Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks.

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device's conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems.

Enhanced Graphene Oxide Electrical Properties for Thin-Film Electronics Using an Active/Shrinkable Substrate.

The advances in material science along with the development of fabrication techniques have enabled the realization of thin-film-based electronics on active substrates. This has substantially enhanced and supported the deployment of electronic devices in several emerging applications with flexible functionality. In this work, we report a novel fabrication of graphene oxide (GO)-based memristor devices on an active/shrinkable substrate. The standard lithography process is used to fabricate planar Au-rGO-Au devices on a polymer substrate that has the ability to shrink at a certain temperature (i.e., 170 °C). Upon heating, the devices are shrunk to 50% of their original size. A detailed electrical characterization has been carried out to study the switching behavior of the fabricated devices before and after shrinking. The results prove that upon shrinking, the device preserves its switching ability with enhanced electrical parameters (i.e., switching voltage). Also, material characterization performed for the deposited GO on the active substrate shows improved properties of the GO film due to the enhanced arrangement of GO flakes after shrinking. The novel approach proposed in this work provides new insights into and offers the ability to scale thin-film electronics postfabrication and thus overcome some of the device scaling challenges due to manufacturing limitations. It also unfolds a new path for the realization of GO-based electronic devices with improved electrical properties, which is a crucial aspect of the development of highly flexible and lightweight green electronics.

Artificial Intelligence-Aided Low Cost and Flexible Graphene Oxide-Based Paper Sensor for Ultraviolet and Sunlight Monitoring.

The adverse effect of ultraviolet (UV) radiation on human beings has sparked intense interest in the development of new sensors to effectively monitor UV and solar exposure. This paper describes a novel low-cost and flexible graphene oxide (GO)-based paper sensor capable of detecting the total amount of UV or sun energy delivered per unit area. GO is incorporated into the structure of standard printing paper, cellulose, via a low-cost fabrication technique. The effect of UV and solar radiation exposure on the GO paper-based sensor is investigated using a simple color change analysis. As a result, users can easily determine the amount of ultraviolet or solar energy received by the sensor using a simple color analysis application. A neural network (ANN) model is also explored to learn the relation between UV color intensity and exposure time, then digitally display the results. The accuracy for the developed ANN reached 96.83%. The disposable, cost-effective, simple, biodegradable, safe, and flexible characteristics of the paper-based UV sensor make it an attractive candidate for a variety of sensing applications. This work provides new vision toward developing highly efficient and fully disposable GO-based photosensors.

Tunable Switching Behavior of GO-Based Memristors Using Thermal Reduction.

This work reports on the fabrication of a novel planar reduced graphene oxide (rGO) memristor (MR) device. For the first time in the literature, the MR tunable resistive switching behavior is controlled by the GO reduction time at a constant temperature. The device is fabricated using standard microfabrication techniques on a flexible cyclic olefin copolymer substrate (COC). Thermal reduction of the GO layer at low temperatures (100 °C) avoids the drawbacks of chemical reduction methods such as toxicity and electrode metal damage during fabrication, while allowing for fine-tuning of the MR's switching behavior. The device has analog switching characteristics, with a range of different resistance states. By taking advantage of the slow nature of GO thermal annealing, the switching properties of the rGO memristors can be precisely controlled by adjusting the reduction period. At short annealing times (i.e., T < 20 h), the devices switch from high to low resistance states, while at longer annealing times the switching behavior is reversed, with the device switching from low to high resistance states (LRS to HRS). Resistive switching occurs as a result of the diffusion and removal of the oxygen functional groups in the GO film caused by Joule heating induced by the electric current. Complete electrical characterization tests are presented along with wettability and X-ray diffraction (XRD) tests. This work opens a new vision for realizing rGO-based MR devices with tunable switching properties, broadening the application horizon of the device.

Memristor-based PUF for lightweight cryptographic randomness.

Physical unclonable functions (PUF) are cryptographic primitives employed to generate true and intrinsic randomness which is critical for cryptographic and secure applications. Thus, the PUF output (response) has properties that can be utilized in building a true random number generator (TRNG) for security applications. The most popular PUF architectures are transistor-based and they focus on exploiting the uncontrollable process variations in conventional CMOS fabrication technology. Recent development in emerging technology such as memristor-based models provides an opportunity to achieve a robust and lightweight PUF architecture. Memristor-based PUF has proven to be more resilient to attacks such as hardware reverse engineering attacks. In this paper, we design a lightweight and low-cost memristor PUF and verify it against cryptographic randomness tests achieving a unique, reliable, irreversible random sequence output. The current research demonstrates the architecture of a low-cost, high endurance Cu/HfO[Formula: see text]Si memristor-based PUF (MR-PUF) which is compatible with advanced CMOS technologies. This paper explores the 15 NIST cryptographic randomness tests that have been applied to our Cu/HfO[Formula: see text]Si MR-PUF. Moreover, security properties such as uniformity, uniqueness, and repeatability of our MR-PUF have been tested in this paper and validated. Additionally, this paper explores the applicability of our MR-PUF on block ciphers to improve the randomness achieved within the encryption process. Our MR-PUF has been used on block ciphers to construct a TRNG cipher block that successfully passed the NIST tests. Additionally, this paper investigated MR-PUF within a new authenticated key exchange and mutual authentication protocol between the head-end system (HES) and smart meters (SM)s in an advanced metering infrastructure (AMI) for smartgrids. The authenticated key exchange protocol utilized within the AMI was verified in this paper to meet the essential security when it comes to randomness by successfully passing the NIST tests without a post-processing algorithm.

