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相关概念视频

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...

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相关实验视频

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一个基于SEM的多模型深度学习框架,用于在[公式:参见文本]中基于SEM的缺陷检测.

Zulfikar Ali Ansari1, Sahil Soni2, Shahin Fatima3

  • 1AIML Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115, India. zulfikar.ansari@sitpune.edu.in.

Scientific reports
|November 25, 2025
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概括

深度学习模型准确地对矿太阳能电池 (PSC) 的缺陷进行分类,改善制造质量控制. 这种自动化方法提高了下一代光伏技术的效率和可靠性.

关键词:
数据增强数据增强深度学习是一种深度学习.在DenseNet169中使用.矿石太阳能电池是如何使用的在ResNet50V2中使用.这就是YOLOv9的意思.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 可再生能源是可再生能源的来源.
  • 人工智能的人工智能

背景情况:

  • 矿太阳能电池 (PSC) 是下一代光伏的前景,因为其高效率和低成本的制造潜力.
  • 化 (FAPbI3) 薄膜中的结构缺陷显著降低PSC的效率和长期稳定性.
  • 目前使用扫描电子显微镜 (SEM) 的缺陷表征方法耗时且主观,阻碍了可扩展的质量控制.

研究的目的:

  • 开发和评估一个多模型深度学习框架,用于混合维度FAPbI3矿膜中关键缺陷的自动分类.
  • 为了对不同深度学习架构 (ResNet50V2,DenseNet169,YOLOv9) 的性能进行基准测试,用于缺陷检测和分类.
  • 为了证明PSC制造中实时质量控制的开发框架的实际适用性.

主要方法:

  • 提出了一个多模型深度学习框架,包括ResNet50V2,DenseNet169用于高精度分类,以及YOLOv9用于实时检测.
  • 该框架在2,380张FAPbI3矿膜的SEM图像上进行了训练,针对五种缺陷类型:纯3D矿,FAPbI3过量,pinholes,3D-2D混合矿和3D-2D混合矿与pinholes.
  • 使用数据增强和转移学习技术来解决数据集稀缺性并增强模型的稳定性.

主要成果:

  • 在缺陷分类方面,ResNet50V2和DenseNet169实现了高测试准确度 (96.7%) 和加权F1得分 (0.966).
  • YOLOv9表现出显著的计算效率,训练时间为8分钟,虽然准确度中等 (45.0%).
  • 经过训练的模型被部署为基于Streamlit的交互式Web应用程序,用于实践实验室和工业用途.

结论:

  • 拟议的深度学习框架允许精确和自动识别矿膜中的形态缺陷.
  • 该研究强调了人工智能驱动的缺陷分析的潜力,以加速PSC技术的优化和商业化.
  • 开发的框架为PSC制造中的质量控制提供了一个可扩展的解决方案,提高了效率和可靠性.