Memtransistor Devices Based on MoS 2 Multilayers with Volatile Switching due to Ag Cation Migration

In the recent years, the need for fast, robust, and scalable memory devices have spurred the exploration of advanced materials with unique electrical properties. Among these materials, 2D semiconductors are promising candidates as they combine atomically thin size, semiconductor behavior, and complementary metal-oxide-semiconductor compatibility. Here a three-terminal memtransistor device, based on multilayer MoS2 with ultrashort channel length, that combines the usual transistor behavior of 2D semiconductors with resistive switching memory operation is presented. The volatile switching behavior is explained by the Ag cation migration along the channel surface. An extensive physical and electrical characterization to investigate the fundamental properties of the device, is presented. Finally, a chain-type memory array architecture similar to a NAND flash structure consisting of memtransistors is demonstrated, where the individual memory devices can be selected for write and read, paving the way for high-density, 3D memories based on 2D semiconductors

Developing a new hybrid MCDM method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents

Breathable air inside the tunnel is an undeniable necessity and beside natural ventilation, the tunnel should be reliably organized, to automatically provide healthy air under different conditions. Among methods of tunnel ventilation, longitudinal and transverse modes are the most common mechanical methods. This research is focused on selection of the optimal method for mechanical longitudinal ventilation of tunnel pollutants from four presented models. In terms of this research, the authors used SWARA (Step-wise Weight Assessment Ratio Analysis) as one of the most versatile MCDM (Multiple-Criteria Decision-Making) methods for managerial decision making in complex situations with multiple and varied measures. Fourteen experts of different fields were involved. The research model was established based on expert ideas and the following criteria: smoke control (C 1), safety level (C 2), design complexity (C 3), investment costs (C4), increasing concentration of pollutants until portal (C5), smoke laden section (C6) and simultaneous evacuation and fire fighting (C7). SWARA method was applied to evaluate criteria while VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method was used to evaluate and rank four alternatives of this research, namely: 1) jet fans with spot extraction by axial fans (A1); 2) axial fans with Saccardo nozzle (A 2); 3) jet fans with shaft axial fans (A 3); 4) jet fans only (A 4). Final results illustrate that jet fans with spot extraction by axial fans is the best choice. Finally, the authors believe that this new hybrid model of MCDM methods can be useful as a new framework in different fields of research

A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity

This paper describes a fully experimental hybrid system in which a [Formula: see text] memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180 nm CMOS technology. The custom memristors used NMOS selector transistors, made available on a second 180 nm CMOS chip. One drawback is that memristors operate with currents in the micro-amperes range, while analogue CMOS neurons may need to operate with currents in the pico-amperes range. One possible solution was to use a compact circuit to scale the memristor-domain currents down to the analogue CMOS neuron domain currents by at least 5-6 orders of magnitude. Here, we proposed using an on-chip compact current splitter circuit based on MOS ladders to aggressively attenuate the currents by over 5 orders of magnitude. This circuit was added before each neuron. This paper describes the proper experimental operation of an SNN circuit using a [Formula: see text] 1T1R synaptic crossbar together with four post-synaptic CMOS circuits, each with a 5-decade current attenuator and an integrate-and-fire neuron. It also demonstrates one-shot winner-takes-all training and stochastic binary spike-timing-dependent-plasticity learning using this small system. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'

Evaluation of Real-Time Intelligent Sensors for Structural Health Monitoring of Bridges Based on Swara-Waspas; a Case in Iran

Now a day, earthquake engineers follow subjects such as structural health monitoring, warning announcement and prediction rather safe-making in the field of structure. In this regard, these three choices are of great goals of Iran in direction of many studies concentrated on. This research is centralized on real time health monitoring system of Iran bridges. In this regard, to evaluate smart real time health monitoring sensors, first all different types were determined using the library resources, and then all the important indices in evaluating these sensors were derived by interviewing experts in construction management fields. After that, to continue the survey, questionnaires were given to 18 experts to weight the effective indices. Through a decision-making method using new hybrid methodology based on SWARA and WASPAS, existential necessity degree of all indices and sensors were obtained and eventually the following result captured: applying piezoelectric sensors is optimal in smart health monitoring to be used in Iran bridges and optical fiber sensor was recognized as the second optimum option

A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems

Pedretti G, Mannocci P, Hashemkhani S, et al. A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 2020;6(1):189-97.Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix-vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing

A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making

Pedretti G, Milo V, Hashemkhani S, et al. A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making. Presented at the 2020 IEEE International Symposium on Circuits & Systems, Seville, Spain

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset

This is the dataset of "In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"Part of this work was supported by the European project MEMQuD, code 20FUN06, funder ID: 10.13039/100014132. This project (EMPIR 20FUN06 MEMQuD) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme