Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

P. Maier, Fabian Hartmann, M. Rebello Sousa Dias, M. Emmerling, C. Schneider, L. K. Castelano, M. Kamp, G. E. Marques, V. Lopez-Richard, L. Worschech, Sven Höfling

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low
conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.
Original languageEnglish
Article number134503
JournalJournal of Applied Physics
Volume120
Issue number13
Early online date4 Oct 2016
DOIs
Publication statusPublished - 7 Oct 2016

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