Objective lens, method of adjusting objective lens, adjusting device, storage medium and lithographic apparatus

Abstract

The application provides a kind of objective lens debugging method, debugging device, storage medium and photoetching equipment, the objective lens debugging method includes: obtaining the current image quality of objective lens, sensitivity matrix and current parameter, output several initial adjustment quantities of adjustable lens;Initial adjustment quantity is encoded as individual, and the initialized population is output;Combined with the sensitivity matrix of objective lens, the image quality fitness function of objective lens is established, and the image quality fitness value of population is calculated;Using genetic algorithm, the image quality fitness function is used to iterate the population to the mature population, and the optimal adjustment quantity of adjustable lens based on the current image quality is output;The optimal adjustment quantity is applied to the objective lens, and the optimized image quality of objective lens is obtained.In the application, the optimal adjustment quantity obtained by the theory is applied to the debugging of objective lens, which not only can use the optimal adjustment quantity to quickly converge the image quality, improve the debugging efficiency of objective lens, but also can simplify the debugging process of objective lens, improve the debugging process of objective lens.

Description

Technical Field

[0001] This invention relates to the field of semiconductor technology, and in particular to a method for adjusting an objective lens, an adjustment device, a storage medium, and a photolithography apparatus. Background Technology

[0002] Photolithography is the most critical process in integrated circuit manufacturing, therefore, photolithography equipment is the most critical equipment in integrated circuit manufacturing.

[0003] Taking projection lithography equipment as an example, the projection lithography objective (hereinafter referred to as the objective) consists of a lens group composed of multiple lenses. It is the core component of the lithography equipment and determines its main performance. In the lithography equipment, after the objective is installed, due to the unavoidable processing errors during lens manufacturing and the assembly errors during assembly, the imaging quality (hereinafter referred to as image quality) of the objective system will decrease, deviating from the theoretical value and reducing the resolution of the lithography equipment.

[0004] To improve image quality, certain specific lenses (such as adjustable lenses) in the objective lens need to be adjusted online with multiple degrees of freedom to correct assembly errors, improve image quality, and thus ensure the image quality of the lithography equipment. However, because the processing and assembly errors of different objectives vary, it is difficult to form a unified and standardized adjustment method, resulting in slow adjustment progress and low adjustment efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, storage medium, and photolithography equipment for adjusting an objective lens, thereby improving the adjustment progress and efficiency during the objective lens adjustment process.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for adjusting an objective lens, comprising:

[0007] S1: Obtain the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output several initial adjustment values ​​of the adjustable lenses;

[0008] S2: Encode the initial adjustment amount as an individual and output the initialized population;

[0009] S3: Based on the sensitivity matrix of the objective lens, establish the image quality fitness function of the objective lens and calculate the image quality fitness value of the population;

[0010] S4: Using a genetic algorithm and the image quality fitness function, the population is iterated until it matures, and the optimal adjustment amount of the adjustable lens based on the current image quality is output.

[0011] S5: Apply the optimal adjustment amount to the objective lens to obtain optimized image quality of the objective lens.

[0012] Optionally, the current parameters and the second parameter of the adjustable lens include the surface eccentricity, surface tilt, and surface spacing of the adjustable lens, and the initial adjustment amount and the optimal adjustment amount include the adjustment amounts of surface eccentricity, surface tilt, and surface spacing.

[0013] Optionally, S2 includes: encoding the initial adjustment amount into individuals using real values, and performing initialization on the population to generate the initialized population, constrained by the adjustable range and adjustment accuracy of the adjustable lens.

[0014] Optionally, the initial population can be formed using a Gaussian distribution or a uniform distribution.

[0015] Optionally, the image quality of the objective lens is evaluated using its wavefront aberration, which is represented by a Zernike polynomial of 1 to 37 terms.

