CNN implemented by nonlinear phase dynamics in nanoscale processes

P. Riechers, R. Kiehl
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引用次数: 2

Abstract

We discuss CNNs in which the states are defined by the electrical phase of a dynamic physical process, such as electron tunneling in ultra-small junctions or integrate-and-fire processes in nanoscale structures or molecules. Such processes produce impulsive "neuron-like" waveforms which can be coupled to nearest neighbors in a 1D, 2D, or 3D array. Input data can be represented by the distribution of dc bias level, initial charge, or coupling strength within the array. Information processing can be realized through the nonlinear dynamics produced by interactions between elements, which give rise to an evolution of complex patterns in the phase-state. In this paper, we discuss information processing for a model physical system based on Coulomb blockade in a 2D array of ultra-small tunnel junctions.
纳米尺度过程中非线性相动力学实现的CNN
我们讨论了由动态物理过程的电相定义状态的cnn,例如超小结中的电子隧穿或纳米级结构或分子中的集成和燃烧过程。这种过程产生脉冲的“神经元样”波形,可以耦合到最近的1D、2D或3D阵列。输入数据可以用直流偏置电平、初始电荷或阵列内耦合强度的分布来表示。信息处理可以通过元件之间相互作用产生的非线性动力学来实现,这种非线性动力学导致复杂模式在相态中的演化。本文讨论了二维超小型隧道结阵列中基于库仑阻塞的模型物理系统的信息处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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