Plasma photoetching imaging training method and device

Abstract

The embodiment of the invention provides a plasma photoetching imaging training method and device, and the method comprises the steps: obtaining a training imaging structure of plasma photoetching imaging, constructing a simulation model according to the training imaging structure, and performing simulation according to the simulation model to obtain a training plasma photoetching image corresponding to the training mask pattern. And training the rapid imaging model according to the training mask pattern and the training plasma photoetching imaging to obtain the rapid imaging model for the training mask pattern with two-dimensional periodic change. Therefore, by establishing the rapid imaging model for the two-dimensional periodically changed mask patterns, the training plasma photoetching image of each mask pattern can be directly obtained by utilizing the rapid imaging model, rapid imaging by utilizing the rapid imaging model is realized, and the subsequent research requirements on the plasma photoetching technology are met.

Description

Technical Field

[0001] The invention relates to the field of semiconductors, and in particular to a plasma lithography imaging training method and device. Background Art

[0002] With the development of semiconductor-related technologies, lithography, one of the important technologies for manufacturing semiconductor devices, is also developing rapidly. As a supplement to mainstream lithography, plasma lithography is very different from traditional optical lithography, such as deep ultraviolet lithography (DUVL) and extreme ultraviolet lithography (EUVL).

[0003] Plasma lithography technology can break through the diffraction limit in traditional lithography by utilizing evanescent near-field imaging containing high-frequency information. Experiments have shown that even with a light source with a wavelength of 365 nanometers (nm), the resolution can reach about 20nm under single exposure conditions, which is about 1 / 17 of the wavelength of light, and it is possible to further improve. This method provides a reliable technical approach for studying low-cost, large-area, and efficient lithography technology, and has therefore received widespread attention.

[0004] In summary, there is currently a research demand for rapid imaging of plasma lithography technology. Summary of the invention

[0005] In view of this, the purpose of the present application is to provide a plasma lithography imaging training method and device, which can train the rapid imaging model of plasma lithography technology, realize rapid imaging using the rapid imaging model, and meet the subsequent research needs for plasma lithography technology.

[0006] To achieve the above purpose, this application has the following technical solutions:

[0007] The present application provides a plasma lithography imaging training method, comprising:

[0008] Acquire a training imaging structure for plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed;

[0009] Building a simulation model according to the training imaging structure, and simulating and obtaining a training plasma lithography imaging corresponding to the training mask pattern according to the simulation model;

[0010] The rapid imaging model is trained according to the training mask pattern and the training plasma lithography imaging to obtain the rapid imaging model for the two-dimensional periodically changing training mask pattern.

[0011] Optionally, the training mask pattern includes a light-transmissive first portion and a light-impermeable second portion, and the second portion surrounds the first portion;

[0012] The training imaging structure for acquiring plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed, includes:

[0013] Acquire a plurality of training mask patterns corresponding to a training imaging structure of plasma lithography imaging, wherein the size of the first part in the plurality of training mask patterns increases sequentially according to a first fixed step size;

[0014] The training mask pattern is converted into a mask matrix.

[0015] Optionally, simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model includes:

[0016] Simulating the simulation model to obtain a training plasma lithography image corresponding to each of the training mask patterns in the plurality of training mask patterns;

[0017] The training plasma lithography image is converted into a light intensity matrix.

[0018] Optionally, converting the training mask pattern into a mask matrix comprises:

[0019] The first part is converted into a first parameter of a mask matrix, and the second part is converted into a second parameter of the mask matrix.

[0020] Optionally, simulating the simulation model to obtain a training plasma lithography image corresponding to each of the training mask patterns includes:

[0021] Simulating the simulation model to obtain a plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle;

[0022] The converting the training plasma lithography imaging into a light intensity matrix comprises:

[0023] A plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle are converted into a light intensity matrix.

[0024] Optionally, the training imaging structure for acquiring plasma lithography imaging, wherein the training imaging structure comprises a plurality of training mask patterns that are two-dimensionally periodically varied, comprises:

[0025] A plurality of training mask patterns corresponding to the training imaging structure of plasma lithography imaging are obtained, and the period lengths of the plurality of training mask patterns are sequentially increased according to a second fixed step length.

