光学接近校正
计算机科学
特征(语言学)
编码器
过程(计算)
卷积(计算机科学)
端到端原则
人工智能
分割
可操作性
水准点(测量)
还原(数学)
集成电路布局
架空(工程)
反向
计算机工程
模式识别(心理学)
人工神经网络
集成电路
数学
软件工程
操作系统
哲学
语言学
地理
大地测量学
几何学
作者
Hui Xu,Fuxin Tang,Qi Pan,Ye Yuan,Huaguo Liang,Zhengfeng Huang
摘要
As the process advances and the minimum linewidth gets closer to the physical limit, inverse lithography technique (ILT) is widely used for optical proximity correction (OPC). However, the computational overhead of the ILT method is high, and the printability of the mask is poor. In response to these limitations, we proposed ERFNet-ILT, a self-training method for an end-to-end learning framework for generating optimized masks directly from layout patterns, which introduces a feature fusion module at the end of the encoder and uses dilated convolution to expand the receptive field, thereby extracting layout pattern information such as edges, vertices and corners of the layout pattern from the feature map. The framework has shorter model building time and higher mask printability. Compared with the state-of-the-art methods, experimental quantitative results show that the proposed framework achieves 2.5% squared L2Â error and 7.9% process variation band reduction within a comparable mask correction time.
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