereby significantly improving consistency of a 3D pattern profile of the resist. 
       FIG.  3 A  is a flowchart illustrating an operation of predicting a shape of a virtual resist pattern of  FIG.  1   , and  FIG.  3 B  is a flowchart illustrating an operation of forming a resist pattern of  FIG.  1   . The descriptions previously provided with reference to  FIGS.  1  and  2    will be simplified or omitted. 
     Referring to  FIG.  3 A , operation S 110  of estimating the shape of the virtual resist pattern S 110  includes operation S 111  in which a material composition of a resist to be simulated is selected to determine a resist. For example, the material composition of the resist may include a molecular weight of a polymer chain, a kind of a protection group of the polymer chain, a kind and a mixing ratio of PAG molecules, a kind and a mixing ratio of a quencher, a kind and mixing ratio of other resist material, and/or the like. However, the material composition of the resist is not limited to the above examples. 
     In operation S 112 , a unit lattice cell of the selected resist is modeled using a molecular scale simulation. The molecular scale simulation may be, for example, a molecular dynamics (MD) simulation. The unit lattice cell may be modeled by mixing a polymer chain, a PAG, and/or a quencher. The unit lattice cell may further other components such as a surfactant. A unit lattice cell of a resist may be modeled from a system including at least three kinds of component. 
     A corresponding unit lattice cell may be stabilized by applying a conjugate gradient method, and then may reach an equilibrium state under a corresponding process condition. 
     In operation S 113 , a PAG dissociation energy curve, a reaction rate constant of an acid-base neutralization reaction, a reaction rate constant of deprotection are calculated using a quantum scale simulation. The quantum scale simulation may be, for example, a density function theory (DFT) simulation. The PAG dissociation energy curve in accordance with secondary electron absorption occurring in exposure may be calculated by stabilizing a molecular structure. 
     The dissociation energy curve of the PGA may be applied to a force field between PAG cations and anions in an exposed region of the lattice unit cell (e.g., modeled by the molecular scale simulation), and may be reproduced as a dissociation reaction of the PAG in an exposed region in an NPT ensemble. Accordingly, in operation S 112  of modeling the unit lattice cell of the selected resist, the PAG dissociation energy curve may be used to simulate the PAG dissociation reaction and/or to obtain acid concentration distribution information. In some embodiments, the reaction rate constant of the acid-base neutralization reaction may be calculated under an assumption that the same amount of acid and base are neutralized to disappear through the acid-base neutralization reaction as soon as the acid and the base react with each other. The reaction rate constant of the acid-base neutralization reaction may be used to simulate the acid-base neutralization reaction in a continuum scale simulation. 
     The reaction rate constant of deprotection may be calculated by applying, for example, energy and a vibrational frequency of main molecules (for example, a protection group and an acid molecule) to a transition state theory. For example, the reaction rate constant of deprotection (e.g., the rate of cleaving and/or removing of the protection group from the main molecule) may be used to simulate the deprotection in the continuum scale simulation. 
     In operation S 114 , the acid-base neutralization reaction, the acid-base diffusion, and deprotection are simulated using a continuum scale simulation. The continuum scale simulation may be, for example, a finite difference method (FDM) simulation. 
     In operation S 112  of modeling the unit lattice cell of the selected resist (e.g., through an MD simulation), an acid concentration profile activated from the PAG anions may be quantified and/or positions of the protection group, the acid, and/or the quencher in the unit lattice cell may be calculated. In the continuum scale simulation, a lattice cell of a continuum may be modeled by mapping the positions of the protection group, the acid, and the acid concentration profile from the unit lattice cell of the molecular scale. 
     In operation S 115 , solubility of the polymer chain in the unit lattice cell depending on the deprotection is calculated. The solubility of the polymer chain may be calculated for each chain by obtaining an arithmetic mean value of a protection ratio of the protection group incorporated in the polymer chain. When the protection ratio of the polymer chain is less than or equal to a specific reference value, a developing solvent may be set to be soluble. 
     In operation S 116 , the soluble polymer chain is removed from the unit lattice cell according to the solubility, and then a virtual resist pattern is formed. The virtual resist pattern may be formed by stabilizing unit lattice cells remaining after removing a polymer chain selected to be soluble from a unit lattice cell. 
     In operation S 117 , the shape of the virtual resist pattern is estimated and a numerical value for the shape of the virtual pattern is calculated. The estimation of the shape of the virtual resist pattern may be estimation of a shape and/or a size of the virtual resist pattern. In addition, the numerical value for the shape of the virtual resist pattern may include CD, LER, LWR, and/or LCDU. However, the numerical value for the shape of the virtual resist pattern is not limited thereto. Ability of patterning the resist material selected by calculating the estimation and the numerical value for the shape of the virtual resist pattern may be quantified. 
     In operation S 118 , a determination is made as to whether the shape of the virtual resist pattern is the same as and/or within an allowable range of an object resist pattern. When the shape of the virtual resist pattern is the same as and/or within the allowable range of the shape of the object resist pattern (YES), the flow proceeds to operation S 120  in which the test resist pattern of  FIG.  1    is formed. When the shape of the virtual resist pattern is different from and/or outside the allowable range of the shape of the object resist pattern in the allowable range (NO), the flow returns to operation S 111  in which the material composition of the resist is selected, and thus, subsequent operations are repeated. 
