Artificial photoelectric synaptic devices with ferroelectric diode effect for high-performance neuromorphic computing

Abstract

With the rapid advancement of artificial intelligence and machine learning, the demand for neuromorphic computing systems has intensified. High-performance artificial synaptic devices are crucial to achieving this objective. Ferroelectric diode artificial synaptic devices were prepared using aluminum-doped BaTiO₃ (BTAO) thin films by a low-cost sol-gel method. These devices mimic the basic properties of biological synapses, such as long-term plasticity (LTP) and short-term plasticity (STP), under electrical stimulation. Additionally, the devices exhibit an excellent UV light response, enabling the transition from STP to LTP by adjusting the light pulse intensity, width, and number of pulses. Aluminum doping significantly enhances the ferroelectricity of BaTiO₃ films, increasing the P_r from 2.31 µC/cm² to 9.08 µC/cm². By modulating the Schottky barrier through polarization, the BTAO films exhibit a switchable diode effect, which facilitates fine-tuning of the synaptic connection strength while maintaining synaptic stability. Recognition accuracies of 97.32 % for the MNIST dataset and 87.48 % for the Fashion-MNIST dataset were achieved by convolutional neural network simulations. These results suggest new possibilities for brain-like processing with ferroelectric diodes.