Extreme ultraviolet photoresist contour prediction method, prediction device and electronic equipment

Abstract

The invention discloses an extreme ultraviolet photoresist contour prediction method and device and electronic equipment, and the method comprises the steps: dividing a target photoresist into a preset number of cell units with the same volume, and carrying out the step-by-step grouping; calculating a tendency function value of each calculation group in each level of group according to initial distribution data of each particle in each cellular unit after exposure of the target photoresist; a tendency function value updating step: according to the current tendency function value of each calculation group in each level of group, performing random simulation on the target photoresist through a minimum process method, and updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group comprising the cell unit; the updating step is repeated for multiple times until the post-baking end moment; and predicting the contour of the developed target photoresist according to the distribution data of each particle in the post-baked target photoresist. According to the technical scheme provided by the invention, the post-baking simulation efficiency is improved, and the prediction speed of the extreme ultraviolet photoresist contour is accelerated.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of photolithography technology, and in particular to a prediction method, a prediction device and an electronic device for an extreme ultraviolet photoresist profile. Background Art

[0002] Chemically Amplified Resists (CAR) are a type of photoresist commonly used in photolithography. The main components are photoacid generators (PAG), polymer resins, alkaline quenchers, and solvents. When the photoresist is exposed, PAG will produce an acidic product. In the subsequent post-exposure bake (PEB), the photoacid produced by PAG acts as a catalyst to catalyze the deprotection reaction of the polymer resin (the protecting groups on the polymer are consumed), affecting the solubility of the polymer. Finally, a liquid developer (such as an aqueous alkaline solution) can be used to remove the soluble part of the photoresist, thereby forming a three-dimensional graphic outline.

[0003] In the related technology, the profile after development can be predicted by performing PEB random simulation on the photoresist; Extreme Ultraviolet Lithography (EUV) lithography technology is a nanoscale lithography technology that uses extreme ultraviolet light with a wavelength of 13.4nm as a light source; In the prior art, the PEB random simulation method for extreme ultraviolet photoresist uses a Gillespie algorithm based on logarithmic classification to accelerate random simulation in order to solve the inefficiency problem of solving the main equation; However, the algorithm will have the problem of class table crossing, and it is necessary to frequently add and delete operations on the class list. For a large-volume extreme ultraviolet photoresist system, this will also consume a lot of time, affecting the simulation efficiency. Summary of the invention

[0004] The embodiments of the present invention provide a prediction method, a prediction device and an electronic device for an extreme ultraviolet photoresist profile, so as to improve the efficiency of post-bake simulation and accelerate the prediction speed of the extreme ultraviolet photoresist profile.

[0005] According to one aspect of the present invention, a method for predicting an extreme ultraviolet photoresist profile is provided, comprising:

[0006] Divide the target photoresist into a preset number of cellular units with the same volume, and group the preset number of cellular units level by level; wherein each level of grouping includes multiple calculation groups;

[0007] Determining initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist;

[0008] According to the initial distribution data of each particle in each cell unit, the tendency function value of each calculation group in each level of grouping is calculated;

[0009] a tendency function value updating step, performing random simulation on the target photoresist by a minimum process method according to the current tendency function value of each calculation group in each level of grouping, and updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit;

[0010] Repeat the trend function value updating step multiple times until the next moment after the current moment is the post-bake end moment of the target photoresist, and obtain the distribution data of each particle in the target photoresist after post-bake;

[0011] The profile of the target photoresist after development is predicted based on the distribution data of each particle in the target photoresist after post-baking.

[0012] Optionally, grouping a preset number of cell units level by level includes:

[0013] Setting the number of grouping levels and the number of groupings each time; wherein the number of groupings at different levels is the same, or the number of groupings at different levels is different, or the number of groupings at different levels is partially different;

[0014] The preset number of cell units are grouped level by level according to the number of grouping levels and the number of groups in each level, and the position index of each cell unit after each grouping is recorded.

[0015] Optionally, a preset number of cell units are grouped level by level according to the number of grouping levels and the number of groups in each level, and a position index of each cell unit after each grouping is recorded, including:

[0016] Performing a first average grouping of the preset number N of cell units according to the number N1 of the first-level groups, and sequentially numbering the N1 first-level calculation groups, and recording the number of each first-level calculation group as the position index n1 of the corresponding first-level calculation group; wherein 1≤n1≤N1;

[0017] The M1 cell units in each first-level calculation group are grouped equally for the second time according to the number of second-level groups N2, and the N2 second-level calculation groups in each first-level calculation group are numbered in sequence, and the combination of the number of each second-level calculation group and the number of the first-level calculation group to which it belongs is recorded as the position index (n1, n2) of the corresponding second-level calculation group; wherein 1≤n2≤N2;

[0018] The M2 cell units in each second-level calculation group are grouped for the third time according to the number of third-level groups N3, and the N3 third-level calculation groups in each second-level calculation group are numbered in sequence, and the combination of the number of each third-level calculation group and the numbers of the second-level calculation group and the first-level calculation group to which it belongs is recorded as the position index (n1, n2, n3) of the corresponding third-level calculation group; wherein 1≤n3≤N3;

[0019] And so on, until the number of groups meets the set number of levels T, and calculate the N in each T-1th level group T The T-th level calculation groups are numbered in sequence, and the combination of the number of each T-th level calculation group and the number of each level calculation group to which it belongs is recorded as the position index (n1, n2, ..., n T ), where 1≤n T ≤N T ;

[0020] For each of the T-th level calculation groups M T The cell units are numbered in sequence, and the position index of each cell unit (n1, n2, ..., n T , n T+1 ), where 1≤n T+1 ≤M T .

[0021] Optionally, determining the initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information includes:

[0022] Acquire the initial state information of the target photoresist; wherein the initial state information includes the concentration distribution information of the photoacid generator, the concentration distribution information of the base quencher and the concentration distribution information of the protecting group of the target photoresist;

[0023] According to the initial state information of the target photoresist and the volume of each cell unit, the initial number of acid molecules, the initial number of base quenchers and the initial number of protective groups in each cell unit after the target photoresist is exposed are calculated.

[0024] Optionally, according to the initial distribution data of each particle in each cell unit, the tendency function value of each calculation group in each level of grouping is calculated, including:

[0025] According to the initial distribution data of each particle in each cell unit, the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit are calculated;

[0026] Determine the tendency function value of the corresponding cell unit according to the sum of the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit;

[0027] The tendency function value of each calculation group in each level of grouping is determined according to the sum of the tendency function values ​​of all cell units included in each calculation group in each level of grouping.

[0028] Optionally, in the tendency function value updating step, according to the current tendency function value of each calculation group in each level of grouping, the target photoresist is randomly simulated by a minimum process method to update the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit, including:

[0029] Determining a current total tendency function value of the target photoresist according to the sum of current tendency function values ​​of all cell units in the target photoresist;

[0030] Obtaining a first random number, and calculating a time interval according to the first random number and a current total tendency function value of the target photoresist;

[0031] Determine whether the sum of the accumulated time after the last update and the time interval exceeds the total post-baking time; if not,

[0032] then obtaining a second random number, calculating a first-level selected value according to the second random number and the current total tendency function value, and selecting a target first-level calculation group from all first-level calculation groups according to the first-level selected value;

[0033] Calculate a second-level selected value according to the sum of the tendency function values ​​of the first-level calculation groups whose numbers are located before the target first-level calculation group and the first-level selected value, and select a target second-level calculation group from all the second-level calculation groups under the target first-level calculation group according to the second-level selected value;

[0034] Calculate a third-level selected value according to the sum of the tendency function values ​​of the second-level calculation groups whose numbers are located before the target second-level calculation group and the second-level selected value, and select a target third-level calculation group from all the third-level calculation groups under the target second-level calculation group according to the third-level selected value;

[0035] And so on, until a target cell unit is selected from the calculation group selected at the last level;

[0036] According to the weights of the reaction events and the molecular diffusion events in the target cell unit, determining the currently occurring random event from the reaction events and the molecular diffusion events;

[0037] Update the number of each particle in the target cell unit according to the currently occurring random event;

[0038] According to the updated number of each particle in the target cell unit, the updated tendency function value of the target cell and the updated tendency function values ​​of each level of calculation group including the target cell are determined.

[0039] Optionally, the time interval is calculated according to the first random number and the current total tendency function value of the target photoresist, and is determined based on the following:

[0040]

[0041] Wherein, Δt is the time interval; W N is the current total tendency function value of the target photoresist; r1 is the first random number, r1∈[0,1);

[0042] The first level selected value is calculated according to the second random number and the current total tendency function value, based on the following determination:

[0043] R1=r2W N ;

[0044] Wherein, R1 is the first level selected value; r2 is the second random number, r2∈[0,1);

[0045] The selecting of the target first-level calculation group from all the first-level calculation groups according to the first-level selected value is based on the following determination:

[0046]

[0047] Wherein, n1 is the number of the first-level computing group, and m1 is the number of the target first-level computing group; is the tendency function value of the n1th first-level calculation group;

[0048] The second level selected value is calculated based on the sum of the tendency function values ​​of the first level calculation group located before the target first level calculation group and the first level selected value, based on the following determination:

[0049]

[0050] Wherein, R2 is the second level selected value;

[0051] The selecting of the target second-level computing group from all the second-level computing groups under the target first-level computing group according to the second-level selected value is based on the following determination:

[0052]

[0053] Wherein, n2 is the number of the second-level computing group, and m2 is the number of the target second-level computing group; is the tendency function value of the n2th second-level calculation group in the target first-level calculation group;

[0054] The calculation of the third level selected value based on the sum of the tendency function values ​​of the second level calculation group located before the target second level calculation group and the second level selected value is based on the following determination:

[0055]

[0056] Wherein, R3 is the third level selected value;

[0057] The selecting of the target third-level computing group from all the third-level computing groups under the target second-level computing group according to the third-level selected value is based on the following determination:

[0058]

[0059] Wherein, n3 is the number of the third-level computing group, and m3 is the number of the target second-level computing group; The tendency function value of the n3th third-level calculation group in the target second-level calculation group.

[0060] Optionally, based on the distribution data of each particle in the target photoresist after post-bake, predicting the contour of the target after photolithography development, including: determining a preliminary distribution image of the protecting groups after the target photoresist is developed based on the distribution data of the protecting groups in the target photoresist after post-bake; performing Gaussian convolution and normalization processing on the preliminary distribution image of the protecting groups after the target photoresist is developed to obtain a target distribution image of the protecting groups after the target photoresist is developed; and predicting the contour of the target photoresist after development based on the target distribution image of the protecting groups.

[0061] According to another aspect of the present invention, there is provided a prediction device for an extreme ultraviolet photoresist profile, comprising:

[0062] A cell unit division unit is used to divide the target photoresist into a preset number of cell units with the same volume, and group the preset number of cell units level by level; wherein each level of grouping includes a plurality of calculation groups;

[0063] An initial distribution data determination unit, used to determine the initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist;

[0064] A tendency function value calculation unit, used for calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cell unit;

[0065] An updating unit, used for updating the tendency function value multiple times until the next moment after the current moment is the post-bake end moment of the target photoresist; in each update, the updating unit is used for randomly simulating the target photoresist through the minimum process method according to the current tendency function value of each calculation group in each level of grouping, and updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit;

[0066] The prediction unit is used to obtain the distribution data of each particle in the target photoresist after post-baking, and predict the profile of the target after photolithography development according to the distribution data of each particle in the target photoresist after post-baking.

[0067] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0068] at least one processor; and

[0069] a memory communicatively connected to the at least one processor; wherein,

[0070] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the extreme ultraviolet photoresist profile described in any embodiment of the present invention.

[0071] The embodiment of the present invention provides a prediction method, prediction device and electronic device for an extreme ultraviolet photoresist profile, wherein the prediction method for the extreme ultraviolet photoresist profile comprises: dividing a target photoresist into a preset number of cellular units with the same volume, and grouping the preset number of cellular units step by step; wherein each level of grouping comprises a plurality of calculation groups; determining the initial distribution data of each particle in each cellular unit after the target photoresist is exposed according to the initial state information of the target photoresist; calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cellular unit; a tendency function value updating step, according to the current tendency function value of each calculation group in each level of grouping, performing random simulation on the target photoresist through a minimum process method, updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit; repeating the tendency function value updating step multiple times until the next moment after the current moment is the post-baking end moment of the target photoresist, obtaining the distribution data of each particle in the target photoresist after the post-baking; and predicting the profile of the target photoresist after development according to the distribution data of each particle in the target photoresist after the post-baking. The technical solution provided by the embodiment of the present invention solves the random model based on the minimum process method of classification cascade, replacing the solution method in the prior art. It does not have the problem of class table crossing, and there is no need to frequently add or delete class lists. It can greatly improve the simulation efficiency and speed up the prediction of the extreme ultraviolet photoresist profile.

[0072] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0074] Figure 1 It is a flow chart of a method for predicting an extreme ultraviolet photoresist profile provided by an embodiment of the present invention;

[0075] Figure 2 It is a flowchart of performing two-level grouping of 1000 cell units provided by an embodiment of the present invention;

[0076] Figure 3 It is a schematic diagram of a preliminary distribution image of a protecting group after development of a target photoresist provided by an embodiment of the present invention;

[0077] Figure 4 It is a schematic diagram of a target distribution image of a protecting group after development of a target photoresist provided by an embodiment of the present invention;

[0078] Figure 5 is a comparison chart of the time consumed for simulating a system composed of different numbers of cellular units by a related technology provided by an embodiment of the present invention and a prediction method provided by an embodiment of the present invention;

[0079] Figure 6 It is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 only 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 should fall within the scope of protection of the present invention.

[0081] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0082] As the background technology, chemical amplification glue is a commonly used photoresist, which is often used in DUV (Deep Ultraviolet Lithography) lithography and EUV lithography with an exposure wavelength less than 300nm. For positive chemical amplification glue, when exposed to light, PAG will produce an acidic product. The kinetic equation of the exposure reaction is:

[0083]

[0084] Among them, the photosensitivity C Dill is the basic material parameter of photoresist, and I is the intensity of incident light. The PAG concentration [PAG] gradually decreases with the increase of exposure time t. The concentration of PAG obtained by solving this equation is:

[0085]

[0086] The concentration of photogenerated acid [A] can be obtained from the following formula:

[0087]

[0088] In the subsequent post-baking, the photogenerated acid will act as a catalyst to catalyze the deprotection reaction of the polymer resin (protective group M on the polymer, which is used to make the polymer insoluble in water), and the protective group M will be consumed. This process is usually accompanied by the diffusion of acid molecules (acid molecules of photogenerated acid) and base quencher molecules. A single acid molecule will catalyze the reaction of more protective groups M, and the base quencher molecules will also react with the acid molecules. The kinetics and diffusion model of the PEB (post-baking) process of CAR can be expressed by the following equations:

[0089]

[0090] Formula (4) describes the acid-catalyzed deprotection reaction of the protecting group M, k1 is the deprotection reaction constant; [M] is the concentration of the protecting group M. Formulas (5) and (6) represent the spontaneous loss of acid and the neutralization reaction with the base quencher, k2 and k3 are the corresponding reaction constants, [B] is the concentration of the base quencher; in addition, a nonlinear diffusion term, D A and D B are the diffusion rates of the acid molecules and the base quencher molecules, respectively.

[0091] Based on the assumption of continuous approximation, DUV lithography usually uses numerical algorithms such as finite differences to solve the above equations. However, this assumption gradually breaks down in EUV lithography technology. Due to the shorter wavelength of EUV, the number of EUV photons in the same volume will be much smaller than the number of DUV photons (about 1 / 14), which makes EUV lithography susceptible to photon noise. Therefore, the discreteness and randomness of particles must be taken into account, which requires random modeling of EUV photoresist. Random modeling of EUV photoresist is to describe the physical and chemical behavior within the photoresist at the molecular scale, establish the chemical master equations describing molecular dynamics and diffusion probability, and solve these equations through the Gillespie algorithm. The Gillespie algorithm is an accurate random simulation algorithm that uses Monte Carlo to simulate molecular reactions and diffusion motions. It is widely used in systems biology and can model intracellular biochemical reaction systems. When the number of molecules in the cell is small, the probability theory method is used to discretize each molecule. It is assumed that within a very small time interval Δt, the system has only one intracellular molecular reaction event or a molecular material exchange event between cells. The occurrence of this event is a random event, and its probability is related to the concentration of molecules in the cell and the type of reaction, which is called the tendency function or conversion probability. The calculation method of the tendency function of some reaction types is a conventional technology in this field and will not be repeated here. Δt is determined by formula (7):

[0092]

[0093] Where a0 is the sum of the tendency functions of the entire system, and r1 is a random number between 0 and 1. The original stochastic simulation algorithm (SSA) determines the index μ of the reaction occurring at a certain time according to formula (8):

[0094]

[0095] The SSA algorithm simulates each reaction time. For a cell system with a large number of molecules, the algorithm will take up a lot of time, especially in a non-uniform spatial system considering the diffusion effect. The situation will be more complicated. Frequent diffusion events will seriously reduce the efficiency of the algorithm. On this basis, various variant algorithms to improve efficiency have also been proposed. Müulders et al. used an improved Gillespie algorithm to stochastically model EUV photoresist. The PEB process of EUV photoresist is modeled as a master equation that describes the probability of the system state changing due to reaction or diffusion. In order to solve the inefficiency of SSA in solving the master equation, a Gillespie algorithm based on logarithmic classification is used to accelerate the random simulation, which divides all cells into different categories according to the logarithm of the tendency function. The cell indexes of the same category are saved in a class list. In subsequent simulations, the value of the cell's tendency function due to reaction or diffusion will change with the change of the number of particles, and the class list also needs to be kept updated. The core of the algorithm is to first randomly extract the α class from a large number of categories, then use the von Neumann rejection method to extract a cell from the α class, and finally select the reaction or diffusion event that occurs in the cell. However, during the iteration process, the algorithm will encounter the problem of class list crossing (that is, a cell jumps from the current class list to another class list, or even a new class list), and the class list needs to be frequently added and deleted, which consumes a lot of time; for large photoresist systems, the number of cells contained is large, which will also consume a lot of time and affect the simulation efficiency.

[0096] In view of this, an embodiment of the present invention provides a method for predicting an extreme ultraviolet photoresist profile. Figure 1 is a flowchart of a method for predicting an extreme ultraviolet photoresist profile provided by an embodiment of the present invention, with reference to Figure 1 , prediction method for extreme ultraviolet photoresist profile, including:

[0097] S10, dividing the target photoresist into a preset number of cellular units with the same volume, and grouping the preset number of cellular units level by level; wherein each level of grouping includes multiple calculation groups.

