基于支持向量机的反向光刻版图重定向算法

Kai-sheng LUO; Zheng SHI; Xiao-lang YAN; Zhen GENG

doi:10.1631/jzus.C1300357

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基于支持向量机的反向光刻版图重定向算法

电子工程 | Updated：2022-05-19

- 基于支持向量机的反向光刻版图重定向算法
- SVM based layout retargeting for fast and regularized inverse lithography
- 信息与电子工程前沿（英文） 2014年15卷第5期页码：390-400
- Affiliations：
  
  Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Kai-sheng LUO, luoks@vlsi.zju.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1300357
  中图分类号： TN47
- 收稿：2013-12-09，
  
  修回：2014-;4-11，
  
  纸质出版：2014-05
- Accepted：
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基于支持向量机的反向光刻版图重定向算法[J]. 信息与电子工程前沿（英文）, 2014,15(5):390-400.

Kai-sheng LUO, Zheng SHI, Xiao-lang YAN, et al. SVM based layout retargeting for fast and regularized inverse lithography[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(5): 390-400.
基于支持向量机的反向光刻版图重定向算法[J]. 信息与电子工程前沿（英文）, 2014,15(5):390-400. DOI： 10.1631/jzus.C1300357.

Kai-sheng LUO, Zheng SHI, Xiao-lang YAN, et al. SVM based layout retargeting for fast and regularized inverse lithography[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(5): 390-400. DOI： 10.1631/jzus.C1300357.

摘要

研究目的

反向光刻技术又被称为基于点的光学邻近校正技术。由于它脱离了原始设计版图拓扑结构的束缚，可以对版图上的每一个点进行优化，相比于传统光学邻近校正技术，可以得到更好校正效果。但是，基于点的校正机制也使反向光刻技术比传统校正技术更复杂，需要更长校正时间。针对这一缺陷，本文提出一种基于支持向量机的反向光刻版图重定向算法，以减少反向光刻所需的迭代次数和校正时间。

创新要点

与传统版图重定向方法不同，本文提出的版图重定向方法使用了与反向光刻匹配的基于点的版图预偏移机制，试图通过改变版图上每个点的值，得到与最终优化版图接近的重定向版图。由于掩模上的点只有0和1两种取值，对版图上点的值进行优化等同于对版图上的点进行分类；使用支持向量机实现此功能。

方法提亮

针对反向光刻技术，首次提出一种版图重定向方法，通过对传统反向光刻优化方法得到的优化结果进行学习，得到支持向量机模型。使用这些模型，对需要进行重定向的版图上的每个点，根据他们的环境进行分类。

重要结论

在不增加优化版图复杂度的条件下，我们提出的版图重定向方法可以得到十分接近最终优化版图的重定向版图，同时减少70.8%的反向光刻优化所需要的迭代次数以及69.0%的优化时间。

Abstract

Inverse lithography technology (ILT)

also known as pixel-based optical proximity correction (PB-OPC)

has shown promising capability in pushing the current 193 nm lithography to its limit. By treating the mask optimization process as an inverse problem in lithography

ILT provides a more complete exploration of the solution space and better pattern fidelity than the traditional edge-based OPC. However

the existing methods of ILT are extremely time-consuming due to the slow convergence of the optimization process. To address this issue

in this paper we propose a support vector machine (SVM) based layout retargeting method for ILT

which is designed to generate a good initial input mask for the optimization process and promote the convergence speed. Supervised by optimized masks of training layouts generated by conventional ILT

SVM models are learned and used to predict the initial pixel values in the 'undefined areas' of the new layout. By this process

an initial input mask close to the final optimized mask of the new layout is generated

which reduces iterations needed in the following optimization process. Manufacturability is another critical issue in ILT; however

the mask generated by our layout retargeting method is quite irregular due to the prediction inaccuracy of the SVM models. To compensate for this drawback

a spatial filter is employed to regularize the retargeted mask for complexity reduction. We implemented our layout retargeting method with a regularized level-set based ILT (LSB-ILT) algorithm under partially coherent illumination conditions. Experimental results show that with an initial input mask generated by our layout retargeting method

the number of iterations needed in the optimization process and runtime of the whole process in ILT are reduced by 70.8% and 69.0%

respectively.

关键词

Keywords

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