使用交叉耦合微电极阵列和二维码分复用的1024通道,64互连,电容性神经接口

Woojun Choi, Yiyang Chen, Donghwan Kim, Sean Weaver, Tilman Schlotter, Can Livanelioglu, Jiawei Liao, Rosario M. Incandela, Parham Davami, Gabriele Atzeni, Sina Arjmandpour, Seonghwan Cho, Taekwang Jang
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引用次数: 0

摘要

本文提出了一种神经接口,它可以感知双电层(EDL)电容作为神经元放电动作电位(AP)所产生离子浓度的函数。与传统的微电极阵列(mea)检测电压不同,电容传感允许使用单根电线使用码分复用(CDM)访问多个记录站点,从而显着减少所需互连的数量。在这项工作中,我们实现了32个驱动器和32个模拟前端电路(afe)来实现1024通道并发神经记录,同时使用总共64个互连,并提高了大规模集成的区域效率。这项工作实现了$9.7 \mu \ mathm {W}$功率/ch和0.005mm2面积/ch的效率,最高电极密度为10,000mm-2,并且是作者所知的最少的互连。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 1,024-Channel, 64-Interconnect, Capacitive Neural Interface Using a Cross-Coupled Microelectrode Array and 2-Dimensional Code-Division Multiplexing
This paper presents a neural interface that senses the electrical double layer (EDL) capacitance as a function of the ion concentration produced by neurons firing action potentials (AP). Unlike conventional microelectrode arrays (MEAs) detecting voltage, capacitance sensing allows access to multiple recording sites with a single wire using code-division multiplexing (CDM), thereby significantly reducing the number of required interconnects. In this work, we implemented 32 drivers and 32 analog front-end circuits (AFEs) to realize 1,024 channel concurrent neural recordings while using a total of 64 interconnects and improving area efficiency for large-scale integration. This work achieves $9.7 \mu \mathrm{W}$ power/ch and 0.005mm2 area/ch efficiency with the highest electrode density of 10,000mm-2, and the fewest interconnects to the authors’ best knowledge.
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