基于多式融合的CZ单晶异常状态检测研究
Lei Jiang1,2, Haotan Wei1, Ding Liu1,2
1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了一种先进的AI模型,用于检测晶生长过程中的异常. 该系统识别了水晶损失和扭曲等问题,提高了生产效率和质量.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 半导体制造业 半导体制造业
背景情况:
- 对于单晶的生产,Czochralski方法至关重要.
- 异常状态 (晶体损失,扭曲,摇摆,正方形) 会降低质量和效率.
研究的目的:
- 开发一种增强的多式融合分类模型,用于检测和分类晶生长中的四种特定异常状态.
- 提高识别和管理生产缺陷的准确性和效率.
主要方法:
- 将1D信号 (直径,温度,拉动速度) 转换为时间频率图像,使用连续波形变换.
- 使用密集ECA-Swin转换器网络进行时间频率图像特征提取.
- 从视频源中利用了阴囊和间差异图像,由ConvNeXt网络处理.
- 来自信号和视觉数据的融合特征,用于多类分类.
主要成果:
- 拟议的模型有效地检测和分类晶体生长过程中的各种异常状态.
- 证明有能力协助工作人员进行准确的判断和针对异常的个性化治疗计划.
- 展示了提高生产效率,节约资源和保护设备的潜力.
结论:
- 增强的多式融合模型为Czochralski生长中的实时异常检测提供了强大的解决方案.
- 通过这种人工智能系统进行有效的异常管理,可以显著改善制造业的结果.
- 这种方法支持优化生产流程和半导体制造中的先进质量控制.
相关概念视频
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