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用于布尔逻辑和神经形态计算的纳米级、CMOS 集成、热导向结构的设计。

Design of a Nanoscale, CMOS-Integrable, Thermal-Guiding Structure for Boolean-Logic and Neuromorphic Computation.

机构信息

Science Faculty, Singapore University of Technology and Design , 8 Somapah Road, Singapore 487372, Singapore.

Department of Chemistry, University of Bath , Claverton Down, Bath BA2 7AY, U.K.

出版信息

ACS Appl Mater Interfaces. 2016 Dec 21;8(50):34530-34536. doi: 10.1021/acsami.6b10667. Epub 2016 Dec 7.

Abstract

One of the requirements for achieving faster CMOS electronics is to mitigate the unacceptably large chip areas required to steer heat away from or, more recently, toward the critical nodes of state-of-the-art devices. Thermal-guiding (TG) structures can efficiently direct heat by "meta-materials" engineering; however, some key aspects of the behavior of these systems are not fully understood. Here, we demonstrate control of the thermal-diffusion properties of TG structures by using nanometer-scale, CMOS-integrable, graphene-on-silica stacked materials through finite-element-methods simulations. It has been shown that it is possible to implement novel, controllable, thermally based Boolean-logic and spike-timing-dependent plasticity operations for advanced (neuromorphic) computing applications using such thermal-guide architectures.

摘要

实现更快的 CMOS 电子学的要求之一是减轻为将热量从关键节点引导开(或最近,引导至关键节点)所需的无法接受的大芯片面积。热导向(TG)结构可以通过“超材料”工程有效地引导热量;然而,这些系统的一些关键方面的行为尚未完全理解。在这里,我们通过有限元方法模拟,展示了使用纳米级、CMOS 可集成、堆叠在硅上的石墨烯材料来控制 TG 结构的热扩散特性。已经表明,使用这种热导向架构,可以为先进的(神经形态)计算应用实现新型的、可控的、基于热的布尔逻辑和尖峰时间依赖可塑性操作。

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