Plasma lithography imaging method and apparatus
By establishing a rapid imaging model and using training mask patterns and training plasma lithography imaging to train target mask patterns, the problem of low imaging efficiency in plasma lithography technology was solved, and efficient plasma lithography imaging was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-11-10
- Publication Date
- 2026-03-24
AI Technical Summary
There is limited research on rapid imaging using existing plasma lithography technology, resulting in low imaging efficiency that fails to meet research needs.
By establishing a rapid imaging model, training is performed using the training mask pattern of the target imaging structure and training plasma lithography imaging, thereby achieving plasma lithography imaging corresponding to the target mask pattern and improving output efficiency.
It achieves rapid output of plasma lithography imaging with accuracy loss controlled within 2% and computational efficiency improved by about 70 times, solving the problem of rapid imaging in plasma lithography technology.
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Figure CN116027639B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of semiconductor, in particular to a method and device for plasma lithography imaging. BACKGROUND
[0002] With the development of semiconductor technology, the lithography technology, one of the important technologies for manufacturing semiconductor devices, is also developing rapidly. As a complementary mainstream lithography technology, the plasma lithography technology is quite different from the traditional optical lithography technology, such as deep ultraviolet lithography (DUVL) and extreme ultraviolet lithography (EUVL).
[0003] The plasma lithography technology can break through the diffraction limit in the traditional lithography by using evanescent near-field imaging containing high-frequency information. Experiments have shown that even if a light source with a wavelength of 365 nanometers (nm) is used, the resolution can reach about 20 nm under single exposure conditions, which is about 1 / 17 of the light wavelength, and can be further improved. This method provides a reliable technical approach for the study of low-cost, large-area, and high-efficiency lithography technology, and therefore has attracted widespread attention.
[0004] In summary, there is a need for research on the rapid imaging of the plasma lithography technology. SUMMARY
[0005] Therefore, the purpose of the present application is to provide a method and device for plasma lithography imaging, which can realize rapid imaging of the plasma lithography technology and meet the subsequent research needs of the plasma lithography technology.
[0006] To achieve the above purpose, the present application has the following technical solutions:
[0007] The present application provides a method for plasma lithography imaging, comprising:
[0008] Obtaining a target imaging structure for plasma lithography imaging, the target imaging structure comprising a target mask pattern that varies periodically;
[0009] Inputting the target mask pattern into a rapid imaging model to obtain a target plasma lithography imaging corresponding to the target mask pattern, the rapid imaging model being trained using training mask patterns of the target imaging structure and training plasma lithography imaging corresponding to the training mask patterns.
[0010] Optionally, the plurality of training mask patterns comprises a first training mask pattern, the training plasma lithography imaging comprises a first training plasma lithography imaging, and the method further comprises:
[0011] obtaining a plurality of the first training mask patterns, a period length of the plurality of first training mask patterns being sequentially increased by a first fixed step;
[0012] converting the plurality of first training mask patterns into a first matrix;
[0013] obtaining a plurality of first training plasma lithography images corresponding to each of the plurality of first training mask patterns;
[0014] converting the plurality of first training plasma lithography images into a second matrix;
[0015] training the fast imaging model according to the first matrix and the second matrix.
[0016] Optionally, the first training mask pattern comprises a first part that is transparent and a second part that is not transparent.
[0017] The converting the plurality of first training mask patterns into a first matrix comprises:
[0018] converting the first part into a first parameter of the first matrix and converting the second part into a second parameter of the first matrix.
[0019] Optionally, the method further comprises:
[0020] constructing a simulation model according to the target imaging structure;
[0021] simulating a plurality of light intensity values of the first training plasma lithography imaging in a single period according to the simulation model.
[0022] Optionally, the converting the plurality of first training plasma lithography images into a second matrix comprises:
[0023] converting the plurality of light intensity values of the plurality of first training plasma lithography images in a single period into the second matrix.
[0024] Optionally, the plurality of training mask patterns comprises a second training mask pattern, the training plasma lithography imaging comprises a second training plasma lithography imaging, and the method further comprises:
[0025] A plurality of second training mask patterns are acquired, wherein the period length of the plurality of second training mask patterns is increased sequentially according to a second fixed step size, the second fixed step size being less than the first fixed step size, and the period length range of the first training mask pattern includes the period length range of the second training mask pattern.
[0026] Convert the plurality of second training mask patterns into a third matrix;
[0027] Acquire multiple second training mask images, each corresponding to a second training plasma lithography image;
[0028] The plurality of second training plasma lithography images are converted into a fourth matrix;
[0029] Continue training the fast imaging model based on the third and fourth matrices.
