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用于神经传感器应用的基于石墨烯有源电极的频分复用技术

Frequency-Division Multiplexing with Graphene Active Electrodes for Neurosensor Applications.

作者信息

Kim Jinyong, Fengel Carly V, Yu Siyuan, Minot Ethan D, Johnston Matthew L

机构信息

School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331 USA.

Department of Physics, Oregon State University, Corvallis, OR 97331 USA.

出版信息

IEEE Trans Circuits Syst II Express Briefs. 2021 May;68(5):1735-1739. doi: 10.1109/tcsii.2021.3066556. Epub 2021 Mar 17.

Abstract

Multielectrode arrays are used broadly for neural recording, both and for cultured neurons. In most cases, recording sites are passive electrodes wired to external read-out circuitry, and the number of wires is at least equal to the number of recording sites. We present an approach to break the conventional N-wire, N-electrode array architecture using graphene active electrodes, which allow signal upconversion at the recording site and sharing of each interface wire among multiple active electrodes using frequency-division multiplexing (FDM). The presented work includes the design and implementation of a frequency modulation and readout architecture using graphene FET electrodes, a custom integrated circuit (IC) analog front-end (AFE), and digital demodulation. The AFE was fabricated in 0.18 m CMOS; electrical characterization and multi-channel FDM results are provided, including GFET-based signal modulation and IC/DSP demodulation. Long-term, this approach can simultaneously enable high signal count, high spatial resolution, and high temporal precision to infer functional interactions between neurons while markedly decreasing access wires.

摘要

多电极阵列广泛用于神经记录,无论是在体内还是用于培养的神经元。在大多数情况下,记录位点是连接到外部读出电路的无源电极,并且导线数量至少等于记录位点的数量。我们提出了一种方法,使用石墨烯有源电极打破传统的N线、N电极阵列架构,该架构允许在记录位点进行信号上变频,并使用频分复用(FDM)在多个有源电极之间共享每条接口线。所展示的工作包括使用石墨烯场效应晶体管(FET)电极的频率调制和读出架构、定制集成电路(IC)模拟前端(AFE)以及数字解调的设计与实现。该AFE采用0.18μm互补金属氧化物半导体(CMOS)工艺制造;提供了电学特性和多通道FDM结果,包括基于石墨烯场效应晶体管(GFET)的信号调制以及IC/数字信号处理器(DSP)解调。从长远来看,这种方法可以在显著减少接入导线的同时,实现高信号数量、高空间分辨率和高时间精度,以推断神经元之间的功能相互作用。

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