Extreme ultraviolet lithography mask multilayer film defect detection method based on photoelectron image

Abstract

The invention relates to the technical field of extreme ultraviolet lithography masks, in particular to an extreme ultraviolet lithography mask multilayer film defect detection method based on a photoelectron image, which comprises the following steps: S1, simulating to generate m phase type mask multilayer film defect structures with different morphological characteristics; s2, using an interpolation approximation method to extract electric field intensity distribution near the surface layer of the mask multilayer film defect structure, and establishing a simulation photoelectron image data set; s3, constructing a neural network model for defect detection, training the network, and obtaining an optimal network weight parameter; and S4, detecting the type and morphology parameters of the defect structure of the multi-layer film of the mask. The method has the characteristics that the photoelectron image is easy to obtain, the neural network training data set is small, and the detection efficiency is high, and high-precision nondestructive detection of the extreme ultraviolet lithography mask multilayer film defects can be realized.

Description

Technical Field

[0001] This invention relates to the field of extreme ultraviolet (EUV) lithography mask technology, and more particularly to a method for detecting defects in multilayer films of EUV lithography masks based on photoelectronic images. Background Technology

[0002] Among the many technical challenges in extreme ultraviolet (EUV) lithography, EUV mask technology is considered one of the most critical steps in the successful implementation of EUV lithography in semiconductor manufacturing processes. It is directly related to the final lithography result and determines the success or failure of the lithography. If defects cannot be accurately detected, mitigated, and compensated, even a tiny defect can cause the final lithographic device to fail. Therefore, multilayer film defects on the mask whiteboard have always been considered one of the problems that urgently need to be solved. Among these, the effective detection of defect location, defect type, and morphological size is an important basis for defect mitigation and compensation, and a fundamental prerequisite for achieving "zero-defect" mask manufacturing.

[0003] Prior art 1 (Harada T, Tanaka Y, Watanabe T, et al. Phase defect characterization on an extreme-ultraviolet blank mask using microcoherentextreme-ultraviolet scatterometry microscope[J]. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena, 2013, 31(6): 06F605.) used microcoherent extreme ultraviolet scattering microscopy (micro-CSM) to detect the surface morphology information of phase defects in multilayer films, but it could not detect the morphology of defects located at the bottom of the multilayer film. However, simply detecting the surface information of defects is insufficient. Therefore, research combining neural networks and spatial imaging has been carried out at home and abroad to realize the detection of information such as the type and morphology of phase defects located at the bottom of multilayer films. Prior technology 2 (Xu D, Evanschitzky P, Erdmann A. Extreme ultraviolet multilayer defect analysis and geometry reconstruction[J]. Journal of Micro / Nanolithography, MEMS, and MOEMS, 2016, 15(1): 014002.) uses extreme ultraviolet projection images at different focal positions to characterize the morphological parameters of multilayer film defects. The phase information of the defects is extracted from the image intensity using the Transmission Intensity Equation (TIE), and the intensity and phase distributions are correlated with the morphological parameters of the defects using Principal Component Analysis (PCA). The morphological parameters of the defects are reconstructed from the principal component coefficients of the intensity and phase using an Artificial Neural Network (ANN). However, in its modeling process, the bottom height and full width at half maximum (FWHM) of the defects are set to the same value. In reality, the two values ​​are not necessarily equal, and the bottom height and FWHM of the defects have different effects on the lithographic image. Furthermore, the movement of the spatial image sensor during the detection process introduces errors, affecting the detection accuracy.Prior technique 3 (Zheng H, Li S, Cheng W, et al. Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography[J]. Applied Optics, 2023, 62(5): 1243.) uses a series of generative adversarial networks (GANs) to characterize multilayer film defects on a mask white plate in extreme ultraviolet lithography. A method based on a neural network framework is proposed to reconstruct the morphological parameters of the defects by using spatial images of the mask white plate obtained under different illumination angles. However, this method depends on the choice of illumination angle. For a set of morphological parameters of the defects, spatial images under multiple illumination angles need to be collected for characterization, and a large amount of data is required for network training, which increases the difficulty of data collection and is time-consuming. In addition, the imaging detection equipment for spatial images is expensive and technically complex. Only a limited number of institutions internationally can realize the actual detection of spatial images, and the imaging results are difficult to obtain. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing a method for detecting defects in multilayer films of extreme ultraviolet (EUV) lithography masks based on optoelectronic images, enabling low-cost, high-efficiency, and high-precision non-destructive testing of defects in multilayer films of EUV lithography masks.

[0005] The method for detecting defects in multilayer films of extreme ultraviolet lithography masks based on photoelectronic imaging provided by this invention specifically includes the following steps:

[0006] S1: Simulate and generate m (m≥480) phase-type mask multilayer film defect structures with different morphological characteristics:

[0007] The multilayer film defect structure of the mask multilayer film consists of l (l≥40) pairs of bilayer films. Each pair of bilayer films contains a Mo layer and a Si layer. The thickness of the Mo layer is 2.78 nm, and the thickness of the Si layer is 4.17 nm. Gaussian defects are used to characterize the defect features of the extreme ultraviolet lithography mask multilayer film. Phase defects of convex and concave mask multilayer films are modeled, and the defect height (h) at the top of the multilayer film is set. top ), Full width at half maximum (WHM) of the top defect in the multilayer film (ω) top ), the height of the bottom defect in the multilayer film (h) bot ), Full width at half maximum (WHM) of bottom defects in multilayer films (ω) bot Four parameters are used to characterize the three-dimensional morphology of the phase defect, including the defect height h at the top of the multilayer film. top The depth h of the bottom defect in the multilayer film is set within the range of 1.5–3.5 nm. botThe full width at half maximum (FWHM) of the top defect in the multilayer film is set in the range of 5–20 nm. top The full width at half maximum (FWHM) of the bottom defect in the multilayer film is set in the range of 30–60 nm. bot It is set in the range of 30 to 50 nm.

[0008] S2: The electric field intensity distribution near the surface of the defect structure in the mask multilayer film is extracted using an interpolation approximation method to establish a simulated optoelectronic image dataset.

[0009] The electric field intensity distribution in a multilayer film structure with a defective mask is simulated using the finite-time difference method with rigorous electromagnetic field simulation. An interpolation approximation extraction method is then used to extract the electric field intensity on the surface of the multilayer film structure with the defective mask. The calculation process of the interpolation approximation extraction method is as follows:

[0010] d1=z defect -z p1 (1)

[0011] d2=z p2 -z defect (2)

[0012]

[0013] |E|=|E1|·r2+|E2|·r1 (7)

[0014] Among them, z defect Interpolation points in the z-direction on the surface of a multilayer film structure containing a defect mask. p1 and z p2 These represent the positions of sampling points P1 and P2 along the z-direction on the surface of the multilayer film structure containing the defective mask, respectively. p2 >z defect >z p1 d1 and d2 are the distances between the interpolation point and the two sampling points, respectively; r1 and r2 are the influence factors of the two electric field intensities; |E1| and |E2| are the electric field intensities at sampling points P1 and P2, respectively; |Ex1| and |Ex2| are the components of the electric field intensities at sampling points P1 and P2 in the x-direction, respectively; |Ey1| and |Ey2| are the components of the electric field intensities at sampling points P1 and P2 in the y-direction, respectively; |Ez1| and |Ez2| are the components of the electric field intensities at sampling points P1 and P2 in the z-direction, respectively; and |E| is the electric field intensity at the interpolation point. Using all electric field data within a 1nm range on and below the surface of the multilayer film structure containing the defect mask, photoelectron imaging simulation is performed based on the linear relationship between photoelectrons and electric field intensities, and the imaging results are converted into grayscale images.

[0015] S3: Construct a neural network model for defect detection, train the network, and obtain the optimal network weight parameters:

[0016] Using transfer learning, a pre-trained neural network was selected as the base model. The weights and structure of the base model were imported into the neural network model for defect detection. The convolutional layers of the base model were retained, but the fully connected layers were replaced. A new fully connected layer was added as the output layer. The learning rate factor for both the weights and the threshold was set to 10. The number of neurons in the output layer was matched with the number of parameters to be detected. The cross-entropy loss function was used for defect type identification, and the mean squared error (MSE) loss function was used for defect morphology parameter detection. The entire network was fine-tuned on a photoelectron image dataset of mask multilayer film defect structures. The learning rate and training cycle were adjusted to balance the model's performance and the risk of overfitting, resulting in the trained neural network model.

[0017] S4: Detect the type and morphological parameters of defects in multilayer films using masks.

[0018] The surface of a defective mask multilayer film structure is inspected using photoemission electron microscopy. A high-sensitivity electron collection device is used to capture minute changes in photoelectrons on the surface of the defective mask multilayer film structure. A charge-coupled device (CCD) is used to capture the imaging results of the photoemission electron microscopy to form the final detection image. The photoemission electron microscopy imaging results are grayscaled and noise-reduced, and then input into a trained neural network model to output the type and morphological parameters of the defects in the mask multilayer film structure, thus completing the detection of defects in extreme ultraviolet lithography mask multilayer films.

[0019] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0020] First, compared to spatial imaging equipment, photoemission electron microscopy (AEEM) imaging results are easier to obtain and less expensive. Second, the application of neural network transfer learning in the characterization of defects in multilayer films of extreme ultraviolet (EUV) lithography masks significantly reduces the amount of data required. Transfer learning is a machine learning method that can quickly train existing pre-trained neural network models on data for new tasks. Compared to traditional methods, transfer learning reduces training time and improves efficiency. With the support of transfer learning, this invention can detect defects in multilayer films of EUV lithography masks with less data, which undoubtedly provides great convenience for the study of defects in multilayer films of EUV lithography masks. This invention proposes a low-cost, high-efficiency, and high-precision non-destructive testing method for defects in multilayer films of EUV lithography masks using photoemission electron microscopy imaging technology combined with neural network transfer learning. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of a method for detecting defects in multilayer films using extreme ultraviolet lithography masks based on optoelectronic images, provided in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of phase defects in a multilayer film of a raised mask provided according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a phase defect in a multilayer film of a recessed mask according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the interpolation approximation extraction method provided in an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of a type recognition neural network provided according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a morphology parameter detection neural network provided according to an embodiment of the present invention;

[0028] Figure 7 This is the result of defect type identification in the test set provided according to an embodiment of the present invention;

[0029] Figure 8 The results of the detection of morphological parameters of the protruding phase defect provided in the embodiments of the present invention;

[0030] Figure 9 The results are the detection results of the morphological parameters of the concave phase defect provided in the embodiments of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] This invention combines the non-destructive characterization of photoemission electron microscopy imaging, its high sensitivity and high-resolution detection capability for phase defects, and the powerful image feature extraction capability of neural networks to propose a method for detecting defects in multilayer films of extreme ultraviolet (EUV) lithography masks based on photoemission electron microscopy (PEEEM). Compared with existing detection technologies, this invention features readily available photoemission images, a small neural network training dataset, and high detection efficiency, enabling high-precision non-destructive detection of defects in multilayer films of EUV lithography masks.

[0033] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0034] Figure 1 The flowchart of the method for detecting defects in multilayer films of extreme ultraviolet lithography masks based on photoelectronic images according to an embodiment of the present invention is shown, specifically including the following steps:

[0035] S1: Simulate and generate phase-type mask multilayer film defect structures with different morphological characteristics.

