Memcomputing with membrane memcapacitive systems

Abstract

We show theoretically that networks of membrane memcapacitive systems -- capacitors with memory made out of membrane materials -- can be used to perform a complete set of logic gates in a massively parallel way by simply changing the external input amplitudes, but not the topology of the network. This polymorphism is an important characteristic of memcomputing (computing with memories) that closely reproduces one of the main features of the brain. A practical realization of these membrane memcapacitive systems, using, e.g., graphene or other 2D materials, would be a step forward towards a solid-state realization of memcomputing with passive devices.

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Cited by in corpus (7)

• Memcomputing: Leveraging memory and physics to compute efficiently
• Kinks and antikinks of buckled graphene: A testing ground for phi^4 field model
• Snap-through transition of graphene membranes for memcapacitor applications: A combined study using MD, DFT and elasticity theory
• An Adiabatic Capacitive Artificial Neuron with RRAM-based Threshold Detection for Energy-Efficient Neuromorphic Computing
• A Demonstration of Implication Logic Based on Volatile (Diffusive) Memristors
• Computing with volatile memristors: An application of non-pinched hysteresis
• Metastable Memristive Lines for Signal Transmission and Information Processing Applications