Design of a Nanoscale, CMOS-Integrable, Thermal-Guiding Structure for Boolean-Logic and Neuromorphic Computation
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Quantum-Dot Cellular Automata · Graphene research and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
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.
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Funding
Engineering and Physical Sciences Research Council · Singapore University of Technology and Design
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