Nowadays, the increasing popularity of extremely energy demanding AI models raises serious concerns in terms of environmental sustainability [1]. At the same time, the need to process vast amounts of often unstructured data highlights the urgency of exploring new and efficient computational paradigms. The mammalian brain performs complex tasks with remarkable efficiency compared to digital computers, exhibiting adaptability and learning capabilities. These features arise from brain peculiar architecture, comprising hundred billions neurons interconnected through an enormous number of synapses. Neurons integrate signals in a nonlinear fashion, while synapses modulate their transmission efficacy through plasticity, thus enabling learning. Neuromorphic Computing (NC) draws inspiration from the brain to develop viable alternatives to conventional digital computing.

Recently, gold cluster–assembled films (ns-Au) have attracted significant attention due to their topological complexity, memory effects, and non-linear electrical response, making them promising physical substrates for NC applications. Their intriguing neuromorphic properties arise from Resistive Switching (RS) phenomena displayed by the nano-junctions distributed thorough the network [2][3]. Notably, the implementation of a reconfigurable nonlinear threshold logic gate based on ns-Au has already been demonstrated [4].

We focused our study on the characterization of the switching behaviour exhibited by ns-Au with the aim to investigate the emergent electrical response of these systems. We employed a novel microthermography based imaging technique to track the regions responsible for the RS activity during electrical stimulation, referred as “switching sites”. This analysis allowed us to reliably study the correlation between different switching sites, gaining insight in the functional connectivity of the network [5]. Moreover, we highlighted that the geometry of the deposit has also a strong impact over the RS activity, particularly influencing the number of switching sites and the statistics of the RS, providing meaningful insight for future design and engineering of ns-Au based devices.

Furthermore, our experiments allowed us to optimize electrical stimulation protocols for reliably tuning the electrical resistance map of multi-electrode device and the connectivity of the network by means of selectively triggering the formation and destruction of conductive paths. This way, we demonstrate that we have achieved a fine reversible control over the conduction state of the devices, which has never been previously reported for self-assembled materials. This characterization paved the way for the implementation of optimized stimulation protocols for implementing specific computing functionalities in reconfigurable ns-Au based multiterminal devices.