Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour

Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour
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具有模拟突触行为的滞后负微分电导的动态分子开关

DOI:
10.1038/s41563-022-01402-2
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发表时间:
2022
期刊:
影响因子:
41.2
通讯作者:
Braunschweig, Björn
Braunschweig, Björn
中科院分区:
材料科学1区
文献类型:
--
作者:
Wang, Yulong;Zhang, Qian;Astier, Hippolyte P.;Nickle, Cameron;Soni, Saurabh;Alami, Fuad A.;Borrini, Alessandro;Zhang, Ziyu;Honnigfort, Christian;Braunschweig, Björn

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为了实现超越冯·诺伊曼瓶颈的分子规模的电子操作,需要一种新型的多功能开关,通过在依赖于它们过去的多个操作之间动态切换,来模仿自我学习或神经形态计算。在这里,我们报告了一个分子,它从高电导状态切换到低电导状态,具有大量的负记忆行为,这取决于驱动速度和过去切换事件的数量,所有的测量都完全使用原子模型和分析模型进行建模。这种动态分子开关模拟突触行为和巴甫洛夫学习,所有这些都在2.4纳米厚的层内,比神经元突触薄三个数量级。动态分子开关提供了深度学习所需的所有基本逻辑门,因为它具有时域和电压相关的可塑性。模拟突触的多功能动态分子开关代表了一种可在固态设备中操作的适应性分子规模的硬件,并开辟了一条简化在单个超紧凑组件中编码的动态复杂电子操作的途径。
To realize molecular-scale electrical operations beyond the von Neumann bottleneck, new types of multifunctional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here, we report a molecule that switches from high to low conductance states with massive negative memristive behaviour that depends on the drive speed and number of past switching events, with all the measurements fully modelled using atomistic and analytical models. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning, all within a 2.4-nm-thick layer that is three orders of magnitude thinner than a neuronal synapse. The dynamic molecular switch provides all the fundamental logic gates necessary for deep learning because of its time-domain and voltage-dependent plasticity. The synapse-mimicking multifunctional dynamic molecular switch represents an adaptable molecular-scale hardware operable in solid-state devices, and opens a pathway to simplify dynamic complex electrical operations encoded within a single ultracompact component.
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发表时间: 2020-10-19
影响因子: 38.3
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通讯作者: van der Wiel, Wilfred G.
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发表时间: 2017-04-01
期刊: NATURE MATERIALS
影响因子: 41.2
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发表时间: 2003-09
影响因子: 3.3
作者:
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通讯作者: Rong Wang;T. Okajima;F. Kitamura;N. Matsumoto;T. Thiemann;S. Mataka*;T. Ohsaka