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

Abstract 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

Novel secret key generation techniques using memristor devices

This paper proposes novel secret key generation techniques using memristor devices. The approach depends on using the initial profile of a memristor as a master key. In addition, session keys are generated using the master key and other specified parameters. In contrast to existing memristor-based security approaches, the proposed development is cost effective and power efficient since the operation can be achieved with a single device rather than a crossbar structure. An algorithm is suggested and demonstrated using physics based Matlab model. It is shown that the generated keys can have dynamic size which provides perfect security. Moreover, the proposed encryption and decryption technique using the memristor based generated keys outperforms Triple Data Encryption Standard (3DES) and Advanced Encryption Standard (AES) in terms of processing time. This paper is enriched by providing characterization results of a fabricated microscale Al/TiO2/Al memristor prototype in order to prove the concept of the proposed approach and study the impacts of process variations. The work proposed in this paper is a milestone towards System On Chip (SOC) memristor based security

MemSens: Memristor-Based Radiation Sensor

© 2001-2012 IEEE. Resistive random-access memory (RRAM) technology has been gaining importance due to scalability, low power, non-volatility, and the ability to perform in-memory computing. The RRAM sensing applications have also emerged to enable single RRAM technology platforms which include sensing, data storage, and computing. This paper reports on sol-gel drop coated low-power μ -thick Ag/TiO2/Cu memristor, named MemSens, developed for radiation sensing. MemSens exhibits a bipolar memristive switching behavior within a small voltage window, ranging up to +0.7 V for the turn-ON, and down to -0.2 V for the turn-OFF. Under these operating conditions, MemSens has 67% less switching voltage, 20% drop in ON switching current, 75% reduced active area and \u3e 3x improved device endurance, compared to the best characteristics reported in the literature for μ -thick memristors. The device is tested under direct exposure to ionizing Cs-137 662keV γ -rays, during which a significant increase in the electrical conductivity of the device is observed. MemSens circuit is proposed to allow a relatively real time and cost-effective radiation detection. This provides a first insight to the advancement of reliable memristors that could potentially be deployed in future low-power radiation sensing technologies for medical, personal protection, and other field applications

Subthreshold Continuum Conductance Change in NbO Pt Memristor Interfaces

Bioinspired semiconductor-based devices with adaptive and dynamic properties will have many advantages over conventional static digital silicon-based technologies. The ability to compute, process, and retain information in parallel, without referencing other circuit elements, offers enhanced speed, storage density, energy efficiency, and functionality benefits. A novel crossbar microwire-based device consisting of Nb/NbO/Pt structure that exhibits neural synapse-like adaptive conductivity (i.e., synaptic plasticity) is presented. The neuromorphic memristive junction, formed at the interface between the Pt metal wire and the thermally annealed core–shell Nb–NbO wire, demonstrates 1000 times conductivity change with an effective continuum of resistance levels. The device can also be fully activated to display standard resistance switching between two states. In the subthreshold regime, the voltage flux applied through the ∼400 nm thick NbO junction is shown to have a linear relationship to the charge produced within the device. The conductance value G is a function of the total flux history applied. The linear flux–charge relationship is exploited to demonstrate the voltage–pulse invariance. This suggests that only the integrated flux produced during a voltage–pulse application determines the charge generated within the junction, regardless of the operational parameters like voltage amplitude and time interval. Variation in current onset voltage as a function of flux is discussed with reference to carrier extraction at the intrinsically doped metal–semiconductor interface. The observed flux invariance has implications in emerging neuromorphic semiconductor hardware. Enabling pulse stimuli to be designed to have equal flux through the device from nonvoltage sources such as light may increase functionality in bioinspired computing and applications