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Micromachines
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New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications
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New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications

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

Deadline for manuscript submissions: closed (27 February 2022) | Viewed by 29749

Special Issue Editors

Dr. Andrey Sokolov

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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
Guest Editor
Department of Materials Science & Engineering, Hanyang University, Seoul 133-791, Republic of Korea
Interests: memristor; neuromorphic computing; in memory computing; RRAM; resistive switching
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
Prof. 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 & Volatile Memory
  • Memristors
  • Synapse & Neuron Devices
  • In Memory Computing
  • Crossbar Resistive Memory
  • Neuromorphic Computing
  • Circuit & CAD Mask Design for Emerging Memory
  • Applications of Resistive Switching Memory
  • Resistive Random Access Memory

Related Special Issue

Published Papers (10 papers)

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Research

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10 pages, 4192 KiB  
Article
Memristive Switching and Density-Functional Theory Calculations in Double Nitride Insulating Layers
by Sobia Ali Khan, Fayyaz Hussain, Daewon Chung, Mehr Khalid Rahmani, Muhammd Ismail, Chandreswar Mahata, Yawar Abbas, Haider Abbas, Changhwan Choi, Alexey N. Mikhaylov, Sergey A. Shchanikov, Byung-Do Yang and Sungjun Kim
Micromachines 2022, 13(9), 1498; https://doi.org/10.3390/mi13091498 - 09 Sep 2022
Viewed by 1725
Abstract
In this paper, we demonstrate a device using a Ni/SiN/BN/p+-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p+-Si and Ni/BN/p+-Si devices. Its switching [...] Read more.
In this paper, we demonstrate a device using a Ni/SiN/BN/p+-Si structure with improved performance in terms of a good ON/OFF ratio, excellent stability, and low power consumption when compared with single-layer Ni/SiN/p+-Si and Ni/BN/p+-Si devices. Its switching mechanism can be explained by trapping and de-trapping via nitride-related vacancies. We also reveal how higher nonlinearity and rectification ratio in a bilayer device is beneficial for enlarging the read margin in a cross-point array structure. In addition, we conduct a theoretical investigation for the interface charge accumulation/depletion in the SiN/BN layers that are responsible for defect creation at the interface and how this accounts for the improved switching characteristics. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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18 pages, 4574 KiB  
Article
SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices
by Fernando Leonel Aguirre, Jordi Suñé and Enrique Miranda
Micromachines 2022, 13(2), 330; https://doi.org/10.3390/mi13020330 - 19 Feb 2022
Cited by 21 | Viewed by 3358
Abstract
This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based [...] Read more.
This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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12 pages, 7203 KiB  
Article
FPGA Implementation of Threshold-Type Binary Memristor and Its Application in Logic Circuit Design
by Liu Yang, Yuqi Wang, Zhiru Wu and Xiaoyuan Wang
Micromachines 2021, 12(11), 1344; https://doi.org/10.3390/mi12111344 - 31 Oct 2021
Cited by 4 | Viewed by 2058
Abstract
In this paper, a memristor model based on FPGA (field programmable gate array) is proposed, by using which the circuit of AND gate and OR gate composed of memristors is built. Combined with the original NOT gate in FPGA, the NAND gate, NOR [...] Read more.
In this paper, a memristor model based on FPGA (field programmable gate array) is proposed, by using which the circuit of AND gate and OR gate composed of memristors is built. Combined with the original NOT gate in FPGA, the NAND gate, NOR gate, XOR gate and the XNOR gate are further realized, and then the adder design is completed. Compared with the traditional gate circuit, this model has distinct advantages in size and non-volatility. At the same time, the establishment of this model will add new research methods and tools for memristor simulation research. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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12 pages, 2865 KiB  
Article
Chitosan-Based Flexible Memristors with Embedded Carbon Nanotubes for Neuromorphic Electronics
by Jin-Gi Min and Won-Ju Cho
Micromachines 2021, 12(10), 1259; https://doi.org/10.3390/mi12101259 - 17 Oct 2021
Cited by 12 | Viewed by 2846
Abstract
In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics. These flexible transparent memristors were applied to a polyethylene naphthalate (PEN) substrate using low-temperature solution processing. The chitosan-based flexible memristors have a bipolar resistive switching [...] Read more.
In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics. These flexible transparent memristors were applied to a polyethylene naphthalate (PEN) substrate using low-temperature solution processing. The chitosan-based flexible memristors have a bipolar resistive switching (BRS) behavior due to the cation-based electrochemical reaction between a polymeric chitosan electrolyte and mobile ions. The effect of SWCNT addition on the BRS characteristics was analyzed. It was observed that the embedded SWCNTs absorb more metal ions and trigger the conductive filament in the chitosan electrolyte, resulting in a more stable and wider BRS window compared to the device with no SWCNTs. The memory window of the chitosan nanocomposite memristors with SWCNTs was 14.98, which was approximately double that of devices without SWCNTs (6.39). Furthermore, the proposed SWCNT-embedded chitosan-based memristors had memristive properties, such as short-term and long-term plasticity via paired-pulse facilitation and spike-timing-dependent plasticity, respectively. In addition, the conductivity modulation was evaluated with 300 synaptic pulses. These findings suggest that memristors featuring SWCNT-embedded chitosan are a promising building block for future artificial synaptic electronics applications. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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16 pages, 3407 KiB  
