A multi-timescale synaptic weight based on ferroelectric hafnium zirconium oxide

Abstract Brain-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.