Designing strategies to reach monodispersity in fabrication of semiconductor nanowire ensembles is essential for numerous applications. When Ga-catalyzed GaAs nanowire arrays are grown by molecular beam epitaxy with help of droplet-engineering, we observe a significant narrowing of the diameter distribution of the final nanowire array with respect to the size distribution of the initial Ga droplets. Considering that the droplet serves as a nonequilibrium reservoir of a group III metal, we develop a model that demonstrates a self-equilibration effect on the droplet size in self-catalyzed III-V nanowires. This effect leads to arrays of nanowires with a high degree of uniformity regardless of the initial conditions, while the stationary diameter can be further finely tuned by varying the spacing of the array pitch on patterned Si substrates.

The brain’s efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network’s global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Neurobiology-inspired phenomena such as winner-takes-all competition and critical dynamics have been recently reported to arise in neuromorphic nanowire networks. These are unique systems where interactions between memristive elements creates emergent conductance pathways between discrete electrodes. This mode of operation can offer substantial advantages to create a truly concomitant plastic-static system for integration in neuromorphic devices. However, critical aspects such as pathway controllability and stability are yet to be explored. In this study, pathway formation in self-assembled neuromorphic networks formed by Ag nanowires decorated with TiO 2 nanoparticles is investigated. Direct visualization of pathway formation through a neuromorphic network is attained using the lock-in thermography technique. Using this technique, it is demonstrated that how networks preserve information from previously used pathways through increased local junction connectivity. This effect directly reshapes subsequent formation of pathways whenever the spatial location of the electrodes is dynamically changed. Combining these results with conventional current-voltage measurements, which show that the network electrically acts as a volatile switching memristor, a unique interaction between short-term and long-term memory arises. This produces unexpected collective dynamical states of potentiation and inhibition of network conductance whenever different spatiotemporal signals are dynamically fed to the network.

Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire-node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small-world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.

Resistance in neuromorphic nanowire networks can be decreased when activated by voltage as multiple pathways of low resistance interconnected nanowires form, increasing nanowire to nanowire connectivity. We show that high connectivity regions are retained for a few minutes after the energy source is switched off. We have used this property to devise an associative device. With a multielectrode array, we send current through the network to connect together areas that are spatially associated with a given electrode combination forming a pattern. We correctly retrieve the stored patterns by passing a small current through the network at a later time even when we input a faulty or incomplete pattern as the network groups stored patterns into cluster of high associativity, in analogy with semantic memory association in the human brain.

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

As supervisor for the candidature upon which this thesis is based, I can confirm that the authorship attribution statements above are correct.