Graphite-based selectorless RRAM: improvable intrinsic nonlinearity for array applications. Issue 33 (9th August 2018)

• Record Type: Journal Article
• Authors: Chen, Ying-Chen; Hu, Szu-Tung; Lin, Chih-Yang; Fowler, Burt; Huang, Hui-Chun; Lin, Chao-Cheng; Kim, Sungjun; Chang, Yao-Feng; Lee, Jack C.
• Is Part Of: Nanoscale. Volume 10:Issue 33(2018)
• Page Start: 15608
• Page End: 15614
• Publication Date: 2018-08-09
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/c8nr04766a
• Languages: English
• ISSNs: 2040-3364

Abstract

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. 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 1 R -only selectorless RRAMs, which are applicable to the low-power memory array, ultrahigh density storage, and in-memory neuromorphic computational configurations.