Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing-a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.

Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

Recent advances in nanoscale science and technology provide possibilities to directly self-assemble and integrate functional circuit elements within the wiring scheme of devices with potentially unique architectures. Electroionic resistive switching circuits comprising highly interconnected fractal electrodes and metal-insulator-metal interfaces, known as atomic switch networks, have been fabricated using simple benchtop techniques including solution-phase electroless deposition. These devices are shown to activate through a bias-induced forming step that produces the frequency dependent, nonlinear hysteretic switching expected for gapless-type atomic switches and memristors. By eliminating the need for complex lithographic methods, such an approach toward device fabrication provides a more accessible platform for the study of ionic resistive switches and memristive systems.

Resistive switching devices have garnered significant consideration for their potential use in nanoelectronics and non-volatile memory applications. Here we investigate the nonlinear current–voltage behavior and resistive switching properties of composite nanoparticle films comprising a large collective of metal–insulator–metal junctions. Silver nanoparticles prepared via the polyol process and coated with an insulating polymer layer of tetraethylene glycol were deposited onto silicon oxide substrates. Activation required a forming step achieved through application of a bias voltage. Once activated, the nanoparticle films exhibited controllable resistive switching between multiple discrete low resistance states that depended on operational parameters including the applied bias voltage, temperature and sweep frequency. The films’ resistance switching behavior is shown here to be the result of nanofilament formation due to formative electromigration effects. Because of their tunable and distinct resistance states, scalability and ease of fabrication, nanoparticle films have a potential place in memory technology as resistive random access memory cells.

The self-organization of dynamical structures in complex natural systems is associated with an intrinsic capacity for computation. Beginning from the context of modern trends in neuromorphic engineering, this work introduces an effort toward the construction of purpose-built dynamical systems. Known as atomic switch networks (ASN), these systems consist of highly interconnected, physically recurrent networks of inorganic synapses (atomic switches). By combining the advantages of controlled design with those of self-organization, the functional topology of ASNs has been shown to produce emergent system-wide dynamics and a diverse set of complex behaviors with striking similarity to those observed in many natural systems including biological neural networks and assemblies. Numerical modeling and experimental investigations of their operational characteristics and intrinsic dynamical properties have facilitated progress toward implementation in neuromorphic reservoir computing. These achievements demonstrate the utility of ASNs as a uniquely scalable physical platform capable of exploring the dynamical interface of complexity, neuroscience, and engineering.