[0098] Specifically, the target photoresist is an extreme ultraviolet photoresist of a to-be-predicted contour. Extreme ultraviolet photoresist is a photosensitive material used in extreme ultraviolet lithography. The step of dividing the target photoresist into a preset number of cellular units of the same volume may include: obtaining spatial data of the target photoresist; determining the length (x direction) information, width (y direction) information and height (z direction) information of the target photoresist according to the spatial data of the target photoresist. In this embodiment, the target photoresist is in the shape of a cuboid. The space occupied by the target photoresist is evenly divided based on the length information, width information and height information, that is, the same length is taken in the three directions of x, y and z to determine a plurality of cuboid-shaped unit blocks, each of which is a cellular unit.

[0099] The step of dividing the target photoresist into a preset number of cellular units of the same volume and then grouping the preset number of cellular units step by step may include: setting the number of grouping levels and the number of groupings each time; and grouping the preset number of cellular units step by step according to the number of grouping levels and the number of groupings per level. The number of groupings at different levels is the same, or the number of groupings at different levels is different, or the number of groupings at different levels is partially different.

[0100] S20 , determining initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist.

[0101] Specifically, the target photoresist is mainly composed of a photoacid generator, a polymer resin, an alkali quencher and a solvent. When the photoresist is exposed, the photoacid generator will produce an acidic product. In the subsequent baking, the photoacid generated by the photoacid generator acts as a catalyst to catalyze the deprotection reaction of the polymer resin (the protecting group on the polymer is consumed), affecting the solubility of the polymer, so that the soluble part in the target photoresist can be removed using a liquid developer (such as an aqueous alkaline solution) to form a three-dimensional graphic outline. Therefore, the initial state information of the target photoresist may include the concentration distribution information of the acid molecules formed after the target photoresist is exposed, the concentration distribution information of the alkali quencher and the concentration distribution information of the protecting group. According to the initial state information of the target photoresist and the volume of each cellular unit, the initial number of acid molecules, the initial number of alkali quenchers and the initial number of protecting groups in each cellular unit after the target photoresist is exposed are calculated. The initial distribution data of particles in each cellular unit after exposure include: the initial number of acid molecules, the initial number of alkali quenchers and the initial number of protecting groups.

[0102] S30, calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cellular unit.

[0103] Specifically, within a very small time interval Δt, only one cell unit in the target photoresist reacts between molecules or a molecular material exchange event occurs between cell units. The occurrence of this event is a random event, and its probability is related to the concentration of molecules in the cell unit and the type of reaction, which is called a tendency function, or also called a conversion probability. During the post-baking process, the chemical behavior in the target photoresist includes acid-base neutralization reaction, and the acid molecule catalyzes the deprotection reaction of the protecting group (the protecting group is consumed); the physical behavior in the target photoresist includes: the diffusion of acid molecules and base quencher molecules. According to the initial distribution data of each particle in each cell unit, the tendency function value of each cell unit is calculated to achieve the description of the physical and chemical behavior in the target photoresist at the molecular scale. After calculating the tendency function value of each cell unit, the tendency function value of each calculation group in each level of grouping is determined according to the sum of the tendency function values ​​of the cell units included in each calculation group in each level of grouping.

[0104] S40, a tendency function value updating step, according to the current tendency function value of each calculation group in each level of grouping, randomly simulates the target photoresist through the minimum process method, updates the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit.

[0105] S50, repeat the trend function value updating step multiple times until the next moment after the current moment is the post-bake end moment of the target photoresist, and obtain the distribution data of each particle in the target photoresist after the post-bake.

[0106] Specifically, in the tendency function value updating step, a random number is obtained, and based on the random number and the current tendency function value of each calculation group, specific groups are selected from the first level downwards in sequence until a minimum group is selected, and then a cell unit is selected from the minimum group as the target cell unit; once the target cell unit is selected, the reaction or diffusion event in the target cell unit can be selected according to the weight of each event. Then, according to the selected event, the tendency function value of the corresponding cell (target cell unit) and the tendency function values ​​of the calculation groups at all levels containing the cell are updated. The tendency function value updating step is repeated many times until the next moment after the current moment is the end moment of the post-baking of the target photoresist, and the distribution data of each particle in the target photoresist after post-baking is obtained.

[0107] S60, predicting the profile of the target photoresist after development according to the distribution data of each particle in the target photoresist after post-baking.

[0108] Specifically, the distribution data of each particle in the target photoresist after post-baking includes the distribution data of the protecting group, and the distribution data of the protecting group affects the solubility of the polymer. Therefore, the profile of the target photoresist after development can be predicted based on the distribution data of the protecting group in the target photoresist after post-baking.

[0109] The method for predicting the profile of an extreme ultraviolet photoresist provided by an embodiment of the present invention comprises: dividing a target photoresist into a preset number of cellular units of the same volume, and grouping the preset number of cellular units step by step; wherein each level of grouping includes multiple calculation groups; determining the initial distribution data of each particle in each cellular unit after the target photoresist is exposed according to the initial state information of the target photoresist; calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cellular unit; a tendency function value updating step, according to the current tendency function value of each calculation group in each level of grouping, randomly simulating the target photoresist through a minimum process method, updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group containing the cell unit; repeating the tendency function value updating step multiple times until the next moment after the current moment is the post-baking end moment of the target photoresist, obtaining the distribution data of each particle in the target photoresist after post-baking; and predicting the profile of the target photoresist after development according to the distribution data of each particle in the target photoresist after post-baking. The method for predicting the extreme ultraviolet photoresist profile provided by the embodiment of the present invention solves the random model based on the minimum process method of classification cascade, replacing the solution method in the prior art. It does not have the problem of class table crossing and does not need to frequently add or delete class lists. It can greatly improve the simulation efficiency and speed up the prediction of the extreme ultraviolet photoresist profile.

[0110] The above is the core inventive concept of the present invention. The steps in the method for predicting the EUV photoresist profile are described in detail below.

[0111] On the basis of the above embodiment, optionally, in step S10, the step of grouping a preset number of cell units level by level includes:

[0112] S110, setting the levels of grouping and the number of groupings each time; wherein the numbers of groupings at different levels are the same, or the numbers of groupings at different levels are different, or the numbers of groupings at different levels are partially different.

[0113] Specifically, the grouping method is not unique. An embodiment of the present invention provides a grouping method, including: dividing a given target photoresist volume V into N small blocks, decomposing N into prime factors, and arranging all prime factors in descending order from large to small, and saving them as a vector F; setting the number of splits T, and initializing a unit vector G with a length of T. Traversing the elements in the prime factor vector, multiplying the prime factor with a minimum element in the vector G each time and saving them to the corresponding position, and finally obtaining the number of groups each time. The above grouping method can ensure that the number of groups at each level is not much different. Exemplarily, N is 36; 36 is decomposed into prime factors, and all prime factors are arranged in descending order from large to small, which are 3, 3, 2, 2; the number of splits T is set to 2, then a vector [1,1] with a length of T=2 is initialized, and 3, 3, 2, 2 are assigned to the smallest elements in the vector G in sequence. In the process, the first prime factor 3 is multiplied with a smallest element in the vector until [3,1], the second prime factor 3 is multiplied with a smallest element in the vector [3,1] until [3,3], the third prime factor 2 is multiplied with a smallest element in the vector [3,3] until [6,3], and the fourth prime factor 2 is multiplied with a smallest element in the vector [6,3] until [6,6]; therefore, the final grouping method is: the first grouping is evenly divided into 6 groups to obtain 6 first-level calculation groups; in the second grouping, each first-level calculation group is evenly divided into 6 groups to obtain 6 second-level calculation groups, and each second-level calculation group includes 1 cell unit.

[0114] S120 , grouping a preset number of cell units level by level according to the number of grouping levels and the number of groups in each level, and recording the position index of each cell unit after each grouping.

[0115] Optionally, a preset number of cell units are grouped level by level according to the number of grouping levels and the number of groups in each level, and a position index of each cell unit after each grouping is recorded, including:

[0116] Performing a first average grouping of a preset number N of cell units according to the number N1 of first-level groups, obtaining N1 first-level calculation groups, and sequentially numbering the N1 first-level calculation groups, and recording the number of each first-level calculation group as the position index n1 of the corresponding first-level calculation group; wherein 1≤n1≤N1;

[0117] The M1 cell units in each first-level calculation group are averagely grouped for a second time according to the number of second-level groups N2 to obtain N2 second-level calculation groups, and the N2 second-level calculation groups in each first-level calculation group are numbered in sequence, and the combination of the number of each second-level calculation group and the number of the first-level calculation group to which it belongs is recorded as the position index (n1, n2) of the corresponding second-level calculation group; wherein 1≤n2≤N2;

[0118] The M2 cell units in each second-level calculation group are averagely grouped for the third time according to the number of third-level groups N3 to obtain N3 third-level calculation groups, and the N3 third-level calculation groups in each second-level calculation group are numbered in sequence, and the combination of the number of each third-level calculation group and the number of the second-level calculation group and the first-level calculation group to which it belongs is recorded as the position index (n1, n2, n3) of the corresponding third-level calculation group; wherein 1≤n3≤N3;

[0119] And so on, until the number of groups meets the set number of levels T, and calculate the N in each T-1th level group T The T-th level calculation groups are numbered in sequence, and the combination of the number of each T-th level calculation group and the number of each level calculation group to which it belongs is recorded as the position index (n1, n2, ..., n T ), where 1≤n T ≤N T ;

[0120] For each T-th level calculation group M T The cell units are numbered in sequence, and the position index of each cell unit (n1, n2, ..., n T , n T+1 ), where 1≤n T+1 ≤M T .

[0121] Specifically, a three-dimensional target photoresist system is divided into a preset number of cellular units of equal volume, and the cellular units are numbered in order, and N is the number of cellular units of the system. In the first grouping, the N cellular units are evenly divided into N1 small groups, and N1 first-level computing groups are obtained, each group contains M1=N / N1 cellular units, and these N1 first-level computing groups are numbered n1 (n1=1,2,3,…,N1); in the second grouping, the M1 cellular units in each first-level computing group are further divided into N2 small groups, and each first-level computing group obtains N2 second-level computing groups, each group contains M2=M1 / N2 cellular units, and these N2 second-level computing groups are all numbered n2 (n2=1,2,3,…,N2); and so on. After the Tth grouping, each smallest group (the last level computing group) should contain M T =M T-1 / N T =N / (N1N2…N T ) cell units, this N T The smallest group is also numbered n T (n T =1,2,3…,N T), each smallest group contains cells that can be numbered as n T+1 (n T+1 =1,2,3,…,M T ), therefore, through a multidimensional coordinate (n1,n2,…,n T+1 ) can be located in one of the smallest groups of cells. Exemplarily, Figure 2 This is a two-level step-by-step grouping process for 1000 cell units provided by an embodiment of the present invention, referring to Figure 2 , a group of cells numbered 1-1000 is grouped T = 2 times, each time into 10 groups, that is, N1 = N2 = 10, and the smallest group contains M T =10 cell units.

[0122] Based on the above embodiments, optionally, in step S30, the step of calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cell unit includes:

[0123] S310, calculating the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit according to the initial distribution data of each particle in each cell unit.

[0124] Specifically, during the post-bake process, the chemical behavior in the target photoresist includes an acid-base neutralization reaction, and the acid molecule catalyzes the deprotection reaction of the protecting group (the protecting group is consumed); the physical behavior in the target photoresist includes the diffusion of the acid molecule and the base quencher molecule. In the step of calculating the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit according to the initial distribution data of each particle in each cell unit, the calculation formula of the tendency function value of the acid-base neutralization reaction is determined based on the following:

[0125] a3=c3X j X k ;

[0126] Among them, a3 is the tendency function value of acid-base neutralization reaction; X j is the number of acid molecules; X k is the number of base quencher molecules; c3 is the acid-base neutralization reaction coefficient. The calculation formula for the tendency function value of the acid molecule catalyzing the deprotection reaction of the protecting group is determined based on the following:

[0127] a4=c4X j X k ;

[0128] Wherein, a4 is the tendency function value of the protective group to undergo deprotection reaction; X j is the number of acid molecules; X kis the number of base quencher molecules; c4 is the deprotection reaction coefficient.

[0129] The calculation formula of the value of the tendency function for acid molecules to diffuse is determined based on the following:

[0130]

[0131] Among them, a5 is the tendency function value of acid molecules to diffuse, D A is the diffusion coefficient of the acid molecules, is the number of acid molecules; l x is the length of the cell unit in the x direction; l y is the length of the cell unit in the y direction; l z is the length of the cell unit in the z direction.

[0132] The calculation formula of the value of the propensity function for the diffusion of the base quencher molecules is determined based on the following:

[0133]

[0134] Where a6 is the tendency function value of the base quencher molecule, D B is the diffusion coefficient of the base quencher molecules, is the number of base quencher molecules.

