Artificial neuromorphic systems aim at the reproduction of the brain performances in terms of classification and pattern recognition, typically using components obtained by top-down lithographic technologies regularly employed by digital computers. Unconventional in-materia computing has been proposed as an alternative strategy, exploiting the complexity and collective phenomena originating from various classes of physical substrates. The employment of materials composed by a large number of non-linear nanoscale junctions, random assembled according to a non-deterministic strategy on a substrate, are of particular interest for the implementation of in-materia computing systems. In particular, the employment of random-assembled memristive materials is a strategic solution for the development of energy efficient and neuromorphic computing devices, well beyond the von Neuman bottleneck. 

In CIMaINa, we develop neuromorphic devices based on films obtained by the assembling of metallic nanoparticles and, in particular, Au cluster-assembled films. They have shown interesting non-linear electrical properties and complex resistive switching phenomena. This has been exploited for the fabrication of devices (Receptrons) able to perform binary classification of boolean functions. Furthermore we develop memristive switching devices based on nanostructured gold able to integrate and process voltage pulse trains autonomously.

However, in loco processing of analog data and the need of computing unit integrated with sensor devices in the same compact hardware system exceed the Boolean algebra and the information coding based on binary inputs. Furthermore, hardware computing solutions able to perceive and to adapt to external stimuli are strategic for the implementation of embodied intelligent system. We prove the integration of such a type of memristive device into a polymer matrix of polydimethylsiloxane (PDMS), which is one of the most common flexible and stretchable silicon elastomers, widely used for stretchable electronics and biomedical applications, microfluidic devices, prostheses, as also as food additive. The composite Au/PDMS device preserves the electrical conductivity even in stress conditions and adapts it in response to the different mechanical stimuli the system receives in a non-trivial manner. Even more interestingly, the memristive flexible and stretchable composite device processes and integrates differently specific train pulses according to the external mechanical stimuli applied to the device.  The employment of such a neuromorphic device is a key solution for the development of embodied adaptive intelligent system.

Referent: Francesca Borghi