[0026] Optionally, the training of the rapid imaging model according to the training mask pattern and the training plasma lithography imaging comprises:

[0027] Converting a plurality of mask matrices with different first part sizes under the same cycle length into a first column vector of a target number, where the target number is the number of a plurality of training mask patterns under the same cycle length;

[0028] Converting the light intensity matrices corresponding to the plurality of mask matrices with different first part sizes under the same period length into the second column vector of the target number;

[0029] Arrange the first column vector combination of the target number into an input matrix, and arrange the second column vector combination of the target number into an output matrix;

[0030] A fast imaging model is trained according to the input matrix and the output matrix.

[0031] Optionally, the first portion is square.

[0032] Optionally, the method further comprises:

[0033] Acquire a target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that changes periodically in two dimensions, and the target mask pattern is the same as the training mask pattern;

[0034] The target mask pattern is input into the rapid imaging model to obtain a target plasma lithography image corresponding to the target mask pattern.

[0035] The present application provides a plasma lithography imaging training device, comprising:

[0036] An acquisition unit, used for acquiring a training imaging structure for plasma lithography imaging, wherein the training imaging structure comprises a plurality of training mask patterns that are two-dimensionally periodically changed;

[0037] A simulation unit, used for constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model;

[0038] A training unit is used to train a fast imaging model according to the training mask pattern and the training plasma lithography imaging to obtain the fast imaging model for the two-dimensional periodically changing training mask pattern.

[0039] The present application provides a training method for plasma lithography imaging, including: obtaining a training imaging structure for plasma lithography imaging, the training imaging structure including a plurality of training mask patterns that are periodically changing in two dimensions, constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask patterns according to the simulation model. That is, the simulation model constructed by the training imaging structure is used to simulate the training mask patterns that are periodically changing in two dimensions to obtain the training plasma lithography imaging corresponding to each training mask pattern. Then, the rapid imaging model is trained according to the training mask patterns and the training plasma lithography imaging to obtain a rapid imaging model for the training mask patterns that are periodically changing in two dimensions. It can be seen that by establishing a rapid imaging model for the mask patterns that are periodically changing in two dimensions, the training plasma lithography imaging of each mask pattern can be directly obtained using the rapid imaging model, so as to realize rapid imaging using the rapid imaging model and meet the subsequent research needs for plasma lithography technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A schematic structural diagram of an imaging structure for plasma lithography imaging is shown;

[0042] Figure 2 A schematic flow chart of a plasma lithography imaging training method provided in an embodiment of the present application is shown;

[0043] Figure 3 A schematic diagram of a top view of a training imaging structure provided in an embodiment of the present application is shown;

[0044] Figure 4 A schematic diagram of a simulation of a local structure of a training imaging structure for plasma lithography imaging provided in an embodiment of the present application is shown;

[0045] Figure 5 A schematic diagram of an observation surface result of plasma lithography imaging provided in an embodiment of the present application is shown;

[0046] Figure 6 A schematic diagram of a mask matrix provided in an embodiment of the present application is shown;

[0047] Figure 7 A schematic diagram of a light intensity matrix provided in an embodiment of the present application is shown;

[0048] Figure 8 A schematic diagram of processing a mask matrix and a light intensity matrix provided in an embodiment of the present application is shown;

[0049] Fig. 9 A schematic diagram of a matrix multiplication method provided in an embodiment of the present application is shown;

[0050] Fig.10 A schematic structural diagram of a plasma lithography imaging training device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0053] This application is described in detail with reference to schematic diagrams. When describing the embodiments of this application in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of this application. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0054] With the development of semiconductor-related technologies, lithography, one of the important technologies for manufacturing semiconductor devices, is also developing rapidly. Plasmonic lithography, as a supplement to mainstream lithography, is very different from traditional optical lithography, such as deep ultraviolet lithography (DUVL) and extreme ultraviolet lithography (EUVL).

[0055] Plasma lithography, also known as surface plasmon lithography, is a lithography technology that excites surface plasmon polaritons (SPPs) or localized surface plasmons on the surfaces of metals and dielectrics, causing the evanescent waves at the object (mask) to be resonantly amplified and participate in imaging, allowing exposure and imaging in the photoresist, ultimately achieving pattern transfer.