[0016] Optionally, the step of calculating the image quality fitness value of the population includes:

[0017] Calculate the RMS values ​​of various wavelet aberrations of the population, including RMS(Z5-Z5). 37 ), Spherical aberration Sph_RMS, Coma_RMS, Astigmatism Ast_RMS, and Trifoliate aberration Trefoil_RMS, Z5-Z 37 These are the Zernike polynomials from the 5th to the 37th term;

[0018] Establish the image quality fitness function D for the population, D = d1 * RMS(Z5 - Z). 37 )+d2*Sph_RMS+d3*Coma_RMS+d4*Ast_RMS+d5*Trefoil_RMS, where d1, d2, d3, d4 and d5 are their respective weighting coefficients, and d1, d2, d3, d4 and d5 are all 0~1;

[0019] The function value is obtained according to the image quality fitness function, and the reciprocal of the function value is taken as the image quality fitness value of the population.

[0020] Optionally, a genetic algorithm is used to iterate over the population, including a genetic algorithm selection step, a genetic algorithm crossover step, and a genetic algorithm mutation step to update the population, thereby obtaining an updated population. The fitness value of the updated population is then calculated again, and the genetic algorithm is iteratively executed until the image quality of the updated population converges.

[0021] Optionally, the genetic algorithm selection step may include selection using a roulette wheel or tournament method.

[0022] Optionally, after performing step S1, an optimization algorithm is used to select the optimal set from several sets of initial adjustment amounts as the input for step S2.

[0023] Optionally, during the execution of the genetic algorithm, after each mutation step, an elite retention strategy is adopted to replace the worst individual in the current population with the historically best individual, and then the current population is iterated.

[0024] According to another aspect of the present invention, an objective lens adjustment device is also provided, the objective lens comprising a plurality of adjustable lenses, including:

[0025] The acquisition module is used to acquire the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output a set of initial adjustment values ​​for the adjustable lenses.

[0026] An initialization module is used to encode the initial adjustment amount output by the acquisition module to generate an initialized population;

[0027] The evaluation module is used to output the image quality change of the objective lens in response to individuals in the population, and to output the image quality fitness value of the population.

[0028] The iteration module is used to perform iterative calculations on the image quality fitness value of the population calculated by the evaluation module using a genetic algorithm until the population matures, and outputs the optimal adjustment amount based on the current image quality.

[0029] Based on another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium storing computer-executable instructions for causing a computer to perform the objective lens adjustment method as described in any of the above claims.

[0030] According to another aspect of the present invention, a photolithography apparatus is also provided, including an objective lens and an objective lens adjustment device. The objective lens includes a plurality of adjustable lenses, and the objective lens adjustment device is used to adjust the objective lens. The objective lens adjustment device includes:

[0031] The acquisition module is used to acquire the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output a set of initial adjustment values ​​for the adjustable lenses.

[0032] An initialization module is used to encode the initial adjustment amount output by the acquisition module to generate an initialized population;

[0033] The evaluation module is used to output the image quality change of the objective lens in response to individuals in the population, and to output the image quality fitness value of the population.

[0034] The iteration module is used to perform iterative calculations on the image quality fitness value of the population calculated by the evaluation module using a genetic algorithm until the population matures, and outputs the optimal adjustment amount based on the current image quality.

[0035] In summary, the objective lens adjustment method and apparatus provided by this invention can generate individuals and an initial population for objective lens adjustment by obtaining the current image quality, sensitivity matrix, and current parameters of the adjustable lenses of the objective lens. Simultaneously, an image quality fitness function of the objective lens is established to calculate the image quality fitness value of the population for evaluation. A genetic algorithm is then used to iteratively operate the population using the aforementioned image quality fitness function until the population matures, and its optimal adjustment value is output for actual objective lens adjustment. Compared to online adjustment, applying the theoretically obtained optimal adjustment value obtained in this embodiment to objective lens adjustment not only allows for rapid convergence of image quality and improves objective lens adjustment efficiency but also simplifies the objective lens adjustment process and accelerates the adjustment process. Attached Figure Description

[0036] Those skilled in the art will understand that the accompanying drawings are provided to better understand the invention and do not constitute any limitation thereof. Wherein:

[0037] Figure 1a This is a schematic diagram of the position detection device provided in Embodiment 1;

[0038] Figure 1b The flowcharts are for the corresponding steps of the objective lens adjustment method provided in Example 1.