[0036] The multilayer film defect structure of the mask consists of 40 pairs of bilayer films, each pair containing a Mo layer and a Si layer. The thickness of the Mo layer is 2.78 nm, and the thickness of the Si layer is 4.17 nm. The simulation area is 300 nm × 300 nm. The light source used in the simulation is the same as that used in extreme ultraviolet lithography, with an incident wavelength of 13.5 nm, an incident angle of 6°, and planar light. Gaussian defects are used to characterize the defect features of the extreme ultraviolet lithography mask multilayer film, and phase defects of the convex mask multilayer film (such as...) are investigated. Figure 2 (as shown) and phase defects in multilayer films of recessed masks (such as Figure 3 Modeling as shown), setting the top defect height (h) of the multilayer film. top ), Full width at half maximum (WHM) of the top defect in the multilayer film (ω) top ), the height of the bottom defect in the multilayer film (h) bot ), Full width at half maximum (WHM) of bottom defects in multilayer films (ω) bot Four parameters are used to characterize the three-dimensional morphology of the phase defect. The defect height h at the top of the multilayer film... top Five values ​​were taken within the range of 1.5–3.5 nm, with an interval of 0.5 nm; the bottom defect height h of the multilayer film. bot Within the range of 5–20 nm, with intervals of 1 nm, 16 values ​​were taken; the full width at half maximum (FWHM) of the defect at the top of the multilayer film ω top Within the range of 30–60 nm, with intervals of 1 nm, 31 values ​​were taken; the full width at half maximum (FWHM) of the bottom defect in the multilayer film ω tot Within the range of 30–50 nm, with an interval of 1 nm, 21 values ​​were selected and combined to create a total of 480 mask whiteboard models with different bottom and top morphological parameters containing multilayer film defects.

[0037] S2: The electric field intensity distribution near the surface of the defect structure in the mask multilayer film is extracted using an interpolation approximation method to establish a simulated optoelectronic image dataset.

[0038] The electric field intensity distribution in a multilayer film structure with a defective mask is simulated using the finite-time difference method with rigorous electromagnetic field simulation. An interpolation approximation extraction method is then used to extract the electric field intensity on the surface of the multilayer film structure with the defective mask. The calculation process of the interpolation approximation extraction method is as follows:

[0039] d1=z defect -z p1 (1)

[0040] d2=z p2 -z defect (2)

[0041]

[0042]

[0043] |E|=|E1|·r2+|E2|·r1 (7)

[0044] Among them, z defect Interpolation points in the z-direction on the surface of a multilayer film structure containing a defect mask. p1 and z p2 These represent the positions of sampling points P1 and P2 along the z-direction on the surface of the multilayer film structure containing the defective mask, respectively. p2 >z defect >z p1 d1 and d2 are the distances between the interpolation point and the two sampling points, respectively; r1 and r2 are the influence factors of the two electric field intensities; |E1| and |E2| are the electric field intensities at sampling points P1 and P2, respectively; |Ex1| and |Ex2| are the components of the electric field intensities at sampling points P1 and P2 in the x-direction, respectively; |Ey1| and |Ey2| are the components of the electric field intensities at sampling points P1 and P2 in the y-direction, respectively; |Ez1| and |Ez2| are the components of the electric field intensities at sampling points P1 and P2 in the z-direction, respectively; and |E| is the electric field intensity at the interpolation point. Using all electric field data within a 1nm range on and below the surface of the multilayer film structure containing the defect mask, photoelectron imaging simulation is performed based on the linear relationship between photoelectrons and electric field intensities, and the imaging results are converted into grayscale images.

