arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.03707quant-phcond-mat.mes-hallphysics.app-phphysics.ins-det
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基于量子金刚石显微镜的集成电路活动磁场指纹

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

Matthew J. Turner, Nicholas Langellier, Rachel Bainbridge, Dan Walters, Srujan Meesala, Thomas M. Babinec, Pauli Kehayias, Amir Yacoby, Evelyn Hu, Marko Lončar,… 展开作者

Matthew J. Turner, Nicholas Langellier, Rachel Bainbridge, Dan Walters, Srujan Meesala, Thomas M. Babinec, Pauli Kehayias, Amir Yacoby, Evelyn Hu, Marko Lončar, Ronald L. Walsworth, Edlyn V. Levine

摘要:

有源集成电路(IC)中的电流密度分布导致包含关于IC的结构和功能信息的磁场图案。磁场可穿透半导体行业使用的标准材料,为安全和故障分析应用提供了一种强大的识别IC活动的手段。在这里,我们展示了高空间分辨率,宽视场,矢量磁场成像的静态(DC)磁场发射从IC在不同的活动状态,使用量子金刚石显微镜(QDM)。QDM在IC上放置的透明金刚石衬底的表面附近采用密集的荧光氮空位(NV)量子缺陷层来成像磁场。我们表明,QDM成像实现了同时$\sim10$ $μ$ m分辨率的所有三个矢量磁场组件在3.7毫米$\times$ 3.7毫米的钻石视场。我们研究活动所产生的空间相关的电流在完整的和解封的现场可编程门阵列(FPGA),并发现QDM图像可以确定预编程的IC活动状态与高保真度使用机器学习分类方法。

英文摘要:

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 $\sim10$ $μ$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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