     Referring to  FIG.  3 B , operation S 140  of forming the resist pattern on the patterning object includes operation in which the selected resist is coated on an object to be patterned (hereinafter referred to as a “patterning object”) (see W of  FIG.  12 B ). The selected resist may be, for example, an EUV resist. In some example embodiments, the selected resist may be coated on the patterning object W by a spin coating process. 
     After the resist is applied, the resist is exposed by an exposure device (see  200  of  FIG.  11 B ), in operation S 144 . The exposure device  200  may be, for example, an EUV exposure device. Chemical characteristics of the resist may be changed through a series of reactions such as PAG dissociation, acid activation and diffusion, an acid-base neutralization reaction, and/or deprotection by an exposure process. Such a process may be simulated through a multiscale simulation. 
     In operation S 146 , a post-exposure-bake (PEB) is performed on the exposed resist. After the PEB process is performed, the resist may be developed by a developing solvent to complete a test resist pattern. In the development process, a soluble polymer chain in the developing solvent may be removed to form a final test resist pattern. The developing solvent may include a polar and/or a non-polar solvent. The solubility of the polymer chain and solubility of the polymer chain in the development solvent in accordance with the solubility of the polymer chain may be determined based on a protection ratio of the protection group. The protection ratio and the solubility of the polymer chain in accordance with the solubility of the polymer chain will be described in more detail with reference to  FIGS.  6 A to  6 F . 
       FIGS.  4 A and  4 B  are a flowchart illustrating a correlation between simulations used in an operation of predicting a shape of a virtual resist pattern of  FIG.  3 B  and a graph illustrating a comparison between scales treated in the simulations, respectively. The descriptions previously provided with reference to  FIGS.  1  and  2    will be simplified or omitted. 
     Referring to  FIGS.  4 A and  4 B , a lithography method according to the present embodiment will be described through the respective scale simulation processes. 
     In a quantum scale simulation operation S 310 , PAG dissociation caused by the generation of secondary electrons after exposure and an acid-base neutralization reaction are simulated to calculate material properties such as a PAG dissociation energy curve S 310 -R 1  and a reaction rate constant of the acid-base neutralization reaction S 310 -R 2  required in a molecular scale simulation. In addition, a deprotection reaction between the activated acid and a protection group inside a polymer chain may be simulated to calculate physical properties (such as a deprotection reaction rate constant S 310 -R 3  used in a continuum scale simulation). For example, molecular structures of a reactant, a product, and/or a transition state may be estimated using a DFT simulation to calculate physical properties. In addition, while stabilizing each of the molecular structures to calculate a dissociation energy curve of PAG, energy and a vibration frequency in a structure of main molecules may be applied to a transition state to calculate a reaction rate constant of deprotection. 
     In a molecular scale simulation operation S 330 , a unit lattice cell of a selected resist is modeled at a molecular level in accordance with a material composition (for example, kinds, amounts, and mixing ratios of a polymer chain, PAG, and/or a quencher), and an acid concentration profile, a base concentration profile, and/or a protection group position S 330 -R 1  used in the continuum scale simulation are calculated. In addition, the soluble polymer chains selected in the continuum scale simulation may be removed to form and stabilize a shape of a final virtual resist pattern. For example, by performing an MD simulation, an amorphous polymer chain having a specific protection ratio, a PAG molecule, and a quencher molecule may be mixed to constitute a unit lattice cell. Then, a dissociation energy curve of the PAG calculated in the quantum scale simulation may be applied to simulate a dissociation reaction of the PAG, and position information of the observed major molecules (for example, the protection group, the acid, and/or the quencher) may be transmitted to the continuum scale simulation. 
     An initial acid concentration and an initial quencher concentration on each node of a unit lattice cell may be derived as a function in accordance with a distance between the protection group and the PAG anion and a function in accordance with a distance between the PAG anion and the quencher, respectively. Interaction energy E int  in accordance with a distance “r” between reactors of respective chemical reactions may be obtained through molecular dynamics calculation, as illustrated in the following equations 1 and 2, and a local concentration of the acid f acid (r) and a concentration of the quencher f quencher (r) may be represented as a difference ΔE int  between Boltzmann activation energy in a chemical reaction and energy in a most stabilized structure to be obtained as illustrated in the following equations 3 and 4. An initial acid concentration concentrated in the exposed region of the unit lattice cell, an initial quencher concentration distributed over the exposed region, and/or an unexposed region may be quantified through the following equations 1 to 4. The initial acid concentration and the initial quencher concentration calculated in such a manner may be applied to A and Q in the following equations 5 and 6, as an initially quantified concentration of a nodal point of each material.
 
 E   int,acid   =E   all   −E   PR   −E   PAG   Equation 1
 
 E   int,quencher   =E   all   −E   PAG   −E   quencher   Equation 2
 
 f   acid ( r )=exp (Δ E   int,quencher ( r )/ k   B   T )  Equation 3
 
 f   quencher ( r )=exp (Δ E   int,quencher ( r )/ k   B   T )  Equation 4,
 
     where E all , E PR , E PAG , and E quencher  may denote energies of an entire system, a polymer chain, a PAG, and a quencher, respectively, k B  may denote a Boltzmann&#39;s constant, and T may denote temperature. 