[0135] S320, determining the tendency function value of the corresponding cell unit according to the sum of the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit.

[0136] S330 , determining the tendency function value of each calculation group in each level of grouping according to the sum of the tendency function values ​​of the cell units included in each calculation group in each level of grouping.

[0137] Specifically, the coordinates are (n1,n2,…,n S ) is calculated by Indicates that the tendency function value is determined by the sum of the tendency function values ​​of all the cell units in the calculation group. Among them, the tendency function value of the first-level calculation group is Determined based on the following formula:

[0138]

[0139] The second level calculates the tendency function value of the group Determined based on the following formula:

[0140]

[0141] The tendency function value of the T-th level calculation group (the last level calculation group) Determined based on the following formula:

[0142]

[0143] The total tendency function value W of the target photoresist N , determined based on the following formula:

[0144]

[0145] On the basis of the above embodiments, optionally, in the tendency function value updating step S40, according to the current tendency function value of each calculation group in each level of grouping, the target photoresist is randomly simulated by the minimum process method to update the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit, including:

[0146] S410 , determining a current total tendency function value of the target photoresist according to the sum of the current tendency function values ​​of all the cell units in the target photoresist.

[0147] S420, obtaining a first random number, and calculating a time interval according to the first random number and the current total tendency function value of the target photoresist. Specifically, calculating the time interval according to the first random number and the current total tendency function value of the target photoresist is based on the following determination:

[0148]

[0149] Where Δt is the time interval; W N is the current total tendency function value of the target photoresist; r1 is the first random number, r1∈[0,1).

[0150] S430, determine whether the sum of the accumulated time after the last update and the time interval exceeds the total post-baking time; if not, execute step S440; if exceeded, stop the trend function value update step.

[0151] S440, obtaining a second random number, calculating a first-level selected value according to the second random number and the current total tendency function value, and selecting a target first-level calculation group from all first-level calculation groups according to the first-level selected value.

[0152] Specifically, the first level selected value is calculated according to the second random number and the current total tendency function value, based on the following determination:

[0153] R1=r2W N ;

[0154] Among them, R1 is the first-level selected value; r2 is the second random number, r2∈[0,1).

[0155] A target first-level calculation group is selected from all first-level calculation groups according to the first-level selected value, based on the following determinations:

[0156]

[0157] Where n1 is the number of the first-level computing group, and m1 is the number of the target first-level computing group; The value of the tendency function for the n1th first-level calculation group.

[0158] S450. Calculate the second-level selected value based on the sum of the tendency function values ​​of the first-level calculation groups whose numbers are located before the target first-level calculation group and the first-level selected value, and select the target second-level calculation group from all the second-level calculation groups under the target first-level calculation group based on the second-level selected value.

[0159] Specifically, the second-level selected value is calculated based on the sum of the propensity function values ​​of the first-level calculation group before the target first-level calculation group and the first-level selected value, based on the formula Determine, where R2 is the second level selected value.

[0160] The target second-level calculation group is selected from all second-level calculation groups under the target first-level calculation group according to the second-level selected value, based on the following determination:

[0161]

[0162] Where n2 is the number of the second-level computing group, and m2 is the number of the target second-level computing group; The tendency function value of the n2th second-level calculation group in the target first-level calculation group.

[0163] S460. Calculate the third-level selected value based on the sum of the tendency function values ​​of the second-level calculation groups whose numbers are located before the target second-level calculation group and the second-level selected value, and select the target third-level calculation group from all the third-level calculation groups under the target second-level calculation group based on the third-level selected value.

[0164] Specifically, the third-level selected value is calculated based on the sum of the propensity function values ​​of the second-level calculation group before the target second-level calculation group and the second-level selected value, based on the formula Determine; wherein R3 is the third level selected value.

[0165] A target third-level calculation group is selected from all third-level calculation groups under the target second-level calculation group according to the third-level selected value, based on the following determination:

[0166]

[0167] Where n3 is the number of the third-level computing group, and m3 is the number of the target second-level computing group; The tendency function value of the n3th third-level calculation group in the target second-level calculation group.

[0168] S470, and so on, until a target cell unit is selected from the calculation group selected at the last level.

[0169] S480, determining a currently occurring random event from among the reaction events and the molecular diffusion events according to the weights of the reaction events and the molecular diffusion events in the target cell unit.

[0170] Specifically, the selection algorithm is repeated until the minimum calculation group m is obtained. T Select a cell m T+1 Finally, a random event is selected in the cell according to the weights of each reaction event and molecular diffusion event. The selected random event is the acid-base neutralization reaction random event, the acid molecule catalyzes the deprotection reaction of the protecting group (the protecting group is consumed) random event, the acid molecule diffusion random event or the base quencher molecule diffusion random event.

[0171] S490, updating the number of each particle in the target cell unit according to the currently occurring random event.

[0172] S4100. Determine the updated tendency function value of the target cell and the updated tendency function values ​​of each level of calculation groups including the target cell according to the updated number of each particle in the target cellular unit.

[0173] Specifically, according to the updated number of each particle in the target cell unit, the updated tendency function value of the target cell and the updated tendency function value of each level of calculation group containing the target cell can be calculated by referring to the formula in step S310, which will not be repeated here. Since the position of each cell unit is fixed after each grouping, the tendency function value of the target cell unit can be quickly updated according to the position index of the target cell unit, thereby improving the prediction efficiency of the extreme ultraviolet photoresist profile.

[0174] In summary, in the process of solving the random model based on the minimum process method of classification cascade, the selection algorithm is to select specific groups from top to bottom until a minimum group is selected, and then select a cell unit from the minimum group. Only one random number is needed to implement the selection process. First, according to the probability Select one of the N1 largest groups obtained from the first grouping, for example, the m1th group, and then From the N2 second-level calculation groups obtained from the second grouping, select one, such as the m2th group, and so on, until the probability is reached after the last grouping. From the smallest group m T Group selects the mth T+1From this process, we can infer that the mth T+1 The probability of a cell unit is This probability is exactly the selection probability in the SSA algorithm, so the splitting algorithm produces the correct master equation. Once a specific cell unit is selected, the reaction or diffusion event in the cell unit can be selected according to the weight of each event, thereby updating the tendency function value of the corresponding cell unit and the tendency function value of the calculation group containing the cell unit.

