Ag2Se Nanowire Network as an Effective In-Materio Reservoir Computing Device

Abstract Modern applications of artificial intelligence (AI) are generally algorithmic in nature and implemented using either general-purpose or application-specific hardware systems that have high power requirements. In the present study, physical (in-materio) reservoir computing (RC) implemented in hardware was explored as an alternative to software-based AI. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions, demonstrated the requisite characteristics of an in-materio reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. As a hardware reservoir, the devices successfully performed waveform generation tasks, where tasks conducted at elevated network temperatures were found to be more stable than those conducted at room temperature. Finally, a comparison of voice classification, with and without the network device, showed that classification performance increased in the presence of the network device.