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用于神经形态计算的温度适应性固态有机人工突触

Temperature-resilient solid-state organic artificial synapses for neuromorphic computing.

作者信息

Melianas A, Quill T J, LeCroy G, Tuchman Y, Loo H V, Keene S T, Giovannitti A, Lee H R, Maria I P, McCulloch I, Salleo A

机构信息

Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.

Zernike Institute for Advanced Materials, University of Groningen, 9747AG Groningen, Netherlands.

出版信息

Sci Adv. 2020 Jul 3;6(27). doi: 10.1126/sciadv.abb2958. Print 2020 Jul.

Abstract

Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (80 femtojoules per write) operation combined with excellent endurance (>10 write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs.

摘要

具有可调电阻的器件在神经形态计算领域备受青睐。然而,传统的电阻式存储器存在电阻调谐非线性和不对称以及写入噪声过大的问题,这会降低人工神经网络(ANN)加速器的性能。新兴的电化学随机存取存储器(ECRAM)具有写入线性特性,通过并行阵列编程能够显著加快ANN的训练速度。然而,目前最先进的ECRAM在封装电子设备所需的温度(约90°C)下尚未展示出稳定且高效的运行性能。在此,我们展示了(半)导电聚合物与离子凝胶电解质薄膜相结合可实现固态ECRAM,其在高达90°C的温度下能稳定运行且几乎不受温度影响。这些ECRAM在大于2倍的动态范围内呈现线性电阻调谐,具有20纳秒的开关速度、亚微秒级的读写循环、低噪声、低电压(±1伏)以及低能量(每次写入约80飞焦)运行,同时具备出色的耐久性(在90°C下可进行超过10次读写操作)。这些高性能ECRAM的展示是其在硬件ANN中实现的关键一步。

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