     In a continuum scale simulation operation S 350 , a position of the protection group, a concentration profile of the activated acid, and a concentration profile of the quencher may be mapped from the unit lattice cell of the resist to model a lattice cell of the continuum. In the continuum scale simulation operation S 350 , FDM simulation(s) may be used, and acid/base diffusion, an acid-base neutralization reaction, and/or a deprotection reaction may be simulated. 
     For example, the acid/base diffusion and the acid-base neutralization may be simulated through Equations 5 and 6, Fick&#39;s second law, and the deprotection reaction between the protection group and the acid may be simulated by the following Equation 7, an Arrhenius equation. For example, in the simulation, the acid concentration and the quencher concentration may be changed by simulating diffusion and/or neutralization reaction(s) of the acid and quencher in a lattice cell, a continuum, based Equations 5 and 6, and a protection ratio of the protection group may be changed based on Equation 7. In this case, time iteration may be performed by applying an explicit method, and a boundary condition, in which acid and base are not introduced and discharged on a cell outer surface, may be applied. 
     
       
         
           
             
               
                 
                   
                     
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                     7 
                     ) 
                   
                 
               
             
           
         
       
     
     where A denotes an acid concentration, D A  denotes a diffusion coefficient of acid, Q denotes a concentration of a quencher, D Q  denotes a diffusion coefficient of the quencher, k quen  denotes a reaction rate constant of an acid-base neutralization reaction, R denotes a protection ratio of a protection group, k p  denotes a reaction rate constant of deprotection, and A pro  denotes a local acid concentration in the protection group. 
     The protection ratio may decrease from 1 to 0 during a deprotection reaction. For example, as illustrated in Equations 5 and 6, a model of a continuum may be configured to reflect the concentrations of the acid and the quencher decreased by the acid-base neutralization reaction through k quen AG and to reflect diffusion of the acid and the quencher into a photoresist at diffusion coefficients of D A  and D Q , by way of the acid concentration A and the quencher concentration Q. In Equation 7, a model of a continuum may be configured to obtain a local concentration A pro  in a position of the protection group by trilinear interpolation for the acid concentration A derived from Equations 5 and 6 and to gradually decrease a protection ratio R from 1 to 0 with time. 
     Solubility (S 350 -R) of the polymer chain in the unit lattice cell (e.g., in accordance with the deprotection reaction) may be calculated through Equation 7. The above equations may construct a continuum model to perform a chemical reaction more rapidly in the protection group disposed in a high-concentration region by way of the concentration of the acid. 
     In a continuum scale simulation operation S 350  of the post-exposure bake (PEB) process, operations illustrated in  FIG.  4 B  may be performed. In the continuum scale simulation operation S 350 , an acid-base neutralization reaction S 350 A and acid/base diffusion  350 B may be taken into account. Since the acid-base neutralization reaction is basically known as a chemically rapid reaction, it may be assumed, stoichiometrically, that the same amount of acid and base are neutralized to disappear through an acid-base neutralization reaction immediately when the acid and the base react with each other, rather than calculating an actual value. For example, a simple stoichiometric assumption may be established that the acid and the base disappear by neutralizing the same amount. 
     In addition, when the concentration of one of the acid and the base is high, the concentration of the other having a low concentration was set to be calculated as zero in the next operation. Equations 5 and 6, connected to each other by way of the acid concentration A and the quencher concentration Q, include two operations, an acid-base neutralization reaction  350 A and acid/base diffusion  350 B, as illustrated in  FIG.  4 B . Accordingly, a continuum model may be constructed to yield numerical solutions of the above equations. Then, a simulation operation of the acid-base neutralization reaction S 350 A and the acid/base diffusion  350 B may be repeatedly performed until more time than the PEB time elapses. Then, in the deprotection reaction S 350 C, the solubility (S 350 -R) of the polymer chain may be yielded as in  FIG.  4 A  through operation S 351  of obtaining a local concentration A pro  in the position of the protection group using trilinear interpolation for the acid concentration A yielded from Equations 5 and 6, operation S 353  of calculating a deprotection ratio R t , and operation S 353  of calculating a deprotection ratio of each chain. 
     As can be seen from  FIG.  4 A , calculation results may be used for each other between simulations of each scale. For example, the solubility (S 350 -R) of the polymer chain calculated in the continuum scale simulation operation S 350  may be transmitted to the molecular scale simulation operation S 330 , and the soluble polymer chain in the unit cell may be removed to estimate a shape of a final virtual resist pattern (S 330 -R 2 ). 
       FIGS.  5 A and  5 B  are conceptual diagrams of a unit lattice cell of a resist. 
