Leaky Integrate‐and‐Fire Model and Short‐Term Synaptic Plasticity Emulated in a Novel Bismuth‐Based Diffusive Memristor

Author:

Zawal Piotr12,Abdi Gisya1,Gryl Marlena3,Das Dip4,Sławek Andrzej1,Gerouville Emilie A.5,Marciszko‐Wiąckowska Marianna1,Marzec Mateusz1,Hess Grzegorz6,Georgiadou Dimitra G.5,Szaciłowski Konrad1ORCID

Affiliation:

1. Academic Centre for Materials and Nanotechnology AGH University of Krakow al. Mickiewicza 30 Kraków 30‐059 Poland

2. Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Kraków 30‐059 Poland

3. Crystal Engineering and Structural Analysis Group Department of Crystal Chemistry and Crystal Physics Faculty of Chemistry Jagiellonian University in Kraków ul. Gronostajowa 9 Kraków 30‐387 Poland

4. Department of Electronic and Electrical Engineering University College London Torrington Place London WC1E7JE UK

5. Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

6. Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research Jagiellonian University ul. Gronostajowa 9 Kraków 30‐387 Poland

Abstract

AbstractMemristors, being prospective work‐horses of future electronics offer various types of memory (volatile and nonvolatile) along with specific computational functionalities. Further development of memristive technologies depends on the availability of suitable materials. These materials should be easily available, stable, and preferably of low toxicity. Commonly used materials are lead halide perovskites, however, they are highly toxic and unstable under ambient conditions. Therefore a novel material is developed on the basis of bismuth iodide. In reaction with butylammonium iodide, it yields a novel compound, butylammonium iodobismuthate (BABI). Here, a diffusive memristor is introduced based on this compound and evaluates its memristive and neuromorphic properties. In contrast to nonvolatile memristors, the BABI memristors exhibit diffusive dynamics, which enable them to store the information only for short periods of time. This property is utilized to mimic the short‐term synaptic plasticity described by the leaky integrate‐and‐fire model of a biological neuron. Combined with high switching uniformity and self‐rectifying behavior, these devices show high classification accuracy for MNIST handwritten datasets, paving the way for their application in neuromorphic computing systems.

Funder

Narodowe Centrum Nauki

Publisher

Wiley

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