[0056] Plasma lithography technology can break through the diffraction limit in traditional lithography by utilizing evanescent near-field imaging containing high-frequency information. Experiments have shown that even with a light source with a wavelength of 365 nanometers (nm), the resolution can reach about 20nm under single exposure conditions, which is about 1 / 17 of the wavelength of light, and it is possible to further improve. This method does not require the introduction of complex and expensive optical lenses and short-wavelength light sources, and is compatible with traditional lithography materials and processes. It has gradually developed into a new high-resolution, low-cost nano-optical processing technology.

[0057] Plasma lithography mainly includes plasma imaging lithography, interference lithography and direct writing lithography. Direct writing lithography usually has no imaging structure, while imaging lithography and interference lithography have imaging structures including mask patterns. Figure 1 shown. Figure 1 The imaging structure shown is a single-layer metal film superlens structure, including a quartz substrate (Glass), a mask pattern, polymethyl methacrylate (PMMA), a metal film, a photoresist (PR) and a reflective layer stacked in sequence, wherein the mask pattern is, for example, a chromium (Cr) mask, the metal film is, for example, silver (Ag), and the reflective layer is, for example, silver.

[0058] The plasma lithography imaging process including the imaging structure is roughly as follows: the light source is incident on the mask pattern and diffracts, generating various diffraction orders, including low-frequency transmission waves and high-frequency evanescent waves. These diffracted light waves will continue to propagate through the single-layer metal film behind the mask pattern until the photoresist layer, transferring the information of the mask pattern to the photoresist. In the process of the diffraction order being transferred to the photoresist, if the wave vector of the high-frequency evanescent diffraction order matches the wave vector of the surface plasmon polaritons at the metal / medium interface, the surface plasmon polaritons can be excited at the metal / medium interface, so that the high-frequency evanescent wave is resonantly amplified and transferred to the photoresist layer, thereby achieving an improvement in the lithography resolution. In addition, a reflective layer is often placed behind the photoresist layer to further improve the imaging effect of the photoresist layer through the reflection resonance effect.

[0059] Being able to accurately and quantitatively describe the imaging of different mask patterns in the photoresist layer under different imaging structures and different material parameters is the basis for in-depth research on plasma lithography. Based on this, we can better understand and explain the relevant experimental phenomena, and can further guide how to optimize process parameters and improve imaging performance (such as resolution, depth of focus, contrast, etc.). Specifically, an imaging model of plasma lithography can be established, and the imaging model can be studied by numerical or analytical methods. Numerical methods can be, for example, finite difference time domain method (FDTD), finite element method (FEM), etc. Analytical methods can be, for example, optical transfer function method (OTF). However, although the numerical method is more accurate, the calculation time is long and the efficiency is not high, especially when expanding the simulation range or solving the three-dimensional model (the corresponding mask pattern is a two-dimensional graphic). Although the analytical method has a faster calculation speed, the accuracy of the imaging result is reduced due to the approximate treatment of the mask, and the analytical method currently only has a two-dimensional model for one-dimensional periodic patterns, and the three-dimensional model for two-dimensional patterns has not been studied accordingly.

[0060] Therefore, there is currently a need for research on rapid imaging of two-dimensional patterns using plasma lithography.

[0061] Based on this, the present application provides a training method for plasma lithography imaging, including: obtaining a training imaging structure for plasma lithography imaging, the training imaging structure including a plurality of training mask patterns that are periodically changing in two dimensions, constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model. That is, the simulation model constructed by the training imaging structure is used to simulate the training mask pattern that is periodically changing in two dimensions to obtain the training plasma lithography imaging corresponding to each training mask pattern. Then, the rapid imaging model is trained according to the training mask pattern and the training plasma lithography imaging to obtain a rapid imaging model for the training mask pattern that is periodically changing in two dimensions. It can be seen that by establishing a rapid imaging model for the mask pattern that is periodically changing in two dimensions, the training plasma lithography imaging of each mask pattern can be directly obtained using the rapid imaging model, and rapid imaging using the rapid imaging model can be achieved, meeting the subsequent research needs for plasma lithography technology.

[0062] In order to better understand the technical solution and technical effects of the present application, specific embodiments will be described in detail below with reference to the accompanying drawings.