Article
Research and Development of Parameter Extraction Approaches for Memristor Models
by Dmitry Alexeevich Zhevnenko, Fedor Pavlovich Meshchaninov, Vladislav Sergeevich Kozhevnikov, Evgeniy Sergeevich Shamin, Oleg Alexandrovich Telminov and Evgeniy Sergeevich Gornev
Micromachines 2021, 12(10), 1220; https://doi.org/10.3390/mi12101220 - 06 Oct 2021
Cited by 4 | Viewed by 1935
Abstract
Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often [...] Read more.
Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often determined by the accuracy of their parameter extraction from experiment results. In this paper, a review of existing extraction methods was performed and new parameter extraction algorithms for an adaptive compact model were proposed. The effectiveness of the developed methods was confirmed for the volt-ampere characteristic of a memristor with a vertical structure: TiN/HfxAl1−xOy/HfO2/TiN. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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21 pages, 19155 KiB  
Article
A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials
by Fedor Pavlovich Meshchaninov, Dmitry Alexeevich Zhevnenko, Vladislav Sergeevich Kozhevnikov, Evgeniy Sergeevich Shamin, Oleg Alexandrovich Telminov and Evgeniy Sergeevich Gornev
Micromachines 2021, 12(10), 1201; https://doi.org/10.3390/mi12101201 - 30 Sep 2021
Cited by 1 | Viewed by 1286
Abstract
The use of low-dimensional materials is a promising approach to improve the key characteristics of memristors. The development process includes modeling, but the question of the most common compact model applicability to the modeling of device characteristics with the inclusion of low-dimensional materials [...] Read more.
The use of low-dimensional materials is a promising approach to improve the key characteristics of memristors. The development process includes modeling, but the question of the most common compact model applicability to the modeling of device characteristics with the inclusion of low-dimensional materials remains open. In this paper, a comparative analysis of linear and nonlinear drift as well as threshold models was conducted. For this purpose, the assumption of the relationship between the results of the optimization of the volt–ampere characteristic loop and the descriptive ability of the model was used. A global random search algorithm was used to solve the optimization problem, and an error function with the inclusion of a regularizer was developed to estimate the loop features. Based on the characteristic features derived through meta-analysis, synthetic volt–ampere characteristic contours were built and the results of their approximation by different models were compared. For every model, the quality of the threshold voltage estimation was evaluated, the forms of the memristor potential functions and dynamic attractors associated with experimental contours on graphene oxide were calculated. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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13 pages, 3280 KiB  
Article
Noise and Memristance Variation Tolerance of Single Crossbar Architectures for Neuromorphic Image Recognition
by Minh Le, Thi Kim Hang Pham and Son Ngoc Truong
Micromachines 2021, 12(6), 690; https://doi.org/10.3390/mi12060690 - 13 Jun 2021
Cited by 1 | Viewed by 1841
Abstract
We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neuromorphic image recognition and conducted an experiment to determine the performance [...] Read more.
We performed a comparative study on the Gaussian noise and memristance variation tolerance of three crossbar architectures, namely the complementary crossbar architecture, the twin crossbar architecture, and the single crossbar architecture, for neuromorphic image recognition and conducted an experiment to determine the performance of the single crossbar architecture for simple pattern recognition. Ten grayscale images with the size of 32 × 32 pixels were used for testing and comparing the recognition rates of the three architectures. The recognition rates of the three memristor crossbar architectures were compared to each other when the noise level of images was varied from −10 to 4 dB and the percentage of memristance variation was varied from 0% to 40%. The simulation results showed that the single crossbar architecture had the best Gaussian noise input and memristance variation tolerance in terms of recognition rate. At the signal-to-noise ratio of −10 dB, the single crossbar architecture produced a recognition rate of 91%, which was 2% and 87% higher than those of the twin crossbar architecture and the complementary crossbar architecture, respectively. When the memristance variation percentage reached 40%, the single crossbar architecture had a recognition rate as high as 67.8%, which was 1.8% and 9.8% higher than the recognition rates of the twin crossbar architecture and the complementary crossbar architecture, respectively. Finally, we carried out an experiment to determine the performance of the single crossbar architecture with a fabricated 3 × 3 memristor crossbar based on carbon fiber and aluminum film. The experiment proved successful implementation of pattern recognition with the single crossbar architecture. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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Review

