• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nanotechnology >Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning
【24h】

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

机译：通过深机学习在指向电子束诱导石墨烯中的Si-原子运动期间跟踪原子结构演变

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at
机译：通过电子束操纵，我们可以确定石墨烯中单个硅原子沿预定轨迹的运动。研究了掺杂剂运动过程中的结构演化，提供了原子运动过程中Si原子邻域变化的信息，并提供了可能的缺陷组态的统计信息。将高斯混合模型和主成分分析应用于深度学习处理的实验数据，可以解开两种不同石墨烯亚晶格的原子畸变。这种方法展示了电子束操纵的潜力，可以创建同一缺陷的多个实现的缺陷库，并探索对称破缺物理的潜力。通过深度学习网络实现的快速图像分析进一步增强了电子束控制的逐原子制造仪器的能力。论文中描述的分析可以通过一个互动的Jupyter笔记本在

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号