基于AFM中的压力传感的自适应块成像

基于AFM中的压力传感的自适应块成像

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Yuchuan Zhang¹, Yongjian Chen¹, Teng Wu¹

¹School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, People's Republic of China.

Microscopy research and technique

|June 15, 2024

概括

本研究介绍了原子力显微镜 (AFM) 的自适应式块压缩传感 (BCS) 方法,以提高成像速度和质量. 新方法通过对不同样本区域的采样率进行智能调整,确保统一,高分辨率的表面形态图像.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
影像科学影像科学

背景情况:

原子力显微镜 (AFM) 提供高精度的表面形态测量,但标准扫描耗时.
区块压缩传感 (BCS) 加快AFM成像,但在平衡局部图像质量方面存在困难,导致结果不均.
现有的BCS-AFM方法经常过量采样平面区域或过少采样详细区域,从而损害效率和图像保真度.

研究的目的:

开发一种创新的自适应BCS-AFM成像方法,用于统一,高质量和快速的表面形态分析.
为了克服常规BCS-AFM在实现不同样本拓的一致图像质量的局限性.
为了提高复杂样品的AFM成像的自动化和效率.

主要方法:

一种可适应的BCS-AFM方法,利用重叠的块来减轻文物.
整合特征参数 (GTV,Lu,SD) 来预测本地样本形态.
使用反向传播神经网络来确定子块的最佳采样率.
在自适应补充扫描后,使用TVAL3算法重建子块图像.

主要成果:

拟议的自适应BCS方法在所有测试样本中实现了统一和优良的图像质量.
与非适应性和其他适应性方案相比,成像速度显著改善.
有效平衡的抽样率,防止平面地区的过量抽样和详细地区的不足抽样.
在七个不同的样本上通过视觉检查和定量指标 (PSNR,SSIM) 验证.

结论:

适应性BCS-AFM方法提供了一个高度自动化的解决方案,用于快速,高质量的表面成像.
整合特征参数和BP神经网络使智能适应本地样本特征成为可能.
这种方法显著提高了AFM在详细形态学研究中的实际实用性.

关键词:

适应性采样率适应性采样率原子力显微镜 (AFM) 的使用背向传播的神经网络 (BPNN)区块压缩传感器 (BCS) 的使用连续的随机扫描连续的随机扫描.

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