Scientia et Securitas
Volume/Issue:
Volume 4: Issue 4
Információfeldolgozás nanoskálájú memrisztív eszközökkel
Authors:
Tímea Nóra Török Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary
HUN-REN Energiatudományi Kutatóközpont, Műszaki Fizikai és Anyagtudományi Intézet Budapest Magyarország; HUN-REN Centre for Energy Research, Institute of Technical Physics and Materials Science Budapest Hungary

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https://orcid.org/0000-0001-5238-5895
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László Pósa Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary
HUN-REN Energiatudományi Kutatóközpont, Műszaki Fizikai és Anyagtudományi Intézet Budapest Magyarország; HUN-REN Centre for Energy Research, Institute of Technical Physics and Materials Science Budapest Hungary

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Dániel Molnár Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary
HUN-REN-BME Kondenzált Anyagok Fizikája Kutatócsoport Budapest Magyarország; HUN-REN-BME Condensed Matter Research Group Budapest Hungary

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János Gergő Fehérvári Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary

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Roland Kövecs Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary

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Zoltán Balogh Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary

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János Volk HUN-REN Energiatudományi Kutatóközpont, Műszaki Fizikai és Anyagtudományi Intézet Budapest Magyarország; HUN-REN Centre for Energy Research, Institute of Technical Physics and Materials Science Budapest Hungary
Védelmi Innovációs Kutatóintézet Budapest Magyarország; Defence Innovation Research Institute Budapest Hungary

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András Halbritter Budapesti Műszaki és Gazdaságtudományi Egyetem, Fizikai Intézet, Fizika Tanszék Budapest Magyarország; Budapest University of Technology and Economics, Institute of Physics, Department of Physics Budapest Hungary
HUN-REN-BME Kondenzált Anyagok Fizikája Kutatócsoport Budapest Magyarország; HUN-REN-BME Condensed Matter Research Group Budapest Hungary

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Pages:
312–320
Online Publication Date:
05 Aug 2024
Publication Date:
05 Aug 2024
Article Category:
Research Article
DOI:
https://doi.org/10.1556/112.2023.00179
Keywords:
memristor; resistive switching memory; edge computing;
Keywords (Hungarian):
memrisztor; rezisztív kapcsoló memória; peremhálózati információfeldolgozás;
Open access

Összefoglalás.

Napjainkra az információs technológiák fejlődése elérte azt a szintet, ahol a gyorsuló ütemben létrejövő adattömeg feldolgozásához már sok esetben elégtelenek a klasszikus, Neumann-elvek alapján működő számítógépek. A jelenség újszerű szoftveres megoldások, biológiai ihletésű algoritmusok, neurális hálózatok elterjedéséhez vezetett, ám ezek hatékony alkalmazásához teljesen új hardveres megoldások szükségesek. Jelen kézirat ilyen újszerű architektúrákhoz fejlesztett, Si-mikrochip-alapú memóriatulajdonsággal rendelkező nanoméretű áramköri elemek kísérleti eredményeit mutatja be, illetve azok egy-egy specifikus információfeldolgozási feladatra történő alkalmazhatóságával foglalkozik.

Summary.

Resistive switching memory devices, also known as memristors, are generally metal-insulator-metal nanostructures whose conductivity can be varied via electrical signals, enabling information storage in the value of the conductivity. Based on this property, memristive devices provide a promising platform for hardware-level encoding of large matrices. With a network of memristors, computationally intensive vector-matrix operations can be performed in a single step, considerably speeding up the operation of an artificial neural network (ANN). Memristors can also serve as real physical building blocks for biologically inspired algorithms through their neuromorphic properties. Via building simple circuits, such devices can be used to create oscillators or artificial neurons, which can be utilized for the implementation of oscillatory neural networks (ONN) or spiking neural networks (SNN). Another interesting feature of these memristive neuromorphic circuits is that they can be directly used for information processing tasks at the edge of a network. Memristors facilitate such edge computing applications which usually require energy-efficient operation and small size of the processor unit. Edge computing approaches have several advantages over centralized data processing from the aspect of security, e.g., significantly reducing time latency of sending large amounts of data to a central hub, and by processing sensitive data locally and independently of the central servers.

Present work focuses on the experimental investigation of purpose-built nanoscale memristive devices, revealing their physical processes. A superconducting spectroscopy measurement technique is developed for the non-destructive detection of atomic scale memristive filaments during device operation (Török et al. 2020; Török et al. 2023). In addition, the tunable stochasticity of the nucleation process is revealed by statistical studies of the set process in silicon oxide memristors (Török et al. 2022). This finding provides a basis for the physical realization of neural activation functions, stochastically firing neurons or energy-efficient true random number generation. Finally, the applicability of the investigated nanoscale memristive devices is illustrated through two examples. The concept of a neuromorphic, memristor-based auditory sensing unit is presented, leading towards medical application in a fully implantable cochlear implant. Last, the feasibility of a hardware-level stochastic optimization procedure is introduced (Fehérvári et al. 2023), based entirely on memristive elements, utilizing tunable noise characteristics of the devices (Sánta et al. 2021).

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