Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing

被引：621

作者：

Fuller, Elliot J. ^{[1
]}

Keene, Scott T. ^{[2
]}

Melianas, Armantas ^{[2
]}

Wang, Zhongrui ^{[3
]}

Agarwal, Sapan ^{[1
]}

Li, Yiyang ^{[1
]}

Tuchman, Yaakov ^{[2
]}

James, Conrad D. ^{[4
]}

Marinella, Matthew J. ^{[4
]}

Yang, J. Joshua ^{[3
]}

Salleo, Alberto ^{[2
]}

Talin, A. Alec ^{[1
]}

机构：

[1] Sandia Natl Labs, Livermore, CA 94550 USA

[2] Stanford Univ, Dept Mat Sci & Engn, Stanford, CA 94305 USA

[3] Univ Massachusetts, Dept Comp Sci & Elect Engn, Amherst, MA 01003 USA

[4] Sandia Natl Labs, POB 5800, Albuquerque, NM 87185 USA

来源：

SCIENCE | 2019年 / 364卷 / 6440期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1126/science.aaw5581

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.

引用

页码：570 / +

页数：27

共 36 条

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