Neuromorphic one-shot learning utilizing a phase-transition material.

ABSTRACT: Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO₂), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the

SUBMITTER: Galloni AR 

PROVIDER: S-EPMC11047090 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature