Application of Maxwell-Wagner polarisation in monolithic technologies

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A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices

In this paper we present a biorealistic model for the first part of the early vision processing by incorporating memristive nanodevices. The architecture of the proposed network is based on the organisation and functioning of the outer plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive devices are indeed a valuable building block for neuromorphic architectures, as their highly non-linear and adaptive response could be exploited for establishing ultra-dense networks with similar dynamics to their biological counterparts. We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing-effect occurring at the OPL for enhancing the dynamic range of the system. In addition, we employ a memristor-based thresholding scheme for detecting the edges of grayscale images, while the proposed system is also evaluated for its adaptation and fault tolerance capacity against different light or noise conditions as well as distinct device yields

Computing shortest paths in 2D and 3D memristive networks

Global optimisation problems in networks often require shortest path length computations to determine the most efficient route. The simplest and most common problem with a shortest path solution is perhaps that of a traditional labyrinth or maze with a single entrance and exit. Many techniques and algorithms have been derived to solve mazes, which often tend to be computationally demanding, especially as the size of maze and number of paths increase. In addition, they are not suitable for performing multiple shortest path computations in mazes with multiple entrance and exit points. Mazes have been proposed to be solved using memristive networks and in this paper we extend the idea to show how networks of memristive elements can be utilised to solve multiple shortest paths in a single network. We also show simulations using memristive circuit elements that demonstrate shortest path computations in both 2D and 3D networks, which could have potential applications in various fields

An RRAM biasing parameter optimizer

Research on memory devices is a highly active field, and many new technologies are being constantly developed. However, characterizing them and understanding how to bias for optimal performance are becoming an increasingly tight bottleneck. Here, we propose a novel technique for extracting biasing parameters, conducive to desirable switching behavior in a highly automated manner, thereby shortening the process development cycles. The principle of operation is based on: 1) applying variable amplitude, pulse-mode stimulation on a test device in order to induce switching multiple times; 2) collecting the data on how pulsing parameters affect the device’s resistive state; and 3) choosing the most suitable biasing parameters for the application at hand. The utility of the proposed technique is validated on TiOx-based prototypes, where we demonstrate the successful extraction of biasing parameters that allow the operation of our devices both as multistate and binary resistive switches

Emulating long-term synaptic dynamics with memristive devices

The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS technology, the properties of the memristive material should be harnessed in the context of such computation, under the view that biological synapses are memristors. Here we demonstrate that single solid-state TiO2 memristors can exhibit associative plasticity phenomena observed in biological cortical synapses, and are captured by a phenomenological plasticity model called triplet rule. This rule comprises of a spike-timing dependent plasticity regime and a classical hebbian associative regime, and is compatible with a large amount of electrophysiology data. Via a set of experiments with our artificial, memristive, synapses we show that, contrary to conventional uses of solid-state memory, the co-existence of field- and thermally-driven switching mechanisms that could render bipolar and/or unipolar programming modes is a salient feature for capturing long-term potentiation and depression synaptic dynamics. We further demonstrate that the non-linear accumulating nature of memristors promotes long-term potentiating or depressing memory transitions

An Adiabatic Capacitive Artificial Neuron With RRAM-Based Threshold Detection for Energy-Efficient Neuromorphic Computing

In the quest for low power, bio-inspired computation both memristive and memcapacitive-based Artificial Neural Networks (ANN) have been the subjects of increasing focus for hardware implementation of neuromorphic computing. One step further, regenerative capacitive neural networks, which call for the use of adiabatic computing, offer a tantalising route towards even lower energy consumption, especially when combined with `memimpedace' elements. Here, we present an artificial neuron featuring adiabatic synapse capacitors to produce membrane potentials for the somas of neurons; the latter implemented via dynamic latched comparators augmented with Resistive Random-Access Memory (RRAM) devices. Our initial 4-bit adiabatic capacitive neuron proof-of-concept example shows 90% synaptic energy saving. At 4 synapses/soma we already witness an overall 35% energy reduction. Furthermore, the impact of process and temperature on the 4-bit adiabatic synapse shows a maximum energy variation of 30% at 100 degree Celsius across the corners without any functionality loss. Finally, the efficacy of our adiabatic approach to ANN is tested for 512 & 1024 synapse/neuron for worst and best case synapse loading conditions and variable equalising capacitance's quantifying the expected trade-off between equalisation capacitance and range of optimal power-clock frequencies vs. loading (i.e. the percentage of active synapses).Comment: This work has been accepted to the IEEE TCAS-