A Multiterminal Setup for Complex Dynamics Characterization and Unconventional Computing in Self-Organizing Memristive Networks

The recent growing interest in neuromorphic architectures based on emergent dynamics of self-organizing memristive networks has posed some challenges regarding the spatiotemporal characterization of these multiterminal systems. This work presents a versatile measurement platform specifically designed for the characterization of memristive nanowire networks and for testing the implementation of unconventional computing paradigms in these systems. By integrating an FPGA controlled, parallel multiterminal array of source-measure units with a custom fixture based on spring-loaded electrodes, the system allows for real-time, reconfigurable voltage and current measurements across 16 terminals without hardware reconnections. The platform supports seamless transition between conventional two-terminal characterization, multiterminal characterization and testing computational properties in the framework of physical reservoir computing. Local conductance measurements, voltage mapping, and real-time dynamic monitoring offer unique insights into the spatiotemporal behavior of the networks. Furthermore, we show that the system enables to correlate electrical properties of the multiterminal network in terms of conductance matrices and voltage maps with computational performances, allowing also adaptive control over the network's operating state. The here reported setup provides a versatile platform for computing at the matter level (i.e., in materia) with multiterminal systems based on self-organizing memristive networks.