Wide-field AC magnetic field imaging using continuous-wave optically detected magnetic resonance of nitrogen-vacancy centers in diamond

Author(s):  
Karl Joel Hallbäck ◽  
Tatsuma Yamaguchi ◽  
Yuichiro Matsuzaki ◽  
Hideyuki Watanabe ◽  
Norikazu Mizuochi ◽  
...  
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yulei Chen ◽  
Zhonghao Li ◽  
Hao Guo ◽  
Dajin Wu ◽  
Jun Tang

AbstractQuantum sensing based on nitrogen-vacancy centers in diamond has shown excellent properties. Combined with the imaging technique, it shows exciting practicability. Here, we demonstrate the simultaneously imaging technique of magnetic field and temperature using a wide-field quantum diamond microscope. We describe the operating principles of the diamond microscope and report its sensitivity (magnetic field ${\sim}1.8~\mu \mbox{T/Hz}^{1/2}$ ∼ 1.8 μ T/Hz 1 / 2 and temperature ${\sim}0.4~\mbox{K/Hz}^{1/2}$ ∼ 0.4 K/Hz 1 / 2 ), spatial resolution (1.3 μm), and field of view ($400 \times 300~\mu \mbox{m}^{2}$ 400 × 300 μ m 2 ). Finally, we use the microscope to obtain images of an integrated cell heater and a PCB, demonstrating its ability in the application of magnetic field and temperature simultaneously imaging at wide-field.


Author(s):  
Edlyn V. Levine ◽  
Matthew J. Turner ◽  
Nicholas Langellier ◽  
Thomas M. Babinec ◽  
Marko Lončar ◽  
...  

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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