基于残余动态感知网络的晶圆复合缺陷识别框架,具有不对称的多标签损失
1Glasgow College, University of Electronic Science and Technology of China, Chengdu, China.
ISA transactions
|September 19, 2025
概括
这项研究引入了剩余动态感知网络 (RDP-Net),用于自动识别晶圆图案,在混合缺陷上达到99.13%的准确性. 这种新的方法提高了半导体制造中的缺陷检测.
科学领域:
- 半导体制造业 半导体制造业
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 半导体制造要求在晶圆图案识别 (DPR) 中具有高精度,以提高生产产量.
- 现有的DPR方法在工业环境中常见的混合模式缺陷,噪音和复杂缺陷类别变化方面扎.
- 当前DPR的局限性需要先进的自动化解决方案.
研究的目的:
- 提出一种基于深度学习的新方法,即剩余动态感知网络 (RDP-Net),用于自动化的晶圆图案识别.
- 为了应对混合模式缺陷,噪音和缺陷检测中类间/类内变化的挑战.
- 提高半导体制造中缺陷模式识别的准确性和可解释性.
主要方法:
- 开发一个包含动态感知机制 (DPM) 的残余动态感知网络 (RDP-Net).
- DPM可自适应地融合多源信息 (内核,层,通道,分辨率) 以实现强大的特征提取和融合.
- 引入非对称多标签损失 (ASL) 以平衡样本概率并减轻错误标签的影响.
主要成果:
- 在具有挑战性的混合WM38数据集上,RDP-Net实现了99.13%的高精度.
- 拟议的方法显著优于当前最先进的DPR技术.
- 视觉化实验证实了DPM的特征学习机制的可解释性.
结论:
- RDP-Net为半导体制造业的自动缺陷模式识别提供了可行和有效的解决方案.
- DPM和ASL组件成功地解决了诸如混合缺陷和噪音数据之类的复杂性.
- 该方法证明了对噪声,错误标签和复杂缺陷类别特征的稳定性.
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