[0030] Optionally, acquiring a plurality of first training mask images, wherein the period length of the plurality of first training mask images increases sequentially according to a first fixed step size, includes:
[0031] Multiple first training mask images are acquired, wherein the length of the first part of the multiple first training mask images remains unchanged, and the length of the second part of the multiple first training mask images increases sequentially according to a first fixed step.
[0032] Optionally, acquiring a plurality of first training mask images, wherein the period length of the plurality of first training mask images increases sequentially according to a first fixed step size, includes:
[0033] Multiple first training mask images are acquired, wherein the length of the second part of the multiple first training mask images remains unchanged, and the length of the first part of the multiple first training mask images is increased sequentially according to a first fixed step.
[0034] Optionally, the method further includes:
[0035] Multiple test mask patterns are acquired, and the period length of the multiple test mask patterns is increased sequentially according to a third fixed step size, wherein the third fixed step size is less than the first fixed step size;
[0036] Convert the multiple test mask patterns into a fifth matrix;
[0037] Acquire multiple test mask images, each corresponding to a test plasma lithography image;
[0038] The plurality of test plasma lithography images are converted into a sixth matrix;
[0039] The fast imaging model is tested based on the fifth and sixth matrices.
[0040] This application also provides a plasma lithography imaging apparatus, including:
[0041] The first acquisition unit is used to acquire the target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that changes periodically.
[0042] The simulation unit is used to input the target mask pattern into the fast imaging model to obtain the target plasma lithography image corresponding to the target mask pattern. The fast imaging model is trained using the training mask pattern of the target imaging structure and the training plasma lithography image corresponding to the training mask pattern.
[0043] This application provides a plasma lithography imaging method, comprising: acquiring a target imaging structure for plasma lithography imaging, the target imaging structure including a periodically changing target mask pattern; inputting the target mask pattern into a fast imaging model to obtain a target plasma lithography image corresponding to the target mask pattern; the fast imaging model is trained using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern; that is, in this application, a fast imaging model is trained using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern, so that the model can be used to quickly output the target plasma lithography image corresponding to the target mask pattern, improving the output efficiency of plasma lithography imaging, providing an effective model for subsequent research on plasma lithography imaging, and greatly facilitating research on plasma lithography technology. Attached Figure Description
[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0045] Figure 1A A schematic diagram of an imaging structure for plasma lithography imaging is shown.
[0046] Figure 1B A schematic diagram of another imaging structure for plasma lithography imaging is shown;
[0047] Figure 2 A schematic flowchart of a plasma lithography imaging method provided in an embodiment of this application is shown;
[0048] Figure 3This paper shows a simulated structural diagram of a target imaging structure for plasma lithography imaging according to an embodiment of this application.
[0049] Figure 4 This illustration shows a schematic diagram of the light intensity distribution of a target imaging structure for plasma lithography imaging according to an embodiment of this application;
[0050] Figure 5 A schematic diagram of a first matrix provided in an embodiment of this application is shown;
[0051] Figure 6 A schematic diagram of a second matrix provided in an embodiment of this application is shown;
[0052] Figure 7 A schematic diagram of a plasma lithography imaging device provided in an embodiment of this application is shown. Detailed Implementation
[0053] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0054] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.
[0055] This application is described in detail with reference to the schematic diagrams. When detailing the embodiments of this application, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of this application. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.
[0056] With the development of semiconductor-related technologies, photolithography, one of the key technologies in the manufacture of semiconductor devices, is also developing rapidly. Plasma lithography, as a supplement to mainstream photolithography, differs significantly from traditional optical lithography techniques such as deep ultraviolet lithography (DUVL) and extreme ultraviolet lithography (EUVL).
[0057] Plasma lithography, by utilizing evanescent near-field imaging that incorporates high-frequency information, can overcome the diffraction limit in traditional lithography. Experiments have shown that even using a 365-nanometer (nm) light source, a resolution of approximately 20 nm can be achieved in a single exposure, about 1 / 17th of the light wavelength, with the potential for further improvement. This method provides a reliable technical approach for researching low-cost, large-area, and efficient lithography techniques, and has therefore attracted widespread attention.
[0058] Compared to traditional ultraviolet projection lithography, plasma lithography has a different principle. For example, the imaging range is localized in the near field, evanescent waves containing high-frequency information of the object (mask) can be resonantly amplified and participate in imaging, and the imaging lens is no longer an ordinary optical lens but a single-layer metal thin film with a thickness at the nanometer level or a multilayer film with alternating metal / dielectric materials.
