Applications of large field of view e-beam metrology to contour-based optical proximity correction modeling

被引:0
作者
Wei, Chih-, I [1 ]
Kang, Seulki [2 ]
Das, Sayantan [3 ]
Oya, Masahiro [2 ]
Okamoto, Yosuke [2 ]
Maruyama, Kotaro [2 ]
Fenger, Germain [4 ]
Latypov, Azat [5 ]
Kusnadi, Ir [5 ]
Khaira, Gurdaman [4 ]
Yamazaki, Yuichiro [2 ]
Gillijns, Werner [3 ]
Halder, Sandip [3 ]
Lorusso, Gian [3 ]
机构
[1] Siemens Digital Ind Software, Leuven, Belgium
[2] TASMIT Inc, Yokohama, Japan
[3] IMEC, Leuven, Belgium
[4] Siemens Digital Ind Software, Wilsonville, OR USA
[5] Siemens Digital Ind Software, Fremont, CA USA
来源
JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 | 2023年 / 22卷 / 04期
关键词
extreme ultraviolet lithography; image contour; e-beam metrology; hotspot modeling; resist three-dimensional; machine learning;
D O I
10.1117/1.JMM.22.4.041603
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Background: For complex two-dimensional (2D) patterns, optical proximity correction (OPC) model calibration flows cannot always satisfy accuracy requirements with the standard cutline-based input data. Utilizing after-development inspection e-beam metrology image contours, better model predictions of 2D shapes and wafer hotspots can be realized.Aim: We compare model accuracy performance of conventional cutline-based and contour-based OPC models on the regular and hotspots patterns.Approach: By utilizing image contours that are directly extracted from large field of view (LFoV) e-beam metrology, OPC models were calibrated and verified with both cutline-based and contour-based modeling flows. We also used a wafer sampling plan that contained bridging hotspots. Using that sampling plan, a hotspot-aware three-dimentional resist (R3D) compact model was created.Results: First, a contour-based OPC model was generated with <1 nm root mean square error of contour sites. Compared with cutline-based models, it shows better predictions on 2D feature corners. Second, when combined with a hotspot sampling plan, a hotspot-aware compact model could be generated. The accuracy of hotspot predictions on false positives and false negatives was reduced to around 1% with this approach.Conclusions: OPC model calibration and verification with LFoV image contours provide improved predictions on corner rounding shapes and great potential to increase design space coverage. We also observed improved accuracy of hotspot predictions when using an update hotspot aware model when comparing with that of the OPC model. Furthermore, the combination of R3D and stochastic compact models also demonstrated great potential on predictions of rare wafer failure events.
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页数:19
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