轻量级热点检测模型融合了SE和ECA机制.
Yanning Chen1, Yanjiang Li2, Bo Wu1
1Beijing Smartchip Microelectronics Technology Co., Ltd., Beijing 100192, China.
Micromachines
|October 26, 2024
概括
我们开发了一种轻量级的机器学习模型用于光刻热点检测,通过Squeeze-and-Excitation (SE) 和高效通道注意力 (ECA) 机制改进特征提取. 与现有方法相比,这种模型提供了更高的性能和效率.
科学领域:
- 半导体制造业 半导体制造业
- 机器学习应用程序 机器学习应用程序
- 集成电路设计 集成电路设计
背景情况:
- 热点检测在石版中至关重要,以确保集成电路 (IC) 的产量.
- 现有的深度学习模型可能是计算密集型,对实时应用构成挑战.
研究的目的:
- 提出一个轻量级和高效的机器学习模型用于 lithography 热点检测.
- 增强特征提取能力,以提高识别热点的准确性.
主要方法:
- 在卷积神经网络 (CNN) 架构中整合挤压和刺激 (SE) 和高效通道注意力 (ECA) 机制.
- 使用七个卷积层和四个聚合层来提取特征,然后是三个完全连接的层.
- 在定制布局数据集和ICCAD 2012数据集上进行培训和评估.
主要成果:
- 拟议的模型展示了一个轻量级的架构,简化了CNN结构.
- 通过SE和ECA机制进行自适应式通道重量调整,可以提高特征提取,而不会减少维度.
- 实验结果显示,与ConvNeXt,Swin变压器和ResNet 50相比,整体精度,回忆和运行时间性能都更好.
结论:
- 开发的轻量级模型有效地检测了光刻热点,并增强了特征表示.
- 集成SE和ECA注意力机制提供了显著的性能优势.
- 这种方法为半导体制造中的热点检测提供了更有效,更准确的解决方案.
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