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Electronics
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Memristor Devices and Semiconductor: Models, Developments and Applications
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Memristor Devices and Semiconductor: Models, Developments and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Semiconductor Devices".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 4399

Special Issue Editor

Dr. Young Pyo Jeon

E-Mail Website
Guest Editor
Advanced Institute of Convergence Technology, Seoul National University, Gyeonggi-do, Suwon 16229, Republic of Korea
Interests: resistive switching device based on lithium ion; oxygen vacancy of amorphous molybdenum trioxide; memristor behavior on artificial synaptic devices

Special Issue Information

Dear Colleagues,

Since the start of the fourth industrial revolution, neural process-based artificial intelligence and internet of things have rapidly increased the demand for data processing. Modern computing systems have encountered a “bottleneck” and high-power consumption due to a number of the sequential data processing. In this context, a novel design of computing architectures (or system) based on memristors have shown excellent potential in replacing conventional computing systems and the emergence of new hardware such as memristors is a necessary and interest and efforts in related research are needed.

However, there are several challenges to using memristors in computing systems. In this Special Issue, we focus on memristors. In addition, recent demonstrations of novel models of the computing and integration using memristors are surveyed.

Considering this situation, the aim of this Special Issue is to gather the most recent research and structural developments of memristors for models, developments, and applications, including the following topics:

  • Electrical characteristics for memristors;
  • Nanomaterials and nanocomposites for memristors;
  • Two-dimensional structures for memristors;
  • Crossbar array of memristors;
  • Neuromorphic-behaving memristors;
  • Integration and neuromorphic computing system based on memristors;
  • Low-power consumption of memristors.

Dr. Young Pyo Jeon
Guest Editor

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. Electronics 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 2400 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.

Published Papers (2 papers)

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Research

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15 pages, 3008 KiB  
Article
A Physics-Informed Recurrent Neural Network for RRAM Modeling
by Yanliang Sha, Jun Lan, Yida Li and Quan Chen
Electronics 2023, 12(13), 2906; https://doi.org/10.3390/electronics12132906 - 2 Jul 2023
Cited by 1 | Viewed by 1490
Abstract
Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to [...] Read more.
Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to enhance the modeling capabilities. The integration strategy of Verilog-A equivalent circuits, is also developed for compatibility with existing general-purpose circuit simulators. The Verilog-A model can be easily adopted into the SPICE-type simulator for the circuit design with a variable step that differs from the training process. Numerical experiments with real RRAM devices data demonstrate the feasibility and advantages of the proposed methodology. Full article
(This article belongs to the Special Issue Memristor Devices and Semiconductor: Models, Developments and Applications)
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Review

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21 pages, 4210 KiB  
Review
Research Progress of Neural Synapses Based on Memristors
by Yamin Li, Kang Su, Haoran Chen, Xiaofeng Zou, Changhong Wang, Hongtao Man, Kai Liu, Xin Xi and Tuo Li
Electronics 2023, 12(15), 3298; https://doi.org/10.3390/electronics12153298 - 31 Jul 2023
Cited by 7 | Viewed by 2589
Abstract
The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and [...] Read more.
The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and analog memristors. Synapses based on digital memristors have been utilized to construct positive, zero, and negative weights for artificial neural networks, while synapses based on analog memristors have demonstrated their ability to simulate the essential functions of neural synapses, such as short-term memory (STM), long-term memory (LTM), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation (PPF). Furthermore, synapses based on analog memristors have shown potential for performing advanced functions such as experiential learning, associative learning, and nonassociative learning. Finally, we highlight some challenges of building large-scale artificial neural networks using memristors. Full article
(This article belongs to the Special Issue Memristor Devices and Semiconductor: Models, Developments and Applications)
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