基于改进的Styleganv3网络的晶圆缺陷图像生成方法
Jialin Zou1, Hongcheng Wang1, Jiajin Zhong2
1School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China.
Micromachines
|August 28, 2025
概括
这项研究使用了新的StyleGANv3框架来增强晶圆缺陷图像的生成. 改进的模型从有限的数据集中生成高准确度图像,帮助下游任务.
科学领域:
- 半导体制造业
- 人工智能
- 计算机视觉
背景情况:
- 高保真晶圆缺陷图像生成至关重要,但由于有限的现实数据而具有挑战性.
- 现有的方法往往缺乏物理真实性,
研究的目的:
- 为高保真晶圆缺陷图像合成开发一个增强的生成模型.
- 用有限的数据提高晶圆数据集的重建能力.
- 在生成模型中解决数据稀缺和物理真实性的挑战.
主要方法:
- 一个增强的StyleGANv3框架,包含一个异质内核融合单元 (HKFU),用于多个尺度的特性改进.
- 集成动态适应性注意模块 (DAAM) 来增强区分器的灵敏度.
- 关于Mixtype-WM38和MIR-WM811K数据集的培训和评估
主要成果:
- 在基准数据集上实现的最先进的性能.
- FID分数为25.20和28.70,SDS值为36.00和35.45,证明了高保真度的产生.
- 从有限的数据中成功生成现实的晶圆缺陷图像.
结论:
- 提出的方法有效地缓解了生成模型中的有限数据集问题.
- 增强的StyleGANv3框架为晶圆缺陷的分类和检测做出了重大贡献.
- 这项工作推动了半导体质量控制的合成数据生成领域.
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