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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: 10 June 2025 | Viewed by 17298

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

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Published Papers (8 papers)

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Research

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16 pages, 12347 KiB  
Article
Nanoscale Titanium Oxide Memristive Structures for Neuromorphic Applications: Atomic Force Anodization Techniques, Modeling, Chemical Composition, and Resistive Switching Properties
by Vadim I. Avilov, Roman V. Tominov, Zakhar E. Vakulov, Daniel J. Rodriguez, Nikita V. Polupanov and Vladimir A. Smirnov
Nanomaterials 2025, 15(1), 75; https://doi.org/10.3390/nano15010075 - 6 Jan 2025
Viewed by 502
Abstract
This paper presents the results of a study on the formation of nanostructures of electrochemical titanium oxide for neuromorphic applications. Three anodization synthesis techniques were considered to allow the formation of structures with different sizes and productivity: nanodot, lateral, and imprint. The mathematical [...] Read more.
This paper presents the results of a study on the formation of nanostructures of electrochemical titanium oxide for neuromorphic applications. Three anodization synthesis techniques were considered to allow the formation of structures with different sizes and productivity: nanodot, lateral, and imprint. The mathematical model allowed us to calculate the processes of oxygen ion transfer to the reaction zone; the growth of the nanostructure due to the oxidation of the titanium film; and the formation of TiO, Ti2O3, and TiO2 oxides in the volume of the growing nanostructure and the redistribution of oxygen vacancies and conduction channel. Modeling of the nanodot structure synthesis process showed that at the initial stages of growth, a conductivity channel was formed, connecting the top and bottom of the nanostructure, which became thinner over time; at approximately 640 ms, this channel broke into upper and lower nuclei, after which the upper part disappeared. Modeling of the lateral nanostructure synthesis process showed that at the initial stages of growth, a conductivity channel was also formed, which quickly disappeared and left a nucleus that moved after the moving AFM tip. The simulation of the imprint nanostructure synthesis process showed the formation of two conductivity channels at a distance corresponding to the dimensions of the template tip. After about 460 ms, both channels broke, leaving behind embryos. The nanodot, lateral, and imprint nanostructure XPS spectra confirmed the theoretical calculations presented earlier: in the near-surface layers, the TiO2 oxide was observed, with the subsequent titanium oxide nanostructure surface etching proportion of TiO2 decreasing, and proportions of Ti2O3 and TiO oxides increasing. All nanodot, lateral, and imprint nanostructures showed reproducible resistive switching over 1000 switching cycles and holding their state for 10,000 s at read operation. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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11 pages, 4012 KiB  
Article
Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing
by Li Zhu, Junchen Lin, Yixin Zhu, Jie Wu, Xiang Wan, Huabin Sun, Zhihao Yu, Yong Xu and Cheeleong Tan
Nanomaterials 2024, 14(14), 1195; https://doi.org/10.3390/nano14141195 - 12 Jul 2024
Viewed by 1293
Abstract
Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as [...] Read more.
Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device’s electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at −0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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17 pages, 7510 KiB  
Article
Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions
by Dmitrii S. Pashin, Marina V. Bastrakova, Dmitrii A. Rybin, Igor. I. Soloviev, Nikolay V. Klenov and Andrey E. Schegolev
Nanomaterials 2024, 14(10), 854; https://doi.org/10.3390/nano14100854 - 14 May 2024
Cited by 1 | Viewed by 1498
Abstract
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between [...] Read more.
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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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://doi.org/10.3390/nano13212856 - 28 Oct 2023
Cited by 3 | Viewed by 1407
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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34 pages, 7122 KiB  
Review
Optical Bio-Inspired Synaptic Devices
by Pengcheng Li, Kesheng Wang, Shanshan Jiang, Gang He, Hainan Zhang, Shuo Cheng, Qingxuan Li, Yixin Zhu, Can Fu, Huanhuan Wei, Bo He and Yujiao Li
Nanomaterials 2024, 14(19), 1573; https://doi.org/10.3390/nano14191573 - 29 Sep 2024
Viewed by 1097
Abstract
The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous “von Neumann storage wall” problem. Inspired by neural [...] Read more.
The traditional computer with von Neumann architecture has the characteristics of separate storage and computing units, which leads to sizeable time and energy consumption in the process of data transmission, which is also the famous “von Neumann storage wall” problem. Inspired by neural synapses, neuromorphic computing has emerged as a promising solution to address the von Neumann problem due to its excellent adaptive learning and parallel capabilities. Notably, in 2016, researchers integrated light into neuromorphic computing, which inspired the extensive exploration of optoelectronic and all-optical synaptic devices. These optical synaptic devices offer obvious advantages over traditional all-electric synaptic devices, including a wider bandwidth and lower latency. This review provides an overview of the research background on optoelectronic and all-optical devices, discusses their implementation principles in different scenarios, presents their application scenarios, and concludes with prospects for future developments. Full article
(This article belongs to the Special Issue Neuromorphic Devices: Materials, Structures and Bionic Applications)
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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://doi.org/10.3390/nano14070584 - 27 Mar 2024
Viewed by 1811
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://doi.org/10.3390/nano14060527 - 15 Mar 2024
Cited by 2 | Viewed by 4905
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://doi.org/10.3390/nano13192720 - 7 Oct 2023
Cited by 4 | Viewed by 3827
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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