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)