A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications

Kim, Kuk Hwan; Gaba, Siddharth; Wheeler, Dana; Cruz-Albrecht, Jose; Hussain, T.; Srinivasa, Narayan; Lü, Wei

doi:10.1021/nl203687n

Cited by 800 publications

(549 citation statements)

References 30 publications

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“…These milestones, in turn, revived interest in the development of memristor-based ANNs and led to numerous few-or single-memristor demonstrations of synaptic functionality and simple associative memory [21][22][23][24][25][26][27][28] , as well as the theoretical modelling of large-scale networks 10,[29][30][31][32][33] . Despite significant progress in memristor crossbar memories 20,[34][35][36][37] , memristor-based ANNs have proven to be significantly more challenging and have yet to be demonstrated. This paper reports the first successful experimental demonstration of pattern classification by a single-layer perceptron network implemented with a memristive crossbar circuit, in which synaptic weights are realized as conductances of titanium dioxide memristors with nanoscale-active regions.…”

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confidence: 99%

Pattern classification by memristive crossbar circuits using ex situ and in situ training

2013

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Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

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confidence: 99%

Pattern classification by memristive crossbar circuits using ex situ and in situ training

2013

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“…Memristive synapses have been interfaced with CMOS neurons in [7][8] The on/off ratio and LRS are likely to increase, which is critical for low power operation, by manipulating the thicknesses and stoichiometries in the ReRAM device film stack.…”

Section: Resultsmentioning

confidence: 99%

Multifunctional Nanotechnology Research

Nostrand¹,

Joseph²

2016

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“…After the application of 10 pulses with Δtp < -2.5 ms, the conductance is non-zero and varying time differences allow programming different memductance states, which may be exploited to realize multilevel memories. 44,45 The horizontal lines in Fig. 2(c) indicate eight different states that can be programmed solely by tuning the time difference between pre-and postsynaptic pulses in step sizes of 0.2 ms.…”

Section: Pulse Shape -Dependent Stdpmentioning

confidence: 99%

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

Maier

Hartmann

Dias

et al. 2016

Journal of Applied Physics

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We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.

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A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications

Cited by 800 publications

References 30 publications

Pattern classification by memristive crossbar circuits using ex situ and in situ training

Pattern classification by memristive crossbar circuits using ex situ and in situ training

Multifunctional Nanotechnology Research

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

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