Carbon-Based Smart Materials

Constantinos A. Charitidis, Elias P. Koumoulos, Dimitrios A. Dragatogiannis, Constantinos A. Charitidis, Elias P. Koumoulos, Dimitrios A. Dragatogiannis

• Publisher: De Gruyter
• Year: 2020
• Print ISBN: 9783110477740
• eBook ISBN: 9783110477757
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Industrial & Technical Chemistry
• Index: Technology & Engineering

Presents technologies and key concepts to produce suitable smart materials and intelligent structures for sensing, information and communication technology, biomedical applications (drug delivery, hyperthermia therapy), self-healing, flexible memories and construction technologies. Novel developments of environmental friendly, cost-effective and scalable production processes are discussed by experts in the field.

1 New class of graphene-based devices for the next generation of nonvolatile memories

Paolo Bondavalli

Thales Research and Technology, Palaiseau Cedex, France

1.1 Introduction

Graphene is a one-atom thick layer of carbon atoms arranged in a hexagonal lattice. Graphene potentialities are attracting a lot of researchers to probe opportunities in a number of directions in the “more Moore” or “beyond CMOS” optics in order to identify the new future technologies [1, 2, 3]. Another promising utilization of graphene and related nanomaterials is to fabricate nonvolatile memories (NVM) exploiting their “memresistive” behavior storing a value of electrical resistance in a permanent way. This happens when a current passing through the materials changes the level of resistance. Therefore, resistive memory exploits the change in the resistance of a material under the effect of an electric field as an information write/erase principle for nonvolatile data storage. The reading of resistance states is nondestructive, and the memory devices can be operated without transistors in every cell [4, 5], as for flash-type memories [6, 7, 8, 9, 10] (see Section 1.2.2), thus achieving a classic cross-bar structure. This kind of memories is called resistive random-access memory (RRAM or ReRAM) and is only one of the possible types of nonvolatile ways to store information in a permanent way. One of the most important advantages of these new classes of 2D materials is that these materials can be implemented in flexible electronics [11, 12, 13, 14, 15], in the form of one-thick atom layers as for graphene or in the form of layers of flakes of graphene oxide (GO) or reduced GO (R-GO), thereby reducing the final cost of the final device exploiting roll-to-roll fabrication [16]. Another great advantage of ReRAMs is their potential to implement them by exploiting only two terminals to work (two contacts and not three as a common transistor, which has drain, source and gate, e.g., flash-type memories), which could dramatically reduce the circuitry and allow to implement easily in 3D architectures by using the roll-to-roll fabrication technique. Potentially, this is applied in various fields such as health monitoring [17, 18, 19, 20, 21], intelligent packaging [22, 23], cards, labels, badges, value paper and medical disposables. Another potential field is the radiofrequency identification (RFID). This is a technology to electronically record the presence of an object using radio signals. Indeed, an innovative alternative pathway to reduce RFID costs and integrate a memory chip to store data is to eliminate the silicon substrate completely, and produce RFID and memory on the same flexible plastic substrate as the antenna [24, 25, 26]. Thanks to graphitic layers, the antenna and chip can be built on the same low-cost substrate, and attachment costs can be removed.

1.2 Graphene-based NVM

As outlined in the International Technology Roadmap for Semiconductors 2011 section concerning Emerging Research Devices, “ultrathin graphite layers are interesting materials for macromolecular memories thanks to the potential fabrication costs that are considered as the primary driver for this type of memory, while extreme scaling is de-emphasized.” The main drawback is related to the fact that memory operation mechanisms and the physics are still unclear and that a deeper research in this field is necessary to improve the comprehension of the phenomenon and the efficiency of the devices. These are not the same physical mechanisms exploited in memories based on graphene-related materials, which will be discussed in the next paragraphs.

1.2.1 Graphene and graphitic layers

The first paper highlighting the utilization of graphene for resistive NVM exploiting two terminal structures was issued in 2008 by Stadley et al. at Caltech [27]. They reported the development of an NVM element based on graphene break junctions. These junctions were obtained by achieving two-terminal devices, transferring graphene sheets on SiO₂/Si substrate and depositing metal electrodes on them using e-beam lithography process. After applying a specific voltage under ultravacuum condition (10⁻⁷ torr), they were able to create a break in the graphene layer (see Figure 1.1). In these papers, all the measurements were performed under ultravacuum to avoid environmental gas interferences, which strongly reduces the potential utilization of the device.

Figure 1.1: (a) SEM image of the device before (left panel) and after breakdown (right panel). The arrows indicate the edges of the break in the graphene layer. (b) Repeatable programming over hundreds of cycles. Upper left panel: Voltage applied to the junction versus time. A ramp with a peak value of ∼4 V corresponds to an ON pulse, while a ramp with a peak value of ∼6 V corresponds to an OFF pulse. A small sawtooth-shaped read-out pulse is applied after each write to determine the junction conductance. Lower left panel: Current flow through the junction, with the low-bias conductance labeled above each read-out pulse. Right panels: Similar data taken after approximately 500 intervening cycles, demonstrating the reproducibility of the switching behavior [27].

After cycling the memories, they observed that the resistance values clearly changed as a function of the voltage (see Figure 1.1b): they moved from a high resistance state (off state) to a lower one as a function of the voltage (on state). This effect was nonvolatile and the devices were cycled thousands of writing cycles (10⁵), showing long retention times (more than 24 h, only limited by measurements).

The reproducibility of the switching behavior was very promising. One of the more coherent explanations was related to the fact that the conductance occurred along a small graphene ribbon that bridges the contacts. To better understand the underlying physical mechanism, researchers studied the time-resolved behavior of the switch from the OFF to ON states, shown in Figure 1.2a. The conductance I/V showed well-defined steps, with magnitude ∼G_Q. Here G_Q is 2e²/h, the conductance quantum, where e is the electron charge and h is Planck’s constant. Since G_Q is the conductance of a spin-degenerate one-dimensional conductor, for example, a linear chain of gold atoms [28], observation of steps in the conductance suggests that the conductance states of a device are likely multiples of highly transmitting quantum channels. Authors proposed a model for device operation based on the formation and breaking of carbon atomic chains that bridge the junctions (see Figure 1.2a, right figure). This mode and the results seem to put in evidence the potential for multiresistive states.

Figure 1.2: (a) Left side: Change in I/V as a function of V pointing out the quantic behavior linked to the formation of atomic chains. Right side: Proposed physical mechanism at the base of the nonvolatile resistive behavior (formation of atomic carbon chains). (b) Tilted-view SEM images of two different devices before and after applying the voltage difference that is necessary to break the layers defined as V_break. The arrow shows the fracture across the stripe due to V_break [29].

The same physical explanation can be evoked fo...

Table of contents

• Title Page
• Copyright
• Contents
• Preface
• 1 New class of graphene-based devices for the next generation of nonvolatile memories
• 2 Plasma surface activation and functionalization of carbon-based materials
• 3 Modeling carbon-based smart materials
• 4 Carbon nanotube-based materials for space applications
• 5 Molecular dynamics simulations of graphene-based polymer nanocomposites
• 6 Carbon from waste source for Li-ion battery
• Index