[0059] Plasma lithography mainly includes plasma imaging lithography, interference lithography, and direct-write lithography. Direct-write lithography typically lacks an imaging structure, while imaging lithography and interference lithography have imaging structures including mask patterns. (See reference...) Figure 1A and Figure 1B As shown. Figure 1A The imaging structure shown is a single-layer metal thin-film superlens structure, which includes a quartz substrate (Glass), a mask pattern, polymethyl methacrylate (PMMA), a metal thin film, photoresist (PR), and a reflective layer stacked sequentially. The mask pattern is, for example, a chromium (Cr) mask, the metal thin film is, for example, silver (Ag), and the reflective layer is, for example, silver. Figure 1B The imaging structure shown is a hyperbolic metamaterial (HMM) structure with alternating metal / dielectric layers, including a quartz substrate, a mask pattern, plexiglass, alternating metal / dielectric thin films, photoresist (PR), and a reflective layer stacked sequentially. The mask pattern is, for example, a chromium (Cr) mask, the alternating metal / dielectric thin films are, for example, alternating aluminum (Al) / silicon oxide (SiO2) thin films, and the reflective layer is, for example, aluminum.
[0060] The plasma lithography imaging process, including the imaging structure, is roughly as follows: A light source is incident on a mask pattern, causing diffraction and generating various diffraction orders, including low-frequency propagating waves and high-frequency evanescent waves. These diffracted light waves continue to propagate through a single-layer metal film or a multilayer film with alternating metal / dielectric structures behind the mask pattern until they reach the photoresist layer, transferring the information from the mask pattern to the photoresist. During the transmission of diffraction orders to the photoresist, if the wave vector of the high-frequency evanescent diffraction order matches the wave vector of the surface plasmon polariton (SPP) at the metal / dielectric interface, the SPP can be excited at the metal / dielectric interface. This causes the high-frequency evanescent wave to be resonantly amplified and transmitted to the photoresist layer, thereby improving the lithographic resolution. Furthermore, a reflective layer is often placed behind the photoresist layer to further enhance the imaging effect of the photoresist layer through reflection resonance.
[0061] Accurately and quantitatively describing the imaging of different mask patterns on photoresist layers under various imaging structures and material parameters is fundamental to in-depth research on plasma lithography. This allows for a better understanding and explanation of related experimental phenomena and provides further guidance on optimizing process parameters and improving imaging performance (such as resolution, depth of focus, and contrast). However, because the entire lithography system is a nanoscale thin-film stacked structure, the imaging range is localized to the near field. Imaging results can only be obtained by rigorously solving Maxwell's equations. Commonly used algorithms include rigorous coupled-wave analysis (RCWA), finite-difference time-domain (FDTD), and finite element method (FEM). While these methods are relatively accurate, they are computationally expensive and inefficient, especially when expanding the simulation scope or solving three-dimensional models (corresponding to two-dimensional mask patterns). Therefore, establishing a rapid imaging model for plasma lithography based on machine learning and deep learning algorithms is essential. Currently, research on rapid imaging in lithography mainly focuses on rapid imaging of the near field of mask diffraction in DUV / EUV lithography. Research on rapid imaging specifically for plasma lithography is limited.
[0062] In summary, there is a current research need for rapid imaging using plasma lithography technology.
[0063] Based on this, this application provides a plasma lithography imaging method, including: acquiring a target imaging structure for plasma lithography imaging, the target imaging structure including a periodically changing target mask pattern; inputting the target mask pattern into a fast imaging model to obtain a target plasma lithography image corresponding to the target mask pattern; the fast imaging model is trained using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern. In other words, this application trains a fast imaging model using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern. This model can then be used to quickly output the target plasma lithography image corresponding to the target mask pattern, improving the output efficiency of plasma lithography imaging and providing an effective model for subsequent research on plasma lithography imaging, greatly facilitating research on plasma lithography technology.
[0064] To better understand the technical solution and effects of this application, the specific embodiments will be described in detail below with reference to the accompanying drawings.
[0065] refer to Figure 2 The diagram shown is a flowchart of a plasma lithography imaging method provided in an embodiment of this application. The method includes the following steps:
[0066] S101, acquire the target imaging structure for plasma lithography imaging.
[0067] In the embodiments of this application, the imaging structure of plasma lithography imaging has various forms, and a target imaging structure can be obtained. The target imaging structure includes a target mask pattern that changes periodically, so that plasma lithography imaging can be performed based on the target mask pattern.
[0068] As an example, the target imaging structure can be a hyperbolic multilayer film structure with alternating metal / dielectric layers, including a quartz substrate, a mask pattern, alternating metal / dielectric thin films, a spacer layer, a photoresist layer, and a reflective layer stacked sequentially. The parameters of the target imaging structure are as follows: the mask pattern is a 40nm thick Cr mask, with transparent titanium oxide (TiO2) between adjacent Cr masks; the alternating metal / dielectric thin films are TiO2 / Ag multilayer films; and the spacer layer thickness is... The photoresist thickness is 20 nm, 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 and -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 conditions used for plasma lithography imaging are normally incident 365 nm wavelength TM polarized light.
