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New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd...
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New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 8438

Special Issue Editors

Dr. Andrey Sokolov

E-Mail Website
Guest Editor
Department of Materials Science & Engineering, Hanyang University, Seoul 133-791, Republic of Korea
Interests: resistive switching device; ReRAM; neuromorphic computing; electronic synapse; semiconductor memory; AI
Special Issues, Collections and Topics in MDPI journals
Dr. Haider Abbas

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore
Interests: neuromorphic computing; emerging nanotechnology devices; electronic synapse
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is open to valuable contributions in the area of emerging memory devices and their applications, such as ReRAM, CBRAM, Schottky diodes, and other memories based on the resistive switching phenomenon. The Special Issue will cover the fundamentals of ionic-driven resistive switching behavior found in emerging memory devices that are designed and fabricated via thin-film deposition techniques, such as ALD, PVD, CVD, thermal oxidation, drop-casting, spin-coating, electrodeposition, sol–gel, etc. It will provide leading research on recent developments in fabrication, miniaturization, and applications of single- and complex-based resistive switching memories utilized as a general memory for its extension to perform synapse, neuron, and other neuromorphic functioning. Freshly given explanations on memory ionic-driven mechanisms would help to tune and benefit the state of the art of resistive switching technology, specifically applicable as a memory or synapse device. Overall, the Special Issue is interested in original research that focuses on new concepts, ideas, and recent progress, covering thin-film materials, memory devices, in-depth physics of the resistive switching mechanism, memory crossbar systems, neuromorphic devices and circuits, and advances in synapse and neuron devices. Contributions to all these related subjects are highly encouraged and appreciated.

Dr. Andrey Sokolov
Dr. Haider Abbas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • resistive switching
  • non-volatile and volatile memory
  • memristors
  • synapse and neuron devices
  • in memory computing
  • crossbar resistive memory
  • neuromorphic computing
  • circuit and cad mask design for emerging memory
  • applications of resistive switching memory
  • resistive random access memory

Related Special Issue

Published Papers (6 papers)

