  Neuromorphic hardware facilitates rapid and energy-efficient training and
operation of neural network models for artificial intelligence. However,
existing analog in-memory computing devices, like memristors, continue to face
significant challenges that impede their commercialization. These challenges
include high variability due to their stochastic nature. Microfabricated
electrochemical synapses offer a promising approach by functioning as an analog
programmable resistor based on deterministic ion-insertion mechanisms. Here, we
developed an all-solid-state oxide-ion synaptic transistor employing
$\text{Bi}_2\text{V}_{0.9}\text{Cu}_{0.1}\text{O}_{5.35}$ as a superior
oxide-ion conductor electrolyte and
$\text{La}_\text{0.5}\text{Sr}_\text{0.5}\text{F}\text{O}_\text{3-$\delta$}$ as
a variable resistance channel able to efficiently operate at temperatures
compatible with conventional electronics. Our transistor exhibits essential
synaptic behaviors such as long- and short-term potentiation, paired-pulse
facilitation, and post-tetanic potentiation, mimicking fundamental properties
of biological neural networks. Key criteria for efficient neuromorphic
computing are satisfied, including excellent linear and symmetric synaptic
plasticity, low energy consumption per programming pulse, and high endurance
with minimal cycle-to-cycle variation. Integrated into an artificial neural
network (ANN) simulation for handwritten digit recognition, the presented
synaptic transistor achieved a 96% accuracy on the MNIST dataset, illustrating
the effective implementation of our device in ANNs. These findings demonstrate
the potential of oxide-ion based synaptic transistors for effective
implementation in analog neuromorphic computing based on iontronics.
