Impact of X-Ray Tomography on the Reliability of Integrated Circuits

Alam, Mahbub; Shen, Haoting; Asadizanjani, Navid; Tehranipoor, Mark; Forte, Domenic

doi:10.1109/tdmr.2017.2656839

Cited by 22 publications

(6 citation statements)

References 29 publications

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“…Common anomalies include wrong markings in package, dents, and reworked leads, missing bond wires, and wrong die [1]. As part of the physical inspection procedure, the component's interior and exterior are thoroughly inspected using imaging techniques [35]. Common imaging techniques include X-ray, scanning electron microscope (SEM), and tomography.…”

Section: Physical Inspectionmentioning

confidence: 99%

Challenges and Opportunities in Analog and Mixed Signal (AMS) Integrated Circuit (IC) Security

et al. 2017

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In the last decade and so, a large amount of research has been done to secure hardware. Security features such as physically unclonable function (PUF), hardware metering, and obfuscation have been developed to protect hardware from threats. Detection and avoidance techniques for IC counterfeiting and hardware Trojan have been introduced to protect the IC supply chain. Till now, research has focused on digital ICs, but analog and mixed signal (AMS) ICs which hold the highest share in the market have been neglected. The solutions developed in digital domain for digital ICs do not extend well to AMS ICs. Thus, a major portion of the IC market remains unsecured. In this paper, we described the challenges and limitations associated with AMS IC security research focusing on three major sections: AMS-enabled security, counterfeiting, and AMS hardware Trojans. We also express a vision for AMS security research.

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Section: Physical Inspectionmentioning

confidence: 99%

Challenges and Opportunities in Analog and Mixed Signal (AMS) Integrated Circuit (IC) Security

et al. 2017

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“…X-ray tomography is a powerful method for imaging the internal details of objects in three dimensions non-invasively [1][2][3], and it has wide applications in biomedical imaging [4], materials science [5], electronic inspection [6], and other fields. The penetrating ability of X-rays makes it possible to obtain a series of two-dimensional Radon transforms (commonly known as radiographs) of the object viewed from different angles [7].…”

Section: Introductionmentioning

confidence: 99%

Physics-assisted Generative Adversarial Network for X-Ray Tomography

Guo,

Song,

Barbastathis

et al. 2022

Preprint

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X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory reconstructions. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Synthetic objects with spatial correlations are integrated circuits (IC) from a proposed model CircuitFaker. Compared with maximum-likelihood estimation, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. We further attribute the improvement to the learned prior by reconstructing objects created without spatial correlations. The advantages of using a prior from deep learning in X-ray tomography may further enable low-photon nanoscale imaging.

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“…The external inspection of packaging texture, material corrosion, lead pins damage and surface marking has been used in the supply chain [5][6][7]. But it has a fundamental drawback of the incapability in dealing with those ECs with internal structural anomalies.…”

Section: Introductionmentioning

confidence: 99%