[0063] refer to Figure 2 FIG. 1 is a flow chart of a plasma lithography imaging training method provided in an embodiment of the present application, the method comprising the following steps:

[0064] S101, obtaining a training imaging structure for plasma lithography imaging.

[0065] In the embodiment of the present application, there are multiple imaging structures for plasma lithography imaging. First, a training imaging structure for plasma lithography imaging may be acquired. The training imaging structure includes multiple training mask patterns that are two-dimensionally periodically changed.

[0066] As an example, the training imaging structure may be a structure in which metal / medium are alternately arranged and a reflective layer is provided behind the photoresist, as shown in FIG. Figure 3 As shown, according to the propagation direction of the light wave, it includes a quartz substrate, a mask, a metal / dielectric stacked alternating film, a spacer layer, a photoresist and a reflective layer stacked in sequence, wherein the parameter information of the training imaging structure is: the mask is a 40nm thick Cr mask, and the adjacent Cr masks are interposed with light-transmitting titanium oxide (TiO2), the metal / dielectric stacked alternating film is a TiO2 / Ag multilayer film, the spacer layer thickness is 20nm, the photoresist thickness is 20nm, the reflective layer is Ag, the dielectric constant of the quartz substrate is 2.25, the dielectric constant of the Cr mask is -8.55+8.96i, the dielectric constants of the TiO2 / Ag multilayer film are 7.8375+0.2800i / -2.3879+0.1573i respectively, the dielectric constant of the spacer layer is 1, the dielectric constant of the photoresist is 2.59, and the dielectric constant of the reflective layer is -2.4+0.45i. The illumination condition used in plasma lithography imaging is a plane wave with a wavelength of 365 nm at normal incidence, such as linearly polarized light.

[0067] refer to Figure 3 As shown, Figure 3 A schematic diagram of the top view of the training imaging structure is provided. It can be seen from the figure that the training imaging structure in the embodiment of the present application can be a through-hole layer mask structure. The training imaging structure includes a plurality of training mask patterns that change in two dimensions periodically. The training mask patterns can be selected to include through holes and partial areas around the through holes. That is, the training mask patterns can include a first part that is transparent and a second part that is opaque. The second part surrounds the first part. The first part corresponds to the through hole. The material of the first part is transparent titanium oxide. The second part corresponds to the partial area around the through hole. The material of the second part is opaque Cr. Specifically, the shape of the through hole can be determined according to actual conditions. The embodiment of the present application adopts a square through hole, that is, the first part is square.

[0068] As an example, the training mask image is Figure 3 The part surrounded by the dotted box is the first part.

[0069] In the embodiment of the present application, in the same training imaging structure, the period of the training mask pattern may be different, and the size of the first part of the training mask pattern may also be different, that is, the same training imaging structure may have multiple different training mask patterns. The following is a specific introduction to obtaining multiple training mask patterns:

[0070] A first possible implementation method is to obtain multiple training mask patterns, and the period lengths of the multiple training mask patterns are sequentially increased according to a second fixed step size, that is, multiple training mask patterns with sequentially changing periods can be obtained and used as input training for the rapid imaging model.

[0071] As an example, the second fixed step length is 5 nm, and the period length of the training mask pattern can be gradually increased from 170 nm to 300 nm in an amplitude of 5 nm, so that 27 groups of training mask patterns can be obtained.

[0072] A second possible implementation method is to obtain multiple training mask patterns, in which the size of the first part increases sequentially according to a first fixed step size. That is, multiple training mask patterns in which the size of the first part changes sequentially can be obtained and used as input training for the rapid imaging model.

[0073] As an example, the first part is a square, the first fixed step size is 10 nm, and the size of the first part of the training mask pattern can be gradually increased from 10 nm to 140 nm in an amplitude of 10 nm, so that 11 groups of training mask patterns can be obtained.

[0074] In practical applications, the period of the training mask pattern is variable, and the size of the first part is also variable, that is, the size of the first part can be different at the same period length, so that multiple training mask patterns with different first part sizes at different period lengths can be obtained.

[0075] As an example, the period length of the training mask pattern is adjusted to obtain 27 groups of training mask patterns, and the size of the first part is adjusted to obtain 11 groups of training mask patterns, so that 27×11=294 groups of training mask patterns can be obtained.

