Mott memristor based stochastic neurons for probabilistic computing.

Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor's inherent stochastic switching dynamics.

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing.

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks.

A review of bio-impedance devices.

Bio-impedance measurement analysis primarily refers to a safe and a non-invasive technique to analyze the electrical changes in living tissues on the application of low-value alternating current. It finds applications both in the biomedical and the agricultural fields. This paper concisely reviews the origin and measurement approaches for concepts and fundamentals of bio-impedance followed by a critical review on bio-impedance portable devices with main emphasis on the embedded system approach which is in demand due to its miniature size and present lifestyle preference of monitoring health in real time.

Dielectrically Modulated III-V Compound Semiconductor Based Pocket Doped Tunnel FET for Label Free Biosensing Applications.

In this paper, a novel structure of double gate tunnel FET has been proposed and simulated for biosensing applications. The device uses III-V compound semiconductors and an n+ doped pocket at the source channel junction. Biomolecules of different dielectric constants (K) with different charge densities (Nbio), both negative and positive, are inserted in the nano-gap cavities (15 nm ×1.

L-Shaped High Performance Schottky Barrier FET as Dielectrically Modulated Label Free Biosensor.

In this work, we demonstrate the realization of L-Shaped Schottky Barrier FET as a biosensing device with improved sensitivity. The proposed device uses dual material gate with work functions of 4.2 eV (Al) and 4.

Ternary Arithmetic Logic Unit Design Utilizing Carbon Nanotube Field Effect Transistor (CNTFET) and Resistive Random Access Memory (RRAM).

Due to the difficulties associated with scaling of silicon transistors, various technologies beyond binary logic processing are actively being investigated. Ternary logic circuit implementation with carbon nanotube field effect transistors (CNTFETs) and resistive random access memory (RRAM) integration is considered as a possible technology option. CNTFETs are currently being preferred for implementing ternary circuits due to their desirable multiple threshold voltage and geometry-dependent properties, whereas the RRAM is used due to its multilevel cell capability which enables storage of multiple resistance states within a single cell.

A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control.

Industrialization has led to a huge demand for a network control system to monitor and control multi-loop processes with high effectiveness. Due to these advancements, new industrial wireless sensor network (IWSN) standards such as ZigBee, WirelessHART, ISA 100.11a wireless, and Wireless network for Industrial Automation-Process Automation (WIA-PA) have begun to emerge based on their wired conventional structure with additional developments.

Implementation of an efficient magnetic tunnel junction-based stochastic neural network with application to iris data classification.

Stochastic neuromorphic computation (SNC) has the potential to enable a low power, error tolerant and scalable computing platform in comparison to its deterministic counterparts. However, the hardware implementation of complementary metal oxide semiconductor (CMOS)-based stochastic circuits involves conversion blocks that cost more than the actual processing circuits. The realization of the activation function for SNCs also requires a complicated circuit that results in a significant amount of power dissipation and area overhead.

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications.

In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed. First, a brief overview of the field of emerging memory technologies is provided. The material properties, resistance switching mechanism, and electrical characteristics of RRAM are discussed.

Low-Voltage Low-Power Integrable CMOS Circuit Implementation of Integer- and Fractional-Order FitzHugh-Nagumo Neuron Model.

The low-voltage low-power sinh-domain (SD) implementations of integer- and fractional-order FitzHugh-Nagumo (FHN) neuron model have been introduced in this paper. Besides, the effect of fractional-orders on the external excitation current and dynamics of the neuron has been examined in this paper. The proposed SD designs of FHN neuron model have the benefits of: 1) low-voltage operation; 2) integrability, due to resistor-less design and the employment of only grounded components; 3) electronic tunability of performance parameters; and 4) low-power implementation due to the inherent properties of SD technique.

An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function.

The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN.