2018
DOI: 10.1002/adma.201802353
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Abstract: Brain-inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal-oxide-semiconductor devices (with "0" and "1" states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an elec… Show more

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Cited by 219 publications
(242 citation statements)
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“…Figure 4e shows the weight change of the excitatory PSC as a function of the presynaptic input pulse with the amplitude of 4 V and base of 0 V. As the presynaptic input pulse width increases from 1µs to 100 ms, the weight change shows a monotonous increase trend from 5% up to over ≈10 4 % as more and more carriers being captured into the PO x trapping layer, resulting in a stronger effect for Fermi level movement. The largest long‐term synaptic weight change reaches 23 300%, exceeding the previously reported results . Continuous pulse stimulations are applied to the device to mimic the long‐term potentiation (LTP) and depression (LTD) of synapses, which are essential synaptic functions for neuromorphic computing.…”
Section: Resultsmentioning
confidence: 68%
“…Figure 4e shows the weight change of the excitatory PSC as a function of the presynaptic input pulse with the amplitude of 4 V and base of 0 V. As the presynaptic input pulse width increases from 1µs to 100 ms, the weight change shows a monotonous increase trend from 5% up to over ≈10 4 % as more and more carriers being captured into the PO x trapping layer, resulting in a stronger effect for Fermi level movement. The largest long‐term synaptic weight change reaches 23 300%, exceeding the previously reported results . Continuous pulse stimulations are applied to the device to mimic the long‐term potentiation (LTP) and depression (LTD) of synapses, which are essential synaptic functions for neuromorphic computing.…”
Section: Resultsmentioning
confidence: 68%
“…[108][109][110][111] In addition, electrochemical random-access memory (ECRAM) based on ion intercalation has recently been reported as a promising synaptic cell, showing multi-states and incremental switching with near-ideal switching symmetry and linearity. [29,[112][113][114][115][116][117][118][119] The electrochemically driven ion intercalation process is more controllable than filament-related ion movements in RRAM; therefore, ECRAM also exhibits a much smaller stochasticity. In addition, by borrowing the battery concept, those devices successfully decouple the read and write operations and thus realize low programming energy and long retention time simultaneously.…”
Section: Artificial Synapsesmentioning
confidence: 99%
“…Based on the ionic intercalation mechanism, Yang et al used quasi-2D insulating molybdenum oxide (α-MoO 3 ) to fabricate ionic liquid controlled transistors [132] and successfully emulate PPF and EPSP/IPSP with different shapes of input gate voltage pulse. [144] Although the ionic intercalation mechanisms have been proposed to explain the observed resistance change, the direct evidence has been lacking. [144] Although the ionic intercalation mechanisms have been proposed to explain the observed resistance change, the direct evidence has been lacking.…”
Section: Wwwadvelectronicmatdementioning
confidence: 99%