Dynamic-Widefield-Magnetometry using Nitrogen-Vacancy Defects in Diamond

Parashar, Madhur; Shishir, Dasika; Gokhale, Alok; Bathla, Anuj; Bandyopadhyay, Sambaran; Saha, Kasturi

doi:10.1364/cleo_at.2021.jw1a.115

Cited by 1 publication

(1 citation statement)

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“…During the preparation of our manuscript, we became aware of contemporary work by Parashar et al [52], independently following a similar procedure as we outline in this work, resolving AC signals from a bulk magnetic field applied across the diamond using a field coil rather than a microcircuit. Both their work and ours highlight the key advantages of the simplicity of the lock-in camera based technique and the limitations, particularly in terms of the possibility of significantly higher sensitivity through improved diamond growth and irradiation.…”

Section: Discussionmentioning

confidence: 99%

High speed microcircuit and synthetic biosignal widefield imaging using nitrogen vacancies in diamond

Webb,

Troise,

Hansen

et al. 2021

Preprint

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The ability to measure the passage of electrical current with high spatial and temporal resolution is vital for applications ranging from inspection of microscopic electronic circuits to biosensing. Being able to image such signals passively and remotely at the same time is of high importance, to measure without invasive disruption of the system under study or the signal itself. A new approach to achieve this utilises point defects in solid state materials, in particular nitrogen vacancy (NV) centres in diamond. Acting as a high density array of independent sensors, addressable opto-electronically and highly sensitive to factors including temperature and magnetic field, these are ideally suited to microscopic widefield imaging. In this work we demonstrate such imaging of signals from a microscopic lithographically patterned circuit at the micrometer scale. Using a new type of lock-in amplifier camera, we demonstrate sub-millisecond (up to 3500 frames-per-second) spatially resolved recovery of AC and pulsed electrical current signals, without aliasing or undersampling. Finally, we demonstrate as a proof of principle the recovery of synthetic signals replicating the exact form of signals in a biological neural network: the hippocampus of a mouse.

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Section: Discussionmentioning

confidence: 99%