Automatic localization of signal sources in photon emission images for integrated circuit analysis

Anthony Boscaro 1, 2 Sabir Jacquir 1 Samuel Chef 3 Kevin Sanchez 2 Philippe Perdu 3 Stéphane Binczak 1
1 Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508]
2 CNES - Centre National d’Études Spatiales [Paris]
3 Temasek life sciences laboratory [Singapore]
Abstract : Defects localization is a key step in failure analysis of highly scaled complementary oxide semiconductor integrated circuits (ICs). It gives prior information on VLSI circuits and allows the designers to improve their diagnostic. Light emission techniques are efficient to localize defects in modern ICs. The identification of the emission spots is an essential step of the process because it shows where is located the electrical activity in the chip. Due to scaling, more and more emission nodes are located within the acquisition area so that large variations of emission intensity can exist. Thresholding techniques have been implemented, but they fail to provide an exhaustive localization. To overcome this problem, we introduce in this paper an automatic unsupervised process. It is based on a combination of median filtering, mathematical morphology and local maxima research. This new approach is evaluated and tested on 20 photon emission images (real and simulated). The final result is compared to an expert evaluation, and the detection quality is quantified. Keywords
Keywords : Photon emission Microelectronics reliability Failure analysis Local maxima
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Article dans une revue
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01861306
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : vendredi 24 août 2018 - 11:57:59
Dernière modification le : jeudi 25 juillet 2019 - 16:16:03

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UNIV-BOURGOGNE | CNRS | UNIV-BM | LE2I | UNIV-BM-THESE | UNIV-HESAM

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Anthony Boscaro, Sabir Jacquir, Samuel Chef, Kevin Sanchez, Philippe Perdu, et al.. Automatic localization of signal sources in photon emission images for integrated circuit analysis. Signal, Image and Video Processing, Springer Verlag, 2018, 12 (4), pp.775 - 782. ⟨10.1007/s11760-017-1219-z⟩. ⟨hal-01861306⟩

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