Modeling of Graphene Oxide Memory based on Hybridization State Modulation
Ee Wah Lim

TL;DR
This paper introduces a new compact SPICE model for graphene oxide RRAM that captures the bulk switching mechanism based on hybridization state modulation, aligning well with experimental data.
Contribution
It provides the first compact model for GO-RRAM based on bulk hybridization state change, filling a gap in existing localized-effect models.
Findings
Model accurately simulates I-V characteristics
Strong correlation with experimental data
Validates bulk switching mechanism in GO-RRAM
Abstract
Graphene oxide (GO)-based resistive random access memory (RRAM) is one of the most promising emerging non-volatile memories for flexible electronics because of its simple structure and low fabrication cost. The reported switching mechanism can be classified into either localized or bulk effect. The localized effect is commonly observed in GO-RRAM with active electrode e.g. Cu and Al. The switching mechanism includes metallic conduction filament evolution or modification of interfacial resistance between GO layer and electrodes. On the other hand, the bulk effect involves hybridization state change of the entire GO layer and is typically observed in devices with inert electrode e.g. Pt and Au. Numerous compact models have been proposed for RRAM that are based on localized switching mechanism. However, compact modeling of GO-RRAM based on bulk mechanism is still lacking. Thus, this paper…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Ferroelectric and Negative Capacitance Devices