[0069] Based on the parameter information of the target imaging structure, simulation software can be used to perform two-dimensional modeling and simulation of the target imaging structure, resulting in a simulated schematic diagram of the target imaging structure. (Refer to...) Figure 3 As shown.
[0070] S102, input the target mask pattern into the fast imaging model to obtain the target plasma lithography image corresponding to the target mask pattern.
[0071] In embodiments of this application, the target mask pattern of the acquired target imaging structure can be input into a pre-trained fast imaging model, which can output a target plasma lithography image corresponding to the target mask pattern. The fast imaging model is trained using the training mask pattern of the target imaging structure and the training plasma lithography image corresponding to the training mask pattern; that is, the input to the fast imaging model during training is the training mask pattern, and the output is the training plasma lithography image.
[0072] Unlike the rapid imaging of the near-field of mask diffraction in ultraviolet lithography, considering the characteristics of plasma lithography, where the entire imaging range is localized in the near field and the electromagnetic field coupling resonance between various stacks is strong, a rapid imaging model can be established that directly transforms the mask pattern into a plasma lithography image. Specifically, the plasma lithography image obtained through the rapid imaging model can be a photoresist spatial image. Then, the photoresist spatial image obtained by the rapid imaging model can be subjected to threshold exposure to obtain the final photoresist exposure image. To avoid ambiguity, the following explanation will take the plasma lithography image output by the rapid imaging model as a photoresist spatial image as an example.
[0073] In the embodiments of this application, after obtaining the simulated structure of the target imaging structure using simulation software, a simulation model can be constructed based on the simulated structure using simulation software. The light intensity distribution corresponding to the target imaging structure is then simulated using the simulation model. This light intensity distribution represents plasma lithography imaging. The light intensity distribution is obtained from the observation surface of the photoresist, specifically the center of the photoresist. (Reference) Figure 4 As shown, the light intensity distribution within a single mask pattern period can be obtained using a simulation model.
[0074] In other words, the input to training the fast imaging model can be the simulated structure of the training mask pattern of the target imaging structure, and the output of training the fast imaging model can be the light intensity distribution corresponding to the training mask pattern obtained through the simulation model.
[0075] In the embodiments of this application, the multiple training mask patterns include a first training mask pattern and a second training mask pattern, and the training of plasma lithography imaging includes a first training plasma lithography imaging and a second training plasma lithography imaging. The period length of the multiple first training mask patterns increases sequentially according to a first fixed step size, and the period length of the multiple second training mask patterns increases sequentially according to a second fixed step size. The second fixed step size is smaller than the first fixed step size. That is to say, when training the fast imaging model, mask patterns with different period lengths and different increases in period length can be used for training, so as to enrich the training content of the fast imaging model and improve the accuracy of the fast imaging model.
[0076] The training process for the fast imaging model will be described in detail below:
[0077] Multiple first training mask patterns and the first training plasma lithography image corresponding to each of the multiple first training mask patterns can be obtained. The period length of the multiple first training mask patterns can be increased sequentially according to the first fixed step size. In other words, multiple first training mask patterns with sequentially changing periods can be obtained and used as input training for a fast imaging model.
[0078] Multiple first training mask patterns can be converted into a first matrix, and multiple first training plasma lithography images can be converted into a second matrix. Then, the first matrix and the second matrix can be used to train a fast imaging model.
[0079] As an example, the first matrix of the input fast imaging model can be A, and the second matrix of the input fast imaging model can be B. The input A and the output C can be fitted by least squares to obtain the intermediate transformation matrix B from A to C, where A, B and C approximately satisfy the relationship C = A·B, where · represents matrix multiplication.
[0080] In the embodiments of this application, the target mask pattern in the target mask structure includes a transparent part and an opaque part. The training mask pattern is similar to the target mask pattern, also including a transparent part and an opaque part. Accordingly, the first training mask pattern may include a transparent first part and an opaque second part. When converting multiple first training mask patterns into a first matrix, the first part can be converted into the first parameter of the first matrix, and the second part can be converted into the second parameter of the first matrix. Based on the longest single period among multiple first training mask patterns, the first training mask pattern with insufficient period is supplemented with a third parameter.
[0081] As one possible implementation, refer to Figure 3The simulation structure shown uses a Cr mask pattern, comprising a transparent first part and an opaque second part. The first part is titanium oxide, and the second part is Cr. The period length of the first training mask pattern can range from 150nm to 400nm, the first fixed step size can be 10nm, the first parameter can be 1, the second parameter can be 0, and the third parameter can be -1. This means the first part can be converted to 1 in the first matrix, and the second part to 0 in the first matrix. For first training mask patterns with a period length less than 400nm, the insufficient portion can be converted to -1 in the first matrix. (Refer to...) Figure 5 The diagram shown is a schematic of a first matrix provided in an embodiment of this application, where the horizontal axis represents the period length, the vertical axis represents the number of first training mask patterns, and multiple first training mask patterns are distinguished by training numbers. Figure 5 It can be seen that the period length of multiple first training mask patterns gradually increases from 150nm to 400nm and then repeats the process of increasing the value to 400nm again starting from 150nm. The position of the first matrix corresponding to the first part that is transparent is the position of the first parameter, the position of the first matrix corresponding to the second part that is opaque is the position of the second parameter, and the position of the first matrix corresponding to the period length of less than 400nm is the position of the third parameter.
