基于编码器-解码器卷积神经网络的原子力显微镜针尖估计  

Tip estimation of atomic force microscopy based on encoder-decoder convolutional neural networks

作　　者：雷艺彤 陈宇航[1] Lei Yitong;Chen Yuhang(Department of Precision Machinery and Precision Instruments,School of Engineering Science,University of Science and Technology of China,Hefei 230027,China)

机构地区：[1]中国科学技术大学工程科学学院精密机械与精密仪器系,合肥230027

出　　处：《仪器仪表学报》2025年第1期105-113,共9页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(52075517)项目资助。

摘　　要：原子力显微镜(AFM)探针针尖形貌尺寸是表面微纳结构精确测量、局域物理化学特性准确表征中的关键参数。基于数学形态学的传统方法,针尖盲估计方法可以仅根据扫描图像来评估针尖形状尺寸,但其往往能给出的是针尖尺寸上限值并非真正的针尖尺寸。而且此种方法受扫描噪声影响较大,获得的尺寸精度难以完全满足需求。针对该问题,基于编码器-解码器架构的卷积神经网络,进行了AFM针尖形貌尺寸的稳定、准确的估计研究。在网络的监督学习训练中,以包含不同半径和数量的纳米颗粒结构,应用数学形态学膨胀算法模拟一系列设定半径针尖的扫描图像作为训练数据集,并以平均绝对误差作为损失函数来更新网络参数。结果表明,卷积神经网络模型对于针尖半径包含在训练集范围内的探针所得扫描图像具备准确预测针尖尺寸的能力。但是当扫描图像的对应针尖尺寸超出该范围时,预测的准确性会降低。此外,通过引入叠加噪声的训练数据,模型的预测能力显著提高,可以准确预测含噪声的扫描图像所用探针的针尖尺寸,且无需额外去噪处理。随后在实际AFM扫描图像上的测试结果证实了该方法预测针尖形貌尺寸的有效性。最后通过模拟和实验数据验证了同样的方法还可以用在受针尖效应影响的图像处理上。The geometry and dimensions of atomic force microscopy(AFM)probe tips are critical parameters for precise measurement of surface micro-nanostructures and accurate characterization of local physicochemical properties.While conventional blind tip estimation methods based on mathematical morphology can evaluate tip geometry solely from scanning images,they typically provide upper-bound estimates rather than true tip dimensions and suffer from significant sensitivity to scanning noise,resulting in insufficient measurement accuracy.To overcome these limitations,this study proposes a robust convolutional neural network(CNN)with an encoder-decoder architecture for stable and accurate AFM tip characterization.During supervised learning,a training dataset was generated by simulating scanning images of nanoparticle structures with varying radii and densities through mathematical morphology dilation operations,representing tips with predefined dimensions.The network parameters were optimized using mean absolute error as the loss function.Experimental results demonstrate that the CNN model achieves accurate tip radius predictions for scanning images when the tip dimensions fall within the training range.However,the model exhibits reduced accuracy for tip sizes outside the training distribution.Notably,the model′s predictive capability is significantly enhanced through noise-augmented training data,enabling precise tip dimension estimation from noisy scanning images without requiring additional denoising procedures.Validation using actual AFM scanning images confirms the method′s effectiveness in practical applications.Furthermore,simulations and experimental data verify the method′s extensibility for processing tip-effect-distorted images.The geometry and size of atomic force microscopy(AFM)tips are crucial for measuring surface micro-nanostructures precisely and characterizing local physical and chemical properties exactly.Traditional blind tip estimation methods based on mathematical morphology only estimate tip size

关 键 词：卷积神经网络 监督学习 原子力显微镜 针尖形状预测 深度学习 图像处理 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置] TH742[自动化与计算机技术—控制科学与工程]