Atomistic-level hysteresis-aware graphene structures electron transport model

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
1 Downloads (Pure)


Hysteretic behavior has been experimentally observed in graphene-based structures and has a major influence on graphene surface potential and gate field modulation ability. Thus, a graphene electronic transport modelling methodology, which incorporates hysteresis effects is crucial in order to properly assess gated-controlled graphene structures response and performance. To this end, we propose an atomistic-level electronic transport model, which is non restricted to rectangular graphene geometries and captures hysteretic effects caused by near-interfacial traps, provided that interface traps trapping/detrapping time constant and density are known. We apply the model on a rectangular graphene shape and validate our results against experimentally measured drain current vs. top gate voltage hysteresis curves. Moreover, to demonstrate model's versatility we consider two non-rectangular Graphene NanoRibbons (GNRs) and investigate their hysteresis behaviour. Our experiments indicate good agreement between simulated and measured results, which qualifies the model appropriate for traps-aware exploration of the conduction behaviour of graphene-based devices and circuits.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781728103976
Publication statusPublished - 2019
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019


Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019


  • Carbon-Nanoelectronics
  • Graphene
  • Hysteresis
  • Interface traps


Dive into the research topics of 'Atomistic-level hysteresis-aware graphene structures electron transport model'. Together they form a unique fingerprint.

Cite this