Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing
<|reference_start|>Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing: 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.<|reference_end|>
@article{langner2024solid-state,
    title={Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing},
    author={Philipp Langner, Francesco Chiabrera, Nerea Alayo, Paul Nizet, Luigi
  Morrone, Carlota Bozal-Ginesta, Alex Morata, Albert Taranc`on},
    journal={arXiv preprint arXiv:2408.01469},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01469},
    primaryClass={cs.ET physics.app-ph}
}