[0175] After step S4100, the process returns to step S410 until the next moment after the current moment is the end moment of the post-bake of the target photoresist, and the trend function value updating step is stopped. According to the distribution data of each particle in the target photoresist after the last update of the number of each particle in the target cell unit, the distribution data of each particle in the target photoresist after the post-bake is determined.

[0176] On the basis of the above embodiments, optionally, in step S60, according to the distribution data of each particle in the target photoresist after post-baking, predicting the profile of the target photoresist after development specifically includes:

[0177] S610, determining a preliminary distribution image of the protecting groups after the target photoresist is developed according to the distribution data of the protecting groups in the target photoresist after post-baking.

[0178] S620 , performing Gaussian convolution processing and normalization processing on the preliminary distribution image of the protecting groups after the target photoresist is developed, to obtain a target distribution image of the protecting groups after the target photoresist is developed.

[0179] S630, predicting the target profile after photolithography development according to the target distribution image of the protecting group.

[0180] Among them, Gaussian convolution and normalization are two important technologies in image processing. Gaussian convolution smoothes the image through the convolution kernel calculated by the Gaussian function, while normalization is to ensure the scale consistency of the data during image processing. The combination of the two can effectively achieve image smoothing and denoising functions. Figure 3 is a schematic diagram of a preliminary distribution image of a protecting group after development of a target photoresist provided by an embodiment of the present invention, Figure 4 is a schematic diagram of a target distribution image of a protective group after a target photoresist is developed according to an embodiment of the present invention, with reference to Figure 3 and Figure 4 , the preliminary distribution image of the protecting group after the target photoresist is developed is subjected to Gaussian convolution processing and normalization processing, and a clear target distribution image of the protecting group can be obtained.

[0181] For target photoresist systems with larger volumes, this technology can greatly improve simulation efficiency. Figure 5FIG. 1 is a comparison chart of the time taken to simulate a system composed of different numbers of cell units by a related technology provided by an embodiment of the present invention and a prediction method provided by an embodiment of the present invention. When running on the matlab platform, the running time of the two schemes is compared as follows: Figure 5 As shown, curve a is a time curve consumed in the related art for simulating a system composed of different numbers of cellular units, and curve b is a time curve consumed in simulating a system composed of different numbers of cellular units through the technical solution provided by the embodiment of the present invention; the technical solution provided by the embodiment of the present invention can quickly predict the target photoresist profile, guide and optimize the photolithography process, and reduce costs.

[0182] The embodiment of the present invention also provides a prediction device for an extreme ultraviolet photoresist profile, comprising: a cell unit division unit, used to divide the target photoresist into a preset number of cell units of the same volume, and group the preset number of cell units step by step; wherein each level of grouping includes multiple calculation groups; an initial distribution data determination unit, used to determine the initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist; a tendency function value calculation unit, used to calculate each calculation group in each level of grouping according to the initial distribution data of each particle in each cell unit The tendency function value of the cell unit currently selected and the tendency function value of the calculation group including the cell unit are updated; the prediction unit is used to obtain the distribution data of each particle in the target photoresist after post-baking, and predict the contour of the target after photolithography development according to the distribution data of each particle in the target photoresist after post-baking.

[0183] The device for predicting the EUV photoresist profile provided in the embodiment of the present invention can execute the method for predicting the EUV photoresist profile provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0184] An embodiment of the present invention further provides an electronic device, Figure 6A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0185] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14. Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0186] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a prediction method for an extreme ultraviolet photoresist profile.

[0187] In some embodiments, the prediction method of the extreme ultraviolet photoresist profile can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the prediction method of the extreme ultraviolet photoresist profile described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the prediction method of the extreme ultraviolet photoresist profile by any other appropriate means (e.g., by means of firmware).

[0188] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for predicting an extreme ultraviolet photoresist profile, characterized in that: include: Divide the target photoresist into a preset number of cellular units with the same volume, and group the preset number of cellular units level by level; wherein each level of grouping includes multiple calculation groups; Determining initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist; According to the initial distribution data of each particle in each cell unit, the tendency function value of each calculation group in each level of grouping is calculated; a tendency function value updating step, performing random simulation on the target photoresist by a minimum process method according to the current tendency function value of each calculation group in each level of grouping, and updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit; Repeat the trend function value updating step multiple times until the next moment after the current moment is the post-bake end moment of the target photoresist, and obtain the distribution data of each particle in the target photoresist after post-bake; The profile of the target photoresist after development is predicted based on the distribution data of each particle in the target photoresist after post-baking.

2. The method for predicting an extreme ultraviolet photoresist profile according to claim 1, wherein: The step of grouping a preset number of cell units level by level includes: Setting the number of grouping levels and the number of groupings each time; wherein the number of groupings at different levels is the same, or the number of groupings at different levels is different, or the number of groupings at different levels is partially different; The preset number of cell units are grouped level by level according to the number of grouping levels and the number of groups in each level, and the position index of each cell unit after each grouping is recorded.

3. The method for predicting an extreme ultraviolet photoresist profile according to claim 2, wherein: The preset number of cell units are grouped level by level according to the number of grouping levels and the number of groups in each level, and the position index of each cell unit after each grouping is recorded, including: Performing a first average grouping of the preset number N of cell units according to the number N1 of the first-level groups, and sequentially numbering the N1 first-level calculation groups, and recording the number of each first-level calculation group as the position index n1 of the corresponding first-level calculation group; wherein 1≤n1≤N1; The M1 cell units in each first-level calculation group are grouped equally for the second time according to the number of second-level groups N2, and the N2 second-level calculation groups in each first-level calculation group are numbered in sequence, and the combination of the number of each second-level calculation group and the number of the first-level calculation group to which it belongs is recorded as the position index (n1, n2) of the corresponding second-level calculation group; wherein 1≤n2≤N2; The M2 cell units in each second-level calculation group are grouped for the third time according to the number of third-level groups N3, and the N3 third-level calculation groups in each second-level calculation group are numbered in sequence, and the combination of the number of each third-level calculation group and the numbers of the second-level calculation group and the first-level calculation group to which it belongs is recorded as the position index (n1, n2, n3) of the corresponding third-level calculation group; wherein 1≤n3≤N3; And so on, until the number of groups meets the set number of levels T, and calculate the N in each T-1th level group T The T-th level calculation groups are numbered in sequence, and the combination of the number of each T-th level calculation group and the number of each level calculation group to which it belongs is recorded as the position index (n1, n2, ..., n T ), where 1≤n T ≤N T ; For each of the T-th level calculation groups M T The cell units are numbered in sequence, and the position index of each cell unit (n1, n2, ..., n T , n T+1 ), where 1≤n T+1 ≤M T .

4. The method for predicting an extreme ultraviolet photoresist profile according to claim 1, characterized in that: Determining the initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information includes: Acquire the initial state information of the target photoresist; wherein the initial state information includes the concentration distribution information of the photoacid generator, the concentration distribution information of the base quencher and the concentration distribution information of the protecting group of the target photoresist; According to the initial state information of the target photoresist and the volume of each cell unit, the initial number of acid molecules, the initial number of base quenchers and the initial number of protective groups in each cell unit after the target photoresist is exposed are calculated.

5. The method for predicting the EUV photoresist profile according to claim 4, characterized in that: According to the initial distribution data of each particle in each cell unit, the tendency function value of each calculation group in each level of grouping is calculated, including: According to the initial distribution data of each particle in each cell unit, the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit are calculated; Determine the tendency function value of the corresponding cell unit according to the sum of the reaction tendency function value and the molecular diffusion tendency function value of each reaction occurring in each cell unit; The tendency function value of each calculation group in each level of grouping is determined according to the sum of the tendency function values ​​of all cell units included in each calculation group in each level of grouping.

6. The method for predicting the extreme ultraviolet photoresist profile according to claim 5, characterized in that: In the tendency function value updating step, according to the current tendency function value of each calculation group in each level of grouping, the target photoresist is randomly simulated by the minimum process method to update the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit, including: Determining a current total tendency function value of the target photoresist according to the sum of current tendency function values ​​of all cell units in the target photoresist; Obtaining a first random number, and calculating a time interval according to the first random number and a current total tendency function value of the target photoresist; Determine whether the sum of the accumulated time after the last update and the time interval exceeds the total post-baking time; if not, then obtaining a second random number, calculating a first-level selected value according to the second random number and the current total tendency function value, and selecting a target first-level calculation group from all first-level calculation groups according to the first-level selected value; Calculate a second-level selected value according to the sum of the tendency function values ​​of the first-level calculation groups whose numbers are located before the target first-level calculation group and the first-level selected value, and select a target second-level calculation group from all the second-level calculation groups under the target first-level calculation group according to the second-level selected value; Calculate a third-level selected value according to the sum of the tendency function values ​​of the second-level calculation groups whose numbers are located before the target second-level calculation group and the second-level selected value, and select a target third-level calculation group from all the third-level calculation groups under the target second-level calculation group according to the third-level selected value; And so on, until a target cell unit is selected from the calculation group selected at the last level; According to the weights of the reaction events and the molecular diffusion events in the target cell unit, determining the currently occurring random event from the reaction events and the molecular diffusion events; Update the number of each particle in the target cell unit according to the currently occurring random event; According to the updated number of each particle in the target cell unit, the updated tendency function value of the target cell and the updated tendency function values ​​of each level of calculation group including the target cell are determined.

7. The method for predicting the EUV photoresist profile according to claim 6, characterized in that: The time interval is calculated according to the first random number and the current total tendency function value of the target photoresist, based on the following determination: Wherein, Δt is the time interval; W N is the current total tendency function value of the target photoresist; r1 is the first random number, r1∈[0,1); The first level selected value is calculated according to the second random number and the current total tendency function value, based on the following determination: R1=r2W N ; Wherein, R1 is the first level selected value; r2 is the second random number, r2∈[0,1); The selecting of the target first-level calculation group from all the first-level calculation groups according to the first-level selected value is based on the following determination: Wherein, n1 is the number of the first-level computing group, and m1 is the number of the target first-level computing group; is the tendency function value of the n1th first-level calculation group; The second level selected value is calculated based on the sum of the tendency function values ​​of the first level calculation group located before the target first level calculation group and the first level selected value, based on the following determination: Wherein, R2 is the second level selected value; The selecting of the target second-level computing group from all the second-level computing groups under the target first-level computing group according to the second-level selected value is based on the following determination: Wherein, n2 is the number of the second-level computing group, and m2 is the number of the target second-level computing group; is the tendency function value of the n2th second-level calculation group in the target first-level calculation group; The calculation of the third level selected value based on the sum of the tendency function values ​​of the second level calculation group located before the target second level calculation group and the second level selected value is based on the following determination: Wherein, R3 is the third level selected value; The selecting of the target third-level computing group from all the third-level computing groups under the target second-level computing group according to the third-level selected value is based on the following determination: Wherein, n3 is the number of the third-level computing group, and m3 is the number of the target second-level computing group; The tendency function value of the n3th third-level calculation group in the target second-level calculation group.

8. The method for predicting an extreme ultraviolet photoresist profile according to claim 1, characterized in that: Predicting the profile of the target after photolithography development according to the distribution data of each particle in the target photoresist after post-baking, including: Determining a preliminary distribution image of the protecting groups after the target photoresist is developed according to the distribution data of the protecting groups in the target photoresist after post-baking; Performing Gaussian convolution processing and normalization processing on the preliminary distribution image of the protecting groups after the target photoresist is developed, so as to obtain a target distribution image of the protecting groups after the target photoresist is developed; The profile of the target photoresist after development is predicted based on the target distribution image of the protecting groups.

9. A prediction device for extreme ultraviolet photoresist profile, characterized in that: include: A cell unit division unit is used to divide the target photoresist into a preset number of cell units with the same volume, and group the preset number of cell units level by level; wherein each level of grouping includes a plurality of calculation groups; An initial distribution data determination unit, used to determine the initial distribution data of each particle in each cell unit after the target photoresist is exposed according to the initial state information of the target photoresist; A tendency function value calculation unit, used for calculating the tendency function value of each calculation group in each level of grouping according to the initial distribution data of each particle in each cell unit; An updating unit, used for updating the tendency function value multiple times until the next moment after the current moment is the post-bake end moment of the target photoresist; in each update, the updating unit is used for randomly simulating the target photoresist through the minimum process method according to the current tendency function value of each calculation group in each level of grouping, and updating the tendency function value of the currently selected cell unit and the tendency function value of the calculation group including the cell unit; The prediction unit is used to obtain the distribution data of each particle in the target photoresist after post-baking, and predict the profile of the target after photolithography development according to the distribution data of each particle in the target photoresist after post-baking.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for predicting an extreme ultraviolet photoresist profile according to any one of claims 1 to 8.

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