Nanoarchitectonic atomic switch networks for unconventional computing
Nanoarchitectonic atomic switch networks for unconventional computing
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DOI：
10.7567/jjap.55.1102b2
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发表时间：
2016-11-01
影响因子：
1.5
通讯作者：
Gimzewski, James K.
中科院分区：
文献类型：
--
作者：
Demis, Eleanor C.;Aguilera, Renato;Gimzewski, James K.
计算硬件的发展受到互补金属氧化物半导体（CMOS）技术的操作原理、纳米尺度特征的制造限制以及有效利用高密度互连的困难的限制。这一系列的障碍已经公布了一个寻找替代的，节能的方法来计算的灵感来自自然系统，包括哺乳动物的大脑。原子开关网络（Atomic Switch Network，缩写为ATN）是一个独特的平台，专门开发来克服这些障碍，实现自适应神经形态技术。ASNs是由一个大规模互连的原子开关网络组成的，密度接近10（9）个单位/cm（2），在结构上让人联想到大脑的新皮层。ASN既具有单个忆阻开关的固有能力，如记忆容量和多态切换，又具有大规模复杂系统的特性，如幂律动力学和输入信号的非线性变换。在这里，我们描述了成功的nanoarchitectonic制造的下一代MEMS器件，使用自上而下和自下而上的处理相结合，并通过实验证明其效用作为水库计算硬件。利用它们的内在动力学和变革性输入/输出（I/O）行为，在没有嵌入式算法的情况下实现了周期信号的波形回归，进一步支持了可重构技术作为非常规计算方法平台的潜在效用。(C)2016日本应用物理学会
Developments in computing hardware are constrained by the operating principles of complementary metal oxide semiconductor (CMOS) technology, fabrication limits of nanometer scaled features, and difficulties in effective utilization of high density interconnects. This set of obstacles has promulgated a search for alternative, energy efficient approaches to computing inspired by natural systems including the mammalian brain. Atomic switch network (ASN) devices are a unique platform specifically developed to overcome these current barriers to realize adaptive neuromorphic technology. ASNs are composed of a massively interconnected network of atomic switches with a density of similar to 10(9) units/cm(2) and are structurally reminiscent of the neocortex of the brain. ASNs possess both the intrinsic capabilities of individual memristive switches, such as memory capacity and multi-state switching, and the characteristics of large-scale complex systems, such as power-law dynamics and non-linear transformations of input signals. Here we describe the successful nanoarchitectonic fabrication of next-generation ASN devices using combined top-down and bottom-up processing and experimentally demonstrate their utility as reservoir computing hardware. Leveraging their intrinsic dynamics and transformative input/output (I/O) behavior enabled waveform regression of periodic signals in the absence of embedded algorithms, further supporting the potential utility of ASN technology as a platform for unconventional approaches to computing. (C) 2016 The Japan Society of Applied Physics