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Micromachines
Special Issues
Memristors – from Next Generation Devices to Unconventional and Bio-Inspired...
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Memristors – from Next Generation Devices to Unconventional and Bio-Inspired Circuits and Systems

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 3048

Special Issue Editors

Dr. Stavros G. Stavrinides

E-Mail Website
Guest Editor
School of Science & Technology, International Hellenic University, Thessaloniki, Nea a 570 01 Moudani, Greece
Interests: nonlinear circuits and systems; chaotic electronics; memristors; chaotic synchronization; mixed-signal circuit design; complexity theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rodrigo Picos

E-Mail Website
Guest Editor
1. Industrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma, Majorca, Spain
2. Balearic Islands Health Institute (Idisba), 07021 Palma, Majorca, Spain
Interests: device modelling; memristors; nonlinear electron device
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ronald Tetzlaff

E-Mail Website
Guest Editor
Institute of Circuits and Systems, Technische Universität Dresden, 01062 Dresden, Germany
Interests: circuit theory; memristors; chaotic circuits; cellular neural networks (CNNs); deep learning; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Leon O. Chua

E-Mail Website
Guest Editor
EECS Department, University of California, Berkeley, CA, USA
Interests: circuit theory to nonlinear circuit theory; memristors; chaotic circuits; nonlinear dynamics; cellular neural networks (CNNs); complexity theory

Special Issue Information

Dear Colleagues,

Following Leon Chua’s seminal paper proposing the memristor as the missing fundamental circuit element, Stanley Williams and his HP team first connected the observations made for their fabricated nanodevice to the theory of memristors. Since then, the field of memristors and memristor-related research has grown enormously. Memristors and memristive circuits have been applied in various current research fields, including nonlinear circuits in general, neuroscience, chemical modelling, security, and mainly next-generation memory devices allowing memory computation, to mention a few. The emergence of inherent complexity in memristors and memristor networks will play an important role in the future. The nonlinear dynamic behavior of memristors makes it possible to consider new and unconventional ways to overcome current technological stalemates, such as the end of Moore’s law for CMOS. Such paradigms are synapse emulation and bio-inspired circuit design or in-memory computation.

This Special Issue aims to compile the latest findings and the most promising high-level research results on all of the above-mentioned topics. It covers a wide range of current interdisciplinary topics, ranging from the fundamental theory of memristors to the design of circuits and systems, and also includes new solutions for memristor fabrication, neuromorphic systems, or the design and implementation of logic gates. The topics of interest include, but are not limited to:

Memristor theory, modeling and simulation tools;

  • Functional materials and novel memristive devices (ReRAM, PCM, STT-RAM, etc.);
  • Analog and digital memristor-based circuits, systems, architectures, and applications;
  • Novel or unconventional architectures including memristor–CMOS integration;
  • In-memory computing;
  • Neuromorphic and bio-inspired circuits and systems;
  • Artificial intelligence and neural networks;
  • Memristive sensor and sensory/interfacing platforms;
  • Internet of Things and security applications;
  • Nonlinear dynamics, chaos, and complex networks.

Dr. Stavros G. Stavrinides
Prof. Dr. Rodrigo Picos
Prof. Ronald Tetzlaff
Prof. Leon O. Chua
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

  • Memristors
  • Memcapacitors
  • Meminductors
  • Memristive circuits
  • Memristor device fabrication
  • ReRAM
  • PCM
  • STT
  • Neuromorphic
  • Bio-inspired
  • In-memory computing
  • IoT
  • ANN
  • Nonlinear networks
  • Memristive sensors
  • Nonlinear dynamics
  • Chaos
  • Analog design
  • Digital design
  • Mixed-signal circuits

Published Papers (1 paper)

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Research

18 pages, 3427 KiB  
Article
Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks
by Oscar Camps, Mohamad Moner Al Chawa, Stavros G. Stavrinides and Rodrigo Picos
Micromachines 2022, 13(1), 67; https://doi.org/10.3390/mi13010067 - 31 Dec 2021
Cited by 1 | Viewed by 1571
Abstract
Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and [...] Read more.
Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s capacity for real time operation. Full article
(This article belongs to the Special Issue Memristors – from Next Generation Devices to Unconventional and Bio-Inspired Circuits and Systems)
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