[0039] Figure 2a This is a schematic diagram of the position detection device provided in Embodiment 2;

[0040] Figure 2b The flowcharts are for the corresponding steps of the objective lens adjustment method provided in Example 2.

[0041] Figure 3a This is a schematic diagram of the position detection device provided in Embodiment 3;

[0042] Figure 3b The flowchart shows the corresponding steps of the objective lens adjustment method provided in Example 3. Detailed Implementation

[0043] To make the objectives, advantages, and features of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the drawings are all in a very simplified form and are not drawn to scale, and are only used to facilitate and clarify the explanation of the embodiments of this invention. Furthermore, the structures shown in the drawings are often part of the actual structures. In particular, different figures may emphasize different aspects and may sometimes use different scales.

[0044] It should be understood that when an element or layer is referred to as "on" or "connected to" other elements or layers, it may be directly on or connected to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as "directly on" or "directly connected to" other elements or layers, there are no intervening elements or layers. Although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are only used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this invention, the first element, component, area, layer, or portion discussed below may be referred to as a second element, component, area, layer, or portion. Spatial relation terms such as "below," "under," "below," "above," "on top," "above," etc., may be used herein for convenience of description to describe the relationship between one element or feature shown in the figures and other elements or features. It should be understood that, in addition to the orientations shown in the figures, spatial relational terms are intended to also include different orientations of the devices in use and operation. For example, if the devices in the figures are flipped, then elements or features described as “below,” “under,” or “below” will be oriented “on” other elements or features. Devices may be oriented additionally (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly. The terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “comprising” is used to identify the presence of features, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups. When used herein, the terms “and / or” include any and all combinations of the associated listed items.

[0045] Example 1

[0046] Example 1 provides a method for adjusting an objective lens.

[0047] Figure 1a This is a flowchart of the objective lens adjustment method provided in Example 1.

[0048] like Figure 1a As shown, the objective lens adjustment method provided in this embodiment includes:

[0049] S1: Obtain the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output several initial adjustment values ​​of the adjustable lenses;

[0050] S2: Encode the initial adjustment amount as an individual and output the initialized population;

[0051] S3: Based on the sensitivity matrix of the objective lens, establish the image quality fitness function of the objective lens and calculate the image quality fitness value of the population;

[0052] S4: Using a genetic algorithm and the image quality fitness function, the population is iterated until it matures, and the optimal adjustment amount of the adjustable lens based on the current image quality is output.

[0053] S5: Apply the optimal adjustment amount to the objective lens to obtain optimized image quality of the objective lens.

[0054] Figure 1b The flowchart below shows the corresponding steps of the objective lens adjustment method provided in Example 1. Next, we will combine... Figure 1a and Figure 1b The adjustment method of the objective lens is described in detail.

[0055] First, execute step S1 to obtain the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output several initial adjustment values ​​of the adjustable lenses.

[0056] The objective lens in this embodiment and subsequent embodiments, also referred to as an objective lens optical system, may include multiple lenses, some of which are adjustable lenses used to adjust performance parameters of the objective lens, such as image quality. In this embodiment, improving the image quality of the objective lens is the adjustment goal. Before performing objective lens adjustment, the current image quality of the objective lens can be tested on a test bench, i.e., the current image quality, as the starting point for objective lens adjustment. In practice, the objective lens adjustment process is difficult to complete in one step; it includes multiple adjustment stages, i.e., several coarse adjustment stages and several fine adjustment stages. The objective lens adjustment method of this embodiment is applicable to all adjustment stages in the objective lens adjustment process.

