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

Turner, Matthew J. and Langellier, Nicholas and Bainbridge, Rachel and Walters, Dan and Meesala, Srujan and Babinec, Thomas M. and Kehayias, Pauli and Yacoby, Amir and Hu, Evelyn and Lončar, Marko and Walsworth, Ronald L. and Levine, Edlyn V. (2020) Magnetic Field Fingerprinting of Integrated-Circuit Activity with a Quantum Diamond Microscope. Physical Review Applied, 14 (1). Art. No. 014097. ISSN 2331-7019.

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Current density distributions in active integrated circuits result in patterns of magnetic fields that contain structural and functional information about the integrated circuit. Magnetic fields pass through standard materials used by the semiconductor industry and provide a powerful means to fingerprint integrated-circuit activity for security and failure analysis applications. Here, we demonstrate high spatial resolution, wide field-of-view, vector magnetic field imaging of static magnetic field emanations from an integrated circuit in different active states using a quantum diamond microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (N-V) quantum defects near the surface of a transparent diamond substrate placed on the integrated circuit to image magnetic fields. We show that QDM imaging achieves a resolution of approximately 10μm simultaneously for all three vector magnetic field components over the 3.7×3.7mm² field of view of the diamond. We study activity arising from spatially dependent current flow in both intact and decapsulated field-programmable gate arrays, and find that QDM images can determine preprogrammed integrated-circuit active states with high fidelity using machine learning classification methods.

Item Type:Article
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URLURL TypeDescription Paper
Langellier, Nicholas0000-0003-2107-3308
Babinec, Thomas M.0000-0002-6974-4679
Lončar, Marko0000-0002-5029-5017
Walsworth, Ronald L.0000-0003-0311-4751
Levine, Edlyn V.0000-0003-0419-1863
Additional Information:© 2020 American Physical Society. Received 24 March 2020; revised 24 May 2020; accepted 11 June 2020; published 31 July 2020. We thank Ben Le for FPGA development; Adam Woodbury, Jeff Hamalainen, Maitreyi Ashok, Connor Hart, David Phillips, Greg Lin, CNS staff, Rebecca Cheng, and Amirhassan Shams-Ansari for helpful discussions and support; Raisa Trubko and Roger Fu for assistance on early measurements; Patrick Scheidegger and Raisa Trubko for work applying GPUfit to the analysis; Edward Soucy, Brett Graham, and the Harvard Center for Brain Science for technical support and fabrication assistance. This project was fully funded by the MITRE Corporation through the MITRE Innovation Program. R.L.W. acknowledges support from the Quantum Technology Center (QTC) at the University of Maryland. P.K. acknowledges support from the Sandia National Laboratories Truman Fellowship Program, which is funded by the Laboratory Directed Research and Development (LDRD) Program at Sandia National Laboratories. N-V diamond sensitivity optimization pertinent to this work was developed under the DARPA DRINQS program (Grant No. D18AC00033). This work was performed in part at the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF Grant No. 1541959. CNS is part of Harvard University.
Funding AgencyGrant Number
University of MarylandUNSPECIFIED
Sandia National LaboratoriesUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)D18AC00033
Harvard UniversityUNSPECIFIED
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Record Number:CaltechAUTHORS:20200821-091832077
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105057
Deposited By: Tony Diaz
Deposited On:21 Aug 2020 16:32
Last Modified:21 Aug 2020 16:32

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