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16 pages, 3068 KiB  
Review
Compute-in-Memory for Numerical Computations
by Dongyan Zhao, Yubo Wang, Jin Shao, Yanning Chen, Zhiwang Guo, Cheng Pan, Guangzhi Dong, Min Zhou, Fengxia Wu, Wenhe Wang, Keji Zhou and Xiaoyong Xue
Micromachines 2022, 13(5), 731; https://doi.org/10.3390/mi13050731 - 02 May 2022
Cited by 1 | Viewed by 2146
Abstract
In recent years, compute-in-memory (CIM) has been extensively studied to improve the energy efficiency of computing by reducing data movement. At present, CIM is frequently used in data-intensive computing. Data-intensive computing applications, such as all kinds of neural networks (NNs) in machine learning [...] Read more.
In recent years, compute-in-memory (CIM) has been extensively studied to improve the energy efficiency of computing by reducing data movement. At present, CIM is frequently used in data-intensive computing. Data-intensive computing applications, such as all kinds of neural networks (NNs) in machine learning (ML), are regarded as ‘soft’ computing tasks. The ‘soft’ computing tasks are computations that can tolerate low computing precision with little accuracy degradation. However, ‘hard’ tasks aimed at numerical computations require high-precision computing and are also accompanied by energy efficiency problems. Numerical computations exist in lots of applications, including partial differential equations (PDEs) and large-scale matrix multiplication. Therefore, it is necessary to study CIM for numerical computations. This article reviews the recent developments of CIM for numerical computations. The different kinds of numerical methods solving partial differential equations and the transformation of matrixes are deduced in detail. This paper also discusses the iterative computation of a large-scale matrix, which tremendously affects the efficiency of numerical computations. The working procedure of the ReRAM-based partial differential equation solver is emphatically introduced. Moreover, other PDEs solvers, and other research about CIM for numerical computations, are also summarized. Finally, prospects and the future of CIM for numerical computations with high accuracy are discussed. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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28 pages, 7790 KiB  
Review
Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications
by Haider Abbas, Jiayi Li and Diing Shenp Ang
Micromachines 2022, 13(5), 725; https://doi.org/10.3390/mi13050725 - 30 Apr 2022
Cited by 24 | Viewed by 7118
Abstract
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing [...] Read more.
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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13 pages, 45012 KiB  
Review
Ion-Driven Electrochemical Random-Access Memory-Based Synaptic Devices for Neuromorphic Computing Systems: A Mini-Review
by Heebum Kang, Jongseon Seo, Hyejin Kim, Hyun Wook Kim, Eun Ryeong Hong, Nayeon Kim, Daeseok Lee and Jiyong Woo
Micromachines 2022, 13(3), 453; https://doi.org/10.3390/mi13030453 - 17 Mar 2022
Cited by 9 | Viewed by 3571
Abstract
To enhance the computing efficiency in a neuromorphic architecture, it is important to develop suitable memory devices that can emulate the role of biological synapses. More specifically, not only are multiple conductance states needed to be achieved in the memory but each state [...] Read more.
To enhance the computing efficiency in a neuromorphic architecture, it is important to develop suitable memory devices that can emulate the role of biological synapses. More specifically, not only are multiple conductance states needed to be achieved in the memory but each state is also analogously adjusted by consecutive identical pulses. Recently, electrochemical random-access memory (ECRAM) has been dedicatedly designed to realize the desired synaptic characteristics. Electric-field-driven ion motion through various electrolytes enables the conductance of the ECRAM to be analogously modulated, resulting in a linear and symmetric response. Therefore, the aim of this study is to review recent advances in ECRAM technology from the material and device engineering perspectives. Since controllable mobile ions play an important role in achieving synaptic behavior, the prospect and challenges of ECRAM devices classified according to mobile ion species are discussed. Full article
(This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications)
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