[0057] Preferably, an objective model can be built on optical design software (such as Zemax, CodeV, etc.) based on the structure of the objective to be adjusted, in order to improve the adjustment efficiency of the objective. Based on the established objective model, the current parameters (adjustable parameters) of the adjustable lenses can be obtained, that is, the parameters of the movable mechanism that cooperates with the adjustable lenses, i.e., the current parameters. Depending on the number of adjustable lenses and adjustable parameters, the current parameter set can be a parameter set that includes all adjustable parameters of all adjustable lenses, and a set of changes is randomly generated within the adjustable range of the current parameters as initial adjustment amounts for subsequent adjustment. The current parameters may include the surface eccentricity, surface tilt, and surface spacing of the adjustable lenses; correspondingly, the initial adjustment amounts may include the adjustment amounts for surface eccentricity, surface tilt, and surface spacing.

[0058] Specifically, the wavelet aberration of the objective lens can be used to evaluate (represent) the image quality (current image quality), and the wavelet aberration can be represented by the Zernike polynomial W(x,y).

[0059] W(x,y)=R1Z1(x,y)+R2Z2(x,y)+......R n Z n (x, y)

[0060] Among them, Z n Let R represent the nth Zernike polynomial. n Let (x, y) represent the corresponding Zernike coefficient in the nth Zernike polynomial, and (x, y) represent the coordinate position. Of course, polar coordinates can also be used to represent the coordinate position in other examples.

[0061] In this embodiment, Z1~Z can be used. 37 That is, the wavelet aberration of the objective lens is represented by 37 Zernike polynomials, and the accuracy is improved by increasing its order.

[0062] In addition, the current image quality can be input into the objective model to obtain the sensitivity matrix of the current objective (i.e., under the current parameters), which is the wave aberration change (image quality change) in response to the adjustment amount (change in current parameters). This sensitivity matrix can be composed of a sub-matrix corresponding to each Zernike coefficient.

[0063] Next, step S2 is executed, where the initial adjustment amount is encoded as an individual, and the initialized population is output.

[0064] The initial adjustment values ​​generated above are encoded with real values ​​as individuals in the population. Then, the adjustable range and adjustment accuracy of the adjustable lens (movable mechanism) are used as constraints. Several sets of adjustment values ​​are randomly generated in the population using a Gaussian distribution to expand the number of individuals, thus completing the initialization of the population.

[0065] Next, step S3 is executed, and the image quality fitness function of the objective lens is established by combining the sensitivity matrix of the objective lens, and the image quality fitness value of the population is calculated.

[0066] As previously used, wavelet aberrations of the objective lens are used to represent the image quality of the objective lens. The steps to establish the image quality fitness function of the objective lens include: calculating the RMS values ​​of each wavelet aberration for each group of individuals in the population, including RMS(Z5-Z5). 37 ), spherical aberration Sph_RMS, coma_RMS, astigmatism Ast_RMS, and triceps aberration Trefoil_RMS. RMS (Z5-Z) 37 Taking spherical difference Sph_RMS as an example,

[0067] RMS (Z5-Z) 37 =R1*Z5+R2*Z6+......+R33 *Z 36 +R 34 *Z 37 ,

[0068] Sph_RMS=S1*Z9+S2*Z 16 +S3*Z 25 + S4*Z 36 ,

[0069] Among them, R n S represents the corresponding Zernike coefficient in the nth Zernike polynomial. n Z represents the combined aberration coefficients. n Let n represent the nth Zernikic polynomial.

[0070] The image quality fitness function D is established using the RMS values ​​of various wavelet aberrations.

[0071] D=d1*RMS (Z5-Z37)+d2*Sph_RMS+d3*Coma_RMS+d4*Ast_RMS+d5*Trefoil_RMS,

[0072] Where d1, d2, d3, d4, and d5 are the weighting coefficients for their respective wavefront aberration RMS values, and d1+d2+d3+d4+d5=1. It is worth mentioning that, depending on the specific scene requirements for adjusting the objective lens, only some of the above wavefront aberration RMS values ​​can be selectively set, with one or any of d1 to d5 being 0.

[0073] Preferably, the corresponding function value is obtained according to the above image quality fitness function D, and the reciprocal of the function value is taken as the image quality fitness value of the individual in the group in the population, so that the maximum value is taken as its optimal solution.

[0074] Next, step S4 is executed, in which a genetic algorithm is used to iterate the population until it matures using the image quality fitness function, and the optimal adjustment amount of the adjustable lens based on the current image quality is output.

[0075] Referring to Figure 2, the steps of iteratively updating the population using a genetic algorithm include a genetic selection step, a genetic crossover step, and a genetic mutation step. This yields an updated population. The fitness value of the updated population is then calculated again, and the genetic algorithm is iteratively executed until the image quality of the updated population converges.

[0076] Specifically, this embodiment uses a roulette wheel method for population elimination and genetics: based on S3, the image quality fitness values ​​of all groups of individuals in the population are obtained, and the probability p of each group of individuals being selected is obtained. i p i =f i / (f1+f n+f3+..f n ), where n is the population size, f i Let p be the fitness value of individual i; generate a Monte Carlo random number between 0 and 1, denoted as p. If p i If p >, then individual i will enter the next generation of inheritance; this process continues until all groups of individuals in the population have performed the above selection.

[0077] Then, the individuals that enter the next generation of inheritance are taken as parents, and the initialized population is taken as mothers. Crossover is performed with a certain crossover probability to obtain a new population: the i-th individual in the parent population is denoted as B. i Individuals randomly selected from the maternal population are denoted as C. i Then the new population A obtained by the i-th individual in the population with probability r is... i A i =r*B i +(1-r)*C i , where probability r is the probability of random generation.

[0078] Then, real-valued mutation is performed on the individuals of the new population obtained after the crossover operation to ensure the diversity of the population: the i-th individual of the population is denoted as B. i B i =[b1, b2, b3, b 4...... b i-2 b i-1 b i The k-th individual in the population is denoted as B. k B k =[b1, b2, b3, b 4...... b k-2 b k-1 b k The probability of performing the mutation is P. m If individual B i random probability R m >P m Then individual B i The mutation is performed, and the mutation location is selected with random probability. The mutated individual B i =[b1, b2, b3, b 4...... b k-2 b i-1 b i ].

[0079] Then, the image quality fitness value is calculated for the new population after individual mutation. The selection, crossover, and mutation steps described above are continued until the image quality fitness value of the population meets the index. The adjustment amount of the adjustable parameters at this point is then output as the optimization adjustment amount of the adjustable lens based on the current image quality. In this embodiment, the number of iterations (the number of generations of the population) is not limited, but the goal is to achieve convergence of the image quality fitness value of the population.

[0080] Example 2

[0081] Example 2 provides a method for adjusting an objective lens.

[0082] Figure 2a This is a flowchart of the objective lens adjustment method provided in Example 2.

[0083] like Figure 2a As shown, the objective lens adjustment method provided in this embodiment includes:

[0084] S1: Obtain the current image quality and sensitivity matrix of the objective lens and the current parameters of several adjustable lenses, and use an optimization algorithm to select the optimal set from several initial adjustment values ​​of the adjustable lenses as the output;

[0085] S2: Encode the initial adjustment amount as an individual and output the initialized population;

[0086] S3: Based on the sensitivity matrix of the objective lens, establish the image quality fitness function of the objective lens and calculate the image quality fitness value of the population;

[0087] S4: Using a genetic algorithm and the image quality fitness function, the population is iterated until it matures, and the optimal adjustment amount of the adjustable lens based on the current image quality is output.

[0088] S5: Apply the optimal adjustment amount to the objective lens to obtain optimized image quality of the objective lens.

[0089] Figure 2b The flowchart shows the corresponding steps of the objective lens adjustment method provided in Example 2.

[0090] Please refer to Figure 2a and Figure 2b Compared to the method provided in Embodiment 1, the objective lens adjustment method provided in Embodiment 2 may have the following main differences:

[0091] The initial adjustment value output in step S1 is not randomly generated. It is obtained by using an optimization algorithm based on the image quality of multiple initial adjustment values, so that the output initial adjustment value has better image quality, thereby accelerating the evolution speed of the population. The optimization algorithm used can include any suitable method with optimization effect, such as least squares method, annealing algorithm, or immune algorithm.

[0092] In step S2, using an optimized set of individuals, within the adjustable range and precision of the adjustable parameters, several sets of adjustment amounts are generated in a uniformly distributed manner to increase the number of individuals, thus completing the initialization of the population.

[0093] In step S4, during the iterative process of the genetic algorithm, the selection step adopted can be a tournament-style elimination and genetic selection of individuals in the population.

[0094] For processes and steps not mentioned in Example 2, please refer to Example 1 for details, which will not be repeated here.

[0095] Example 3

[0096] Example 3 provides a method for adjusting an objective lens.

[0097] Figure 3a This is a flowchart of the objective lens adjustment method provided in Example 3.

[0098] like Figure 3a As shown, the objective lens adjustment method provided in this embodiment includes:

[0099] S1: Obtain the current image quality and sensitivity matrix of the objective lens and the current parameters of several adjustable lenses, and use an optimization algorithm to select the optimal set from several initial adjustment values ​​of the adjustable lenses as the output;

[0100] S2: Encode the initial adjustment amount as an individual and output the initialized population;

[0101] S3: Based on the sensitivity matrix of the objective lens, establish the image quality fitness function of the objective lens and calculate the image quality fitness value of the population;

[0102] S4: Using a genetic algorithm, the image quality fitness function is utilized, and an elite retention strategy is employed to iteratively calculate the population until the population matures, and the optimal adjustment amount of the adjustable lens based on the current image quality is output.

[0103] S5: Apply the optimal adjustment amount to the objective lens to obtain optimized image quality of the objective lens.

[0104] Figure 3b The flowchart shows the corresponding steps of the objective lens adjustment method provided in Example 3.

[0105] Please refer to Figure 3a and Figure 3b Compared to the method provided in Embodiment 2, the objective lens adjustment method provided in Embodiment 3 may have the following main differences:

[0106] In step S4, during the iteration of the genetic algorithm, the selection step adopted can be a roulette wheel method to eliminate and heredize individuals in the population. After each mutation step, an elite retention strategy is adopted to replace the worst individual in the current population with the historical best individual, and then the current population is iterated to ensure that the population evolves in the optimal direction and improve the efficiency of objective lens adjustment.

[0107] For processes and steps not mentioned in Example 3, please refer to Example 1 and Example 2 for details, which will not be repeated here.

[0108] Example 4

[0109] Example 4 provides an objective lens adjustment device.

[0110] The objective lens adjustment device provided in this embodiment includes an objective lens that may include several adjustable lenses. The objective lens adjustment device includes an acquisition module, an initialization module, an evaluation module, and an iteration module. The acquisition module acquires the current image quality and sensitivity matrix of the objective lens, the current parameters of the several adjustable lenses, and outputs a set of initial adjustment values ​​for the adjustable lenses. The initialization module encodes the initial adjustment values ​​output by the acquisition module to generate an initialized population. The evaluation module responds to individuals in the population by outputting the image quality changes of the objective lens and outputs the image quality fitness value of the population. The iteration module uses a genetic algorithm to calculate the image quality fitness value of the population using the evaluation module, iterating until the population matures, and outputs the optimal adjustment value based on the current image quality.

