Structural characterisation of semiconductors by computer methods of image analysis

M. A. Hernández-Fenollosa; D. Cuesta-Frau; L. C. Damonte; M. A. Satorre Aznar

doi:10.1117/12.616982

18 August 2005 Structural characterisation of semiconductors by computer methods of image analysis

M. A. Hernández-Fenollosa, D. Cuesta-Frau, L. C. Damonte, M. A. Satorre Aznar

Proceedings Volume 5878, Advanced Characterization Techniques for Optics, Semiconductors, and Nanotechnologies II; 587814 (2005) https://doi.org/10.1117/12.616982
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Analysis of microscopic images for automatic particle detection and extraction is a field of growing interest in many scientific fields such as biology, medicine and physics. In this paper we present a method to analyze microscopic images of semiconductors in order to, in a non-supervised way, obtain the main characteristics of the sample under test: growing regions, grain sizes, dendrite morphology and homogenization. In particular, nanocrystalline semiconductors with dimension less than 100 nm represent a relatively new class of materials. Their short-range structures are essentially the same as bulk semiconductors but their optical and electronic properties are dramatically different. The images are obtained by scanning electron microscopy (SEM) and processed by the computer methods presented. Traditionally these tasks have been performed manually, which is time-consuming and subjective in contrast to our computer analysis. The images acquired are first pre-processed in order to improve the signal-to-noise ratio and therefore the detection rate. Images are filtered by a weighted-median filter, and contrast is enhanced using histogram equalization. Then, images are thresholded using a binarization algorithm in such a way growing regions will be segmented. This segmentation is based on the different grey levels due to different sample height of the growing areas. Next, resulting image is further processed to eliminate the resulting holes and spots of the previous stage, and this image will be used to compute the percentage of such growing areas. Finally, using pattern recognition techniques (contour following and raster to vector transformation), single crystals are extracted to obtain their characteristics.

Citation Download Citation

M. A. Hernández-Fenollosa, D. Cuesta-Frau, L. C. Damonte, and M. A. Satorre Aznar "Structural characterisation of semiconductors by computer methods of image analysis", Proc. SPIE 5878, Advanced Characterization Techniques for Optics, Semiconductors, and Nanotechnologies II, 587814 (18 August 2005); https://doi.org/10.1117/12.616982

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
14 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Zinc oxide

Semiconductors

Image analysis

Image filtering

Scanning electron microscopy

Crystals

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years