Reconstruction of atomic force microscopy image using compressed sensing

An automated vertical drift correction algorithm for AFM images based on morphology prediction

The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian-Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images.

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

Rapid broadband discrete nanomechanical mapping of soft samples on atomic force microscope

In this paper, an approach to achieve rapid broadband discrete nanomechanical mapping of soft samples using an atomic force microscope is developed. Nanomechanical mapping (NM) is needed to investigate, for example, dynamic evolution of the nanomechanical distribution of the sample-provided that the mapping is fast enough. The throughput of conventional NM methods, however, is inherently limited by the continuous scanning involved where the probe visits each sampling location continuously. Thus, we propose to significantly reduce the number of measurements through discrete mapping where only discrete sampling locations of interests are visited and measured. An online-searching learning-based technique is utilized to achieve rapid probe engagement and withdrawal with the interaction force minimized at each sampling location. Then, a control-based nanoindentation measurement technique is used to quickly acquire the nanomechanical property at each location, over frequencies that can be chosen arbitrarily in a broad range. Finally, a decomposition-based learning approach is explored to achieve rapid probe transitions between the sampling locations. The proposed technique is demonstrated through experiments using a Polydimethylsiloxane (PDMS) sample and a PDMS-epoxy sample as examples.