Title: Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications,
Abstract: Nature inspired neuromorphic architectures are being explored as an
alternative to imminent limitations of conventional complementary metal-oxide
semiconductor (CMOS) architectures. Utilization of such architectures for
practical applications like advanced pattern recognition tasks will require
synaptic connections that are both reconfigurable and stable. Here, we report
realization of stable atomic-switch networks (ASN), with inherent complex
connectivity, self-assembled from percolating metal nanoparticles (NPs). The
device conductance reflects the configuration of synapses which can be
modulated via voltage stimulus. By controlling Relative Humidity (RH) and
oxygen partial-pressure during NP deposition we obtain stochastic conductance
switching that is stable over several months. Detailed characterization reveals
signatures of electric-field induced atomic-wire formation within the
tunnel-gaps of the oxidized percolating network. Finally we show that the
synaptic structure can be reconfigured by stimulating at different repetition
rates, which can be utilized as short-term to long-term memory conversion. This
demonstration of stable stochastic switching in ASNs provides a promising route
to hardware implementation of biological neuronal models and, as an example, we
highlight possible applications in Reservoir Computing (RC).