@inproceedings{a7d60dbb026848cfae81c81e00a2acf2,
title = "Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor",
abstract = "In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images.",
keywords = "Pentacene, Radon Transform, defects detection, interdigitated archimedean spirals transistor",
author = "Sciuto, \{Grazia Lo\} and Sivan Linde and Rafi Shikler and Pawel Kowol and Salvo Coco and Giacomo Capizzi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Clean Electrical Power, ICCEP 2023 ; Conference date: 27-06-2023 Through 29-06-2023",
year = "2023",
doi = "10.1109/ICCEP57914.2023.10247427",
language = "English",
series = "2023 International Conference on Clean Electrical Power, ICCEP 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "641--646",
booktitle = "2023 International Conference on Clean Electrical Power, ICCEP 2023",
address = "United States",
}