Mathematical model of remote monitoring for grounding devices on overhead power line poles by measuring grounding device resistance

We report that we have built a mathematical model of a remote monitoring module for the purpose of control grounding devices located on overhead power line poles. The invention is based on measuring grounding device resistance. Resistance measurements conducted by the said monitoring module go directly to a power company’s control room. All measurements are being conducted and transferred to a control room with a specific frequency.

RRAM Random Number Generator Based on Train of Pulses

In this paper, the modulation of the conductance levels of resistive random access memory (RRAM) devices is used for the generation of random numbers by applying a train of RESET pulses. The influence of the pulse amplitude and width on the device resistance is also analyzed. For each pulse characteristic, the number of pulses required to drive the device to a particular resistance threshold is variable, and it is exploited to extract random numbers. Based on this behavior, a random number generator (RNG) circuit is proposed. To assess the performance of the circuit, the National Institute of Standards and Technology (NIST) randomness tests are applied to evaluate the randomness of the bitstreams obtained. The experimental results show that four random bits are simultaneously obtained, passing all the applied tests without the need for post-processing. The presented method provides a new strategy to generate random numbers based on RRAMs for hardware security applications.

Memristors With Controllable Data Volatility by Loading Metal Ion-Added Ionic Liquids

We demonstrate a new memristive device (IL-Memristor), in which an ionic liquid (IL) serve as a material to control the volatility of the resistance. ILs are ultra-low vapor pressure liquids consisting of cations and anions at room temperature, and their introduction into solid-state processes can provide new avenues in electronic device fabrication. Because the device resistance change in IL-Memristor is governed by a Cu filament formation/rupture in IL, we considered that the Cu filament stability affects the data retention characteristics. Therefore, we controlled the data retention time by clarifying the corrosion mechanism and performing the IL material design based on the results. It was found out that the corrosion of Cu filaments in the IL was ruled by the comproportionation reaction, and that the data retention characteristics of the devices varied depending on the valence of Cu ions added to the IL. Actually, IL-Memristors involving Cu(II) and Cu(I) show volatile and non-volatile nature with respect to the programmed resistance value, respectively. Our results showed that data volatility can be controlled through the metal ion species added to the IL. The present work indicates that IL-memristor is suitable for unique applications such as artificial neuron with tunable fading characteristics that is applicable to phenomena with a wide range of timescale.

Large Responsivity of Graphene Radiation Detectors with Thermoelectric Readout: Results of Simulations

Simple estimations show that the thermoelectric readout in graphene radiation detectors can be extremely effective even for graphene with modest charge-carrier mobility ∼1000 cm 2 /(Vs). The detector responsivity depends mostly on the residual charge-carrier density and split-gate spacing and can reach competitive values of ∼ 10 3 – 10 4 V/W at room temperature. The optimum characteristics depend on a trade-off between the responsivity and the total device resistance. Finding out the key parameters and their roles allows for simple detectors and their arrays, with high responsivity and sufficiently low resistance matching that of the radiation-receiving antenna structures.

Exploring the Impact of Variability in Resistance Distributions of RRAM on the Prediction Accuracy of Deep Learning Neural Networks

In this work, we explore the use of the resistive random access memory (RRAM) device as a synapse for mimicking the trained weights linking neurons in a deep learning neural network (DNN) (AlexNet). The RRAM devices were fabricated in-house and subjected to 1000 bipolar read-write cycles to measure the resistances recorded for Logic-0 and Logic-1 (we demonstrate the feasibility of achieving eight discrete resistance states in the same device depending on the RESET stop voltage). DNN simulations have been performed to compare the relative error between the output of AlexNet Layer 1 (Convolution) implemented with the standard backpropagation (BP) algorithm trained weights versus the weights that are encoded using the measured resistance distributions from RRAM. The IMAGENET dataset is used for classification purpose here. We focus only on the Layer 1 weights in the AlexNet framework with 11 × 11 × 96 filters values coded into a binary floating point and substituted with the RRAM resistance values corresponding to Logic-0 and Logic-1. The impact of variability in the resistance states of RRAM for the low and high resistance states on the accuracy of image classification is studied by formulating a look-up table (LUT) for the RRAM (from measured I-V data) and comparing the convolution computation output of AlexNet Layer 1 with the standard outputs from the BP-based pre-trained weights. This is one of the first studies dedicated to exploring the impact of RRAM device resistance variability on the prediction accuracy of a convolutional neural network (CNN) on an AlexNet platform through a framework that requires limited actual device switching test data.

Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform

Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias measurements are demonstrated for lower power considerations. While compatible with resistive elements, we demonstrate a platform based on transitive amorphous indium gallium zinc oxide (aIGZO) pixel elements. Using a lithium based ionic liquid, we demonstrate both potentiation (decrease in device resistance) and depression (increase in device resistance), and propose a 2D platform array that would enable a much higher pixel count via Active Matrix electrowetting.

Temperature Characteristics of a Pressure Sensor Based on BN/Graphene/BN Heterostructure

Temperature is a significant factor in the application of graphene-based pressure sensors. The influence of temperature on graphene pressure sensors is twofold: an increase in temperature causes the substrates of graphene pressure sensors to thermally expand, and thus, the graphene membrane is stretched, leading to an increase in the device resistance; an increase in temperature also causes a change in the graphene electrophonon coupling, resulting in a decrease in device resistance. To investigate which effect dominates the influence of temperature on the pressure sensor based on the graphene–boron nitride (BN) heterostructure proposed in our previous work, the temperature characteristics of two BN/graphene/BN heterostructures with and without a microcavity beneath them were analyzed in the temperature range 30–150 °C. Experimental results showed that the resistance of the BN/graphene/BN heterostructure with a microcavity increased with the increase in temperature, and the temperature coefficient was up to 0.25%°C−1, indicating the considerable influence of thermal expansion in such devices. In contrast, with an increase in temperature, the resistance of the BN/graphene/BN heterostructure without a microcavity decreased with a temperature coefficient of −0.16%°C−1. The linearity of the resistance change rate (ΔR/R)–temperature curve of the BN/graphene/BN heterostructure without a microcavity was better than that of the BN/graphene/BN heterostructure with a microcavity. These results indicate that the influence of temperature on the pressure sensors based on BN/graphene/BN heterostructures should be considered, especially for devices with pressure microcavities. BN/graphene/BN heterostructures without microcavities can be used as high-performance temperature sensors.