Memistor

About: Memistor is a research topic. Over the lifetime, 608 publications have been published within this topic receiving 34905 citations.

Memristor-based relaxation oscillators using digital gates

Abstract: This paper presents two memristor-based relaxation oscillators. The proposed oscillators are designed without the need of any reactive elements, i.e., capacitor or inductor. As the "resistance storage" property of the memristor can be exploited to generate the oscillation. The proposed oscillators have the advantage that they can be fully integrated on-chip giving an area-efficient solution. Furthermore, these oscillators give higher frequency other than the existing reactance-less oscillator and provide a wider range of the resistance. The concept of operation and the mathematical analysis for the proposed oscillators are explained and verified with circuit simulations showing an excellent agreement.

Highly-uniform multi-layer ReRAM crossbar circuits

Abstract: Resistive switching memories have been identified as an enabling technology for a variety of emerging computing applications, including neuromorphic and logic-in-memory computing. For example, analog tuning of the memory state combined with high integration density of memristors is needed for very compact implementation of synapses, the most numerous devices in artificial neural networks and would be essential for low energy implementations of large-scale neuromorphic circuits. One way to increase effective memristor density is by vertical monolithical integration of memristor crossbar circuits. Previous work on such circuits have focused on their use for digital memory applications. Only limited device statistics was typically reported, not sufficient for understanding prospects for computing applications. Here we report fabrication and detailed characterization results for a bilayer stacked metal-oxide memristor crossbar circuits. The experimental results show excellent uniformity for the memristors in both crossbar layers. Moreover, the utilized low temperature process flow is CMOS compatible and can be extended to multi-layer stacking.

An adaptive neural network A/D converter based on CMOS/memristor hybrid design

Abstract: A memristor is regarded as a promising device for modeling synapses in the realization of artificial neural systems for its nanoscale size, analog storage properties, low energy and non-volatility. In this letter, an adaptive T-Model neural network based on CMOS/memristor hybrid design is proposed to perform the analog-to-digital conversion without oscillations. The circuit is composed of CMOS neurons and memristor synapses. The A/D converter (ADC) is trained by the least mean square (LMS) algorithm. The conductance of the memristors can be adjusted to convert input voltages with different ranges, which makes the ADC flexible. Using memristors as synapses in neuromorphic circuits can potentially offer high density.

BDD based synthesis technique for design of high-speed memristor based circuits

Abstract: Recently, a passive device — memristor has received wide attention in nano-scale design due to its applications in the area of nanoelectronic memory design, neuromorphic computing and logic design. This passive element is non-volatile in nature and has dual properties of memory and resistor. In recent time, the application of this device in designing high speed logic circuits has now opened a new research domain in nano-scale design. This work presents an efficient design technique implementing logic functions using memristor. The proposed design methodology not only speed-up the response time of the circuits but can also deal with functions with larger input size (beyond 100 variables). Our entire design scheme is divided in two phases. In the first phase, we use Binary Decision Diagrams (BDDs) to represent input logic functions and in second phase, a technology mapping is performed that generates memristor based circuits corresponding to this BDD graphs. Comparative analysis with existing works is given and we find that our design steadily improves the average performance of circuits.

Memristor plasticity enables emergence of synchronization in neuromorphic networks

Abstract: Besides being at the core of novel ultra-high density low-power non-volatile memories and innovative pattern recognition systems based upon oscillatory associative and dynamic memories, the nano-scale memristor also has the potential to reproduce the behavior of a biological synapse more efficiently and accurately than any conventional electronic emulator. As in a living being the weight of a synapse is adapted by the ionic flow through it, so the conductance of a memristor is adjusted by the flux across it. This article is organized according to the regulations of the ISCAS2014 special session on the state-of-the-art in memristor-based nonlinear circuits and architectures. In this work we focus on arrays of oscillatory cells locally coupled through memristors. Our investigations shows how the nonlinear dynamics of the memristor plays a key role in the mechanisms underlying the synchronization properties of the networks. This work provides new insights into the nonlinear behavior of the still largely unexplored memristor element, which promises to revolutionize integrated circuit design in the incoming years.