反演光刻技术的研究进展

艾飞; 苏晓菁; 韦亚一

doi:10.11999/JEIT240308

反演光刻技术的研究进展

doi: 10.11999/JEIT240308

艾飞¹,
苏晓菁^{1, 2},
韦亚一^{1, 2, ,}

1.
中国科学院大学集成电路学院北京 100049
2.
中国科学院微电子研究所北京 100029

基金项目: 国家自然科学基金 (62204257)，中央高校基本科研业务费专项资金(E3E43802)，中国科学院青促会项目(2021115)

详细信息

作者简介:
艾飞：男，博士，特别研究助理，研究方向为计算光刻

苏晓菁：女，副研究员，研究方向为计算光刻，设计与工艺协同优化

韦亚一：男，特聘教授，研究方向为集成电路技术

通讯作者:
韦亚一　weiyayi@ime.ac.cn

中图分类号: TN405; TP301
计量
- 文章访问数: 40
- HTML全文浏览量: 17
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-22
- 修回日期: 2024-12-06
- 网络出版日期: 2024-12-12

Research Progress of Inverse Lithography Technology

AI Fei¹,
SU Xiaojing^{1, 2},
WEI Yayi^{1, 2
, ,}

1.
School of integrated circuits, University of Chinese Academy of Sciences, Beijing 100049, China
2.
Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China

Funds: The National Natural Science Foundation of China (62204257), The Fundamental Research Funds for the Central Universities (E3E43802), Youth Innovation Promotion Association Chinese Academy of Sciences (2021115)

摘要

摘要: 反演光刻技术(ILT)相比传统的光学临近效应修正(OPC)，生成的掩模具有成像效果更好，工艺窗口更大等优点，在当前芯片制造的工艺尺寸不断减小的背景下，逐渐成为主流的光刻掩模修正技术。该文首先介绍了反演光刻算法的基本原理和几种主流实现方法；其次，调研了当前反演光刻技术应用在光刻掩模优化问题上的研究进展，分析了反演光刻技术的优势和存在的问题。以希望为计算光刻及相关研究领域的研究人员提供参考，为我国先进集成电路产业的发展提供技术支持。
- 先进集成电路技术 /
- 反演光刻技术 /
- 光学临近效应修正 /
- 掩模优化 /
- 研究进展
Abstract: Objective Inverse Lithography Technology (ILT) provides improved imaging effects and a larger process window compared to traditional Optical Proximity Correction (OPC). As chip manufacturing continually reduces process dimensions, ILT has become the leading lithography mask correction technology. This paper first introduces the basic principles and several common implementation methods of the reverse lithography algorithm. It then reviews current research on using reverse lithography technology to optimize lithography masks, as well as analyzes the advantages and existing challenges of this technology. Methods The general process of generating mask patterns in ILT is exemplified using the level set method. First, the target graphics, light sources, and other inputs are identified. Then, the initial mask pattern is created and a pixelated model is constructed. A photolithography model is then established to calculate the aerial image. The general photoresist threshold model is represented by a sigmoid function, which helps derive the imaging pattern on the photoresist. The key element of the ILT algorithm is the cost function, which measures the difference between the wafer image and the target image. The optimization direction is determined based on the chosen cost function. For instance, the continuous cost function can calculate gradients, enabling the use of gradient descent to find the optimal solution. Finally, when the cost function reaches its minimum, the output mask is generated. Results and Discussions This paper systematically introduces several primary methods for implementing ILT. The level set method's main concept is to convert a two-dimensional closed curve into a three-dimensional surface. Here, the closed curve is viewed as the set of intersection lines between the surface and the zero plane. During the ILT optimization process, the three-dimensional surface shape remains continuous. This continuity allows the ILT problem to be transformed into a multivariate optimization problem, solvable using gradient algorithms, machine learning, and other methods. Examples of the level set method's application can be found in both mask optimization and light source optimization. The level set mathematical framework effectively addresses two-dimensional curve evolution. When designing the ILT algorithm, a lithography model determines the optimization direction and velocity for each mask point, employing the level set for mask evolution. Intel has proposed an algorithm that utilizes a pixelated model to optimize the entire chip. However, this approach incurs significant computational costs, necessitating larger mask pixel sizes. Notably, the pixelated model is consistently used throughout the process, with a defined pixelated cost function applicable to multi-color masks. The frequency domain method for calculating the ILT curve involves transforming the mask from the spatial domain into the frequency domain, followed by lithography model calculations. This approach generates a mask with continuous pixel values, which is then gradually converted into a binary mask through multiple steps. When modifying the cost function in frequency domain optimization, all symmetric and repetitive patterns are altered uniformly, preserving symmetry. The transition of complex convolution calculations into multiplication processes within the frequency domain significantly reduces computational complexity and can be accelerated using GPU technology. Due to the prevalent issue of high computational complexity in various lithography mask optimization algorithms, scholars have long pursued machine learning solutions for mask optimization. Early research often overlooked the physical model of photolithography technology, training neural networks solely based on optimized mask features. This oversight led to challenges such as narrow process windows. Recent studies have, however, integrated machine learning with other techniques, enabling the physical model of lithography technology to influence neural network training, resulting in improved optimization results. While the ILT-optimized mask lithography process window is relatively large, its high computational complexity limits widespread application. Therefore, combining machine learning with the ILT method represents a promising research direction. Conclusions Three primary techniques exist for optimizing masks using ILT: the Level Set Method, Intel Pixelated ILT Method, and Frequency Domain Calculation of Curve ILT. The Level Set Method reformulates the ILT challenge into a multivariate optimization issue, utilizing a continuous cost function. This approach allows for the application of established methods like gradient descent, which has attracted significant attention and is well-documented in the literature. In contrast, the Intel method relies entirely on pixelated models and pixelated cost functions, though relevant literature on this method is limited. Techniques in the frequency domain can leverage GPU operations to substantially enhance computational speed, and advanced algorithms also exist for converting curve masks into Manhattan masks. The integration of ILT with machine learning technologies shows considerable potential for development. Further research is necessary to effectively reduce computational complexity while ensuring optimal results. Currently, ILT technology faces challenges such as high computational demands and obstacles in full layout optimization. Collaboration among experts and scholars in integrated circuit design and related fields is essential to improve ILT computational speed and to integrate it with other technologies. We believe that as research on ILT-related technologies advances, it will play a crucial role in helping China's chip industry overcome technological bottlenecks in the future.
- Advanced integrated circuit technology /
- Reverse lithography technology /
- Optical proximity effect correction /
- Mask optimization /
- Research progress

HTML全文

图 1 ILT优化掩模示意图

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图 2 ILT算法的一般流程

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图 3 Level set方法生成SRAF示意图^[48]

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图 4 Level set 方法生成掩模图形的一般流程^[49,50]

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图 5 使用OPC和ILT优化45nm FLASH的结果^[50]

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图 6 使用OPC(未插入SRAF)和ILT(插入SRAF)优化45 nm SRAM连接层^[50]

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图 7 相同目标图形在不同光源下利用Level set ILT方法生成的掩模图形^[50]

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图 8 Intel像素化ILT流程示意图^[51]

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图 9 Intel像素化ILT双色掩模优化结果^[51]

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图 10 连续的像素化掩模二值化过程^[10]

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图 11 OPC与频域中计算曲线 ILT方法的对比^[52]

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图 12 基于梯度的ILT计算流程示意图^[66]

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图 13 模型驱动神经网络方法示意图^[66]

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图 14 一种基线CGAN ILT框架图^[70]

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图 15 包含光刻物理模型信息的深度学习网络ILT方法流程示意图^[72]

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