Adaptive AFM imaging based on object detection using compressive sensing. (March 2022)

• Record Type: Journal Article
• Authors: Han, Guoqiang; Chen, Yongjian; Wu, Teng; Li, Huaidong; Luo, Jian
• Is Part Of: Micron. Volume 154(2022)
• Publication Date: 2022-03
• Subjects: Atomic force microscopy (AFM) -- Compressive sensing (CS) -- Object detection -- Adaptive sampling -- Supplementary scanning -- Reconstruction algorithm; Microscopy -- Periodicals; Electron Probe Microanalysis -- Periodicals; Microscopy -- Periodicals; Microscopie -- Périodiques; Microscopy; Periodicals; 502.82
• DOI: 10.1016/j.micron.2021.103197
• Languages: English
• ISSNs: 0968-4328

Abstract

Highlights: The proposed adaptive CS-AFM method can solve the problem of poor imaging quality in the object area at low sampling rate. Otsu algorithm is used to realize the accurate detection and positioning of the objects. The supplementary scanning is carried out to achieve adaptive sampling on the objects. TVAL3 algorithm can recover AFM images with high-efficiency and high-quality. Five samples with different morphology are used to test the performance of the proposed adaptive algorithm. Abstract: Atomic force microscopy (AFM) is a kind of high-precision nanoscale instrument to measure the surface morphology of various samples. Nevertheless, the standard AFM scanning process takes a very long time to obtain high-resolution images. Compressive sensing (CS) can be used to achieve fast AFM imaging. But, the traditional CS-AFM imaging is difficult to balance the image quality of each local area, resulting in poor quality in the object area at low sampling rate. Therefore, a novel imaging scheme of adaptive CS-AFM is proposed. The fast scanning is first used to generate a low resolution image in a short time, and then bicubic interpolation is performed to obtain a high resolution image. Afterwards, an advanced detection algorithm is used to realize the accurate detection and positioning of the objects. Furthermore, the supplementary scanning is carried out to achieve adaptive sampling on the objects. After sampling, the measurement matrix corresponding to the measurement points is constructed. Finally, Total Variation Minimization by Augmented Lagrangian and Alternating Direction Algorithm (TVAL3) is used to reconstruct the whole AFM image. The imaging quality of the sample is analyzed and assessed by image evaluation metrics (PSNR and SSIM) and visual effect. Compared with two non-adaptive imaging schemes, the proposed scheme is characterized by high automation, short time, and high quality.