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

Chih-I Wei, Seulki Kang, Sayantan Das, Masahiro Oya, Yosuke Okamoto, Kotaro Maruyama, Germain Fenger, Azat Latypov, Ir Kusnadi, Gurdaman Khaira, Yuichiro Yamazaki, Werner Gillijns, Sandip Halder, Gian Lorusso
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Abstract

BackgroundFor 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.AimWe compare model accuracy performance of conventional cutline-based and contour-based OPC models on the regular and hotspots patterns.ApproachBy 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.ResultsFirst, 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.ConclusionsOPC 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.
大视场电子束测量在基于轮廓的光学接近校正建模中的应用
对于复杂的二维(2D)图形,光学接近校正(OPC)模型校准流并不总是满足基于标准切线的输入数据的精度要求。利用显影后检测电子束测量图像轮廓,可以更好地预测二维形状和晶圆热点。在常规模式和热点模式下,比较传统的基于切线的OPC模型和基于轮廓的OPC模型的精度性能。方法利用直接从大视场(LFoV)电子束测量中提取的图像轮廓,使用基于切线和基于轮廓的建模流程对OPC模型进行校准和验证。我们还使用了包含桥接热点的晶圆取样方案。利用该采样方案,建立了热点感知三维电阻(R3D)紧凑模型。结果首先,生成了轮廓点均方根误差<1 nm的基于轮廓的OPC模型;与基于切线的模型相比,该模型对二维特征角的预测效果更好。其次,结合热点采样方案,生成热点感知紧凑模型。通过这种方法,对假阳性和假阴性热点预测的准确率降低到1%左右。结论基于LFoV图像轮廓的sopc模型标定和验证可以改善对圆角形状的预测,并有很大的潜力增加设计空间覆盖率。我们还观察到,与OPC模型相比,使用更新的热点感知模型可以提高热点预测的准确性。此外,R3D和随机紧凑模型的结合也显示出在罕见晶圆失效事件预测方面的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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