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 and a fine-grained weight update of more than 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 dev

SUBMITTER: Halter M 

PROVIDER: S-EPMC9936949 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature