Learning mechanisms in memristor networks based on GaN nanomembranes
Закрыть
Conţinutul numărului revistei
Articolul precedent
Articolul urmator
726 0
SM ISO690:2012
DRAGOMAN, Mircea L., TIGINYANU, Ion, DRAGOMAN, Daniela, DINESCU, Adrian, BRANISTE, Tudor, CIOBANU, Vladimir. Learning mechanisms in memristor networks based on GaN nanomembranes. In: Journal of Applied Physics, 2018, vol. 124, p. 0. ISSN 0021-8979. DOI: https://doi.org/10.1063/1.5034765
EXPORT metadate:
Google Scholar
Crossref
CERIF

DataCite
Dublin Core
Journal of Applied Physics
Volumul 124 / 2018 / ISSN 0021-8979 /ISSNe 1089-7550

Learning mechanisms in memristor networks based on GaN nanomembranes

DOI:https://doi.org/10.1063/1.5034765

Pag. 0-0

Dragoman Mircea L.1, Tiginyanu Ion23, Dragoman Daniela45, Dinescu Adrian1, Braniste Tudor3, Ciobanu Vladimir3
 
1 National Institute for Research and Development in Microtechnology, IMT-Bucharest,
2 Institute of the Electronic Engineering and Nanotechnologies "D. Ghitu",
3 Technical University of Moldova,
4 University of Bucharest,
5 Romanian Academy of Science
 
 
Disponibil în IBN: 4 decembrie 2018


Rezumat

We demonstrate experimentally that single crystalline GaN nanomembranes arranged in simple networks exhibit learning mechanisms such as habituation and dishabituation followed by storage of the response to a certain electrical stimulus. These artificial learning mechanisms are analogous to non-associative learning processes which are identical in simple animals and human beings. We found that the learning time depends on the number of GaN membranes in parallel, and this parameter decreases by 30% when three memristors are connected in parallel compared to the learning time of a single memristor. Moreover, an increased number of parallel memristors reduces the eventual asymmetry in the temporal response of the circuit at positive and negative step voltages.

Cuvinte-cheie
Engineering controlled terms III-V semiconductors, Memristors, Nanostructures Engineering uncontrolled terms Artificial learning, Associative learning, Electrical stimuli, Learning mechanism, Negative steps, Simple networks, Single-crystalline, Temporal response Engineering main heading Gallium nitride