Nature Research, communications materials, 1(4), 2023
DOI: 10.1038/s43246-023-00342-x
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AbstractBrain-inspired computing emerged as a forefront technology to harness the growing amount of data generated in an increasingly connected society. The complex dynamics involving short- and long-term memory are key to the undisputed performance of biological neural networks. Here, we report on sub-µm-sized artificial synaptic weights exploiting a combination of a ferroelectric space charge effect and oxidation state modulation in the oxide channel of a ferroelectric field effect transistor. They lead to a quasi-continuous resistance tuning of the synapse by a factor of $60$ 60 and a fine-grained weight update of more than $200$ 200 resistance values. We leverage a fast, saturating ferroelectric effect and a slow, ionic drift and diffusion process to engineer a multi-timescale artificial synapse. Our device demonstrates an endurance of more than ${10}^{10}$ 10 10 cycles, a ferroelectric retention of more than $10$ 10 years, and various types of volatility behavior on distinct timescales, making it well suited for neuromorphic and cognitive computing.