[0082] In the embodiments of this application, a single period of the first training mask pattern includes a light-transmitting first part and an opaque second part. When the period length of multiple first training mask patterns increases sequentially according to a first fixed step size, the period length of the first training mask pattern can be increased by increasing only the length of the first part, or by increasing only the length of the second part, or by increasing the length of both the first and second parts simultaneously. By using various methods of changing the period length, the types of first training mask patterns obtained are enriched, the amount of data for training the fast imaging model is increased, and the accuracy of the fast imaging model is improved.
[0083] As one possible implementation, the length of the first part of the multiple first training mask patterns remains unchanged, and the length of the second part of the multiple first training mask patterns increases sequentially according to a first fixed step.
[0084] As an example, see reference Figure 5 As shown, within the period of a single mask pattern, the first transparent part is between the second opaque part. The length of the first part is fixed at 75nm, and the length of the second part gradually increases from 75nm to 325nm in a first fixed step of 10nm. This allows the period length of multiple first training mask patterns to gradually increase from 150nm to 400nm.
[0085] As another possible implementation, the length of the second part of the multiple first training mask patterns remains unchanged, and the length of the first part of the multiple first training mask patterns increases sequentially according to a first fixed step.
[0086] As an example, the length of the second part is fixed at 75nm, and the length of the first part gradually increases from 75nm to 275nm in a first fixed step of 10nm, thereby realizing that the period length of multiple first training mask patterns gradually increases from 150nm to 350nm.
[0087] In the embodiments of this application, after constructing a simulation model based on the target imaging structure using simulation software, the light intensity distribution for the target imaging structure can be simulated. That is, multiple light intensity values for the first training plasma lithography imaging within a single period can be simulated, and these multiple light intensity values can represent the first training plasma lithography imaging. Specifically, the accuracy of the simulated multiple light intensity values is 0.1 nm.
[0088] Therefore, when converting multiple first training plasma lithography images into a second matrix, multiple light intensity values of multiple first training plasma lithography images within a single period can be converted into a second matrix. Taking the longest single period among multiple first training mask patterns as the benchmark, the light intensity values of the first training mask patterns with insufficient periods are supplemented with fixed parameters.
[0089] As an example, see reference Figure 6 As shown, the horizontal axis represents the period length, and the vertical axis represents the number of first plasma lithography images. Multiple first plasma lithography images are distinguished by training numbers. Figure 6 It can be seen that the period length of multiple first training mask patterns gradually increases from 150nm to 400nm, and then repeats the process of increasing the value to 400nm again starting from 150nm. The light intensity values of multiple first training plasma lithography images also change accordingly with the change of period. Figure 6 In this context, the fixed parameter can be -1e9. The light intensity value of the first training mask image corresponding to the first training mask image with insufficient period is supplemented by -1e9.
[0090] In the embodiments of this application, after the fast imaging model is trained using multiple first training mask patterns, the fast imaging model can be further trained using second training mask patterns based on the training effect to improve the training effect of the fast imaging model. Since the period growth step of the second training mask pattern is smaller than that of the first training mask pattern, more refined training of the fast imaging model can be achieved, further improving the accuracy of fast imaging.
[0091] Specifically, multiple second training mask patterns can be obtained. The period length of these second training mask patterns increases sequentially according to a second fixed step size, which is smaller than the first fixed step size. The period length range of the first training mask patterns includes the period length range of the second training mask patterns. In other words, for the fast imaging model trained using multiple first training mask patterns with period lengths increased by the first fixed step size, the range of period lengths with poor imaging performance is determined. Multiple second training mask patterns are then obtained by gradually increasing the period length using a second fixed step size smaller than the first fixed step size. Local training is then performed using these multiple second training mask patterns to further improve the accuracy of fast imaging. The obtained multiple second training mask patterns are converted into a third matrix. The second training plasma lithography image corresponding to each of the multiple second training mask patterns is obtained. The multiple second training plasma lithography images are converted into a fourth matrix. The fast imaging model is then trained again based on the third and fourth matrices.
[0092] In practical applications, when training the fast imaging model using multiple second training mask patterns with a second fixed step size, the training method used is the same as that used for the first training mask pattern, and will not be repeated here.