[0076] S102, constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model.

[0077] In the embodiment of the present application, according to the parameter information of the training imaging structure, the training imaging structure can be simulated by using simulation software to obtain a simulation model of the training imaging structure. Considering the periodicity, only a simulation model of a periodic local structure needs to be established. Figure 4 shown. Figure 4 The simulation of the local structure shown corresponds to Figure 3 The training mask image is surrounded by the dashed box. Figure 4 The middle square area is the first part of the training mask pattern.

[0078] After the simulation model is established, the plasma lithography imaging can be simulated according to the simulation model to obtain the training plasma lithography imaging corresponding to the training mask pattern.

[0079] Specifically, simulation software can be used to construct a simulation model of the training imaging structure, and then the simulation software can be used to simulate the light intensity distribution corresponding to the training imaging structure based on the simulation model. The light intensity distribution is used to represent the training plasma lithography imaging. The light intensity distribution is obtained on the observation surface of the photoresist, and the observation surface is specifically the center of the photoresist. Figure 5 As shown, the light intensity distribution of training plasma lithography imaging corresponding to a single training mask pattern can be simulated by using the simulation model.

[0080] In an embodiment of the present application, there may be different training mask patterns in the same training imaging structure, so a simulation model can be used to simulate the training plasma lithography imaging corresponding to each training mask pattern in a plurality of different training mask patterns, so as to subsequently use these different training mask patterns and their corresponding training plasma lithography imaging to train the rapid imaging model.

[0081] S103, training a fast imaging model according to the training mask pattern and the training plasma lithography imaging to obtain a fast imaging model for the two-dimensional periodically changing training mask pattern.

[0082] In an embodiment of the present application, after obtaining a training mask pattern and simulating a training plasma lithography imaging, a fast imaging model can be trained using the training mask pattern and the training plasma lithography imaging to obtain a fast imaging model for a two-dimensional periodically changing training mask pattern.

[0083] Specifically, the training mask pattern can be converted into a mask matrix, and the training plasma lithography imaging can be converted into a light intensity matrix, so as to realize the training of a fast imaging model using the mask matrix and the light intensity matrix.

[0084] When converting the training mask pattern into a mask matrix, the first part of the training mask pattern can be converted into the first parameter of the mask matrix, and the second part can be converted into the second parameter of the mask matrix, so as to distinguish the first part from the second part in the mask matrix. For example, the first parameter can be 1, the second parameter can be 0, and the mask matrix reference Figure 6 shown.

[0085] When converting the training plasma lithography image into a light intensity matrix, since the light intensity distribution on the observation surface of the photoresist is obtained when the training plasma lithography image is obtained through the simulation software, that is, multiple light intensity values ​​of the training plasma lithography image in a single cycle are obtained by simulating the simulation model according to the simulation software. The multiple light intensity values ​​can represent the training plasma lithography image. The multiple light intensity values ​​of the training plasma lithography image in a single cycle can be converted into a light intensity matrix, referring to Figure 7 Specifically, when converting the light intensity value into the light intensity matrix, the light intensity value sampling is performed with a sampling step of 1 nm×1 nm.

[0086] In an embodiment of the present application, after obtaining a mask matrix using a training mask pattern and obtaining a light intensity matrix using training plasma lithography imaging, the mask matrix of the training mask pattern with different first part sizes under the same period length and its corresponding light intensity matrix of training plasma lithography imaging can be processed to form an input matrix and an output matrix, respectively, to train the rapid imaging model.

[0087] Specifically, multiple mask matrices with different first part sizes under the same period length can be converted into a target number of first column vectors, and the light intensity matrices corresponding to the multiple mask matrices with different first part sizes under the same period length can be converted into a target number of second column vectors, where the target number is the number of multiple training mask patterns under the same period length. The target number of first column vectors are combined and arranged as an input matrix, and the target number of second column vectors are combined and arranged as an output matrix.

[0088] That is to say, based on the period length, the mask matrices with different first part sizes and the corresponding light intensity matrices with the same period length can be vectorized, and the vectorized column vectors can be rearranged to obtain the input matrix and the output matrix.