       FIGS.  5 A and  5 B  illustrate a process in which an acid is activated by exposure in a unit lattice cell of a resist in which a polymer chain  1 , a PAG  2 , and quencher  3  are combined with each other. A molecular structure of the polymer chain  1 , the PAG  2 , and the quencher  3  is merely examples, and may be other types of molecule may be used according to some example embodiments. The polymer chain may include a combination of at least one protection group and at least one non-protection group. In  FIG.  5 A , the ovals indicated by the middle arrow represent PAGs  2 , and the ellipses indicated by the right arrow represent quenchers  3 .  FIG.  5 A  illustrates a unit lattice cell of a resist before being exposed, and  FIG.  5 B  illustrates a unit lattice cell immediately after being exposed. In a unit lattice cell, a region indicated by “a” of  FIG.  5 B  represents an exposed region, and a region indicated by “b” of  FIG.  5 B  represents a pristine region (e.g., an unexposed region). Secondary electrons may be generated in the exposure region “a” and may combine with the PAGs  2  to cause a dissociation reaction of the PAGs  2 .  FIG.  5 B  illustrates a process in which the exposed PAGs  2  are dissociated into PAG cations and anions, for example, in an acid activation process. In  FIG.  5 B , the ovals indicated by a left arrow represent PGS cations  2   a  and the ovals indicated by a right arrow represent dissociated PAG anions  2   b.    
     The quencher may be, for example, a photo decomposable quencher (PDQ). When the quencher is a PDQ, the PDQ may be activated with a weak acid in the exposed region and a little neutralization reaction with the acid activated by PAG may slightly or hardly occur. However, the PDQ may be present as a base in the unexposed region and may cause an acid-base neutralization reaction in an interface between the exposed region and the unexposed region to suppress diffusion of the acid to the unexposed region. According to some example embodiments, since not only a behavior of the acid caused by dissociation of a PGS but also a behavior of the quencher in the exposed region and the unexposed region may be simulated, consistency of a resist pattern may be significantly improved. 
     In the lithography method according to embodiments, for example, by applying a multiscale simulation to material compositions of a carbonate-based polymer chain, a sulfonic acid generator based PAG, and a quencher, a photochemical reaction of exposure→PAG dissociation→acid activation→acid-base neutralization reaction→acid/base diffusion is illustrated. The material composition of the resist, to which the multiscale simulation and corresponding lithography method is applied, is not limited to the above-mentioned material composition. A quantum scale DFT simulation and a molecular scale MD simulation may be used for such a photochemical reaction. 
     For example, a unit lattice cell of a resist may be modeled using an MD simulation and a PAG dissociation energy curve, calculated through a DFT simulation, and applied to a force field of each PAG disposed in the exposed region of the unit lattice cell. Within nanoseconds after calculation of an NPT ensemble, a PAG may be dissociated and/or divided into PAG cations and PAG anions. Again, concentration profile information of acid activated from dissociated anions and concentration profile information of a quencher may be quantified through the MD simulation. As a result, the acid concentration profile and the quencher concentration profile may be detected, and corresponding information may be used later in a continuum scale simulation. 
       FIGS.  6 A to  6 F  are conceptual diagrams illustrating acid/base diffusion, acid-base neutralization reaction, and deprotection.  FIG.  6 A  illustrates a concentration profile of an activated acid and a concentration profile of a quencher, a base, through an MD simulation. The acid and quencher concentrations vary as represented by a dark color and light colors for each position, and small circles in a black outline represent protection groups of a polymer chain. 
     Referring to  FIGS.  6 A to  6 F , in a lithography method of the present example embodiments, similarly to the description provided with reference to  FIGS.  5 D and  5 E , by applying a multiscale simulation to a material composition of a carbonate-based polymer chain, a sulfonic acid generator based PAG, a quencher, acid/base diffusion, an acid-base neutralization reaction, and a deprotection reaction are illustrated. A continuum scale FDM simulation may be used for the acid/base diffusion, the acid-base neutralization reaction, and the deprotection reaction. 
     For example, a local acid concentration A pro  within a unit lattice cell and a change in protection ratio R of the protection group may be calculated using a FDM simulation. For example, the acid concentration A pro  may be calculated through Equation 5 and Equation 6, and the change in the protection ratio R may be calculated through Equation 7. As a result, it may be confirmed that the acid/base diffusion and/or the acid-base neutralization reaction may occur in the unit lattice cell, and a chemical reaction (for example, the deprotection reaction) is rapidly performed on only the protection group in a corresponding region due to the acid. In this case, the protection ratio R of the protection group may be used to select soluble polymer chains. 
     In  FIG.  6 A , two rectangular boxes include an upper rectangular box and a lower rectangular box. In the lower box, a protection ratio of a protection group is continuously denoted with dark and light colors. When the protection ratio is 1, the protection group may be insoluble in a polar solvent. As the protection ratio is decreased to zero, solubility of polar solvents may be increased. 
     In the upper rectangular box, an acid/base concentration profile is divided into three levels and denoted with dark and light colors using an equivalent curved surface of quantified local concentration. For example, the acid concentration profile may gradually decrease from a core-surface having an acid concentration A of 0.8 through a mid-surface having an acid concentration of 0.2 to an outer-surface having an acid concentration of 0.0005. Similarly, a base concentration profile may gradually decrease from a core-surface having a base concentration Q of 0.8 through a mid-surface having a base concentration of 0.2 to an outer-surface having a base concentration Q of 0.0005. 
