Selective and quasi-continuous switching of ferroelectric Chern insulator devices for neuromorphic computing.

Quantum materials exhibit dissipationless topological edge state transport with quantized Hall conductance, offering notable potential for fault-tolerant computing technologies. However, the development of topological edge state-based computing devices remains a challenge. Here we report the selective and quasi-continuous ferroelectric switching of topological Chern insulator devices, showcasing a proof-of-concept demonstration in noise-immune neuromorphic computing. We fabricate this ferroelectric Chern insulator device by encapsulating magic-angle twisted bilayer graphene with doubly aligned h-BN layers and observe the coexistence of the interfacial ferroelectricity and the topological Chern insulating states. The observed ferroelectricity exhibits an anisotropic dependence on the in-plane magnetic field. By tuning the amplitude of the gate voltage pulses, we achieve ferroelectric switching between any pair of Chern insulating states in the presence of a finite magnetic field, resulting in 1,280 ferroelectric states with distinguishable Hall resistance levels on a single device. Furthermore, we demonstrate deterministic switching between two arbitrary levels among the record-high number of ferroelectric states. This unique switching capability enables the implementation of a convolutional neural network resistant to external noise, utilizing the quantized Hall conductance levels of the Chern insulator device as weights. Our study provides a promising avenue towards the development of topological quantum neuromorphic computing, where functionality and performance can be drastically enhanced by topological quantum materials.

Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array.

The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.