[0093] As an example, the period length of the first training mask pattern can range from 150nm to 400nm, and the first fixed step size can be 10nm. The period length of the second training mask pattern can range from 196nm to 256nm, and the second fixed step size can be 4nm.
[0094] As another example, the period length of the first training mask pattern can be in the range of 150nm-350nm, and the first fixed step size can be 10nm. The period length of the second training mask pattern can be in the range of 222nm-258nm, and the second fixed step size can be 3nm, 2nm, or 1nm.
[0095] Therefore, by training the fast imaging model using multiple training mask patterns with the first and second fixed step sizes respectively, it is possible to ensure both model speed and training accuracy. In other words, training with non-uniform step sizes can improve computational accuracy without reducing computational efficiency.
[0096] After training, a trained fast imaging model is obtained. Specifically, the intermediate transformation matrix B from input A to output C, fitted by the least squares method, can be obtained. The trained fast imaging model can then be tested, i.e., the intermediate transformation matrix B can be tested, so as to determine the imaging effect of the fast imaging model based on the test results. The fast imaging model can also be further optimized based on the test results.
[0097] In the embodiments of this application, multiple test mask patterns can be obtained. The period length of the multiple test mask patterns increases sequentially according to a third fixed step size, which is smaller than the first fixed step size. That is, a third fixed step size with a smaller increase in period length than when training the fast imaging model can be used to test the fast imaging model, so as to maximize the testing effect of the fast imaging model. The obtained multiple test mask patterns are converted into a fifth matrix, and the test plasma lithography image corresponding to each test mask pattern is obtained. The multiple test plasma lithography images are converted into a sixth matrix, and the fast imaging model is tested according to the fifth matrix and the sixth matrix.
[0098] In practical applications, when testing the fast imaging model using multiple test mask patterns with a third fixed step size, the testing method is similar to that used with multiple first training mask patterns, and will not be repeated here.
[0099] As an example, the period length of multiple test mask patterns is increased sequentially from 150nm to 390nm with a third fixed step of 6nm, resulting in the fifth matrix A1. The sixth matrix C1 = A1·B is obtained by using the intermediate transformation matrix B obtained through training, i.e., through the output of the fast imaging model.
[0100] As another example, the period length of multiple test mask patterns is increased sequentially from 150nm to 350nm with a third fixed step of 1nm, resulting in the fifth matrix A2. The sixth matrix C2 = A2·B is obtained by using the intermediate transformation matrix B obtained from training, i.e., through the output of the fast imaging model.
[0101] In this embodiment of the application, after obtaining the test results of the fast imaging model, the calculation results obtained by using a rigorous simulation calculation method can also be obtained, and the error between the test results and the calculation results can be compared. The imaging effect of the fast imaging model can be obtained through the error.
[0102] In practical applications, since the output of the fast imaging model is a photoresist spatial image, the photoresist spatial image obtained by the fast imaging model can be subjected to threshold exposure to obtain the final photoresist exposure image. Furthermore, the calculation results obtained through rigorous simulation calculation methods can also be subjected to threshold exposure, and the exposure results of the two can be compared to obtain the exposure size (CD) error.
[0103] The table below shows the imaging results of rapid plasma lithography imaging using a fast imaging model and rigorous simulation calculation method.
[0104]
[0105] In this context, the dark field test set refers to a test set where the length of the first part remains constant, while the length of the second part increases sequentially by 10nm or 4nm. The bright field test set refers to a test set where the length of the second part remains constant, while the length of the first part increases sequentially by 10nm, 3nm, 2nm, or 1nm. Test sets with the same period length increasing sequentially by 6nm require fewer test mask patterns compared to test sets increasing sequentially by 1nm.
[0106] The table above shows that when using the same training set but different test sets (numbers 1 and 2), as the number of tests on the test set increases, the average relative error of exposure CD and the running time of the fast imaging model increase, but the error value is less than 1% and the speed is several tens of times faster than the rigorous simulation calculation method. However, when using the same test set to test fast imaging models obtained from different training sets, it was found that the more training samples there are, i.e. the smaller the step size increase, the smaller the error of the fast imaging model, while the running time does not increase significantly, and it is still about 70 times faster than the rigorous simulation calculation method.
[0107] Therefore, the plasma lithography rapid imaging model based on least squares fitting provided in this application can directly obtain the photoresist spatial image from the mask pattern. Compared with rigorous simulation calculation methods, the accuracy loss is controlled within 2% and the computational efficiency is improved by about 70 times. That is, the running speed can be greatly improved without significant accuracy loss. In practical applications, the plasma lithography imaging method provided in this application can solve the imaging simulation problem of various imaging structures.
