Nature Communications · 2020 · 298 citations · 47 references
EngineeringElectrical EngineeringMaterials ScienceElectronic MaterialsMicroelectronicsSemiconductor MaterialsMemory DevicesMemristive MaterialsNanoelectronicsSpike-timing-dependent PlasticityCarrier ConcentrationCurrent DensityFerroelectric Tunnel JunctionsHigh-speed OperationResistive SwitchingSpike-timing PlasticityStorage PerformanceWork Function
Next‑generation non‑volatile memories demand ultrafast speed, low power, and high density for big‑data applications. We present a high‑performance memristor based on an Ag/BaTiO₃/Nb:SrTiO₃ ferroelectric tunnel junction that achieves the fastest operation speed (600 ps) and the most states (32 or 5 bits) per cell reported for FTJs. The device’s speed is enhanced by using a Nb:SrTiO₃ electrode with high carrier concentration and a low‑work‑function metal electrode. The memristor exhibits sub‑nanosecond switching up to 358 K, a low write current density of 4 × 10³ A cm⁻², supports spike‑timing‑dependent plasticity, and offers a path toward bridging memory hierarchy gaps and enabling ultrafast neuromorphic computing.
Abstract Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO 3 /Nb:SrTiO 3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 10 3 A cm −2 . The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO 3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems.
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Memristive devices for computing
J. Joshua Yang, Dmitri B. Strukov, Duncan R. Stewart · Nature Nanotechnology · 2012 · 3.8K citations
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Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
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Enhancement of Ferroelectricity in Strained BaTiO <sub>3</sub> Thin Films
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