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Redox-Based Resistive Nanomemristor for Neuromorphic Computing
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Redox-Based Resistive Nanomemristor for Neuromorphic Computing

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanoelectronics, Nanosensors and Devices".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 21416

Special Issue Editor

Dr. Wan Sik Hwang

E-Mail Website
Guest Editor
Department of Materials Engineering, Korea Aerospace University, Goyang, Republic of Korea
Interests: MOSFET; semiconducotr; Ga2O3; memristor; photocatalyst
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of the artificial intelligence (AI) era, there has been a surge of interest in neuromorphic devices imitating biological neural systems that are presumed to be the most efficient information processors for conducting cognitive tasks such as image/pattern recognition and future prediction. Unlike conventional electronics based on a von Neumann architecture, the distinct advantage of the emerging neuromorphic device is the in-memory computing paradigm, where information is processed directly within the system without an additional energy transfer.

Over the decades, various types of neuromorphic nanodevice have been demonstrated with phase change memory (PCM), magneto-resistive memory (MRAM), resistive random-access memory (RRAM), ferroelectric random-access memory (FeRAM), and resistive random-access memory (ReRAM). Among of them, redox-based resistive memory is the one of the most promising candidates in terms of scalability, power consumption, switching speed, and endurance/retention characteristics.

To expedite the development of neuromorphic nanodevices, further research is necessary from materials to systems architecture.

In this Special Issue on redox-based resistive memory in nanoscale for neuromorphic computing, we expect contributions from a broad community of scientists and engineers working on redox-based resistive memory including materials and device fabrication. We also anticipate manuscripts dealing with new understanding and characterization methods regarding conduction mechanism.

Dr. Wan Sik Hwang
Guest Editor

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Keywords

  • resistive-switching nanodevices
  • nanoscale resistive switching
  • nanoscale memristor device redox active metal oxide nanomaterials
  • nanoscale conductive filaments
  • nanoscale devices for neuromorphic computing

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Published Papers (7 papers)

