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A Deep Learning-Based Optical Proximity Correction Method for Extreme Ultraviolet

A technology of optical proximity correction and deep learning, which is applied in the field of electromagnetic inversion, can solve the problems of reduced imaging fidelity and PBOPC software running time, and achieve the effects of enhancing balance, reducing CPU time and memory, and high computing efficiency

Active Publication Date: 2022-02-11
XIAMEN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Simulation results show that the model can effectively reduce the runtime of the PBOPC software and improve the manufacturability of the mask, but at the expense of reduced imaging fidelity

Method used

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  • A Deep Learning-Based Optical Proximity Correction Method for Extreme Ultraviolet
  • A Deep Learning-Based Optical Proximity Correction Method for Extreme Ultraviolet
  • A Deep Learning-Based Optical Proximity Correction Method for Extreme Ultraviolet

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings.

[0029] see figure 1 As shown, the extreme ultraviolet EUV structure of the present invention is composed of a mask set and a multilayer Bragg reflector set, and a 6-degree incident plane wave is selected as the EUV light. The mask stack includes a mask pattern over the multilayer Bragg reflector stack. The multilayer Bragg reflector consists of 40 double-layer Si-Mo layers, which can effectively reflect the energy of a 13.5nm plane wave at 6 degrees of incidence. Meanwhile, the mask area covered by the absorber absorbs most of the EUV light, while the mask area not covered reflects most of the EUV light into the optical projector. Among them, the optical projector is used to project the layout pattern of the mask onto the wafer. After the photoresist is developed, the layout pattern is printed on the wafer.

[0030] see figure 2 As shown, the deep learning-based ...

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Abstract

The present invention provides an optical proximity correction method for extreme ultraviolet based on deep learning, which includes two parts: a forward modeling module and an inversion module; wherein, the forward modeling module is used to quickly and accurately map the mask to the corresponding plane above the stack The near and far field, inversion module is used to quickly and accurately map the target image to the corrected mask. Compared with the traditional full-wave simulation, the method provided by the present invention can greatly improve the computational efficiency of the forward modeling module, including the required running time and memory; at the same time, unlike the time-consuming iterative OPC method, the trained inversion The module inputs the target image to obtain the corrected mask.

Description

technical field [0001] The present invention relates to the field of electromagnetic inversion methods, in particular to an optical proximity correction (optical proximity correction, OPC) method for extreme ultraviolet (extreme ultraviolet, EUV) based on deep learning. Background technique [0002] In recent years, EUV lithography technology at 13.5nm illumination wavelength, as the most advanced lithography technology after deep ultraviolet lithography, has attracted more and more attention because the critical dimension of integrated circuits is less than 22nm. However, optical proximity effect and mask shadow effect will inevitably affect the imaging performance of EUV lithography system. [0003] OPC can improve the uniformity of imaging in photolithography system, which has attracted more and more attention. By pre-distorting the mask, OPC can compensate for imaging distortion and make it converge to the target style. Generally, OPC methods can be divided into rule-b...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G03F7/20G06N3/04G06N3/08
CPCG03F7/70441G03F7/705G03F7/70508G06N3/08G06N3/045
Inventor 肖理业赵乐一易俊男其他发明人请求不公开姓名
Owner XIAMEN UNIV