Soft Electronic Switches and Adaptive Logic Gates Based on Nanostructured Gold Networks

The emergence of neuromorphic substrates is promoting the development of in materia autonomous and adaptive devices that are used as hardware solutions to improve the energy-related inefficiencies of conventional data processing techniques by exploiting the complexity and collective phenomena originating from various classes of physical substrates.^1,2 Specifically, the employment of materials composed by a huge number of non-linear nanoscale junctions, random assembled according to a non-deterministic strategy, are of particular interest for the implementation of in materia computing systems.^1,3 In this context, thin films built by the assembling of metallic nanoparticles such as gold cluster-assembled films have shown interesting non-linear electrical properties and complex resistive switching phenomena,^4-7 which has been exploited for the fabrication of neuromorphic devices (Receptrons) able to perform binary classification of Boolean functions.⁸ The integration of the gold cluster-assembled films on a polymeric substrate equipped with stretchable electrodes, is here exploited for the production of in materia devices to bridge the gap between data processing and interaction with the environment for the development of the edge computing paradigm that is relevant to wearable technology and soft robotics. The description and the control of the non-linear, resistive switching electrical properties are demonstrated by the development of soft mechano-responsive electronic switches and soft reconfigurable logic gates. Because of the gold network redundant and adaptive connectivity, these devices maintain Boolean function classifications even in the presence of minor mechanical perturbations.⁹ By enabling the direct combination of physical and computational intelligence on the embodied intelligent system to effectively interact with its environment, such findings open up opportunities for the creation of increasingly sophisticated electronic circuits integrated into soft robotic systems.

References.
(1) Alexander Vahl et al 2024 J. Phys. D: Appl. Phys. 57 503001
(2) Jaeger, H. Neuromorphic Comput. Eng. 2021, 1 (1), 012002.
(3) Sillin, H. O. et al. Nanotechnology 2013, 24 (38).
(4) Mirigliano, M. et al. Nanotechnology 2020. 31 234001.
(5) Mirigliano, M.et al. Nanoscale Adv. 2019, 1 (8).
(6) Borghi, F.; et al. Appl. Surf. Sci. 2022, 582 (October 2021), 152485.
(7) Nadalini, G., et al. Sci Rep 13, 19713 (2023)
(8) Mirigliano, M. et al. Neuromorphic Comput. Eng. 2021, 1 (2), 024007.
(9) Nadalini, G., et al. Adv. Elect. Mat. (2024) (submitted)