[0089] As an example, refer to Figure 8 As shown, for 11 sets of training mask patterns with different sizes of the first part and a period length of 170 nm, the 11 sets of 170×170 mask matrices Mi are vectorized into 11 sets of 170 2 The first column vector mi, where i is the group of mask patterns, i = 1, 2, ..., 11, the first column vector mi is arranged in sequence to form 170 2 ×11 input matrix Then the light intensity matrix Pi is processed in the same way, that is, the 11 groups of 170×170 light intensity matrices Pi are vectorized into 11 groups of 170 2 The second column vector pi is arranged in sequence to form 170 2 ×11 output matrix

[0090] In the embodiment of the present application, the input matrix and the output matrix may be fitted using the least square method to obtain an intermediate transformation matrix from the input matrix to the output matrix.

[0091] As an example, the input matrix of the fast imaging model may be A, and the output matrix of the fast imaging model may be C. The input A and the output C may be fitted by the least square method to obtain an intermediate transformation matrix B from A to C, where A, B and C approximately satisfy the relationship C = B·A, where · represents matrix multiplication. Fig. 9 shown.

[0092] In an embodiment of the present application, by vectorizing the mask matrix and the light intensity matrix, and then rearranging the column vectors into an input matrix A and an output matrix C, it is possible to use the above matrix multiplication formula to reflect that the light intensity of each pixel of the output matrix C is the weighted sum of the mask pixel values ​​under the same period length. In addition, vectorization and rearrangement processing can meet the requirements of least squares linear fitting and matrix multiplication for matrix dimensions. The above training process can obtain intermediate transformation matrices under different period lengths, that is, fast imaging models under different period lengths can be trained.

[0093] As an example, the period length of the training mask pattern is adjusted to obtain 27 sets of training mask patterns, so that the intermediate transformation matrix B under each period length can be obtained. j ,j=1,2,3,…,27.

[0094] After training, a trained rapid imaging model is obtained. Specifically, an intermediate transformation matrix B from input A to output C fitted by the least squares method can be obtained. The trained rapid imaging model can be tested later, that is, the intermediate transformation matrix B is tested, so as to determine the imaging effect of the rapid imaging model according to the test results, and the rapid imaging model can be further optimized according to the test results.

[0095] In an embodiment of the present application, a plurality of test mask patterns may be obtained, and the sizes of the first parts of the plurality of test mask patterns are sequentially increased according to a third fixed step length, and the third fixed step length is smaller than the first fixed step length, that is, the rapid imaging model may be tested using a third fixed step length that is smaller than the increase in the size of the first part when training the rapid imaging model, so as to maximize the test effect of the rapid imaging model. The plurality of test mask patterns obtained under the same period length are converted into an input matrix, and a test plasma lithography image corresponding to each of the plurality of test mask patterns is obtained, and the plurality of test plasma lithography images under the same period length are converted into an output matrix, and the rapid imaging model is tested according to the input matrix and the output matrix.

[0096] In practical applications, when the rapid imaging model is tested using a plurality of test mask patterns increased by the third fixed step size, the adopted testing method is similar to that of the plurality of training mask patterns, which will not be described in detail herein.

[0097] As an example, the first part size of the plurality of test mask patterns increases sequentially from 40 nm to 140 nm according to a third fixed step size smaller than 10 nm, such as 5 nm.

[0098] In an embodiment of the present application, after obtaining the test results of the rapid imaging model, the calculation results obtained using a rigorous simulation calculation method can also be obtained, and the errors between the test results and the calculation results can be compared. The imaging effect of the rapid imaging model can be obtained through the errors.

[0099] The plasma lithography fast imaging model based on least squares fitting provided in the embodiment of the present application can directly obtain the photoresist space image from the mask pattern. Compared with the strict simulation calculation method, the operating speed can be greatly improved under the premise of little loss of precision. In practical applications, the operating speed and calculation accuracy of the strict simulation software are related to the size of the grid division, but the calculation accuracy of the fast imaging model in the present application is related to the accuracy and sampling number of the acquired training data, and the operating speed is related to the sampling number, but under basically the same conditions, the fast imaging model provided in the embodiment of the present application can achieve fast imaging. The plasma lithography imaging method provided by the embodiment of the present application can solve the imaging simulation problem of the imaging structure of the two-dimensional periodic mask pattern.

