可制造性设计
平版印刷术
热点(地质)
计算机科学
绘图
电子束光刻
人工智能
模式识别(心理学)
计算机图形学(图像)
工程类
抵抗
材料科学
纳米技术
光电子学
图层(电子)
地球物理学
地质学
机械工程
作者
Qingyue Wu,Jiamin Liu,Hao Jiang,Shiyuan Liu
标识
DOI:10.1109/iwaps57146.2022.9972318
摘要
Lithography hotspot detection plays an important role in the manufacturability design of integrated circuits, which affects the yield of the final product. In this paper, based on deep learning technology named Yolov5, a lithography hotspot detection method is proposed, which can localize the lithography hotspots rapidly. Using ICCAD 2012 contest benchmark 1 and the OPC post-processed simulation graphics as the datasets to conduct the experiments, the results show that the proposed algorithm can effectively improve the detection efficiency, with the recall of 96.3%, the precision of 95.3%, the average detection time of 0.7 h/mm 2 .
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