Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2014, Vol. 15 Issue (5): 390-400    DOI: 10.1631/jzus.C1300357
    
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
Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China
SVM based layout retargeting for fast and regularized inverse lithography
Kai-sheng Luo, Zheng Shi, Xiao-lang Yan, Zhen Geng
Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China
 全文: PDF 
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。
关键词： 反向光刻技术;  光学邻近校正;  版图重定向;  支持向量机    
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.
Key words: Inverse lithography technology    Optical proximity correction    Layout retargeting    Support vector machine
收稿日期: 2013-12-09 出版日期: 2014-05-06
CLC:  TN47  
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Kai-sheng Luo
Zheng Shi
Xiao-lang Yan
Zhen Geng

引用本文:

Kai-sheng Luo, Zheng Shi, Xiao-lang Yan, Zhen Geng. SVM based layout retargeting for fast and regularized inverse lithography. Front. Inform. Technol. Electron. Eng., 2014, 15(5): 390-400.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1300357    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2014/V15/I5/390

[1] Jun-hong Zhang, Yu Liu. 应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 272-286.
[2] Mohammad Mosleh, Hadi Latifpour, Mohammad Kheyrandish, Mahdi Mosleh, Najmeh Hosseinpour. 运用支持向量机的稳健智能音频水印设计[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(12): 1320-1330.
[3] G. R. Brindha, P. Swaminathan, B. Santhi. 一种观点挖掘新词语权重过程性能分析[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(11): 1186-1198.
[4] Gurmanik Kaur, Ajat Shatru Arora, Vijender Kumar Jain. 基于体位特征使用混杂模型预测血压对于无支撑后背的反应[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(6): 474-485.
[5] Qi-rong Mao, Xin-yu Pan, Yong-zhao Zhan, Xiang-jun Shen. 基于Kinect的实时面部情感识别[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(4): 272-282.
[6] Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu. 基于非线性支持向量机和遗传算法的移动ECG质量评估[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(7): 564-573.
[7] Li Chen, Ying-chun Yang, Zhao-hui Wu. 用于情感说话人识别的精细失真特征检测与修正[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(10): 903-916.