Integrated graphene oxide resistive element in tunable RF filters.

Adaptable communication systems are of great interest as they provide dynamic front end to accommodate the tunable spectrum management in advanced wireless systems. Memristor (acronym of memory resistor) is an emerging technology part of resistive RAM (RRAM) that has good potential for application in reconfigurable RF devices. The potentiality of using resistive switches for frequency tuning of high frequency RF filters is successfully explored in this article for the first time. Tunable RF filter is designed with detailed simulation using Ansys HFSS, and then correlated with measured results from experiment. As a proof of concept, a prototype of the tunable RF filter is fabricated by using a graphene oxide (GO) integrated with a conventional microstrip open stub notch filter. The resistor switching ability of the device is exploited for the frequency tuning. The resonating length of the notch filter is varied by changing the resistance of the active GO material between 'HIGH' (OFF) and 'LOW' (ON) resistance states. The measured results demonstrate the great potential of using RRAM in tunable RF devices. It also proves the possibility of tuning RF devices without any localized surface mount device (SMD) element or complex realization technique.

NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks.

Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the RON and ROFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

Micro-Pattern of Graphene Oxide Films Using Metal Bonding.

Recently, graphene has been explored in several research areas according to its outstanding combination of mechanical and electrical features. The ability to fabricate micro-patterns of graphene facilitates its integration in emerging technologies such as flexible electronics. This work reports a novel micro-pattern approach of graphene oxide (GO) film on a polymer substrate using metal bonding. It is shown that adding ethanol to the GO aqueous dispersion enhances substantially the uniformity of GO thin film deposition, which is a great asset for mass production. On the other hand, the presence of ethanol in the GO solution hinders the fabrication of patterned GO films using the standard lift-off process. To overcome this, the fabrication process provided in this work takes advantage of the chemical adhesion between the GO or reduced GO (rGO) and metal films. It is proved that the adhesion between the metal layer and GO or rGO is stronger than the adhesion between the latter and the polymer substrate (i.e., cyclic olefin copolymer used in this work). This causes the removal of the GO layer underneath the metal film during the lift-off process, leaving behind the desired GO or rGO micro-patterns. The feasibility and suitability of the proposed pattern technique is confirmed by fabricating the patterned electrodes inside a microfluidic device to manipulate living cells using dielectrophoresis. This work adds great value to micro-pattern GO and rGO thin films and has immense potential to achieve high yield production in emerging applications.

Bipolar Cu/HfO2/p++ Si Memristors by Sol-Gel Spin Coating Method and Their Application to Environmental Sensing.

In this paper, the memristive switching behavior of Cu/ HfO2/p++ Si devices fabricated by an organic-polymer-assisted sol-gel spin-coating method, coupled with post-annealing and shadow-mask metal sputtering steps, is examined. HfO2 layers of about 190 nm and 80 nm, are established using cost-effective spin-coating method, at deposition speeds of 2000 and 4000 rotations per minute (RPM), respectively. For two types of devices, the memristive characteristics (Von, Ion, and Vreset) and device-to-device electrical repeatability are primarily discussed in correlation with the oxide layer uniformity and thickness. The devices presented in this work exhibit an electroforming free and bipolar memory-resistive switching behavior that is typical of an Electrochemical Metallization (ECM) I-V fingerprint. The sample devices deposited at 4000 RPM generally show less variation in electrical performance parameters compared to those prepared at halved spin-coating speed. Typically, the samples prepared at 4000 RPM (n = 8) display a mean switching voltage Von of 3.0 V (±0.3) and mean reset voltage Vreset of -1.1 V (±0.5) over 50 consecutive sweep cycles. These devices exhibit a large Roff/Ron window (up to 104), and sufficient electrical endurance and retention properties to be further examined for radiation sensing. As they exhibit less statistical uncertainty compared to the samples fabricated at 2000 RPM, the devices prepared at 4000 RPM are tested for the detection of soft gamma rays (emitted from low-activity Cs-137 and Am-241 radioactive sources), by assessing the variation in the on-state resistance value upon exposure. The analysis of the probability distributions of the logarithmic Ron values measured over repeated ON-OFF cycles, before, during and after exposing the devices to radiation, demonstrate a statistical difference. These results pave the way for the fabrication and development of cost-effective soft-gamma ray detectors.

MOMSense: Metal-Oxide-Metal Elementary Glucose Sensor.

In this paper, we present a novel Pt/CuO/Pt metal-oxide-metal (MOM) glucose sensor. The devices are fabricated using a simple, low-cost standard photolithography process. The unique planar structure of the device provides a large electrochemically active surface area, which acts as a nonenzymatic reservoir for glucose oxidation. The sensor has a linear sensing range between 2.2 mM and 10 mM of glucose concentration, which covers the blood glucose levels for an adult human. The distinguishing property of this sensor is its ability to measure glucose at neutral pH conditions (i.e. pH = 7). Furthermore, the dilution step commonly needed for CuO-based nonenzymatic electrochemical sensors to achieve an alkaline medium, which is essential to perform redox reactions in the absence of glucose oxidase, is eliminated, resulting in a lower-cost and more compact device.