磁强计
钻石
显微镜
磁场
材料科学
集成电路
生物磁学
有限元法
炸薯条
光电子学
计算机科学
电子工程
光学
物理
工程类
复合材料
热力学
电信
量子力学
作者
Edlyn V. Levine,Matthew Turner,Nicholas Langellier,Thomas M. Babinec,Marko Lončar,Ronald L. Walsworth
出处
期刊:Proceedings
日期:2020-12-01
被引量:5
标识
DOI:10.31399/asm.cp.istfa2020p0084
摘要
Abstract We present a new method for backside integrated circuit (IC) magnetic field imaging using Quantum Diamond Microscope (QDM) nitrogen vacancy magnetometry. We demonstrate the ability to simultaneously image the functional activity of an IC thinned to 12 µm remaining silicon thickness over a wide fieldof- view (3.7 x 3.7 mm2). This 2D magnetic field mapping enables the localization of functional hot-spots on the die and affords the potential to correlate spatially delocalized transient activity during IC operation that is not possible with scanning magnetic point probes. We use Finite Element Analysis (FEA) modeling to determine the impact and magnitude of measurement artifacts that result from the specific chip package type. These computational results enable optimization of the measurements used to take empirical data yielding magnetic field images that are free of package-specific artifacts. We use machine learning to scalably classify the activity of the chip using the QDM images and demonstrate this method for a large data set containing images that are not possible to visually classify.
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