     In  FIGS.  6 A to  6 D , it can be seen that a neutralization reaction is performed while an acid and a base gradually diffuse, so that the concentrations of the acid and the quencher may be decreased, respectively. It also can be seen that each of the core-surface and the mid-surface is absorbed to the outer-surface while each of the acid and base diffuses, and the outer-surface fills an internal space of the unit lattice cell. The acid-base neutralization reaction may be performed and the acid and quencher may disappear at an interface in which respective outer-surface intersect each other. In  FIGS.  6 C and  6 D , it can be seen that as PEB time elapses, a quencher concentration is continuously decreased to contract the outer-surface and to expand an outer-surface of the acid. From the beginning of the PEB process, the acid may attack surrounding protection groups to cause a deprotection reaction. The deprotection reaction may preferentially start in a protection group surrounded by a high-concentration acid. As illustrated in  FIG.  6 D , performing of a deprotection reaction in an unexposed region, in which a quencher concentration remains, may be slower than performing of a deprotection reaction in an exposed region. The quencher, present in the unexposed region, may control diffusion of the acid from the exposed region to the unexposed region. 
     In  FIGS.  6 E and  6 F , acid diffusions and protection ratios are illustrated. As can be seen in the drawings, a light-colored portion of acid diffuses to an unexposed region to diffuse an acid concentration to the unexposed region and protection groups in the unexposed region may decrease in a protection ratio due to a deprotection reaction. In this case, the protection groups in the exposure region may significantly decrease in a protection ratio because the deprotection reaction is not performed to a great extent. When the protection ratio is about 0.2, a corresponding protection group may be set as having solubility. However, example embodiments are not limited thereto and the protection ratio set as having solubility may vary depending on the kind of resist and/or developer. The term “solubility” may refer to solubility for a developing solvent. 
       FIGS.  7 A and  7 B  are views illustrating a comparison between a result of a multiscale simulation of a 2-component system (hereinafter referred to a “comparative example”) and a result of a multiscale simulation of a 3-component system. 
     The comparative example is a system considering a polymer chain and a PAG, and the 3-component system is a system considering a polymer chain, a PAG, and a quencher. In  FIG.  7 A , X-axis represents PEB time, Y-axis represents a concentration ratio of acid, a dashed-line graph represents a change in acid concentration ratio in an exposed region, and a solid-line graph represents a change in acid concentration ratio in an unexposed region. In  FIG.  7 B , X-axis represents a position, Y-axis represents a protection ratio, and a dashed line extending in a direction, perpendicular to a central portion of the X-axis, separates an exposed region and an unexposed region from each other. 
     In  FIG.  7 A , it can be seen that in the comparative example, 30% of acid was generated for 0 to 1.0 seconds and a concentration of acid in an exposed region diffuses to an unexposed region, and thus, a unit lattice cell slowly reached an equilibrium state in about 6.4 seconds. It also can be seen that in the comparative example, the concentration of the acid was continuously increased in the unexposed region. It also can be seen that in the present disclosure, a concentration of acid in an unexposed region was decreased by an acid-base neutralization reaction for 0.02 to 0.1 seconds and was then increased again. Such a result shows that a quencher suppresses diffusion of acid to the unexposed region. Such a decrease in acid concentration may play an important role in delaying deprotection. In the exposed region, the acid was neutralized with the quencher, and thus, the acid concentration was decreased more rapidly than in the comparative example. Then, the remaining acid diffused to the entire unit lattice cell and reached an equilibrium state in about 7.4 seconds. 
     In  FIG.  7 B , a change in local acid concentration in a protection group in X-axis is illustrated. In an unexposed region, a concentration of acid according to the 3-component system is lower than a concentration of acid according to the comparative example. This is because a quencher serves to suppress diffusion of the acid to the unexposed region. As illustrated in  FIGS.  7 A and  7 B , the present disclosure may further consider the quencher, a base, as compared with the comparative example, so that a development aspect of a chemical reaction changed by an acid-base neutralization reaction and estimation of a pattern shape resulting from the development aspect may be more realistic. 
       FIGS.  8 A and  8 B  are views illustrating LER obtained by a multiscale simulation of a 2-component system and LER obtained by a multiscale simulation of a 3-component system, respectively.  FIGS.  8 A and  8 B  illustrate a shape of a pattern remaining after removal of a dissolved polymer chain in an MD simulation according to the comparative example and the present disclosure. 
     In  FIGS.  8 A and  8 B , it can be seen that interface roughness between an exposed region and an unexposed region is improved in the 3-component system, as compared with the comparative example. For example, LER in the comparative example was at a level of 2.0 nm or less but LER in the present disclosure was at a level of 1.7 nm or less. This is because the quencher simulated suppression of diffusion of acid diffusion to the unexposed region to be closer to an actual situation. 
       FIG.  9    is a flowchart illustrating a method of manufacturing a semiconductor device based on the lithography method of  FIG.  1    according to some example embodiments. The descriptions previously provided with reference to  FIGS.  1  to  8    will be simplified or omitted. 
     Referring to  FIG.  9   , operation S 110  of estimating a shape of a virtual resist pattern to operation S 140  of forming a resist pattern on an object to be patterned (hereinafter referred to as a “patterning object”) are sequentially performed. Details of the operations are the same as those described with reference to  FIGS.  1 ,  3 A, and  3 B . 
     In operation S 160 , a pattern is formed on the patterning object. The pattern on the patterning object (see W of  FIG.  11 B ) may be formed by an etching process using a resist pattern as an etching mask. The etching process may be a dry etching process and/or a wet etching process, and the pattern may be formed on the patterning object using at least one of the dry etching process and/or the wet etching process. 
