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

• URL: http://arxiv.org/abs/2107.14156v1
• Date: Thu, 29 Jul 2021 16:27:39 GMT
• Title: High speed microcircuit and synthetic biosignal widefield imaging using nitrogen vacancies in diamond
• Authors: James L. Webb, Luca Troise, Nikolaj W. Hansen, Louise F. Frellsen, Christian Osterkamp, Fedor Jelezko, Steffen Jankuhn, Jan Meijer, Kirstine Berg-S{\o}rensen, Jean-Fran\c{c}ois Perrier, Alexander Huck, Ulrik Lund Andersen
• Abstract summary: We show how to image signals from a microscopic lithographically patterned circuit at the micrometer scale. Using a new type of lock-in amplifier camera, we demonstrate sub-millisecond spatially resolved recovery of AC and pulsed electrical current signals. Finally, we demonstrate as a proof of principle the recovery of synthetic signals replicating the exact form of signals in a biological neural network.
• Score: 44.62475518267084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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