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用于基于离子液体的神经形态平台的可编程电流体学

Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform.

作者信息

Boldman Walker L, Zhang Cheng, Ward Thomas Z, Briggs Dayrl P, Srijanto Bernadeta R, Brisk Philip, Rack Philip D

机构信息

Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA.

Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

出版信息

Micromachines (Basel). 2019 Jul 17;10(7):478. doi: 10.3390/mi10070478.

Abstract

Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias measurements are demonstrated for lower power considerations. While compatible with resistive elements, we demonstrate a platform based on transitive amorphous indium gallium zinc oxide (aIGZO) pixel elements. Using a lithium based ionic liquid, we demonstrate both potentiation (decrease in device resistance) and depression (increase in device resistance), and propose a 2D platform array that would enable a much higher pixel count via Active Matrix electrowetting.

摘要

由于冯·诺依曼瓶颈导致的计算能力限制,正在开发通过将设备特性与神经元的突触权重相关联来模拟大脑神经生物学处理的计算设备。该平台结合了离子液体门控和电润湿技术,用于离子液体的可编程放置/连接。在这个平台中,短期增强(STP)和长期增强(LTP)分别通过非晶铟镓锌氧化物(aIGZO)的静电和电化学掺杂来实现,并且为了降低功耗进行了脉冲偏置测量演示。虽然与电阻元件兼容,但我们展示了一个基于可传递非晶铟镓锌氧化物(aIGZO)像素元件的平台。使用基于锂的离子液体,我们展示了增强(器件电阻降低)和抑制(器件电阻增加),并提出了一个二维平台阵列,该阵列将通过有源矩阵电润湿实现更高的像素数量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee81/6680446/c3f2cf14cc2c/micromachines-10-00478-g001.jpg

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