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Granulometry is the process of measuring the size distribution of objects in an image of granular material. Usually, algorithms based on mathematical morphology or edge detection are used for this task. We propose a entirely new approach for the granulometry using the cross correlations with circles of different sizes. This technique is primarily adequate for detecting circular-shaped objects, but it can be extended to other shapes using other correlation kernels. Experiments show that the new algorithm is greatly robust to noise and can detect even faint objects. This paper also reports the quantitative structural characteristics of the porous silicon layer based on the proposed algorithm applied to Scanning Electron Microscopy (SEM) images. The new algorithm computes the size distribution of pores and classifies the pores in circular or square ones. We relate these quantitative results to the fabrication process and discuss the square porous silicon formation mechanism. The new algorithm shows to be reliable in SEM images processing and is a promising tool to control the pores formation process. IntroductionGranulometry is the process of measuring sizes of different objects/grains in an image of granular material. The granulometric curve or pattern spectrum of an image is the histogram of objects as the function of radius. The objective of the granulometry is, given an image, to obtain its pattern spectrum. There are two main groups of image-based granulometry algorithms: Mathematical morphology-based algorithms;  Edge detection-based algorithms.Mathematical morphology-based granulometry obtains the pattern spectrum of an image without explicitly segmenting it. Dougherty et al. present a popular morphologybased granulometry for binary images (1). Raimundo et al. used this algorithm to characterize porous material (2). Unfortunately, this algorithm cannot be directly applied to grayscale images. If the original image is grayscale, the algorithm must somehow convert it into a binary image and any binarization discards many important information. Vincent presents a morphology-based granulometry for grayscale images (3). A demonstration program of this algorithm with source code is available at (4). This algorithm seems to be scarcely used in practice. Indeed, the output of this algorithm is highly non-intuitive, difficult to be used in practice. It represents the pattern spectrum as the "sum of pixel values in opened image as a function of radius." Ordinaly, the user wants to obtain simply the "quantity of objects as a function of radius." Moreover, in many applications the spatial localization of each grain is important, and this information is not provided by grayscale morphology granulometry. Edge detection-based granulometry detects the edges of the image using conventional gradient operators and thresholding (5). Then, it delimitates the objects using the edges. Edge-detection is a noise-sensible operation and may not be reliable, especially in blurred low-contrast images. This paper presents a entirely ne...

The present work reports the two types of silicon nanotubes fabrication. The tubular structures based on silicon have been fabricated by immersing mesoporous silicon layer into aqueous electrolyte based on a mixture of NH 4 F and NiSO 4 . Similar results are achieved by immersing mesoporous silicon layer that was formed from the silicon wafer previously metallized on its smooth surface with aluminum and after annealing in a N 2 environment. In this case, either silicon or nickel nanotubes can easily be formed only by tuning the pH level of the ammonium fluoride solution. The range of the inner diameter of these tubes varies from 40 to 110 nm, whereas the mean value of their tube-wall thicknesses is about 25 nm. The SNT characterization by energy-dispersive X-ray spectroscopy, X-ray diffraction spectroscopy and their open-circuit potential behaviors show that the silicon tubes have been formed by an etch-stop process where the space-charge region on the silicon side plays a key role for tubes formation. In this paper, the SNT formation mechanism in the aqueous mixture solution of NH 4 F:NiSO 4 using the mesoporous silicon layer as a starting material is discussed. We show that this proposed procedure is new and easy to implement.

In order to improve the characteristics of future integrated circuits, low dielectric constant (low-k) materials are employed. In this paper we describe in detail the characteristics of k = 2.0, SICOH films treated by capacitively coupled Ar/N2 and Ar/H2 plasmas, which were applied in order to modify the top surface of the film. New insights were obtained about the porous structure of the pristine and the plasma treated films: analyses indicated that the investigated plasma treatments reduced the pore size in the top ten nm of the films, while partial carbon depletion was found down to a few tens of nm inside the film. For the integration of metal barriers deposited by Atomic Layer Deposition in interconnect technologies, precursor penetration into the porous low-k dielectric should be avoided. We investigated precursor penetration in the pores during TaN Atomic Layer Deposition on the pristine and the plasma treated porous low-k films. Detailed analyses showed that the plasma induced modifications resulted in a local growth enhancement and pore sealing during the first cycles of the atomic layer deposition process.