Magnetic Field Fingerprinting of Integrated Circuit Activity with a Quantum Diamond Microscope

• URL: http://arxiv.org/abs/2004.03707v1
• Date: Tue, 7 Apr 2020 20:58:56 GMT
• Title: Magnetic Field Fingerprinting of Integrated Circuit Activity with a Quantum Diamond Microscope
• Authors: Matthew J. Turner, Nicholas Langellier, Rachel Bainbridge, Dan Walters, Srujan Meesala, Thomas M. Babinec, Pauli Kehayias, Amir Yacoby, Evelyn Hu, Marko Lon\v{c}ar, Ronald L. Walsworth, Edlyn V. Levine
• Abstract summary: Current density distributions in active integrated circuits (ICs) result in patterns of magnetic fields that contain structural and functional information about the IC. Magnetic fields pass through standard materials used by the semiconductor industry and provide a powerful means to fingerprint IC activity for security and failure analysis applications. Here, we demonstrate high spatial resolution, wide field-of-view, vector magnetic field imaging of static (DC) magnetic field emanations from an IC in different active states using a Quantum Diamond Microscope.
• Score: 0.9546614537373657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current density distributions in active integrated circuits (ICs) result in patterns of magnetic fields that contain structural and functional information about the IC. Magnetic fields pass through standard materials used by the semiconductor industry and provide a powerful means to fingerprint IC activity for security and failure analysis applications. Here, we demonstrate high spatial resolution, wide field-of-view, vector magnetic field imaging of static (DC) magnetic field emanations from an IC in different active states using a Quantum Diamond Microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (NV) quantum defects near the surface of a transparent diamond substrate placed on the IC to image magnetic fields. We show that QDM imaging achieves simultaneous $\sim10$ $\mu$m resolution of all three vector magnetic field components over the 3.7 mm $\times$ 3.7 mm field-of-view of the diamond. We study activity arising from spatially-dependent current flow in both intact and decapsulated field-programmable gate arrays (FPGAs); and find that QDM images can determine pre-programmed IC active states with high fidelity using machine-learning classification methods.

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