A microcontroller-based system for piezoscanner nonlinearity correction: Atomic force microscope

Cidade, Geraldo A. G.; Weissmüller, Gilberto; Bisch, Paulo Mascarello

doi:10.1063/1.1149144

Cited by 5 publications

References 8 publications

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Correction for piezoelectric creep in scanning probe microscopy images using polynomial mapping

et al. 2003

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Summary:We describe a method for using polynomial mapping to correct scanning probe microscope images for distortion due to piezoelectric creep. Because such distortion varies from image to image, this method can be used when the actual locations of some features within an image are known absolutely, or in a series of images in which the actual locations of some features are known not to vary. While the general case of polynomial mapping of degree N requires the determination of 2(N+1) 2 matrix elements by regression, we find that by understanding the mechanism by which piezoelectric creep distorts scanning probe microscope images, we can fix most of these coefficients at 0 or 1 a priori, leaving only 2(N+1) coefficients to be determined by regression. We describe our implementation of this strategy using the Interactive Data Language (IDL) programming language, and demonstrate our technique on a series of atomic force microscopy (AFM) images of diblock copolymer microdomains. Using our simplified scheme, we are able to reduce the effects of distortion in an AFM image from 5% of the scan width to a single pixel, using only five reference points.

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Correction for piezoelectric creep in scanning probe microscopy images using polynomial mapping

et al. 2003

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An automated glitch-detection/restoration method of atomic force microscope images

Hyon

Kim

et al. 2002

Review of Scientific Instruments

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An automated glitch-detection/restoration method of atomic force microscope images is proposed and implemented. Contrary to other manual methods, our method is based on the probability distribution of the derivative of the scanned image data. The glitches are identified as the points that deviate from a normal probability density function. The essence of the automation is calculating the distribution of the scanned image and removing the points that deviate from the normal distribution. Quantitative analysis of the original and the restored image have been performed and the degree of deformation of the restored images has also been analyzed. This technique can directly be applied to other types of scanning probe microscope equipments.

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Detection and volume estimation of semiconductor quantum dots from atomic force microscope images

Hyon

Sull

et al. 2003

Review of Scientific Instruments

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An automated quantum dot detection and volume estimation method of atomic force microscope images are proposed and implemented. Quantum dot detection is based on the image-segmentation method widely used in image processing. We used a modified local watershed algorithm, which produces stable and effective segmentation results in order to detect the peak position of the quantum dot of the atomic force microscopy image. An effective volume estimation of the quantum dot is performed by intelligently determining the boundary of the quantum dot at each height step and by simply adding small volume fractions lying inside of the quantum dot boundary. Our volume estimation results are quantitatively analyzed by comparison with the quantum dot volume obtained by assuming conical and pyramidal shapes. The detection results of our method are quantitatively compared with the results of two other methods for general grain detection, and with a transmission electron micrograph of the quantum dot. Finally, information such as the number of quantum dots, the density, the size and the height distribution are also provided as a result of implementing our method. This method can be applied to detect and estimate volume of a similar shape with a different size.

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A microcontroller-based system for piezoscanner nonlinearity correction: Atomic force microscope

Cited by 5 publications

References 8 publications

Correction for piezoelectric creep in scanning probe microscopy images using polynomial mapping

Correction for piezoelectric creep in scanning probe microscopy images using polynomial mapping

An automated glitch-detection/restoration method of atomic force microscope images

Detection and volume estimation of semiconductor quantum dots from atomic force microscope images

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