     After the pattern is formed on the patterning object W, subsequent semiconductor processes are performed on the patterning object, in operation S 170 . A semiconductor device may be manufactured by performing the subsequent semiconductor processes on the patterning object W. For example, when the patterning object W is a wafer, a plurality of semiconductor devices may be manufactured from the wafer by performing the subsequent semiconductor processes on the wafer. 
     The subsequent semiconductor processes performed on the wafer may include various processes. For example, the subsequent semiconductor process on the wafer may include a deposition process, an etching process, an ion implantation process, a cleaning process, and/or the like. In addition, the subsequent semiconductor processes performed on the wafer may include a process of testing a wafer-level semiconductor device. Furthermore, the subsequent semiconductor processes performed on the wafer may include a singulation process of dividing the wafer into individual semiconductor chips and a process of packaging the semiconductor chips. The packaging process may refer to a process of mounting the semiconductor chips on a printed circuit board (PCB) and encapsulating the semiconductor chips with an encapsulant, and may include stacking a plurality of semiconductors on the PCB to form a stack package or stacking one stack package on another stack package to form a package-on-package (PoP) structure. Through the process of packaging the semiconductor chips, a semiconductor device or a semiconductor package may be completed. 
       FIG.  10    is a block diagram of exposure equipment based on the lithography method of  FIG.  1    according to some example embodiments. 
     Referring to  FIG.  10   , exposure equipment  1000  according to the some example embodiments may include a simulation device  10 , an exposure device  20 , and a measurement device  30 . The simulation device  10  may estimate a shape of a virtual resist pattern, using a multiscale simulation, in the lithography method described above with reference to  FIG.  1   . According to some example embodiments, though illustrated as included in the exposure equipment  1000 , the simulation device  10  may be excluded from the exposure equipment  1000  and/or distinguished as a separate device. The simulation device  10  will be described in more detail with reference to  FIG.  11 A . 
     The exposure device  200  may perform an exposure process on a resist. The exposure device  20  may perform an exposure process on a layer, formed of a selected resist, to form a test resist pattern. The resist may be selected based on a result of the multiscale simulation of the simulation device  10 . The exposure device  20  may be, for example, an extreme ultraviolet (EUV) exposure device. However, the exposure device  20  is not limited to the EUV exposure device. A structure of the exposure device  20  will be described in more detail with reference to  FIG.  11 B . 
     The measurement device  30  may measure the test resist pattern, formed by the exposure device  20 , or a resist pattern on an object to be patterned (hereinafter referred to as a “patterning object”) to compare the test resist pattern with a virtual resist pattern. For example, the measurement device  30  may measure CD, LER, LWR, and LCDU for the test resist pattern or the resist pattern on the patterning object. In some embodiments, for example, the measurement device  30  may include a probe, a photoreceptor, a light source, and/or a microscope. According some example embodiments, though illustrated as being included in the exposure equipment  1000 , the measurement device  30  may be excluded from the exposure equipment  1000  and/or distinguished as a separate device. 
     The exposure equipment  1000  may include a simulation device  10  performing a multiscale simulation. Accordingly, the exposure equipment  1000  may estimate a shape of the virtual resist pattern using the simulation device  10  to select an optimal resist and to form a resist pattern using the resist selected by the exposure device  20 . For example, the simulation device  10  may simulate PAG dissociation, acid activation, an acid-base neutralization reaction, acid/base diffusion, and/or a deprotection reaction in a unit lattice cell of the resist. As a result, the exposure equipment  1000  may accurately form a resist pattern matching an object resist pattern within an allowable range, and then may pattern the patterning object using the resist pattern to form a precise pattern on the patterning object. Accordingly, the exposure equipment  1000  may contribute to manufacturing of a semiconductor device having improved reliability. 
     The exposure equipment  1000  may be combined with a deep learning and/or artificial intelligence (AI) technology to further improve consistency of the estimation of the pattern shape. For example, a more precise pattern may be formed through machine learning for sampling a semiconductor wafer formed of a resist selected by the simulation device  10  and a plurality of semiconductor elements formed from the wafer. 
       FIG.  11 A  is a block diagram illustrating a simulation device in the exposure equipment of  FIG.  10   , and  FIG.  11 B  is a block diagram illustrating an exposure device in the exposure equipment of  FIG.  10   . 
     Referring to  FIG.  11 A , in the exposure equipment  1000  according to the present embodiment, the simulation device  10  may include first to third simulation units  11 ,  12 , and  13  and a shape estimation unit  14 . The first simulation unit  11  may perform a quantum scale simulation. For example, the first simulation unit  11  may perform a DFT simulation. The second simulation unit  12  may perform a molecular scale simulation. For example, the second simulation unit  12  may perform an MD simulation. The third simulation unit  13  may perform a continuum scale simulation. For example, the third simulation unit  13  may perform an FDM simulation. The first to third simulation units  11 ,  12 , and  13  may be organically connected to each other. 
