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2019
Conference Paper
Titel
Electroforming-free BiFeO3 switches for neuromorphic computing: Spike-timing dependent plasticity (STDP) and cycle-number dependent plasticity (CNDP)
Abstract
Memristor technology will strongly influence the architecture of computer systems in the near future. Its potential in several application domains, e.g. in-memory information processing, neuromorphic computing, hardware cryptography, and machine learning makes it more than ever necessary to understand the underlying resistive switching mechanisms and to look for electroforming-free memristors. We have developed an electroforming-free bipolar memristor, namely BiFeO 3 , which emulates spike-timing dependent plasticity. Neuromorphic engineering takes advantage of artificial neurons and artificial synapses to mimic the most complicated human attributes, learning and unlearning. Here we discuss how BiFeO 3 memristors as artificial synapse and artificial neurons are used to implement both spike-timing dependent plasticity and cycle number dependent plasticity.