As CMOS feature size approaches atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, prompting for research and development on new materials, devices, and/or computation paradigms. Within this context, Graphene Nanoribbons (GNRs), owing to graphene's excellent electronic properties, may serve as basic blocks for carbon-based nanoelectronics. En route to GNR-based logic circuits, the ability to externally control GNRs' conduction to map a basic Boolean logic function onto its electrical characteristics, with a high 1ON/1OFF ratio, and uncompromised carriers mobility, is the main desideratum. To this end, we augment a trapezoidal GNR with top gates as controlling inputs, and investigate its conductance G by means of the NEGF-Landauer formalism. Further, we demonstrate that the butterfly GNR can exhibit conduction maps (high G for logic “1”, and low G for logic “0”) capturing the functionality of 2 and 3-input Boolean gates, by properly adjusting its topology and dimensions. Our simulations prove butterfly GNR structure capability to capture basic Boolean logic transfer functions, while potentially providing 30× and 3000× smaller propagation delay and gate active area, respectively, when compared to 15 nm CMOS equivalent counterparts, establishing GNR's potential as basic building block for future graphene-based logic gates.
Original languageEnglish
Title of host publication2018 IEEE International Symposium on Circuits and Systems (ISCAS)
Subtitle of host publicationProceedings
Place of PublicationPiscataway, NY
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-4881-0
ISBN (Print)978-1-5386-4882-7
DOIs
Publication statusPublished - 2018
EventISCAS 2018: IEEE International Symposium on Circuits and Systems - Florence, Italy
Duration: 27 May 201830 May 2018
http://www.iscas2018.org

Conference

ConferenceISCAS 2018
Abbreviated titleISCAS 2018
CountryItaly
CityFlorence
Period27/05/1830/05/18
Internet address

    Research areas

  • Graphene, GNR, Graphene-based Boolean Gates, Carbon-Nanoelectronics

ID: 45542309