Solid-state thin-film memistor for electronic neural networks

Thakoor, Sarita; Moopenn, A.; Daud, T.; Thakoor, A. P.

doi:10.1063/1.345390

Cited by 76 publications

(57 citation statements)

References 6 publications

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“…After 30 s proton injection at 200°C, the channel resistivity increases from o1 to B2 Â 10 3 mO cm. It is found that a large amount of hydrogen gas pockets develop cumulatively during the anodic reaction of SNO under negative gating, which eventually leads to the rupturing of Pd layer 30 . Overloaded protons facilitate this blistering process and underloading helps to prolong the lifetime of the Pd electrode.…”

Section: Doping-induced Phase Transition By LI and Mg Intercalationmentioning

confidence: 99%

Colossal resistance switching and band gap modulation in a perovskite nickelate by electron doping

2014

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The electronic properties of correlated oxides are exceptionally sensitive to the orbital occupancy of electrons. Here we report an electron doping strategy via a chemical route, where interstitial dopants (for example, hydrogen) can be reversibly intercalated, realizing a sharp phase transition in a model correlated perovskite nickelate SmNiO 3 . The electron configuration of e g orbital of Ni 3 þ t 6 2g e 1 g in SmNiO 3 is modified by injecting and anchoring an extra electron, forming a strongly correlated Ni 2 þ t 6 2g e 2 g structure leading to the emergence of a new insulating phase. A reversible resistivity modulation greater than eight orders of magnitude is demonstrated at room temperature. A solid-state room temperature nonvolatile proton-gated phase-change transistor is demonstrated based on this principle, which may inform new materials design for correlated oxide devices. Electron doping-driven phase transition accompanied by large conductance changes and band gap modulation opens up new directions to explore emerging electronic and photonic devices with correlated oxide systems.

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Section: Doping-induced Phase Transition By LI and Mg Intercalationmentioning

confidence: 99%

Colossal resistance switching and band gap modulation in a perovskite nickelate by electron doping

2014

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“…Current two-terminal metal-insulator-metal artificial synapses operate by separating the signal transmission and selflearning processes in time 8,9 . Three-terminal synaptic devices, being able to realize both functions simultaneously, therefore offer a promising solution for efficient synapse simulation [23][24][25][26][27][28] .…”

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confidence: 99%

A correlated nickelate synaptic transistor

et al. 2013

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Inspired by biological neural systems, neuromorphic devices may open up new computing paradigms to explore cognition, learning and limits of parallel computation. Here we report the demonstration of a synaptic transistor with SmNiO 3 , a correlated electron system with insulator-metal transition temperature at 130°C in bulk form. Non-volatile resistance and synaptic multilevel analogue states are demonstrated by control over composition in ionic liquid-gated devices on silicon platforms. The extent of the resistance modulation can be dramatically controlled by the film microstructure. By simulating the time difference between postneuron and preneuron spikes as the input parameter of a gate bias voltage pulse, synaptic spike-timing-dependent plasticity learning behaviour is realized. The extreme sensitivity of electrical properties to defects in correlated oxides may make them a particularly suitable class of materials to realize artificial biological circuits that can be operated at and above room temperature and seamlessly integrated into conventional electronic circuits.

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“…Hence, the memristor research did not lead to the development of any major hardware types, except for a few notable projects in the 1990s 14,15 . However, the situation has changed dramatically in recent years 8 , and memristor technology is rapidly advancing owing to the large-scale semiconductor industry's effort to develop digital memristor-based crossbar memories.…”

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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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Solid-state thin-film memistor for electronic neural networks

Cited by 76 publications

References 6 publications

Colossal resistance switching and band gap modulation in a perovskite nickelate by electron doping

Colossal resistance switching and band gap modulation in a perovskite nickelate by electron doping

A correlated nickelate synaptic transistor

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

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