[0100] In an embodiment of the present application, after the rapid imaging model is trained, the rapid imaging model can be used to generate a target plasma lithography image corresponding to a target mask pattern of a target imaging structure. Specifically, a target imaging structure of plasma lithography imaging can be obtained, wherein the target imaging structure includes a target mask pattern that changes periodically in two dimensions, and the target mask pattern is the same as the training mask pattern. Then, the target mask pattern is input into the rapid imaging model to obtain a target plasma lithography image corresponding to the target mask pattern.

[0101] The present application provides a training method for plasma lithography imaging, including: obtaining a training imaging structure for plasma lithography imaging, the training imaging structure including a plurality of training mask patterns that are periodically changing in two dimensions, constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask patterns according to the simulation model. That is, the simulation model constructed by the training imaging structure is used to simulate the training mask patterns that are periodically changing in two dimensions to obtain the training plasma lithography imaging corresponding to each training mask pattern. Then, the rapid imaging model is trained according to the training mask patterns and the training plasma lithography imaging to obtain a rapid imaging model for the training mask patterns that are periodically changing in two dimensions. It can be seen that by establishing a rapid imaging model for the mask patterns that are periodically changing in two dimensions, the training plasma lithography imaging of each mask pattern can be directly obtained using the rapid imaging model, so as to realize rapid imaging using the rapid imaging model and meet the subsequent research needs for plasma lithography technology.

[0102] Based on the plasma lithography imaging training method provided in the above embodiment, the present application embodiment also provides a plasma lithography imaging training device, referring to Fig.10 , which is a schematic diagram of the structure of a plasma lithography imaging training device provided in an embodiment of the present application. The plasma lithography imaging training device 200 provided in an embodiment of the present application includes:

[0103] An acquisition unit 210 is used to acquire a training imaging structure for plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed;

[0104] A simulation unit 220, configured to construct a simulation model according to the training imaging structure, and obtain a training plasma lithography imaging corresponding to the training mask pattern by simulating the simulation model;

[0105] The training unit 230 is used to train the fast imaging model according to the training mask pattern and the training plasma lithography imaging to obtain the fast imaging model for the two-dimensional periodically changing training mask pattern.

[0106] Optionally, the training mask pattern includes a light-transmissive first portion and a light-impermeable second portion, and the second portion surrounds the first portion;

[0107] The acquisition unit 210 is used to:

[0108] Acquire a plurality of training mask patterns corresponding to a training imaging structure of plasma lithography imaging, wherein the size of the first part in the plurality of training mask patterns increases sequentially according to a first fixed step size;

[0109] The training mask pattern is converted into a mask matrix.

[0110] Optionally, the simulation unit 220 is used to:

[0111] Simulating the simulation model to obtain a training plasma lithography image corresponding to each of the training mask patterns in the plurality of training mask patterns;

[0112] The training plasma lithography image is converted into a light intensity matrix.

[0113] Optionally, the acquiring unit 210 is configured to:

[0114] The first part is converted into a first parameter of a mask matrix, and the second part is converted into a second parameter of the mask matrix.

[0115] Optionally, the simulation unit 220 is used to:

[0116] Simulating the simulation model to obtain a plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle;

[0117] A plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle are converted into a light intensity matrix.

[0118] Optionally, the acquiring unit 210 is configured to:

[0119] A plurality of training mask patterns corresponding to the training imaging structure of plasma lithography imaging are obtained, and the period lengths of the plurality of training mask patterns are sequentially increased according to a second fixed step length.

[0120] Optionally, the training unit 230 is used to:

[0121] Converting a plurality of mask matrices with different first part sizes under the same cycle length into a first column vector of a target number, where the target number is the number of a plurality of training mask patterns under the same cycle length;

[0122] Converting the light intensity matrices corresponding to the plurality of mask matrices with different first part sizes under the same period length into the second column vector of the target number;

[0123] Arrange the first column vector combination of the target number into an input matrix, and arrange the second column vector combination of the target number into an output matrix;

[0124] A fast imaging model is trained according to the input matrix and the output matrix.

[0125] Optionally, the first portion is square.