[0108] This application provides a plasma lithography imaging method, comprising: acquiring a target imaging structure for plasma lithography imaging, the target imaging structure including a periodically changing target mask pattern; inputting the target mask pattern into a fast imaging model to obtain a target plasma lithography image corresponding to the target mask pattern; the fast imaging model is trained using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern; that is, in this application, a fast imaging model is trained using a training mask pattern of the target imaging structure and a training plasma lithography image corresponding to the training mask pattern, so that the model can be used to quickly output the target plasma lithography image corresponding to the target mask pattern, improving the output efficiency of plasma lithography imaging, providing an effective model for subsequent research on plasma lithography imaging, and greatly facilitating research on plasma lithography technology.
[0109] Based on the plasma lithography imaging method provided in the above embodiments, this application also provides a plasma lithography imaging apparatus, see reference. Figure 7 The diagram shown is a structural schematic of a plasma lithography imaging apparatus provided in an embodiment of this application. The plasma lithography imaging apparatus 700 provided in this embodiment includes:
[0110] The first acquisition unit 710 is used to acquire the target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that varies periodically.
[0111] The simulation unit 720 is used to input the target mask pattern into the fast imaging model to obtain the target plasma lithography image corresponding to the target mask pattern. The fast imaging model is trained using the training mask pattern of the target imaging structure and the training plasma lithography image corresponding to the training mask pattern.
[0112] Optionally, the plurality of training mask patterns includes a first training mask pattern, the training plasma lithography imaging includes a first training plasma lithography imaging, and the apparatus further includes a first training unit for:
[0113] A plurality of first training mask images are acquired, wherein the period length of the plurality of first training mask images is increased sequentially according to a first fixed step size;
[0114] Convert the plurality of first training mask patterns into a first matrix;
[0115] Acquire multiple first training mask images, each corresponding to a first training plasma lithography image;
[0116] The plurality of first training plasma lithography images are converted into a second matrix;
[0117] The fast imaging model is trained based on the first matrix and the second matrix.
[0118] Optionally, the first training mask pattern includes a light-transmitting first part and an opaque second part;
[0119] The first training unit is specifically used for:
[0120] The first part is converted into the first parameter of the first matrix, and the second part is converted into the second parameter of the first matrix.
[0121] Optionally, the device further includes a construction unit for:
[0122] A simulation model is constructed based on the target imaging structure;
[0123] The simulation model was used to obtain multiple light intensity values for the first training plasma lithography imaging within a single period.
[0124] Optionally, the first training unit is specifically used for:
[0125] The multiple light intensity values of the multiple first training plasma lithography images within a single period are converted into the second matrix.
[0126] Optionally, the plurality of training mask patterns includes a second training mask pattern, the training plasma lithography imaging includes a second training plasma lithography imaging, and the apparatus further includes a second training unit for:
[0127] A plurality of second training mask patterns are acquired, wherein the period length of the plurality of second training mask patterns is increased sequentially according to a second fixed step size, the second fixed step size being less than the first fixed step size, and the period length range of the first training mask pattern includes the period length range of the second training mask pattern.
[0128] Convert the plurality of second training mask patterns into a third matrix;
[0129] Acquire multiple second training mask images, each corresponding to a second training plasma lithography image;
[0130] The plurality of second training plasma lithography images are converted into a fourth matrix;
[0131] Continue training the fast imaging model based on the third and fourth matrices.
[0132] Optionally, the first training unit is specifically used for:
[0133] Multiple first training mask images are acquired, wherein the length of the first part of the multiple first training mask images remains unchanged, and the length of the second part of the multiple first training mask images increases sequentially according to a first fixed step.
[0134] Optionally, the first training unit is specifically used for:
[0135] Multiple first training mask images are acquired, wherein the length of the second part of the multiple first training mask images remains unchanged, and the length of the first part of the multiple first training mask images is increased sequentially according to a first fixed step.
[0136] Optionally, the device further includes a testing unit for:
[0137] Multiple test mask patterns are acquired, and the period length of the multiple test mask patterns is increased sequentially according to a third fixed step size, wherein the third fixed step size is less than the first fixed step size;
[0138] Convert the multiple test mask patterns into a fifth matrix;
[0139] Acquire multiple test mask images, each corresponding to a test plasma lithography image;
[0140] The plurality of test plasma lithography images are converted into a sixth matrix;
[0141] The fast imaging model is tested based on the fifth and sixth matrices.
[0142] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0143] The above description is merely a preferred embodiment of this application. Although this application has disclosed preferred embodiments above, it is not intended to limit this application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solutions of this application shall still fall within the protection scope of the technical solutions of this application.
