Model-based OPC using the MEEF matrix III

被引:0
|
作者
Lei, Junjiang [1 ]
Yang, Yi [1 ]
Lippincott, George [1 ]
Zhang, Xima [1 ]
机构
[1] Siemens EDA, Wilsonville, OR 97070 USA
来源
关键词
cross MEEF; Jacobian matrix; curvilinear mask; curvilinear OPC; EPE and local MEEF;
D O I
10.1117/12.3010562
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Siemens EDA (Mentor) has published their pioneering work on matrix OPC at SPIE before, in the same title but part I and II. Based on this work, an OPC feature MatrixOPC has been developed at Siemens EDA (Mentor). The MatrixOPC feature is now used by customers in production recipes routinely. However, this work was only focused on rectilinear OPC or Manhattan masks. In this paper, we present our current effort in generalizing the rectilinear matrix OPC to the curvilinear mask setting and to curvilinear OPC. Our initial test with a particular test case shows a promise that the new version, curvilinear matrix OPC and still under development, may also become a useful supplemental instrument for our curvilinear OPC solutions, compared to the curvilinear OPC practices without it. In this paper we will define the Jacobian matrix for the curvilinear mask setting, and compare the Jacobian matrices obtained from the brute-force definition and from our fast approximation algorithm, by comparing their total differentials. We also compare the OPC results from regular curvilinear OPC and matrix OPC with a fast approximated Jacobian.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Model-based OPC for sub-resolution assist feature enhanced layouts
    LaCour, P
    Pell, E
    Granik, Y
    Do, T
    DESIGN, PROCESS INTEGRATION, AND CHARACTERIZATION FOR MICROELECTRONICS, 2002, 4692 : 540 - 546
  • [32] Model-based statistical depth for matrix data
    Mu, Yue
    Hu, Guanyu
    Wu, Wei
    STATISTICS AND ITS INTERFACE, 2024, 17 (02) : 305 - 316
  • [33] On model-based clustering of skewed matrix data
    Melnykov, Volodymyr
    Zhu, Xuwen
    JOURNAL OF MULTIVARIATE ANALYSIS, 2018, 167 : 181 - 194
  • [34] Model-based transductive learning of the kernel matrix
    Zhihua Zhang
    James T. Kwok
    Dit-Yan Yeung
    Machine Learning, 2006, 63 : 69 - 101
  • [35] Model-based transductive learning of the kernel matrix
    Zhang, ZH
    Kwok, JT
    Yeung, DY
    MACHINE LEARNING, 2006, 63 (01) : 69 - 101
  • [36] Application of reverse scattering bar for memory device, combined with model-based OPC
    Lee, S
    Chen, G
    Lee, R
    ISSM 2005: IEEE International Symposium on Semiconductor Manufacturing, Conference Proceedings, 2005, : 446 - 449
  • [37] Model-based geometry optimization of a Nitsch cell using the Fisher information matrix
    Bertakis, Evangelos
    Kalem, Murat
    Pfennig, Andreas
    CHEMICAL ENGINEERING SCIENCE, 2008, 63 (19) : 4881 - 4887
  • [38] Model-based target classification using polarimetric similarity with coherency matrix elements
    Umemura, Maito
    Yamaguchi, Yoshio
    Yamada, Hiroyoshi
    IEICE COMMUNICATIONS EXPRESS, 2019, 8 (03): : 73 - 80
  • [39] Highly accurate modeling by using 2-dimensional calibration data set for Model-based OPC verification
    Kim, Cheol-Kyun
    Choi, Jae-Seung
    Nam, Byung-Ho
    Yim, DongGyu
    PHOTOMASK AND NEXT GENERATION LITHOGRAPHY MASK TECHNOLOGY XIII, PTS 1 AND 2, 2006, 6283
  • [40] MANAGING SEMANTIC CONSISTENCY IN MODEL-BASED SYSTEMS ENGINEERING USING A MATRIX STRUCTURE
    Ye, Yun
    Jankovic, Marija
    Bocquet, Jean-Claude
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2012, VOL 2, PTS A AND B, 2012, : 1193 - 1204