title: Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs.
abstract: Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one post-synaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25 ms to 125 μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany.
authors.forename: Nan
authors.initials: N
authors.lastname: Du
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Kiani
authors.affiliation: Neuromorphic Cognitive Systems Group, Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
authors.forename: Christian G
authors.initials: CG
authors.lastname: Mayr
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany.
authors.forename: Tiangui
authors.initials: T
authors.lastname: You
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany.
authors.forename: Danilo
authors.initials: D
authors.lastname: Bürger
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany ; Semiconductor Materials, Institute of Ion Beam Physics and Materials Research, HZDR Innovation GmbH Dresden, Germany.
authors.forename: Ilona
authors.initials: I
authors.lastname: Skorupa
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany ; Institute for Integrative Nanosciences, IFW Dresden Dresden, Germany.
authors.forename: Oliver G
authors.initials: OG
authors.lastname: Schmidt
authors.affiliation: Material Systems for Nanoelectronics, Faculty of Electrical and Information Engineering, Chemnitz University of Technology Chemnitz, Germany.
authors.forename: Heidemarie
authors.initials: H
authors.lastname: Schmidt
keywords: BiFeO3 memristor; artificial synapse; learning window; low-power device; memory consolidation; single pairing STDP