• DocumentCode
    626995
  • Title

    A model based comparison of BiFeO3 device applicability in neuromorphic hardware

  • Author

    Cederstrom, Love ; Starke, Paul ; Mayr, Christian ; Yao Shuai ; Schmid, Heinz ; Schuffny, Rene

  • Author_Institution
    Zentrum Mikroelektron. Dresden AG, Dresden, Germany
  • fYear
    2013
  • fDate
    19-23 May 2013
  • Firstpage
    2323
  • Lastpage
    2326
  • Abstract
    Two terminal devices with switchable resistance have been of interest to electrical engineers for a long time, but only in the last few years has this attracted widespread attention. Recently a BiFeOe (BFO) capacitor-like metal-insulator-metal (MIM) structure was proposed as a synthetic synapse in neuromorphic systems, implementing voltage waveform driven spike timing dependent plasticity (STDP). Using a new device model that faithfully reproduces measurements of BFO-MIM structures we analyze how the switching characteristic affects the STDP learning window. Our simulations indicate that the gradual increase in the resistance change of BFO MIM structures result in a robust STDP with a biologically realistic learning window, whereas a distinct threshold followed by a steep hysteresis curve produce a narrow learning window and inflict strict operating conditions. Therefore we conclude that the steepness of the current voltage hysteresis curve is a fundamental characteristic to consider when designing synthetic synapses for neuromorphic hardware.
  • Keywords
    MIM devices; bismuth compounds; capacitors; electric resistance; hysteresis; learning (artificial intelligence); neurophysiology; BFO-MIM structures; STDP learning window; biologically realistic learning window; capacitor-like MIM structure; capacitor-like metal-insulator-metal structure; current voltage hysteresis curve; electrical engineers; neuromorphic hardware; steep hysteresis curve; switchable resistance; switching characteristics; synthetic synapse; voltage waveform driven spike timing dependent plasticity; Adaptation models; Biological system modeling; Hardware; Memristors; Neuromorphics; Neurons; Switches; STDP; device model; memristive device; memristor; neuromorphic systems; synthetic synapse;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems (ISCAS), 2013 IEEE International Symposium on
  • Conference_Location
    Beijing
  • ISSN
    0271-4302
  • Print_ISBN
    978-1-4673-5760-9
  • Type

    conf

  • DOI
    10.1109/ISCAS.2013.6572343
  • Filename
    6572343