基于改进Yolov5s的光刻热点检测算法  

Lithography Hotspot Detection Based on Improved Yolov5s

作　　者：吴清岳 刘佳敏 张松[1] 江浩[1] 刘世元[1,2] Wu Qingyue;Liu Jiamin;Zhang Song;Jiang Hao;Liu Shiyuan(State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China;Hubei Optics Valley Laboratory,Wuhan 430074,Hubei,China)

机构地区：[1]华中科技大学智能制造装备与技术全国重点实验室,湖北武汉430074 [2]湖北光谷实验室,湖北武汉430074

出　　处：《激光与光电子学进展》2023年第24期243-251,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(52130504,51975232,52205592);湖北省重点研发计划(2022BAA013)。

摘　　要：光刻热点检测是实现集成电路可制造性设计,保障集成电路芯片最终良率的关键。鉴于传统基于深度学习的光刻热点检测方法难以满足先进集成电路制造对检测精度的要求,提出了一种基于改进Yolov5s的检测算法,用于光刻版图热点缺陷的精确检测。通过将坐标注意力机制引入骨干网络,提高了Yolov5s模型对版图图形区域的关注度,进而极大地改善了基于Yolov5s的检测算法的光刻热点检测性能。与此同时,采用Sigmoid线性单元激活函数进一步完善整个神经网络的非线性表达,利用Scylla交并比损失函数更快速地定量评估边界框回归损失,提高了热点检测算法的收敛速度和精度。将ICCAD(The International Conference on Computer-Aided Design)2012竞赛基准、经光学邻近校正优化后的光刻图形作为数据集对所提算法开展性能测试实验,验证了热点检测算法的优异检测精度。实验结果表明,该算法的平均准确率、平均召回率、平均F1-score和均值平均精度分别达到97.7%、98.0%、97.8%和98.4%,显著优于其他光刻热点检测算法,展示了良好的应用前景。Lithography hotspot detection plays a critical role in realizing the manufacturability design of integrated circuits(IC)and ensuring the final yield of IC chips.Considering that conventional lithography hotspot detection methods based on deep learning are challenging to meet the inspection precision requirement of advanced IC manufacturing,we propose a detection algorithm based on improved Yolov5s for the precise detection of hotspot defects in the lithography layout.In the algorithm,a coordinate attention mechanism is introduced into the backbone network,which can improve the attention of the Yolov5s model to the patterned area in the layout.Thereby,the performance of the lithography hotspots based on the Yolov5s detection algorithm can be greatly promoted.Meanwhile,the Sigmoid linear unit activation function is used to improve the nonlinear expression of the entire neural network,and the Scylla intersection over union loss function is adopted to realize the quantitative evaluation of the bounding box regression loss more quickly,which can enhance the convergence speed and accuracy of the algorithm.Using the ICCAD(The International Conference on Computer-Aided Design)2012 contest benchmark and the optical proximity correction optimized lithography patterns as the dataset,performance test experiments are carried out to verify the excellent detection accuracy of the proposed algorithm.The experimental results indicate that the mean precision,mean recall,mean F1-score,and mean average precision of the algorithm reach 97.7%,98.0%,97.8%,and 98.4%,respectively,which are significantly better than those of other hotspot detection algorithms and show its good application prospects.

关 键 词：光刻热点检测 改进Yolov5s 检测精度 坐标注意力机制 Sigmoid线性单元激活函数 Scylla交并比损失函数 

分 类 号：TN406[电子电信—微电子学与固体电子学]