YOLO-LA:基于原型的视觉语言对齐用于晶片缺陷检测模式
Ziyue Wang1, Yichen Yang2, Jianning Chu3
1School of Intelligent Manufacturing, Jianghan University, Wuhan 430056, China.
Micromachines
|January 28, 2026
概括
本研究介绍了YOLO-LA,这是用于晶圆缺陷识别的视觉语言框架. 它通过将视觉模式与缺陷描述相结合来提高准确性,提高半导体制造产量.
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:
- 半导体制造需要精确的工艺控制,以减少变化和提高产量.
- 晶圆缺陷模式识别对于识别根本原因至关重要.
- 目前基于视觉的缺陷识别深度学习方法缺乏可解释性和概括性,原因是语义缺陷信息的不足利用.
研究的目的:
- 开发一个轻量级的视觉语言对齐框架 (YOLO-LA),以改进晶圆缺陷识别.
- 通过整合视觉和文本数据,提高缺陷识别模型的可解释性和概括性.
- 通过更好的缺陷分析,提高半导体制造工艺的准确性和效率.
主要方法:
- 提出了YOLO-LA,这是一个整合冷YOLO骨干和冷文本编码器的框架.
- 引入了一个可学习的投影头,将视觉特征映射到共享的嵌入空间中,用于基于等号相似性的分类.
- 利用基于原型的视觉语言对齐来利用缺陷描述中的语义信息.
主要成果:
- 在WM-811K数据集上的各种YOLO骨干中,YOLO-LA始终提高了分类准确性.
- 该框架引入了最小的额外参数,保持了轻量级的配置.
- YOLOv12显示出最快的推断速度,具有竞争力的准确性,而YOLOv10显示出从语义对齐中获得的显著收益.
结论:
- 拟议的YOLO-LA框架通过整合视觉和语义信息,有效地提高了晶圆缺陷识别.
- 轻量化和高效的设计使YOLO-LA适用于实时工业晶圆检查系统.
- 利用视觉语言对齐为提高半导体制造缺陷分析的可解释性和概括性提供了一个有希望的方向.
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