     The shape estimation unit  14  may estimate a shape of a virtual resist pattern formed by the first to third simulation units  11 ,  12 , and  13 . Also, the shape estimation unit  14  may calculates critical dimension (CD), line edge roughness (LER), line width roughness (LWR), local critical dimension uniformity (LCU), and the like, to quantify patterning performance for a material composition of a selected resist. According to example embodiments, a function of the shape estimation unit  14  may be integrated into the second simulation unit  12  to be performed. 
     The simulation device  10  and/or its component elements (e.g., the first to third simulation units  11 ,  12 , and  13  and/or a shape estimation unit  14 ) may include and/or be included in processing circuitry such as hardware including logic circuits; a hardware/software combination such as a processor executing software; and/or a combination thereof. For example, the processing circuitry more specifically may include, but is not limited to, a central processing unit (CPU), an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), and programmable logic unit, a microprocessor, application-specific integrated circuit (ASIC), complex programmable logic devices (CPLD), firmware driven in hardware devices, integrated circuits (IC), an application specific IC (ASIC), etc. 
     Referring to  FIG.  11 B , in the exposure equipment  1000 , the exposure device  20  may be an extreme ultraviolet (EUV) exposure device. The exposure device  20  is not limited to the EUV exposure device. Hereinafter, however, for ease of description, exposure device  20  will be mainly described as an EUV exposure device. The exposure device  20  may include an EUV light source  21 , an illumination optics (Ill Optics)  22 , a projection optics (Pro Optics)  23 , a stage  24 , and a mask support  25 . 
     The EUV light source  210  may generate high-energy-density EUV L 1  within a wavelength range of about 5 nm to 50 nm, and then may output the EUV L 1 . For example, the EUV light source  21  may generate and output high-energy-density EUV L 1  having a wavelength of 13.5 nm. 
     The illumination optics  22  may include a plurality of mirrors, and may transmit the EUV L 1  from the EUV light source  21  to an EUV mask M on the mask support  25 . For example, the EUV L 1  from the EUV light source  21  may be incident on the EUV mask M disposed on the mask support  25  through reflection performed by the mirrors in the illumination optics  22 . 
     The EUV mask M may be a reflective mask including a reflection region, a non-reflection, and/or intermediate reflection region. The EUV mask M may include a reflection multilayer for reflecting EUV on a substrate, formed of a low thermal expansion coefficient material (LTEM) such as quartz, and an absorption layer pattern formed on the reflection multilayer. The reflection multilayer may have, for example, a structure in which at least dozens of molybdenum (Mo) layers and silicon (Si) layers are alternately stacked. The absorption layer may be formed of, for example, TaN, TaNO, TaBO, Ni, Au, Ag, C, Te, Pt, Pd, Cr, and/or the like. However, a material of the reflection multilayer and a material of the absorption layer are not limited to the above-mentioned materials. An absorption layer portion may correspond to the non-reflection and/or intermediate reflection region. 
     The EUV mask M may reflect the EUV L 1  incident through the illumination optics  22 , and may have the reflected EUV L 1  incident on the protection optics  23 . More specifically, the EUV mask M may structuralize the illumination light from the illumination optics  220  to projection light based on a shape of a pattern including the reflection multilayer and the absorption layer on a substrate, and may have the projection light incident on the projection optics  23 . The projection light may be structuralized through at least secondary diffraction order due to the pattern of the EUV mask M. The projection light may be incident on the projection optics  23  with shape information of the pattern on the EUV mask M, and may pass through the projection optics  23  to transcribe an image, corresponding to the pattern of the EUV mask M, onto an object W to be patterned (hereinafter referred to as a “patterning object” W). 
     The patterning object W may be a substrate including a semiconductor material such as silicon (Si), for example, a wafer. The patterning object W exposed to the projection light may be a resist applied on the patterning object W, for example, an EUV resist. Performing an exposure process on the patterning object W will be described in detail later with reference to  FIGS.  12 A to  13   . 
     The patterning object W may be disposed on the stage  240 . The stage  240  may move in X and Y directions on an X-Y plane and may move in a Z direction, perpendicular to the X-Y plane. For example, the stage  240  may be configured to move in the directions of a lateral plane (e.g., forward/backward and/or left/right) and may move up and/or down. Accordingly, the movement of the stage  240  may allow the patterning object W to move in the X, Y, and Z directions. 
     The projection optics  230  may include a plurality of mirrors. In  FIG.  11 B , for easy of description, only two mirrors (a first mirror  23   a  and a second mirror  23   b ) are illustrated in the projection optics  23 . However, the projection optics  23  may include more mirrors. For example, the projection optics  23  may include four to eight mirrors. However, the number of mirrors included in the projection optics  23  is not limited to the above numerical values. 
       FIGS.  12 A to  12 C  are schematic cross-sectional views illustrating a method of manufacturing a semiconductor device according to an example embodiment.  FIGS.  12 A to  12 C  may illustrate examples of a method of manufacturing a semiconductor device using a photoresist layer  120  with a resist composition optimized by a simulation device ( 10  of  FIG.  11 A ) performing a multiscale simulation. 
     Referring to  FIGS.  12 A to  12 C , a photoresist layer  120  may be formed on a surface of a semiconductor wafer  100  by a spin coating process. The semiconductor wafer  100  may include a semiconductor substrate  105 , a lower layer  109  on the semiconductor substrate  105 , and a photoresist layer  120  coated on the lower layer  109 . The photoresist layer  120  may be formed with a resist composition optimized by a simulation device ( 10  of  FIG.  11 A ) performing a multiscale simulation. 
