Van der Waals ferroelectric transistors: the all-round artificial synapses for high-precision neuromorphic computing

State number, operation power, dynamic range and conductance weight update linearity are key synaptic device performance metrics for high-accuracy and low-power-consumption neuromorphic computing in hardware. However, high linearity and low power consumption couldn't be simultaneously achieved by most of the reported synaptic devices, which limits the performance of the hardware. This work demonstrates van der Waals (vdW) stacked ferroelectric field-effect transistors (FeFET) with single-crystalline ferroelectric nanoflakes. Ferroelectrics are of fine vdW interface and partial polarization switching of multi-domains under electric field pulses, which makes the FeFETs exhibit multi-state memory characteristics and excellent synaptic plasticity. They also exhibit a desired linear conductance weight update with 128 conductance states, a sufficiently high dynamic range of G_max/G_min > 120, and a low power consumption of 10 fJ/spike using identical pulses. Based on such an all-round device, a two-layer artificial neural network was built to conduct Modified National Institute of Standards and Technology (MNIST) digital numbers and electrocardiogram (ECG) pattern-recognition simulations, with the high accuracies reaching 97.6% and 92.4%, respectively. The remarkable performance demonstrates that vdW-FeFET is of obvious advantages in high-precision neuromorphic computing applications.