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High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm

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TLDR
Using memristive properties common for titanium dioxide thin film devices, this article designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy within its dynamic range even in the presence of large variations in switching behavior.
Abstract
Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.

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Citations
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Memristive devices for computing

TL;DR: The performance requirements for computing with memristive devices are examined and how the outstanding challenges could be met are examined.
Journal ArticleDOI

The future of electronics based on memristive systems

TL;DR: The state of the art in memristor-based electronics is evaluated and the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing is explored.
Journal ArticleDOI

PRIME: a novel processing-in-memory architecture for neural network computation in ReRAM-based main memory

TL;DR: This work proposes a novel PIM architecture, called PRIME, to accelerate NN applications in ReRAM based main memory, and distinguishes itself from prior work on NN acceleration, with significant performance improvement and energy saving.
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Synaptic electronics: materials, devices and applications

TL;DR: In this paper, the recent progress of synaptic electronics is reviewed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing.
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Memristive crossbar arrays for brain-inspired computing

TL;DR: The challenges in the integration and use in computation of large-scale memristive neural networks are discussed, both as accelerators for deep learning and as building blocks for spiking neural networks.
References
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Journal ArticleDOI

Nanoscale Memristor Device as Synapse in Neuromorphic Systems

TL;DR: A nanoscale silicon-based memristor device is experimentally demonstrated and it is shown that a hybrid system composed of complementary metal-oxide semiconductor neurons and Memristor synapses can support important synaptic functions such as spike timing dependent plasticity.
Journal ArticleDOI

Memristive switching mechanism for metal/oxide/metal nanodevices.

TL;DR: Experimental evidence is provided to support this general model of memristive electrical switching in oxide systems, and micro- and nanoscale TiO2 junction devices with platinum electrodes that exhibit fast bipolar nonvolatile switching are built.
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Resistive switching in transition metal oxides

TL;DR: In this paper, the authors review the current status of one of the alternatives, resistance random access memory (ReRAM), which uses a resistive switching phenomenon found in transition metal oxides.
Journal ArticleDOI

Atomic structure of conducting nanofilaments in TiO2 resistive switching memory

TL;DR: In situ current-voltage and low-temperature conductivity measurements confirm that switching occurs by the formation and disruption of Ti(n)O(2n-1) (or so-called Magnéli phase) filaments, which will provide a foundation for unravelling the full mechanism of resistance switching in oxide thin films.
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