A method includes scanning a probe laterally across a surface so that the probe follows a scanning motion across the surface and steering a detection beam onto the probe via a steering mirror, the detection beam reflecting from the probe in the form of a return beam. The method also includes moving the steering mirror so that the detection beam follows a tracking motion which is synchronous with the scanning motion and the detection beam remains steered onto the probe by the steering mirror and using the return beam to obtain image measurements, each indicative of a measured height of a respective point on the surface. An associated height error measurement is obtained for each point on the surface, each measurement being indicative of a respective error in the measured height. The height error measurements are used to correct the image measurements so as to generate corrected image measurements.

A magnetic distribution detection method includes the steps of providing a magnetic sensor and a sample, selecting a multiple of measuring points on the sample, sensing the measuring points by the magnetic sensor, obtaining a multiple of sense data and a series of the heights of the magnetic sensor from each measuring point, using a signal decomposition algorithm to convert these sense data into data groups, and selecting one of the data groups as the magnetic distribution data of the sample.

An atomic-force-microscope-based apparatus and method including hardware and software, configured to collect, in a dynamic fashion, and analyze data representing mechanical properties of soft materials on a nanoscale, to map viscoelastic properties of a soft-material sample. The use of the apparatus as an addition to the existing atomic-force microscope device.

A method of operating an atomic force microscope (AFM), using a denoising algorithm, real-time, during AFM data acquisition. Total Variation and Non-Local Means denoising are preferred. Real time images with minimized sensor noise needing no post-image acquisition processing to account for noise as described herein results.

A method of operating an atomic force microscope (AFM), using a denoising algorithm, real-time, during AFM data acquisition. Total Variation and Non-Local Means denoising are preferred. Real time images with minimized sensor noise needing no post-image acquisition processing to account for noise as described herein results.

A system and method of operating an atomic force microscope (AFM) that includes providing relative scanning motion between a probe of the AFM and a sample in a slow scan direction of a data scan to generate a reference image (plane) of a region of interest. Then, relative scanning motion between the probe and the sample is provided in a fast scan direction of a final data scan to generate a data image. By mapping the data image against the reference image in real-time during the supplying step, the preferred embodiments generate a final drift corrected data image without post-image acquisition processing.

A system and method of operating an atomic force microscope (AFM) that includes providing relative scanning motion between a probe of the AFM and a sample in a slow scan direction of a data scan to generate a reference image (plane) of a region of interest. Then, relative scanning motion between the probe and the sample is provided in a fast scan direction of a final data scan to generate a data image. By mapping the data image against the reference image in real-time during the supplying step, the preferred embodiments generate a final drift corrected data image without post-image acquisition processing.

Provided are an apparatus and a method for identifying a target position in an atomic microscope. An apparatus is configured to acquire result data identifying the cantilever from an image using an identification model learned to identify the cantilever based on the image photographed by a photographing unit, and calculate a target position from the cantilever using the acquired result data, in which the result data include at least one of bounding box data representing a bounding box including a boundary of the cantilever and segmentation data obtained by segmenting the cantilever and an object other than the cantilever.

The method for detecting mechanical and magnetic features comprises the steps of: aiming a probe of the sensor at a sample; defining several detected points for detection on the sample; detecting one of points and comprising the steps of: approaching the probe to the detected point from a predetermined height; contacting the probe with the detected point and applying a predetermined force on the detected point; making the probe far away from the detected point until to the predetermined height; shifting the probe to the next point for detection and repeating the detection; collecting the data of each of the detected points while the probe rapidly approaches to the points from the predetermined height; using a signal decomposition algorithm to transform the collected data to a plurality of data groups; and choosing a part of the data groups to be as data of feature distributions of the sample.

The method for detecting mechanical and magnetic features comprises the steps of: aiming a probe of the sensor at a sample; defining several detected points for detection on the sample; detecting one of points and comprising the steps of: approaching the probe to the detected point from a predetermined height; contacting the probe with the detected point and applying a predetermined force on the detected point; making the probe far away from the detected point until to the predetermined height; shifting the probe to the next point for detection and repeating the detection; collecting the data of each of the detected points while the probe rapidly approaches to the points from the predetermined height; using a signal decomposition algorithm to transform the collected data to a plurality of data groups; and choosing a part of the data groups to be as data of feature distributions of the sample.