Claims
1. A plasma lithography imaging method, characterized in that, include: To acquire a target imaging structure for plasma lithography imaging, the target imaging structure comprising a target mask pattern that varies periodically; The target mask pattern is input into the fast imaging model to obtain the target plasma lithography image corresponding to the target mask pattern. The fast imaging model is trained using the training mask pattern of the target imaging structure and the training plasma lithography image corresponding to the training mask pattern. The training mask pattern includes a first training mask pattern, the training plasma lithography imaging includes a first training plasma lithography imaging, and the method further includes: A plurality of first training mask images are acquired, wherein the period length of the plurality of first training mask images is increased sequentially according to a first fixed step size; Convert the plurality of first training mask patterns into a first matrix; Acquire multiple first training mask images, each corresponding to a first training plasma lithography image; The plurality of first training plasma lithography images are converted into a second matrix; The fast imaging model is trained based on the first matrix and the second matrix; The training mask pattern includes a second training mask pattern, the training plasma lithography imaging includes a second training plasma lithography imaging, and the method further includes: A plurality of second training mask patterns are acquired, wherein the period length of the plurality of second training mask patterns is increased sequentially according to a second fixed step size, the second fixed step size is less than the first fixed step size, and the period length range of the first training mask pattern includes the period length range of the second training mask pattern. Convert the plurality of second training mask patterns into a third matrix; Acquire multiple second training mask images, each corresponding to a second training plasma lithography image; The plurality of second training plasma lithography images are converted into a fourth matrix; Continue training the fast imaging model based on the third and fourth matrices.
2. The method according to claim 1, characterized in that, The first training mask pattern includes a light-transmitting first part and an opaque second part; The step of converting the plurality of first training mask patterns into a first matrix includes: The first part is converted into the first parameter of the first matrix, and the second part is converted into the second parameter of the first matrix.
3. The method according to claim 1, characterized in that, The method further includes: A simulation model is constructed based on the target imaging structure; The simulation model was used to obtain multiple light intensity values for the first training plasma lithography imaging within a single period.
4. The method according to claim 3, characterized in that, The step of converting the plurality of first training plasma lithography images into a second matrix includes: The multiple light intensity values of the multiple first training plasma lithography images within a single period are converted into the second matrix.
5. The method according to claim 2, characterized in that, The step of acquiring multiple first training mask images, wherein the period length of the multiple first training mask images increases sequentially according to a first fixed step size, includes: Multiple first training mask images are acquired, wherein the length of the first part of the multiple first training mask images remains unchanged, and the length of the second part of the multiple first training mask images increases sequentially according to a first fixed step.
6. The method according to claim 2, characterized in that, The step of acquiring multiple first training mask images, wherein the period length of the multiple first training mask images increases sequentially according to a first fixed step size, includes: Multiple first training mask images are acquired, wherein the length of the second part of the multiple first training mask images remains unchanged, and the length of the first part of the multiple first training mask images is increased sequentially according to a first fixed step.
7. The method according to any one of claims 1-6, characterized in that, The method further includes: Multiple test mask patterns are acquired, and the period length of the multiple test mask patterns is increased sequentially according to a third fixed step size, wherein the third fixed step size is less than the first fixed step size; Convert the multiple test mask patterns into a fifth matrix; Acquire multiple test mask images, each corresponding to a test plasma lithography image; The plurality of test plasma lithography images are converted into a sixth matrix; The fast imaging model is tested based on the fifth and sixth matrices.
8. A plasma lithography imaging device, characterized in that, include: The first acquisition unit is used to acquire the target imaging structure of plasma lithography imaging, wherein the target imaging structure includes a target mask pattern that changes periodically. The simulation unit is used to input the target mask pattern into the fast imaging model to obtain the target plasma lithography image corresponding to the target mask pattern. The fast imaging model is trained using the training mask pattern of the target imaging structure and the training plasma lithography image corresponding to the training mask pattern. The training mask pattern includes a first training mask pattern, the training plasma lithography imaging includes a first training plasma lithography imaging, and the apparatus further includes a first training unit for: A plurality of first training mask images are acquired, wherein the period length of the plurality of first training mask images is increased sequentially according to a first fixed step size; Convert the plurality of first training mask patterns into a first matrix; Acquire multiple first training mask images, each corresponding to a first training plasma lithography image; The plurality of first training plasma lithography images are converted into a second matrix; The fast imaging model is trained based on the first matrix and the second matrix; The training mask pattern includes a second training mask pattern, the training plasma lithography imaging includes a second training plasma lithography imaging, and the apparatus further includes a second training unit for: A plurality of second training mask patterns are acquired, wherein the period length of the plurality of second training mask patterns is increased sequentially according to a second fixed step size, the second fixed step size is less than the first fixed step size, and the period length range of the first training mask pattern includes the period length range of the second training mask pattern. Convert the plurality of second training mask patterns into a third matrix; Acquire multiple second training mask images, each corresponding to a second training plasma lithography image; The plurality of second training plasma lithography images are converted into a fourth matrix; Continue training the fast imaging model based on the third and fourth matrices.
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Photoetching mask optimization method based on residual network
CN114326329A