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Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary switching in nanoscale learning systems. Such devices offer a natural transition between short-term plasticity (STP) and long-term plasticity (LTP). In this work, we show...
The memristor-based neural learning network is considered as one of the candidates for future computing systems thanks to its low power, high density and defect-tolerance. However, its application is still hindered by the limitations of huge neuron structure and complicated learning cell. In this paper, we present a memristor-based neural crossbar circuit to implement on-chip supervised learning rule...
Conventional CMOS integrated circuits suffer from serve power and scalability challenges as technology node scales into ultra-deep-micron technology nodes. Alternative approaches beyond charge-only based circuits. In particular, spin-based devices or integrated circuits show promising merits to overcome these issues by adding the spin freedom of electrons to the electronic circuits. Spintronics has...
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