神经形态工程学
互连性
巨量平行
记忆电阻器
计算机科学
材料科学
高效能源利用
光电子学
CMOS芯片
人工神经网络
人工智能
电子工程
电气工程
工程类
并行计算
作者
Yoeri van de Burgt,Ewout Lubberman,Elliot James Fuller,Scott T. Keene,Gregório Couto Faria,Sapan Agarwal,Matthew Marinella,A. Alec Talin,Alberto Salleo
出处
期刊:Nature Materials
[Springer Nature]
日期:2017-02-20
卷期号:16 (4): 414-418
被引量:1203
摘要
The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
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