     In some examples, when the exposure equipment  1000  is an EUV exposure equipment, the photoresist layer may have a thickness between about 200 nm and about 600 nm. However, according to some example embodiments, the thickness of the photoresist layer is not limited to the thickness between about 200 nm and about 600 nm, but may be less than about 200 nm or greater than about 600 nm. 
     The photoresist layer  120  of the semiconductor wafer  100  may be exposed by sequentially performing a one-shot process, including aligning a wafer and irradiating light generated by a light source, twice or more. The semiconductor wafer  100  may be unloaded from the exposure equipment  1000 . The photoresist layer  120 , to be exposed, of the semiconductor wafer  100  may be developed to form a photoresist pattern  120   a.    
     By performing an etching process using the photoresist pattern  120   a  as an etching mask, the lower layer  109  of the semiconductor wafer  100  may be etched to form lower patterns  110  including an alignment mark  110   a , a test device pattern  110   t , and circuit patterns  110   c.    
     In an example, the lower patterns  110  may further include a guard ring pattern  110   g . A semiconductor wafer  100   a , including a photoresist pattern  120   a  and lower patterns  110 , may be formed. 
     Then, the photoresist pattern  120   a  may be removed. Accordingly, the photoresist pattern  120   a  may be removed and a semiconductor wafer  100   b , including the lower patterns  110 , may be formed. 
     Next, an extreme ultraviolet (EUV) photomask for manufacturing a semiconductor device according to an example embodiment will be described with reference to  FIG.  13   .  FIG.  13    is a schematic cross-sectional view illustrating an EUV photomask for manufacturing a semiconductor device according to some example embodiments. 
     Referring to  FIG.  13   , in an example, an EUV photomask M may include a mask substrate  303 , a stack structure  320  disposed below the mask substrate  303 , a backside layer  315  disposed above the mask substrate  303 , a capping layer  330  disposed below the stacked structure  320 , and a plurality of mask patterns  340  disposed below the capping layer  330 . For example, the mask substrate  303  may have a first surface  303   s   1  and a second surface  303   s   2  opposing each other, and the stack structure  320  may be formed on the first surface  303   s   1  of the mask substrate  303 , the capping layer  330  may be disposed on the stack structure  320 , the plurality of mask patterns  340  may be disposed on the capping layer  330 , and the backside layer  315  may be in contact with the second surface  303   s   2  of the mask substrate  303 . 
     The mask substrate  303  may include a low thermal expansion material (LTM). For example, the mask substrate  303  may include a silicon material. 
     The stacked structure  320  may include a silicon layer  322  and a metal layer  324  stacked alternately and repeatedly. The metal layer  324  may be a molybdenum layer. The capping layer  330  may be a ruthenium layer. 
     Each of the mask patterns  340  may include a first mask pattern  342  in contact with the capping layer  330  and a second mask pattern  344  below the first mask pattern  342 . The first mask pattern  342  may be an absorber including a TaBN material. The second mask pattern  344  may be an anti-reflection layer including a lawrencium material. 
     The EUV photomask M may include a mask layout region  305  and a border region  310  surrounding the mask layout region  305 . The mask layout region  305  may be a region in which a mask layout pattern of the EUV photomask M is formed. The border region  310  may surround the mask layout region  305 , and may be a region in which a mask layout pattern is not formed. As described in  FIG.  1   , the second light  50   b  incident on the photomask M and the third light  50   c  reflected from the photomask M may be inclined with respect to an axis, perpendicular to a surface of the photomask M. 
     The mask layout region  305  of the extreme EUV photomask M may include a mask chip region  305 _C and a mask scribe lane region  305 _S surrounding the mask chip region  305 _C. 
     The mask patterns  340  may include mask circuit layout patterns  340   c  formed in the mask chip region  305 _C, an alignment layout pattern  340   a  and a test layout pattern  340   t  formed in the mask scribe lane region  305 _S, and a border mask pattern  340   d  formed in the border region  310 . The mask patterns  340  may further include a guard ring layout pattern  340   g  disposed in the mask chip region  305 _C adjacent to the mask scribe lane region  305 _S. 
     As described above, in a lithography method using a multiscale simulation, through a multiscale simulation in which a quantum scale, a molecular scale, and a continuum scale are integrated, physical phenomena on a multilevel, such as acid activation, acid/base diffusion, an acid-base neutralization reaction and a change in concentration of acid caused by the neutralization reaction, deprotection, a change in solubility of a polymer chain, and control of acid diffusion by base in unexposed regions, may be simulated. Therefore, selection of a structure or a material composition of the resist to patterning of the resist (for example, resist manufacturing or selection→exposure→post-exposure bake (PEB)→developing) may all be performed together to significantly reduce development time and costs of lithography resist. In addition, the lithography method according to the present embodiment may overcome limitations of conventional single-scale simulations to significantly improve consistency for estimation of a resist pattern shape, as compared with the single scale simulations. 
     While example embodiments have been shown and described above, it will be apparent to those skilled in the art that modifications and variations could be made without departing from the scope of the present inventive concepts as defined by the appended claims.