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Research

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9 pages, 8189 KiB  
Article
Memristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices
by Minsu Park, Beomki Jeon, Jongmin Park and Sungjun Kim
Nanomaterials 2022, 12(23), 4206; https://doi.org/10.3390/nano12234206 - 26 Nov 2022
Cited by 14 | Viewed by 2283
Abstract
As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit [...] Read more.
As artificial intelligence technology advances, it is necessary to imitate various biological functions to complete more complex tasks. Among them, studies have been reported on the nociceptor, a critical receptor of sensory neurons that can detect harmful stimuli. Although a complex CMOS circuit is required to electrically realize a nociceptor, a memristor with threshold switching characteristics can implement the nociceptor as a single device. Here, we suggest a memristor with a Pt/HfO2/TaOx/TaN bilayer structure. This device can mimic the characteristics of a nociceptor including the threshold, relaxation, allodynia, and hyperalgesia. Additionally, we contrast different electrical properties according to the thickness of the HfO2 layer. Moreover, Pt/HfO2/TaOx/TaN with a 3 nm thick HfO2 layer has a stable endurance of 1000 cycles and controllable threshold switching characteristics. Finally, this study emphasizes the importance of the material selection and fabrication method in the memristor by comparing Pt/HfO2/TaOx/TaN with Pt/TaOx/TaN, which has insufficient performance to be used as a nociceptor. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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13 pages, 2645 KiB  
Article
Effect of Post-Annealing on Barrier Modulations in Pd/IGZO/SiO2/p+-Si Memristors
by Donguk Kim, Hee Jun Lee, Tae Jun Yang, Woo Sik Choi, Changwook Kim, Sung-Jin Choi, Jong-Ho Bae, Dong Myong Kim, Sungjun Kim and Dae Hwan Kim
Nanomaterials 2022, 12(20), 3582; https://doi.org/10.3390/nano12203582 - 13 Oct 2022
Cited by 3 | Viewed by 2475
Abstract
In this article, we study the post-annealing effect on the synaptic characteristics in Pd/IGZO/SiO2/p+-Si memristor devices. The O-H bond in IGZO films affects the switching characteristics that can be controlled by the annealing process. We propose a switching model [...] Read more.
In this article, we study the post-annealing effect on the synaptic characteristics in Pd/IGZO/SiO2/p+-Si memristor devices. The O-H bond in IGZO films affects the switching characteristics that can be controlled by the annealing process. We propose a switching model based on using a native oxide as the Schottky barrier. The barrier height is extracted by the conduction mechanism of thermionic emission in samples with different annealing temperatures. Additionally, the change in conductance is explained by an energy band diagram including trap models. The activation energy is obtained by the depression curve of the samples with different annealing temperatures to better understand the switching mechanism. Moreover, our results reveal that the annealing temperature and retention can affect the linearity of potentiation and depression. Finally, we investigate the effect of the annealing temperature on the recognition rate of MNIST in the proposed neural network. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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9 pages, 15001 KiB  
Article
Enhancement of Resistive and Synaptic Characteristics in Tantalum Oxide-Based RRAM by Nitrogen Doping
by Doohyung Kim, Jihyung Kim and Sungjun Kim
Nanomaterials 2022, 12(19), 3334; https://doi.org/10.3390/nano12193334 - 24 Sep 2022
Cited by 5 | Viewed by 2217
Abstract
Resistive random–access memory (RRAM) for neuromorphic systems has received significant attention because of its advantages, such as low power consumption, high–density structure, and high–speed switching. However, variability occurs because of the stochastic nature of conductive filaments (CFs), producing inaccurate results in neuromorphic systems. [...] Read more.
Resistive random–access memory (RRAM) for neuromorphic systems has received significant attention because of its advantages, such as low power consumption, high–density structure, and high–speed switching. However, variability occurs because of the stochastic nature of conductive filaments (CFs), producing inaccurate results in neuromorphic systems. In this article, we fabricated nitrogen–doped tantalum oxide (TaOx:N)–based resistive switching (RS) memory. The TaOx:N–based device significantly enhanced the RS characteristics compared with a TaOx–based device in terms of resistance variability. It achieved lower device–to–device variability in both low-resistance state (LRS) and high–resistance state (HRS), 8.7% and 48.3% rather than undoped device of 35% and 60.7%. Furthermore, the N–doped device showed a centralized set distribution with a 9.4% variability, while the undoped device exhibited a wider distribution with a 17.2% variability. Concerning pulse endurance, nitrogen doping prevented durability from being degraded. Finally, for synaptic properties, the potentiation and depression of the TaOx:N–based device exhibited a more stable cycle–to–cycle variability of 4.9%, compared with only 13.7% for the TaOx–based device. The proposed nitrogen–doped device is more suitable for neuromorphic systems because, unlike the undoped device, uniformity of conductance can be obtained. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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12 pages, 4573 KiB  
Article
Multi-Level Resistive Switching in SnSe/SrTiO3 Heterostructure Based Memristor Device
by Tsz-Lung Ho, Keda Ding, Nikolay Lyapunov, Chun-Hung Suen, Lok-Wing Wong, Jiong Zhao, Ming Yang, Xiaoyuan Zhou and Ji-Yan Dai
Nanomaterials 2022, 12(13), 2128; https://doi.org/10.3390/nano12132128 - 21 Jun 2022
Cited by 11 | Viewed by 2857
Abstract
Multilevel resistive switching in memristive devices is vital for applications in non-volatile memory and neuromorphic computing. In this study, we report on the multilevel resistive switching characteristics in SnSe/SrTiO3(STO) heterojunction-based memory devices with silver (Ag) and copper (Cu) top electrodes. The [...] Read more.
Multilevel resistive switching in memristive devices is vital for applications in non-volatile memory and neuromorphic computing. In this study, we report on the multilevel resistive switching characteristics in SnSe/SrTiO3(STO) heterojunction-based memory devices with silver (Ag) and copper (Cu) top electrodes. The SnSe/STO-based memory devices present bipolar resistive switching (RS) with two orders of magnitude on/off ratio, which is reliable and stable. Moreover, multilevel state switching is achieved in the devices by sweeping voltage with current compliance to SET the device from high resistance state (HRS) to low resistance state (LRS) and RESET from LRS to HRS by voltage pulses without compliance current. With Ag and Cu top electrodes, respectively, eight and six levels of resistance switching were demonstrated in the SnSe/SrTiO3 heterostructures with a Pt bottom electrode. These results suggest that a SnSe/STO heterojunction-based memristor is promising for applications in neuromorphic computing as a synaptic device. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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17 pages, 45392 KiB  
Article
Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures
by Roman V. Tominov, Zakhar E. Vakulov, Nikita V. Polupanov, Aleksandr V. Saenko, Vadim I. Avilov, Oleg A. Ageev and Vladimir A. Smirnov
Nanomaterials 2022, 12(3), 455; https://doi.org/10.3390/nano12030455 - 28 Jan 2022
Cited by 12 | Viewed by 3610
Abstract
This article presents the results of experimental studies of the impact of electrode material and the effect of nanoscale film thickness on the resistive switching in forming-free nanocrystalline ZnO films grown by pulsed laser deposition. It was demonstrated that the nanocrystalline ZnO film [...] Read more.
This article presents the results of experimental studies of the impact of electrode material and the effect of nanoscale film thickness on the resistive switching in forming-free nanocrystalline ZnO films grown by pulsed laser deposition. It was demonstrated that the nanocrystalline ZnO film with TiN, Pt, ZnO:In, and ZnO:Pd bottom electrodes exhibits a nonlinear bipolar effect of forming-free resistive switching. The sample with Pt showed the highest resistance values RHRS and RLRS and the highest value of Uset = 2.7 ± 0.4 V. The samples with the ZnO:In and ZnO:Pd bottom electrode showed the lowest Uset and Ures values. An increase in the number of laser pulses from 1000 to 5000 was shown to lead to an increase in the thickness of the nanocrystalline ZnO film from 7.2 ± 2.5 nm to 53.6 ± 18.3 nm. The dependence of electrophysical parameters (electron concentration, electron mobility, and resistivity) on the thickness of the forming-free nanocrystalline ZnO film for the TiN/ZnO/W structure was investigated. The endurance test and homogeneity test for TiN/ZnO/W structures were performed. The structure Al2O3/TiN/ZnO/W with a nanocrystalline ZnO thickness 41.2 ± 9.7 nm was shown to be preferable for the manufacture of ReRAM and memristive neuromorphic systems due to the highest value of RHRS/RLRS = 2307.8 ± 166.4 and low values of Uset = 1.9 ± 0.2 V and Ures = −1.3 ± 0.5 V. It was demonstrated that the use of the TiN top electrode in the Al2O3/TiN/ZnO memristor structure allowed for the reduction in Uset and Ures and the increase in the RHRS/RLRS ratio. The results obtained can be used in the manufacturing of resistive-switching nanoscale devices for neuromorphic computing based on the forming-free nanocrystalline ZnO oxide films. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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Review

