Memristive systems have been extensively explored for the realization of novel computing hardware architectures. In addition to conventional crossbar arrays based on two-terminal memristive cells, multiterminal memristive systems formed by self-organizing networks of nanoscale components have attracted growing interest. Here, we discuss the concept of transmemristance in multiterminal memristive networks, where emergent memristive behavior arises from the mutual interactions among a large number of nanowires (NWs). We show that transmemristance offers a new perspective for probing the internal dynamics of such systems, providing insights into their spatiotemporal evolution. Moreover, it can serve as a measurable physical quantity that reflects the internal state of the network and can be harnessed for the hardware implementation of unconventional computing paradigms.

Transmemristance in multiterminal memristive nanowire networks / Milano, G., Cultrera, A., Miranda, E., Ricciardi, C.. - (2026), pp. 1-3. (2026 10th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) Penang (Malaysia) 01-04 March 2026) [10.1109/edtm65772.2026.11497048].

Transmemristance in multiterminal memristive nanowire networks

Milano, Gianluca;Miranda, Enrique;Ricciardi, Carlo
2026

Abstract

Memristive systems have been extensively explored for the realization of novel computing hardware architectures. In addition to conventional crossbar arrays based on two-terminal memristive cells, multiterminal memristive systems formed by self-organizing networks of nanoscale components have attracted growing interest. Here, we discuss the concept of transmemristance in multiterminal memristive networks, where emergent memristive behavior arises from the mutual interactions among a large number of nanowires (NWs). We show that transmemristance offers a new perspective for probing the internal dynamics of such systems, providing insights into their spatiotemporal evolution. Moreover, it can serve as a measurable physical quantity that reflects the internal state of the network and can be harnessed for the hardware implementation of unconventional computing paradigms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3010873