Real Time Automated Counterfeit Integrated Circuit Detection using X-ray Microscopy
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Otros documentos de la autoría: Mahmood, Kaleel; Latorre Carmona, Pedro; Shahbazmohamadi, Sina; Pla, Filiberto; Javidi, Bahram
Metadatos
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Título
Real Time Automated Counterfeit Integrated Circuit Detection using X-ray MicroscopyAutoría
Fecha de publicación
2015-05xmlui.dri2xhtml.METS-1.0.item-edition
PostprintEditor
OSA PublishingCita bibliográfica
MAHMOOD, Kaleel; LATORRE CARMONA, Pedro; SHAHBAZMOHAMADI, Sina; PLA BAÑÓN, Filiberto; JAVIDI, Bahram. Real Time Automated Counterfeit Integrated Circuit Detection using X-ray Microscopy. Apllied Optics (2015), v. 54, n. 13, pp. D25-D32Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.osapublishing.org/ao/abstract.cfm?uri=ao-54-13-D25Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Determining the authenticity of integrated circuits is paramount to preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. ... [+]
Determining the authenticity of integrated circuits is paramount to preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. Existing techniques to distinguish between authentic and counterfeit integrated circuits (ICs) often include destructive testing requiring subject matter experts. We present a nondestructive technique to detect ICs using x-ray microscopy and advanced imaging analysis with different pattern recognition approaches. Our proposed method is completely automated, and runs in real time. In our approach, images of an integrated circuit are obtained from an x-ray microscope. Local binary pattern features are then extracted from the x-ray image, followed by dimensionality reduction through principal component analysis, and alternatively through a nonlinear principal component methodology using a stacked autoencoder embedded in a deep neural network. From the reduced dimension features, we train two types of learning machines, a support vector machine with a nonlinear kernel and a deep neural network. We present experiments using authentic and ICs to demonstrate that the proposed approach achieves an accuracy of 100% in distinguishing between the counterfeit and authentic samples. [-]
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Apllied Optics (2015), v. 54, n. 13Derechos de acceso
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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