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18 pages, 3929 KiB  
Review
Ferroelectric Devices for Content-Addressable Memory
by Mikhail Tarkov, Fedor Tikhonenko, Vladimir Popov, Valentin Antonov, Andrey Miakonkikh and Konstantin Rudenko
Nanomaterials 2022, 12(24), 4488; https://doi.org/10.3390/nano12244488 - 19 Dec 2022
Cited by 5 | Viewed by 3704
Abstract
In-memory computing is an attractive solution for reducing power consumption and memory access latency cost by performing certain computations directly in memory without reading operands and sending them to arithmetic logic units. Content-addressable memory (CAM) is an ideal way to smooth out the [...] Read more.
In-memory computing is an attractive solution for reducing power consumption and memory access latency cost by performing certain computations directly in memory without reading operands and sending them to arithmetic logic units. Content-addressable memory (CAM) is an ideal way to smooth out the distinction between storage and processing, since each memory cell is a processing unit. CAM compares the search input with a table of stored data and returns the matched data address. The issues of constructing binary and ternary content-addressable memory (CAM and TCAM) based on ferroelectric devices are considered. A review of ferroelectric materials and devices is carried out, including on ferroelectric transistors (FeFET), ferroelectric tunnel diodes (FTJ), and ferroelectric memristors. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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Graphical abstract

29 pages, 4814 KiB  
Review
Ion-Movement-Based Synaptic Device for Brain-Inspired Computing
by Chansoo Yoon, Gwangtaek Oh and Bae Ho Park
Nanomaterials 2022, 12(10), 1728; https://doi.org/10.3390/nano12101728 - 18 May 2022
Cited by 5 | Viewed by 3483
Abstract
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von [...] Read more.
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices. Full article
(This article belongs to the Special Issue Redox-Based Resistive Nanomemristor for Neuromorphic Computing)
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