Adaptive ferroelectric memristors with high-throughput BaTiO3 thin films for neuromorphic computing

Abstract

Ferroelectric tunnel junctions (FTJs) and ferroelectric diodes (FDs) have been considered as promising artificial synaptic devices for constructing brain-inspired neuromorphic computing systems. However, their functionalities and applications are limited due to their strong dependence on the ferroelectric layer thickness and the thickness optimization is labour-intensive and time-consuming. Here, we demonstrate high-performance electronic synapses based on a high-throughput ferroelectric BaTiO₃ (BTO) thin film. Two-terminal ferroelectric memristors are fabricated on a thickness-gradient BTO film with thickness ranging from 1 to 30 unit cells (UC), and intrinsic ferroelectricity is revealed in regions with thickness >5 UC. Notably, three typical resistive switching behaviors of resistor, FTJ, and FD occur sequentially with increasing BTO thickness, allowing these three basic electronic components to be integrated. High-performance FTJ synapses with adaptive conductance compensation from resistor and FD components are proposed based on an on-chip integration configuration. This approach improves the accuracy of handwritten digit recognition using artificial neural networks (ANNs) from 91.3% to 95.7%. Despite Gaussian noise interference, the ANN based on this adaptive compensation approach remains extremely fault-tolerant, and is expected to meet the increasing demands of contemporary electronic devices, particularly in the fields of memory, logic processing, and neuromorphic computing.