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Nanomaterials
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Neuromorphic Devices: Materials, Structures and Bionic Applications
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Neuromorphic Devices: Materials, Structures and Bionic Applications

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 4974

Special Issue Editors

Prof. Dr. Qing Wan

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
Interests: oxide semiconductor; neuromorphic devices; neuromorphic computing; dendrite integration
Prof. Dr. Liqiang Zhu

E-Mail Website
Guest Editor
School of Physical Science and Technology, Ningbo University, Ningbo 315211, China
Interests: neuromorphic transistor; memristor; synaptic plasticities; perceptual platform; learning activities

Special Issue Information

Dear Colleagues,

With the developments of machine learning, Artificial Intelligence (AI), and Internet of Things (IoTs) technology, it is necessary to process massive amounts of data in an energy-efficient way. Brain-inspired neuromorphic devices have attracted increased attention for artificial intelligent applications. Designing neuromorphic devices that could mimic essential synapse-like functions is of great importance for brain-inspired computation. This is becoming an important branch of artificial intelligence and neuromorphic engineering that will inject new vitality into the development of artificial intelligence in the future. With the development of new materials technology and new conceptual devices, several kinds of neuromorphic devices have been proposed, including two terminal resistance switch devices and three terminal transistors. Moreover, memtransistors have been reported with interesting neuromorphic functions. Especially with the adoption of nanomaterials and nanostructures, including nanodots, nanowires, 2D materials, and hybrid nano-configuration, advanced neural cognitive behaviors have been mimicked. In addition, a multi-terminal structure also endows new neuromorphic system opportunities. All these achievements indicate the great potential of neuromorphic devices in neuromorphic engineering.

Moreover, inspired by the powerful perception functions of human multi-sensory learning activities, developing an artificial perception system is of great significance for artificial intelligence and humanoid robots. So far, neuromorphic devices have been proposed for applications in constructing artificial perception systems with complex sensing functions as this will provide intelligent robots with new vitality.

We are pleased to invite you to contribute original and review articles regarding neuromorphic devices and their applications in an intelligent perception system. Potential topics include, but are not limited to: two terminal memristors for neuromorphic computing applications, three terminal neuromorphic transistors, nano-structure with specific neuromorphic functions, the integration of advanced nanomaterials for advanced neuromorphic computation, neuromorphic device arrays for advanced neural functions, an artificial intelligent perception platform with functional nanomaterials, etc.

We look forward to receiving your contributions.

Prof. Dr. Qing Wan
Prof. Dr. Liqiang Zhu
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. Nanomaterials is an international peer-reviewed open access semimonthly 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 2900 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

  • nanomaterials and nano-structures
  • neuromorphic computing
  • artificial synapse
  • memristor
  • neuromorphic transistor
  • synaptic function
  • perception systems
  • dendrite integration
  • learning activities

Published Papers (4 papers)

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Research

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12 pages, 3885 KiB  
Article
The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfOx/W Bilayer-Structured Memory Device
by Minseo Noh, Dongyeol Ju, Seongjae Cho and Sungjun Kim
Nanomaterials 2023, 13(21), 2856; https://0-doi-org.brum.beds.ac.uk/10.3390/nano13212856 - 28 Oct 2023
Viewed by 854
Abstract
This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments [...] Read more.
This study discusses the potential application of ITO/ZnO/HfOx/W bilayer-structured memory devices in neuromorphic systems. These devices exhibit uniform resistive switching characteristics and demonstrate favorable endurance (>102) and stable retention (>104 s). Notably, the formation and rupture of filaments at the interface of ZnO and HfOx contribute to a higher ON/OFF ratio and improve cycle uniformity compared to RRAM devices without the HfOx layer. Additionally, the linearity of potentiation and depression responses validates their applicability in neural network pattern recognition, and spike-timing-dependent plasticity (STDP) behavior is observed. These findings collectively suggest that the ITO/ZnO/HfOx/W structure holds the potential to be a viable memory component for integration into neuromorphic systems. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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Review

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21 pages, 7728 KiB  
Review
Oxide Ionic Neuro-Transistors for Bio-inspired Computing
by Yongli He, Yixin Zhu and Qing Wan
Nanomaterials 2024, 14(7), 584; https://0-doi-org.brum.beds.ac.uk/10.3390/nano14070584 - 27 Mar 2024
Viewed by 721
Abstract
Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is [...] Read more.
Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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33 pages, 9959 KiB  
Review
Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations
by Kannan Udaya Mohanan
Nanomaterials 2024, 14(6), 527; https://0-doi-org.brum.beds.ac.uk/10.3390/nano14060527 - 15 Mar 2024
Viewed by 1105
Abstract
Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate [...] Read more.
Neuromorphic computing has emerged as an alternative computing paradigm to address the increasing computing needs for data-intensive applications. In this context, resistive random access memory (RRAM) devices have garnered immense interest among the neuromorphic research community due to their capability to emulate intricate neuronal behaviors. RRAM devices excel in terms of their compact size, fast switching capabilities, high ON/OFF ratio, and low energy consumption, among other advantages. This review focuses on the multifaceted aspects of RRAM devices and their application to brain-inspired computing. The review begins with a brief overview of the essential biological concepts that inspire the development of bio-mimetic computing architectures. It then discusses the various types of resistive switching behaviors observed in RRAM devices and the detailed physical mechanisms underlying their operation. Next, a comprehensive discussion on the diverse material choices adapted in recent literature has been carried out, with special emphasis on the benchmark results from recent research literature. Further, the review provides a holistic analysis of the emerging trends in neuromorphic applications, highlighting the state-of-the-art results utilizing RRAM devices. Commercial chip-level applications are given special emphasis in identifying some of the salient research results. Finally, the current challenges and future outlook of RRAM-based devices for neuromorphic research have been summarized. Thus, this review provides valuable understanding along with critical insights and up-to-date information on the latest findings from the field of resistive switching devices towards brain-inspired computing. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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23 pages, 7412 KiB  
Review
Emerging Opportunities for 2D Materials in Neuromorphic Computing
by Chenyin Feng, Wenwei Wu, Huidi Liu, Junke Wang, Houzhao Wan, Guokun Ma and Hao Wang
Nanomaterials 2023, 13(19), 2720; https://0-doi-org.brum.beds.ac.uk/10.3390/nano13192720 - 07 Oct 2023
Cited by 2 | Viewed by 1899
Abstract
Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold [...] Read more.
Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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