Sub-resolution assist features have been widely recognized in lithography patterning. Ingeneral, the insertion of assist features in optically adjacent space around main designed features,will change the aerial image ...Sub-resolution assist features have been widely recognized in lithography patterning. Ingeneral, the insertion of assist features in optically adjacent space around main designed features,will change the aerial image intensity profiles of corresponding main features. Optimizing assistfeature placement lets the main feature obtain optimal or better image contrast, better imagingresolution and depth of focus (DOF). Recent EUV lithography development, however, imposesstrict budget of edge placement error and process window control causing assist features tobecome more and more complex. In this domain, 1D assisting feature can no longer meet suchtight requirements, and 2D assisting features have become necessary in the semiconductor industry.In this paper, the process window and edge placement error evaluations of different 2D assistfeature types are reviewed, along with their associated run time and memory consumption. Varioustypes of 2D assist features are evaluated, including 45-degree disconnected assist features, 45-degree connected assisting features, Manhattan only assist feature arrays, and so on. To generatethe assist features, the model-based assisting feature rule table is first generated using the opticalmodel as the reference. The rule table is then split into different rule sets by considering thedimensions and types of assisting features. Finally, the CD variations across process window areevaluated as the success criteria of each assist feature rule sets. In addition, an inverse lithographytechnology (ILT) based approach is proposed to generate the optimized rule table, as ILT is wellknown to have considerable benefits in finding the best pattern solutions to improve processwindow, 2D CD control, and resolution in the low K1 lithography regime. At the end of this paper,the summary discusses how the assisting feature placement can be further optimized using leadingedgetechnologies like machine learning.展开更多
目的探讨定位像的扫描参数对智能管电压辅助(Scout on Tube Voltage Assist,kV assist)技术联合自动管电流调制技术的影响。方法利用GE Revolution CT机和CT剂量体模,改变定位像的管电压、管电流、球管的投射角度以及床相对于机架等中...目的探讨定位像的扫描参数对智能管电压辅助(Scout on Tube Voltage Assist,kV assist)技术联合自动管电流调制技术的影响。方法利用GE Revolution CT机和CT剂量体模,改变定位像的管电压、管电流、球管的投射角度以及床相对于机架等中心点的距离,研究使用kV assist技术联合自动管电流调制技术时的断层图像管电流曲线和图像质量。利用ImageJ软件获取断层图像的管电流、容积CT剂量指数和剂量长度乘积,分别计算定位像和断层的信噪比并进行比较。结果当定位像的管电压发生变化时,定位像管电压为120 kV,图像质量较好,且辐射剂量较低。当定位像的管电流发生变化时,但曝光量无明显变化,定位像的管电流对图像质量和辐射剂量基本没有影响。当定位像的投射角度发生变化时,90°和180°结合时图像质量较好,且辐射剂量较低。当CT床相对于机架等中心点的距离发生变化时,扫描部位的中心置于机架机架等中心点时,图像质量较好,且辐射剂量较低。结论通过研究定位像的参数对断层的管电流曲线的影响,发现在使用kV assist技术联合自动管电流调制技术时,选择合适参数可以在获得较好的图像质量,同时能降低CT检查的辐射剂量。展开更多
基金The authors of this paper would like to thank Daniel Xu,Yongdong Wang,Guangming Xiao and Travis Brist for their helpful discussions and supports.
文摘Sub-resolution assist features have been widely recognized in lithography patterning. Ingeneral, the insertion of assist features in optically adjacent space around main designed features,will change the aerial image intensity profiles of corresponding main features. Optimizing assistfeature placement lets the main feature obtain optimal or better image contrast, better imagingresolution and depth of focus (DOF). Recent EUV lithography development, however, imposesstrict budget of edge placement error and process window control causing assist features tobecome more and more complex. In this domain, 1D assisting feature can no longer meet suchtight requirements, and 2D assisting features have become necessary in the semiconductor industry.In this paper, the process window and edge placement error evaluations of different 2D assistfeature types are reviewed, along with their associated run time and memory consumption. Varioustypes of 2D assist features are evaluated, including 45-degree disconnected assist features, 45-degree connected assisting features, Manhattan only assist feature arrays, and so on. To generatethe assist features, the model-based assisting feature rule table is first generated using the opticalmodel as the reference. The rule table is then split into different rule sets by considering thedimensions and types of assisting features. Finally, the CD variations across process window areevaluated as the success criteria of each assist feature rule sets. In addition, an inverse lithographytechnology (ILT) based approach is proposed to generate the optimized rule table, as ILT is wellknown to have considerable benefits in finding the best pattern solutions to improve processwindow, 2D CD control, and resolution in the low K1 lithography regime. At the end of this paper,the summary discusses how the assisting feature placement can be further optimized using leadingedgetechnologies like machine learning.
