Microwave method for high-frequency properties of graphene

Graphene is a remarkable material, which is yet to make the transition from unique laboratory phenomenon to useful industrial material. One missing element in the development process is a quick method of quality control of the electrical properties of graphene which may be applied in, or close to, the graphene growth process on an industrial scale. In this study, the authors describe a non-contact method using microwave resonance which potentially solves this problem. They describe the technique, consider its limitations and accuracy and suggest how the method may have future take up.UK NMS Programme, the EU EMRP project ‘GraphOhm’ and ‘MetNEMS’. The EMRP (European Metrology Research Programme

Self-supporting graphene films and their applications

The self-supporting monolayer material which is graphene has excited enormous interest over the ten years since its discovery due to its remarkable electrical, mechanical thermal and chemical properties. In this paper we describe our work to develop chemical vapour deposition methods to grow monolayer graphene on copper foil substrates and the subsequent transfer process. Raman microscopy, scanning electron microscopy and atomic force microscopy (AFM) are used to examine the quality of the transferred material. To demonstrate the process we describe transfer onto patterned SiO2/Si substrates which forms freely suspended graphene with focus on circular wells forming graphene drums. These show interesting mechanical properties which are being explored as nanomechanical resonators.UK NMS Programme, the EU EMRP (European Metrology Research Programme) projects MetNEMS and GraphOh

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe

Realization of Analog Wavelet Filter using Hybrid Genetic Algorithm for On-line Epileptic Event Detection

© 2020 The Author(s). This open access work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.Peer reviewe

Performance Analysis of Millimeter Wave Massive MIMO Systems in Centralized and Distributed Schemes

This paper considers downlink multi-user millimeter-wave massive multiple-input multiple-output (MIMO) systems in both centralized and distributed configurations, referred to as C-MIMO and D-MIMO, respectively. Assuming the fading channel is composite and comprised of both large-scale fading and small-scale fading, a hybrid precoding algorithm leveraging antenna array response vectors is applied into both the C-MIMO system with fully connected structure and the D-MIMO system with partially connected structure. First, the asymptotic spectral efficiency (SE) of an arbitrary user and the asymptotic average SE of the cell for the C-MIMO system are analyzed. Then, two radio access unit (RAU) selection algorithms are proposed for the D-MIMO system, based on minimal distance (D-based) and maximal signal-to-interference-plus-noise-ratio (SINR) (SINR-based), respectively. For the D-MIMO system with circular layout and D-based RAU selection algorithm, the upper bounds on the asymptotic SE of an arbitrary user and the asymptotic average SE of the cell are also investigated. Finally, numerical results are provided to assess the analytical results and evaluate the effects of the numbers of total transmit antennas and users on system performance. It is shown that, from the perspective of the cell, the D-MIMO system with D-based scheme outperforms the C-MIMO system and achieves almost alike performance compared with the SINR-based solution while requiring less complexity.Peer reviewe

Perspective of buried oxide thickness variation on triple metal-gate (TMG) recessed-S/D FD-SOI MOSFET

Recently, Fully-Depleted Silicon on Insulator (FD-SOI) MOSFETs have been accepted as a favourable technology beyond nanometer nodes, and the technique of Recessed-Source/Drain (Re-S/D) has made it more immune in regards of various performance factors. However, the proper selection of Buried-Oxide (BOX) thickness is one of the major challenges in the design of FD-SOI based MOS devices in order to suppress the drain electric penetrations across the BOX interface efficiently. In this work, the effect of BOX thickness on the performance of TMG Re-S/D FD-SOI MOSFET has been presented at 60 nm gate length. The perspective of BOX thickness variation has been analysed on the basis of its surface potential profile and the extraction of the threshold voltage by performing two-dimensional numerical simulations. Moreover, to verify the short channel immunity, the impact of gate length scaling has also been discussed. It is found that the device attains two step-up potential profile with suppressed short channel effects. The outcomes reveal that the Drain Induced Barrier Lowering (DIBL) values are lower among conventional SOI MOSFETs. The device has been designed and simulated by using 2D numerical ATLAS Silvaco TCAD simulator