Ultrahigh drive current and large selectivity in GeS selector

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching (OTS) devices with much higher scalability are currently the most industrially favored selector technology. Despite industrial confidence in OTS selectors, current devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants to remedy the intrinsic demerits of the principal functioning materials. Here, we report on a selector with a large drive current density of 34 MA/cm2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations have revealed Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field. Besides the superior selector function, we have also demonstrated stochastic integrate-and-fire neuron behavior using GeS device, providing an intriguing opportunity for all-chalcogenide neuromorphic electronics. The source data underlying Figs. 1c-e, Figs. 2a–e, and Figs. 3b, d, f are provided as a Source Data file.

Identifier
Source https://archive.materialscloud.org/record/2020.72
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:424
Provenance
Creator Jia, Shujing; Zhu, Min; Li, Huanglong; Gotoh, Tamihiro; Longeaud, Christophe; Zhang, Bin; Lyu, Juan; Lv, Shilong; Song, Zhitang; Liu, Qi; Robertson, John; Liu, Ming
Publisher Materials Cloud
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering