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

IF 5.3 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Piotr Zawal, Gisya Abdi, Marlena Gryl, Dip Das, Andrzej Sławek, Emilie A. Gerouville, Marianna Marciszko-Wiąckowska, Mateusz Marzec, Grzegorz Hess, Dimitra G. Georgiadou, Konrad Szaciłowski
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Abstract

Memristors, 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.

Abstract Image

Abstract Image

新型铋基扩散式晶膜管中的漏电积分-点火模型和短期突触可塑性模拟
忆阻器是未来电子技术的主力军,可提供各种类型的存储器(易失性和非易失性)以及特定的计算功能。忆阻器技术的进一步发展取决于能否获得合适的材料。这些材料应易于获得、稳定,最好是低毒性的。常用的材料是卤化铅包晶石,但它们在环境条件下毒性大且不稳定。因此,我们在碘化铋的基础上开发了一种新型材料。它与碘化丁基铵反应,生成一种新型化合物--碘铋酸丁基铵(BABI)。本文介绍了基于这种化合物的扩散式忆阻器,并对其忆阻和神经形态特性进行了评估。与非易失性忆阻器相比,BABI 忆阻器表现出扩散动态特性,这使其只能在短时间内存储信息。利用这一特性,可以模拟生物神经元的 "泄漏整合-发射 "模型所描述的短期突触可塑性。结合高开关均匀性和自校正行为,这些设备在 MNIST 手写数据集上显示出很高的分类准确性,为它们在神经形态计算系统中的应用铺平了道路。
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来源期刊
Advanced Electronic Materials
Advanced Electronic Materials NANOSCIENCE & NANOTECHNOLOGYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
11.00
自引率
3.20%
发文量
433
期刊介绍: Advanced Electronic Materials is an interdisciplinary forum for peer-reviewed, high-quality, high-impact research in the fields of materials science, physics, and engineering of electronic and magnetic materials. It includes research on physics and physical properties of electronic and magnetic materials, spintronics, electronics, device physics and engineering, micro- and nano-electromechanical systems, and organic electronics, in addition to fundamental research.
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