[0126] Optionally, the device further comprises an application unit, wherein the application unit is configured to:

[0127] Acquire a target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that changes periodically in two dimensions, and the target mask pattern is the same as the training mask pattern;

[0128] The target mask pattern is input into the rapid imaging model to obtain a target plasma lithography image corresponding to the target mask pattern.

[0129] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0130] The above is only a preferred implementation of the present application. Although the present application has been disclosed as a preferred embodiment, it is not intended to limit the present application. Any technician familiar with the art can use the above disclosed methods and technical contents to make many possible changes and modifications to the technical solution of the present application without departing from the scope of the technical solution of the present application, or modify it into an equivalent embodiment of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still falls within the scope of protection of the technical solution of the present application.

Claims

1. A plasma lithography imaging training method, characterized in that: include: Acquire a training imaging structure for plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed; Building a simulation model according to the training imaging structure, and simulating and obtaining a training plasma lithography imaging corresponding to the training mask pattern according to the simulation model; The rapid imaging model is trained according to the training mask pattern and the training plasma lithography imaging to obtain the rapid imaging model for the two-dimensional periodically changing training mask pattern.

2. The method according to claim 1, characterized in that The training mask pattern includes a light-transmitting first portion and a light-impermeable second portion, wherein the second portion surrounds the first portion; The training imaging structure for acquiring plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed, includes: Acquire a plurality of training mask patterns corresponding to a training imaging structure of plasma lithography imaging, wherein the size of the first part in the plurality of training mask patterns increases sequentially according to a first fixed step size; The training mask pattern is converted into a mask matrix.

3. The method according to claim 2, characterized in that The step of simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model comprises: Simulating the simulation model to obtain a training plasma lithography image corresponding to each of the training mask patterns in the plurality of training mask patterns; The training plasma lithography image is converted into a light intensity matrix.

4. The method according to claim 2, characterized in that: The step of converting the training mask pattern into a mask matrix comprises: The first part is converted into a first parameter of a mask matrix, and the second part is converted into a second parameter of the mask matrix.

5. The method according to claim 2, characterized in that: The step of simulating the simulation model to obtain a training plasma lithography image corresponding to each of the training mask patterns in the plurality of training mask patterns comprises: Simulating the simulation model to obtain a plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle; The converting the training plasma lithography imaging into a light intensity matrix comprises: A plurality of light intensity values ​​of the training plasma lithography imaging in a single cycle are converted into a light intensity matrix.

6. The method according to claim 3, characterized in that The training imaging structure for acquiring plasma lithography imaging, wherein the training imaging structure includes a plurality of training mask patterns that are two-dimensionally periodically changed, includes: A plurality of training mask patterns corresponding to the training imaging structure of plasma lithography imaging are obtained, and the period lengths of the plurality of training mask patterns are sequentially increased according to a second fixed step length.

7. The method according to claim 6, characterized in that The training of the fast imaging model according to the training mask pattern and the training plasma lithography imaging comprises: Converting a plurality of mask matrices with different first part sizes under the same cycle length into a first column vector of a target number, where the target number is the number of a plurality of training mask patterns under the same cycle length; Converting the light intensity matrices corresponding to the plurality of mask matrices with different first part sizes under the same period length into the second column vector of the target number; Arrange the first column vector combination of the target number into an input matrix, and arrange the second column vector combination of the target number into an output matrix; A fast imaging model is trained according to the input matrix and the output matrix.

8. The method according to claim 2, characterized in that: The first portion is square.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: Acquire a target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that changes periodically in two dimensions, and the target mask pattern is the same as the training mask pattern; The target mask pattern is input into the rapid imaging model to obtain a target plasma lithography image corresponding to the target mask pattern.

10. A plasma lithography imaging training device, characterized in that: include: An acquisition unit, used for acquiring a training imaging structure for plasma lithography imaging, wherein the training imaging structure comprises a plurality of training mask patterns that are two-dimensionally periodically changed; A simulation unit, used for constructing a simulation model according to the training imaging structure, and simulating the training plasma lithography imaging corresponding to the training mask pattern according to the simulation model; A training unit is used to train a fast imaging model according to the training mask pattern and the training plasma lithography imaging to obtain the fast imaging model for the two-dimensional periodically changing training mask pattern.

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