Reconfigurable 2D-ferroelectric platform for neuromorphic computing

Abstract

To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarization effect of 2D α-In2Se3, we designed a reconfigurable hardware platform, which can switch from continuously modulated conductance for emulating synapse to spiking behavior for mimicking neuron. More crucially, we demonstrate the application of such proof-of-concept reconfigurable 2D ferroelectric devices on a spiking neural network with an accuracy of 95.8% and self-adaptive grow-when required network with an accuracy of 85% by dynamically shrinking its nodes by 72%, which exhibits more powerful learning ability and efficiency than the static neural network.