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
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.
