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应用科学学报 ›› 2026, Vol. 44 ›› Issue (3): 345-357.doi: 10.3969/j.issn.0255-8297.2026.03.001

• 先进通信 • 上一篇    

利用压缩感知减缓AFM光纤探针磨损

叶帅1,2, 商娅娜1,2, 陈娜1,2, 刘书朋1,2, 刘勇1,2   

  1. 1. 上海大学特种光纤与光接入网重点实验室, 上海 200444;
    2. 上海大学特种光纤与先进通信国际合作联合实验室, 上海 200444
  • 收稿日期:2025-03-27 发布日期:2026-06-23
  • 通信作者: 商娅娜，副教授，研究方向为特种光纤与光纤器件。E-mail:ynshang@shu.edu.cn E-mail:ynshang@shu.edu.cn
  • 基金资助:
    国家自然科学基金(No.62175142)

Compressed Sensing-Based Approach for Reducing Wear in AFM Fiber Probes

YE Shuai1,2, SHANG Yana1,2, CHEN Na1,2, LIU Shupeng1,2, LIU Yong1,2   

  1. 1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
    2. Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China
  • Received:2025-03-27 Published:2026-06-23

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摘要： 针对原子力显微镜（atomic force microscope,AFM）光纤探针扫描成像中针尖磨损的问题，提出一种基于压缩感知（compressed sensing,CS）的欠采样扫描方法，减少光纤探针与样品的接触次数，有效延长探针的使用寿命。利用CS算法重构得到完整的AFM图像，并通过引入卷积神经网络（convolutional neural network,CNN）优化CS重构图像的质量，解决了因欠采样导致的图像质量下降问题，在减少光纤探针磨损的同时保持了高质量的成像。实验中，当扫描频率为0.3 Hz，扫描点数由200×200降低至100×100时，针尖磨损量由78nm降低至13 nm，成像时间缩短为原来的1/4，通过卷积神经网络优化后，AFM图像的峰值信噪比（peak signal-to-noise ratio,PSNR）和结构相似性（structural similarity,SSIM）分别为30.12 dB和0.96，实现了光纤探针低磨损、快速、高质量的AFM成像。

关键词: 原子力显微镜, 光纤探针, 针尖磨损, 压缩感知, 卷积神经网络

Abstract: To address the issue of tip wear in atomic force microscope(AFM) fiber probe scanning imaging, an undersampling scanning method based on compressed sensing(CS)was proposed, which reduced the number of contacts between the fiber probe and the sample and effectively prolonged the probe lifetime. Complete AFM images were reconstructed using the CS algorithm. A convolutional neural network(CNN) was introduced to optimize the quality of CS-reconstructed images and mitigate the degradation of image quality caused by undersampling. Fiber probe wear was therefore reduced while maintaining high-quality imaging. In the experiments, when the scanning frequency was 0.3 Hz and the number of scanning points was reduced from 200 × 200 to 100 × 100, the tip wear was reduced from 78 nm to 13 nm, and the imaging time was reduced to one quarter of the original imaging time. After optimization using the CNN, the AFM images had a peak signal-to-noise ratio(PSNR) of 30.12 dB and a structural similarity(SSIM) of 0.96. The results demonstrate that low-wear, fast, and high-quality AFM imaging with fiber probes can be achieved.

Key words: atomic force microscope, fiber probe, tip wear, compressed sensing, convolutional neural network(CNN)

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