Special Issue "Nanostructure-Based Memory Devices"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Electronic Materials".

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editor

Dr. Panagiotis Dimitrakis
E-Mail Website
Guest Editor
Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research “Demokritos”, Athens, Greece
Interests: nonvolatile memories; charge-trapping memories; resistive memories; nanowire-based devices; graphene and 2D devices; memristors; quantum devices

Special Issue Information

Dear Colleagues,

Electronic memory devices have been leading the microelectronics technology for years, having a critical and crucial role in the current computing machines. Today, electronic memory is found in any device we use in our everyday life, starting from coffee and white machines and ending to cars and spacecrafts. Thus, memories affect every facet of our social life, our health, our security, our transportation, our environment, etc.

Since we are living in the period of nanotechnology and nanoelectronics blossom, the size of the electronic devices has shrunk, allowing nanostructured memories to play a significant role. Furthermore, the new nanofabrication techniques and tools allow the utilization of nanoparticles, single molecules, and DNA as materials suitable for information storage. Nanotechnology has revealed new features of materials and devices: the physical and electronic properties of nanoparticles are different from those of bulk materials, and the operation of nanowire transistors becomes more complicated than that of the planar elements. The new nanomaterials and nanodevices developed recently, such as memristors, inspire scientists to think of new computing paradigms that are not compatible with the standard von Neumann computer architecture. The most well-known paradigm is the in-memory computing or logic-in-memory computing. The field of nanostructured-based memories is advancing rapidly, laying the foundations for an age closer to the “internet of things”, artificial intelligence and neuromorphic computing.

High-quality papers on all the above-mentioned types of memory, topics, and open issues should be written and collected in a special volume. This is the aim of this Special Issue in Materials. It is my pleasure to invite you to submit a manuscript for this Special Issue. Communications, full, and review papers are all welcome.

Dr. Panagiotis Dimitrakis
Guest Editor

Manuscript Submission Information

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Keywords

  • nanowire-based memories
  • nanoparticle-based memories
  • graphene and 2D materials memories
  • nanocrossbar memories
  • nanoscale resistive switching memories
  • nanoscale MRAM and FRAM
  • molecular memories
  • hybrid silicon/polymer memories
  • vertical and 3D memories

Published Papers (3 papers)

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Research

Open AccessArticle
Quantum Memristors in Frequency-Entangled Optical Fields
Materials 2020, 13(4), 864; https://doi.org/10.3390/ma13040864 - 14 Feb 2020
Abstract
A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and [...] Read more.
A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up. Full article
(This article belongs to the Special Issue Nanostructure-Based Memory Devices)
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Open AccessFeature PaperArticle
Impact of Line Edge Roughness on ReRAM Uniformity and Scaling
Materials 2019, 12(23), 3972; https://doi.org/10.3390/ma12233972 - 30 Nov 2019
Abstract
We investigate the effects of Line Edge Roughness (LER) of electrode lines on the uniformity of Resistive Random Access Memory (ReRAM) device areas in cross-point architectures. To this end, a modeling approach is implemented based on the generation of 2D cross-point patterns with [...] Read more.
We investigate the effects of Line Edge Roughness (LER) of electrode lines on the uniformity of Resistive Random Access Memory (ReRAM) device areas in cross-point architectures. To this end, a modeling approach is implemented based on the generation of 2D cross-point patterns with predefined and controlled LER and pattern parameters. The aim is to evaluate the significance of LER in the variability of device areas and their performances and to pinpoint the most critical parameters and conditions. It is found that conventional LER parameters may induce >10% area variability depending on pattern dimensions and cross edge/line correlations. Increased edge correlations in lines such as those that appeared in Double Patterning and Directed Self-assembly Lithography techniques lead to reduced area variability. Finally, a theoretical formula is derived to explain the numerical dependencies of the modeling method. Full article
(This article belongs to the Special Issue Nanostructure-Based Memory Devices)
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Open AccessArticle
Fabrication of [email protected]2O3 Core-shell Nanoparticles for Stable Nonvolatile Memory Applications
Materials 2019, 12(19), 3111; https://doi.org/10.3390/ma12193111 - 24 Sep 2019
Abstract
[email protected]2O3 core-shell nanoparticles (NPs) with narrow spatial distributions were synthesized in silicon dioxide (SiO2). These [email protected]2O3 core-shell NPs were self-assembled by thermally annealing a stacked structure of SiOx/Al/Sn/Al/SiOx sandwiched between two SiO [...] Read more.
[email protected]2O3 core-shell nanoparticles (NPs) with narrow spatial distributions were synthesized in silicon dioxide (SiO2). These [email protected]2O3 core-shell NPs were self-assembled by thermally annealing a stacked structure of SiOx/Al/Sn/Al/SiOx sandwiched between two SiO2 layers at low temperatures. The resultant structure provided a well-defined Sn NP floating gate with a SiO2/Al2O3 dielectric stacked tunneling barrier. Capacitance-voltage (C-V) measurements on a metal-oxide-semiconductor (MOS) capacitor with a [email protected]2O3 core-shell NP floating gate confirmed an ultra-high charge storage stability, and the multiple trapping of electron at the NPs, as expected from low-k/high-k dielectric stacked tunneling layers and metallic NPs, respectively. Full article
(This article belongs to the Special Issue Nanostructure-Based Memory Devices)
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