光学接近校正
多边形(计算机图形学)
趋同(经济学)
平版印刷术
计算机科学
计算
GSM演进的增强数据速率
算法
炸薯条
计算机工程
人工智能
光学
电信
物理
帧(网络)
经济
经济增长
作者
Sheng-Wei Chien,Jia-Syun Cai,Chien-Lin Lee,Kuen-Yu Tsai,James P. Shiely,Matt St. John
摘要
Model-based optical proximity correction (MPOPC) has been well adopted in subwavelength lithography for integrated-circuit manufacturing. Typical MBOPC algorithms involve with iteratively moving the layout polygon edges to reduce the edge placement errors (EPEs) predicted by the lithography model. At each iteration, the amounts of movement are mainly determined by the values of the EPEs and the correction factors (CFs). Since full-chip lithography simulation is very computation intensive, it is highly desirable to minimize the number of iterations for acceptable run times, by selecting suitable CFs. In practical applications, the CFs are usually heuristically determined and applied globally throughout the correction regions. This approach efficiently reduces the EPEs at most of the target points but the entire convergence can be hampered at a relatively small number of hot-spot locations. This work investigates the effectiveness of improving the overall convergence by introducing both global and local CFs, and approaches to utilize machine-learning techniques to estimate the hot-spot locations and associated local CF values.
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