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Research

13 pages, 2811 KiB  
Article
Research on the Impact of Data Density on Memristor Crossbar Architectures in Neuromorphic Pattern Recognition
by Minh Le and Son Ngoc Truong
Micromachines 2023, 14(11), 1990; https://doi.org/10.3390/mi14111990 - 27 Oct 2023
Viewed by 792
Abstract
Binary memristor crossbars have great potential for use in brain-inspired neuromorphic computing. The complementary crossbar array has been proposed to perform the Exclusive-NOR function for neuromorphic pattern recognition. The single crossbar obtained by shortening the Exclusive-NOR function has more advantages in terms of [...] Read more.
Binary memristor crossbars have great potential for use in brain-inspired neuromorphic computing. The complementary crossbar array has been proposed to perform the Exclusive-NOR function for neuromorphic pattern recognition. The single crossbar obtained by shortening the Exclusive-NOR function has more advantages in terms of power consumption, area occupancy, and fault tolerance. In this paper, we present the impact of data density on the single memristor crossbar architecture for neuromorphic image recognition. The impact of data density on the single memristor architecture is mathematically derived from the reduced formula of the Exclusive-NOR function, and then verified via circuit simulation. The complementary and single crossbar architectures are tested by using ten 32 × 32 images with different data densities of 0.25, 0.5, and 0.75. The simulation results showed that the data density of images has a negative effect on the single memristor crossbar architecture while not affecting the complementary memristor crossbar architecture. The maximum output column current produced by the single memristor crossbar array decreases as data density decreases while the complementary memristor crossbar array architecture provides stable maximum output column currents. When recognizing images with data density as low as 0.25, the maximum output column currents of the single memristor crossbar architecture is reduced four-fold compared with the maximum currents from the complementary memristor crossbar architecture. This reduction causes the Winner-take-all circuit to work incorrectly and will reduce the recognition rate of the single memristor crossbar architecture. These simulation results show that the single memristor crossbar architecture has more advantages compared with the complementary crossbar architecture when the images do have not many different densities, and none of the images have very low densities. This work also indicates that the single crossbar architecture must be improved by adding a constant term to deal with images that have low data densities. These are valuable case studies for archiving the advantages of single memristor crossbar architecture in neuromorphic computing applications. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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12 pages, 1440 KiB  
Article
Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning
by Seokjin Oh, Jiyong An, Seungmyeong Cho, Rina Yoon and Kyeong-Sik Min
Micromachines 2023, 14(7), 1367; https://doi.org/10.3390/mi14071367 - 03 Jul 2023
Cited by 1 | Viewed by 1322
Abstract
Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of local learning, numerical iteration [...] Read more.
Equilibrium propagation (EP) has been proposed recently as a new neural network training algorithm based on a local learning concept, where only local information is used to calculate the weight update of the neural network. Despite the advantages of local learning, numerical iteration for solving the EP dynamic equations makes the EP algorithm less practical for realizing edge intelligence hardware. Some analog circuits have been suggested to solve the EP dynamic equations physically, not numerically, using the original EP algorithm. However, there are still a few problems in terms of circuit implementation: for example, the need for storing the free-phase solution and the lack of essential peripheral circuits for calculating and updating synaptic weights. Therefore, in this paper, a new analog circuit technique is proposed to realize the EP algorithm in practical and implementable hardware. This work has two major contributions in achieving this objective. First, the free-phase and nudge-phase solutions are calculated by the proposed analog circuits simultaneously, not at different times. With this process, analog voltage memories or digital memories with converting circuits between digital and analog domains for storing the free-phase solution temporarily can be eliminated in the proposed EP circuit. Second, a simple EP learning rule relying on a fixed amount of conductance change per programming pulse is newly proposed and implemented in peripheral circuits. The modified EP learning rule can make the weight update circuit practical and implementable without requiring the use of a complicated program verification scheme. The proposed memristor conductance update circuit is simulated and verified for training synaptic weights on memristor crossbars. The simulation results showed that the proposed EP circuit could be used for realizing on-device learning in edge intelligence hardware. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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12 pages, 3730 KiB  
Article
SET Kinetics of Ag/HfO2-Based Diffusive Memristors under Various Counter-Electrode Materials
by Solomon Amsalu Chekol, Richard Nacke, Stephan Aussen and Susanne Hoffmann-Eifert
Micromachines 2023, 14(3), 571; https://doi.org/10.3390/mi14030571 - 27 Feb 2023
Cited by 1 | Viewed by 1591
Abstract
The counter-electrode (CE) material in electrochemical metallization memory (ECM) cells plays a crucial role in the switching process by affecting the reactions at the CE/electrolyte interface. This is due to the different electrocatalytic activity of the CE material towards reduction–oxidation reactions, which determines [...] Read more.
The counter-electrode (CE) material in electrochemical metallization memory (ECM) cells plays a crucial role in the switching process by affecting the reactions at the CE/electrolyte interface. This is due to the different electrocatalytic activity of the CE material towards reduction–oxidation reactions, which determines the metal ion concentration in the electrolyte and ultimately impacts the switching kinetics. In this study, the focus is laid on Pt, TiN, and W, which are relevant in standard chip technology. For these, the influence of CE metal on the switching kinetics of Ag/HfO2-based volatile ECM cells is investigated. Rectangular voltage pulses of different amplitudes were applied, and the SET times were analyzed from the transient curves. The results show that CE material has a significant effect on the SET kinetics, with differences being observed depending on the voltage regime. The formation of interfacial oxides at the CE/electrolyte interface, particularly for non-noble metals, is also discussed in relation to the findings. Overall, this work highlights the important role of the CE material in the switching process of Ag/HfO2-based diffusive memristors and the importance of considering interfacial oxide formation in the design of these devices. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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14 pages, 7288 KiB  
Article
Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks
by Fernando Leonel Aguirre, Eszter Piros, Nico Kaiser, Tobias Vogel, Stephan Petzold, Jonas Gehrunger, Timo Oster, Christian Hochberger, Jordi Suñé, Lambert Alff and Enrique Miranda
Micromachines 2022, 13(11), 2002; https://doi.org/10.3390/mi13112002 - 17 Nov 2022
Cited by 5 | Viewed by 1395
Abstract
In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite [...] Read more.
In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-Vs obtained from Y2O3-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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8 pages, 2239 KiB  
Article
Ag-Ion-Based Transparent Threshold Switching Selector with Filament-Size-Dependent Rectifying Behavior
by Jongseon Seo, Geonhui Han, Hyejin Kim and Daeseok Lee
Micromachines 2022, 13(11), 1874; https://doi.org/10.3390/mi13111874 - 31 Oct 2022
Cited by 1 | Viewed by 1293
Abstract
A metal–insulator–metal-structured Ag-filament-based transparent threshold switch is developed as a selector device for a crossbar array, which can lead to high-density integration of advanced memory devices. Both threshold switching and rectifying behavior were achieved based on sensitive control of the filament size. Conduction [...] Read more.
A metal–insulator–metal-structured Ag-filament-based transparent threshold switch is developed as a selector device for a crossbar array, which can lead to high-density integration of advanced memory devices. Both threshold switching and rectifying behavior were achieved based on sensitive control of the filament size. Conduction mechanism analyses demonstrated that the rectifying behavior resulted from the Schottky barrier at the interface. From the threshold switching, including the rectifying behavior, the available crossbar array size is 105-times larger. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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17 pages, 7025 KiB  
Article
Memristor Degradation Analysis Using Auxiliary Volt-Ampere Characteristics
by Georgy Teplov, Dmitry Zhevnenko, Fedor Meshchaninov, Vladislav Kozhevnikov, Pavel Sattarov, Sergey Kuznetsov, Alikhan Magomedrasulov, Oleg Telminov and Evgeny Gornev
Micromachines 2022, 13(10), 1691; https://doi.org/10.3390/mi13101691 - 08 Oct 2022
Cited by 2 | Viewed by 1383
Abstract
The memristor is one of the modern microelectronics key devices. Due to the nanometer scale and complex processes physic, the development of memristor state study approaches faces limitations of classical methods to observe the processes. We propose a new approach to investigate the [...] Read more.
The memristor is one of the modern microelectronics key devices. Due to the nanometer scale and complex processes physic, the development of memristor state study approaches faces limitations of classical methods to observe the processes. We propose a new approach to investigate the degradation of six Ni/Si3N4/p+Si-based memristors up to their failure. The basis of the proposed idea is the joint analysis of resistance change curves with the volt-ampere characteristics registered by the auxiliary signal. The paper considers the existence of stable switching regions of the high-resistance state and their interpretation as stable states in which the device evolves. The stable regions’ volt-ampere characteristics were simulated using a compact mobility modification model and a first-presented target function to solve the optimization problem. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, 2nd Edition)
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