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Electroforming-free BiFeO3 switches for neuromorphic computing: Spike-timing dependent plasticity (STDP) and cycle-number dependent plasticity (CNDP)

: Kiani, M.; Du, N.; Bürger, D.; Skorupa, I.; Ecke, R.; Schulz, S.E.; Schmidt, H.


Institute of Electrical and Electronics Engineers -IEEE-:
26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019 : 27-29 November 2019, Genoa, Italy
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0997-8
ISBN: 978-1-7281-0996-1
International Conference on Electronics, Circuits and Systems (ICECS) <26, 2019, Genoa>
Fraunhofer ENAS ()

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.