[0111] The settings of each module in Embodiment 4 can be specifically referred to the objective lens adjustment methods provided in Embodiment 1, Embodiment 2 or Embodiment 3, and will not be repeated here.

[0112] Example 5

[0113] Example 5 provides a computer-readable storage medium.

[0114] The computer-readable storage medium provided in this embodiment stores a computer program. When executed by a processor, the computer program implements the steps of the objective lens debugging method described in any of the above embodiments. The computer-readable storage medium involved in this embodiment includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0115] Example 6

[0116] Example 6 provides a photolithography apparatus.

[0117] The lithography apparatus provided in this embodiment includes an objective lens and an objective lens adjustment device. The objective lens includes several adjustable lenses. The objective lens adjustment device is used to adjust the objective lens and may include an acquisition module, an initialization module, an evaluation module, and an iteration module. The acquisition module acquires the current image quality and sensitivity matrix of the objective lens, the current parameters of the several adjustable lenses, and outputs a set of initial adjustment values ​​for the adjustable lenses. The initialization module encodes the initial adjustment values ​​output by the acquisition module to generate an initialized population. The evaluation module outputs the image quality changes of the objective lens in response to individuals in the population and outputs the image quality fitness value of the population. The iteration module uses a genetic algorithm to calculate the image quality fitness value of the population using the evaluation module, iterating until the population matures, and outputs the optimal adjustment value based on the current image quality.

[0118] In summary, the objective lens adjustment method and apparatus provided by this invention can generate individuals and an initial population for objective lens adjustment by obtaining the current image quality, sensitivity matrix, and current parameters of the adjustable lenses of the objective lens. Simultaneously, an image quality fitness function of the objective lens is established to calculate the image quality fitness value of the population for evaluation. A genetic algorithm is then used to iteratively operate the population using the aforementioned image quality fitness function until the population matures, and its optimal adjustment value is output for actual objective lens adjustment. Compared to online adjustment, applying the theoretically obtained optimal adjustment value obtained in this embodiment to objective lens adjustment not only allows for rapid convergence of image quality and improves objective lens adjustment efficiency but also simplifies the objective lens adjustment process and accelerates the adjustment process.

[0119] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A method for adjusting an objective lens, the objective lens comprising a plurality of adjustable lenses, characterized in that, include: S1: Obtain the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output several initial adjustment values ​​of the adjustable lenses; The sensitivity matrix represents the wavefront aberration change of the objective lens in response to the change in the current parameters; S2: Encode the initial adjustment amount into individuals using real values, and initialize the population using the adjustable range and adjustment accuracy of the adjustable lens as constraints to obtain an initialized population; S3: Combine the sensitivity matrix of the objective lens to calculate the RMS values ​​of each wavefront aberration of the population; establish the image quality fitness function of the population based on the RMS values ​​of each wavefront aberration, and calculate the image quality fitness value of the population. S4: Using a genetic algorithm and the image quality fitness function, the population is iterated until it matures, and the optimal adjustment amount of the adjustable lens based on the current image quality is output. S5: Apply the optimal adjustment amount to the objective lens to obtain optimized image quality of the objective lens.

2. The objective lens adjustment method according to claim 1, characterized in that, The current parameters of the adjustable lens include the surface eccentricity, surface tilt, and surface spacing of the adjustable lens. The initial adjustment amount and the optimal adjustment amount include the adjustment amounts of surface eccentricity, surface tilt, and surface spacing.

3. The objective lens adjustment method according to claim 1, characterized in that, The initial population is formed using either a Gaussian distribution or a uniform distribution.

4. The objective lens adjustment method according to claim 1, characterized in that, The image quality of the objective lens is evaluated using its wavefront aberration, which is represented by Zernike polynomials of 1 to 37 terms.

5. The objective lens adjustment method according to claim 4, characterized in that, The steps for calculating the image quality fitness value of the population include: Calculate the RMS values ​​of various wavelet aberrations of the population, including RMS(Z5-Z5). 37 ), Spherical aberration Sph_RMS, Coma_RMS, Astigmatism Ast_RMS, and Trifoliate aberration Trefoil_RMS, Z5-Z 37 These are the Zernike polynomials from the 5th to the 37th term; Establish the image quality fitness function D for the population, D = d1 * RMS(Z5 - Z). 37 )+d2*Sph_RMS+d3*Coma_RMS+d4*Ast_RMS+d5*Trefoil_RMS, where d1, d2, d3, d4 and d5 are their respective weighting coefficients, and d1, d2, d3, d4 and d5 are all 0~1; The function value is obtained according to the image quality fitness function, and the reciprocal of the function value is taken as the image quality fitness value of the population.

6. The objective lens adjustment method according to claim 1, characterized in that, The steps of iteratively updating the population using a genetic algorithm include a genetic algorithm selection step, a genetic algorithm crossover step, and a genetic algorithm mutation step to obtain an updated population. The fitness value of the updated population is then calculated again, and the genetic algorithm is iteratively executed until the image quality of the updated population converges.

7. The objective lens adjustment method according to claim 6, characterized in that, The genetic algorithm selection step includes using a roulette wheel or tournament selection method.

8. The method for adjusting the objective lens according to any one of claims 1 to 7, characterized in that, After performing step S1, an optimization algorithm is used to select the optimal set from several sets of initial adjustment amounts as the input for step S2.

9. The method for adjusting the objective lens according to any one of claims 1 to 7, characterized in that, During the execution of the genetic algorithm, after each mutation step, an elite retention strategy is adopted to replace the worst individual in the current population with the historical best individual, and then the current population is iterated.

10. An objective lens adjustment device, wherein the objective lens comprises a plurality of adjustable lenses, characterized in that, include: The acquisition module is used to acquire the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output a set of initial adjustment values ​​for the adjustable lenses. The sensitivity matrix represents the wavefront aberration change of the objective lens in response to the change in the current parameters; An initialization module is used to encode the initial adjustment amount into individuals using real values, and to perform initialization on the population using the adjustable range and adjustment accuracy of the adjustable lens as constraints to obtain an initialized population. The evaluation module is used to output the image quality change of the objective lens in response to individuals in the population, calculate the RMS values ​​of various wavelet aberrations of the population, establish the image quality fitness function of the population based on the RMS values ​​of various wavelet aberrations, and output the image quality fitness value of the population. The iteration module is used to perform iterative calculations on the image quality fitness value of the population calculated by the evaluation module using a genetic algorithm until the population matures, and outputs the optimal adjustment amount based on the current image quality.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the objective lens adjustment method as described in any one of claims 1-9.

12. A photolithography apparatus, characterized in that, The device includes an objective lens and an objective lens adjustment device. The objective lens includes a plurality of adjustable lenses. The objective lens adjustment device is used to adjust the objective lens. The objective lens adjustment device includes: The acquisition module is used to acquire the current image quality and sensitivity matrix of the objective lens, the current parameters of several adjustable lenses, and output a set of initial adjustment values ​​for the adjustable lenses; the sensitivity matrix is ​​the wavefront aberration change of the objective lens in response to changes in the current parameters; An initialization module is used to encode the initial adjustment amount into individuals using real values, and to initialize the population by constraining the adjustable range and adjustment accuracy of the adjustable lens, thereby obtaining an initialized population. The evaluation module is used to output the image quality change of the objective lens in response to individuals in the population, calculate the RMS values ​​of various wavefront aberrations of the population, establish the image quality fitness function of the population based on the RMS values ​​of various wavefront aberrations, and output the image quality fitness value of the population. The iteration module is used to perform iterative calculations on the image quality fitness value of the population calculated by the evaluation module using a genetic algorithm until the population matures, and outputs the optimal adjustment amount based on the current image quality.

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