[0045] S3: Construct a neural network model for defect detection, train the network, and obtain the optimal network weight parameters:

[0046] Using transfer learning, a pre-trained ResNet-18 neural network was selected as the base model. The weights and structure of the base model were imported into the defect detection neural network model. The convolutional layers of the base model were retained, but the fully connected layers were replaced. A new fully connected layer was added as the output layer. The learning rate factor for both the weights and the threshold was set to 10. A classification network was used for defect type identification, with cross-entropy loss as the loss function. The output results were divided into two categories: bumps and pits (e.g.,...). Figure 5 As shown), a regression network is used to detect defect morphology parameters, and the loss function is the mean squared error (MSE) loss. The output results are the four morphology parameters of the defect (e.g., ...). Figure 6 As shown, the network is fine-tuned on the dataset generated by steps S1-S2, adjusting the learning rate and training cycle to balance the model's performance and the risk of overfitting, resulting in the trained neural network model.

[0047] S4: Detect the type and morphological parameters of defects in multilayer films using masks.

[0048] Set the top defect height h of the multilayer film. top Within the range of 1.5–3.5 nm, the sampling interval is 0.5 nm, and the bottom defect height h of the multilayer film is set. bot Within the range of 5–20 nm, the sampling interval is 1 nm, and the full width at half maximum (FWHM) of the defect at the top of the multilayer film is set to ω. top Within the range of 30–60 nm, the sampling interval is 1 nm, and the full width at half maximum (FWHM) of the bottom defect in the multilayer film is set to ω. bot Within the range of 30–50 nm, the sampling interval is 1 nm. By combining the samples, 120 convex multilayer film phase defect structures and 120 concave multilayer film phase defect structures are generated. Step S2 is repeated to generate photoelectron images of 240 defect structures, forming a test dataset.

[0049] From 240 photoelectron images obtained from simulation, 60 photoelectron images of convex multilayer film phase defects and 60 photoelectron images of concave multilayer film defects were randomly selected and input into the trained type recognition neural network model. The model outputs the defect type of the mask multilayer film structure, thus completing the type detection of defects in extreme ultraviolet lithography mask multilayer films. Figure 7 The results of defect type identification are shown. The detection accuracy of the defect type identification network model after training is 100%, indicating that the trained network can accurately identify convex multilayer phase defects and concave multilayer phase defects.

[0050] This embodiment uses root mean square error (RMSE) and error rate (Er) to characterize the accuracy of the defect morphology parameter detection results, i.e.

[0051]

[0052] Where x′ t x represents the detected value of the morphological parameter. t The true values ​​of the morphology parameters are given, where n is the sample size of the test set. The 240 photoelectron images obtained from the simulation are input into the trained morphology parameter neural network model to detect convex and concave phase defects in multilayer films, respectively. Figure 8 The results of the detection of phase defect morphology parameters of the protruding multilayer film show that the maximum RMSE of the morphology parameter detection is 0.36 nm, and the maximum error rate is 2.16%. Figure 9 The detection results for the morphology parameters of concave multilayer film phase defects show that the maximum RMSE is 0.65 nm, and the maximum error rate is 2.13%. For both convex and concave multilayer film phase defects, the RMSE values ​​are less than 1 nm, indicating that the overall error of this invention is small and the overall detection accuracy is high across all test datasets. The error rate for both types of phase defects does not exceed 3%, with the lowest error rate reaching 0.41%, demonstrating that this invention maintains high accuracy even in data samples with different defect sizes.

[0053] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for detecting defects in multilayer films of extreme ultraviolet lithography masks based on photoelectronic images, characterized in that, Specifically, the following steps are included: S1: Simulation generates m phase-type mask multilayer film defect structures with different morphological characteristics: The multilayer film defect structure of the mask multilayer film consists of a pair of bilayer films, each pair containing a Mo layer and a Si layer. The thickness of the Mo layer is 2.78 nm, and the thickness of the Si layer is 4.17 nm. Gaussian defects are used to characterize the defect features of the extreme ultraviolet lithography mask multilayer film. Modeling of phase defects in convex and concave mask multilayer films is performed, and the defect height (h) at the top of the multilayer film is set. top ), Full width at half maximum (WHM) of the top defect in the multilayer film (ω) top ), the height of the bottom defect in the multilayer film (h) bot ), Full width at half maximum (WHM) of bottom defects in multilayer films (ω) bot Four parameters are used to characterize the three-dimensional morphology of the phase defect, including the defect height h at the top of the multilayer film. top The depth h of the bottom defect in the multilayer film is set within the range of 1.5–3.5 nm. bot The full width at half maximum (FWHM) of the top defect in the multilayer film is set in the range of 5–20 nm. top The full width at half maximum (FWHM) of the bottom defect in the multilayer film is set in the range of 30–60 nm. bot Set within the 30–50 nm range; S2: The electric field intensity distribution near the surface of the defect structure in the mask multilayer film is extracted using an interpolation approximation method to establish a simulated optoelectronic image dataset. The electric field intensity distribution in a multilayer film structure with a defective mask is simulated using the finite-time difference method with rigorous electromagnetic field simulation. An interpolation approximation extraction method is then used to extract the electric field intensity on the surface of the multilayer film structure with the defective mask. The calculation process of the interpolation approximation extraction method is as follows: d1=z defect -with p1 (1) d2=z p2 -z defect (2) |E|=|E1|·r2+|E2|·r1 (7) Among them, z defect For the z-axis interpolation point on the surface of the multilayer film structure containing the defect mask, z p1 and z p2 These represent the positions of sampling points P1 and P2 along the z-direction on the surface of the multilayer film structure containing the defective mask, respectively. p2 >z defect >z p1 d1 and d2 are the distances between the interpolation point and the two sampling points, r1 and r2 are the influence factors of the two electric field intensities, |E1| and |E2| are the electric field intensities at sampling points P1 and P2, |Ex1| and |Ex2| are the components of the electric field intensities at sampling points P1 and P2 in the x-direction, |Ey1| and |Ey2| are the components of the electric field intensities at sampling points P1 and P2 in the y-direction, |Ez1| and |Ez2| are the components of the electric field intensities at sampling points P1 and P2 in the z-direction, and |E| is the electric field intensity at the interpolation point. Using all electric field data within a 1nm range on and below the surface of the multilayer film structure with defect mask, photoelectronic imaging simulation is performed based on the linear relationship between photoelectrons and electric field intensities, and the imaging results are converted into grayscale images. S3: Construct a neural network model for defect detection, train the network, and obtain the optimal network weight parameters: Using transfer learning, a pre-trained neural network was selected as the base model. The weights and structure of the base model were imported into the neural network model for defect detection. The convolutional layers of the base model were retained, but the fully connected layers were replaced. A new fully connected layer was added as the output layer, and the number of neurons in the output layer was matched with the number of parameters to be detected. The cross-entropy loss function was used for defect type recognition, and the mean squared error (MSE) loss function was used for defect morphology parameter detection. The entire network was fine-tuned on a photoelectron image dataset of mask multilayer film defect structures. The learning rate and training cycle were adjusted to balance the model's performance and the risk of overfitting, resulting in the trained neural network model. S4: Detect the type and morphological parameters of defects in multilayer films using masks. The surface of a defective mask multilayer film structure is inspected using photoemission electron microscopy. A high-sensitivity electron collection device is used to capture minute changes in photoelectrons on the surface of the defective mask multilayer film structure. A charge-coupled device (CCD) is used to capture the imaging results of the photoemission electron microscopy to form the final detection image. The photoemission electron microscopy imaging results are grayscaled and noise-reduced, and then input into a trained neural network model to output the type and morphological parameters of the defects in the mask multilayer film structure, thus completing the detection of defects in extreme ultraviolet lithography mask multilayer films.

2. The method for detecting defects in multilayer films of extreme ultraviolet lithography masks based on photoelectronic images according to claim 1, characterized in that, The m ≥ 480.

3. The method for detecting defects in multilayer films of extreme ultraviolet lithography masks based on photoelectronic images according to claim 1, characterized in that, The value of l is ≥40.

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