Reconfigurable 2D-ferroelectric platform for neuromorphic computing

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 alpha-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.

Automated summary

To meet the requirements of data-intensive computing in the data-explosive era, researchers have extensively investigated brain-inspired neuromorphic computing for the past decade. However, challenges in integrating synaptic and neuronal devices in a single chip due to incompatible preparation processes have limited energy efficiency and scalability. Therefore, the development of a reconfigurable device with synaptic and neuronal functions in a single chip using the same materials and structures is highly desired. In this study, a reconfigurable hardware platform based on the polarization effect of 2D alpha-In2Se3 was designed, which can switch between emulating synapse and mimicking neuron. The application of this proof-of-concept device on a spiking neural network demonstrated its powerful learning ability and efficiency.