Graphite-based selectorless RRAM: Improvable intrinsic nonlinearity for array applications

Ying Chen Chen, Szu Tung Hu, Chih Yang Lin, Burt Fowler, Hui Chun Huang, Chao Cheng Lin, Sungjun Kim, Yao Feng Chang, Jack C. Lee

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Selectorless graphite-based resistive random-access memory (RRAM) has been demonstrated by utilizing the intrinsic nonlinear resistive switching (RS) characteristics, without an additional selector or transistor for low-power RRAM array application. The low effective dielectric constant value (k) layer of graphite or graphite oxide is utilized, which is beneficial in suppressing sneak-path currents in the crossbar RRAM array. The tail-bits with low nonlinearity can be manipulated by the positive voltage pulse, which in turn can alleviate variability and reliability issues. Our results provide additional insights for built-in nonlinearity in 1R-only selectorless RRAMs, which are applicable to the low-power memory array, ultrahigh density storage, and in-memory neuromorphic computational configurations.

Original languageEnglish (US)
Pages (from-to)15608-15614
Number of pages7
JournalNanoscale
Volume10
Issue number33
DOIs
StatePublished - Sep 7 2018
Externally publishedYes

ASJC Scopus subject areas

  • Materials Science(all)

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