Recent advances in the neuromorphic operation of atomic switches as individual synapse-like devices demonstrate the ability to process information with both short-term and long-term memorization in a single two terminal junction. Here it is shown that atomic switches can be self-assembled within a highly interconnected network of silver nanowires similar in structure to Turing’s “B-Type unorganized machine”, originally proposed as a randomly connected network of NAND logic gates. In these experimental embodiments,complex networks of coupled atomic switches exhibit emergent criticality similar in nature to previously reported electrical activity of biological brains and neuron assemblies. Rapid fluctuations in electrical conductance display metastability and power law scaling of temporal correlation lengths that are attributed to dynamic reorganization of the interconnected electro-ionic network resulting from induced non-equilibrium thermodynamic instabilities. These collective properties indicate a potential utility for realtime,multi-input processing of distributed sensory data through reservoir computation. We propose these highly coupled, nonlinear electronic networks as an implementable hardware-based platform toward the creation of physically intelligent machines.

Efforts to emulate the formidable information processing capabilities of the brain through neuromorphic engineering have been bolstered by recent progress in the fabrication of nonlinear, nanoscale circuit elements that exhibit synapse-like operational characteristics. However, conventional fabrication techniques are unable to efficiently generate structures with the highly complex interconnectivity found in biological neuronal networks. Here we demonstrate the physical realization of a self-assembled neuromorphic device which implements basic concepts of systems neuroscience through a hardware-based platform comprised of over a billion interconnected atomic-switch inorganic synapses embedded in a complex network of silver nanowires. Observations of network activation and passive harmonic generation demonstrate a collective response to input stimulus in agreement with recent theoretical predictions. Further, emergent behaviors unique to the complex network of atomic switches and akin to brain function are observed, namely spatially distributed memory, recurrent dynamics and the activation of feedforward subnetworks. These devices display the functional characteristics required for implementing unconventional, biologically and neurally inspired computational methodologies in a synthetic experimental system.

We explore the feasibility of growing a continuous layer of graphene in prepatterned substrates, like an engineered silicon wafer, and we apply this as a mold for the fabrication of AFM probes. This fabrication method proves the fabrication of SU-8 devices coated with graphene in a full-wafer parallel technology and with high yield. It also demonstrates that graphene coating enhances the functionality of SU-8 probes, turning them conductive and more resistant to wear. Furthermore, it opens new experimental possibilities such as studying graphene-graphene interaction at the nanoscale with the precision of an AFM or the exploration of properties in nonplanar graphene layers.

The spontaneous emergence of complex behavior in dynamical systems occurs through the collective interaction of nonlinear elements toward a highly correlated, non-equilibrium critical state. Criticality has been proposed as a model for understanding complexity in systems whose behavior can be approximated as a state lying somewhere between order and chaos. Here we present unique, purpose-built devices, known as atomic switch networks (ASN), specifically designed to generate the class of emergent properties which underlie critical dynamics in complex systems. The network is an open, dissipative system comprised of highly interconnected (∼109/cm2) atomic switch interfaces wired through the spontaneous electroless deposition of metallic silver fractal architectures. The functional topology of ASN architectures self-organizes to produce persistent critical dynamics without fine-tuning, indicating a capacity for memory and learning via persistent critical states toward potential utility in real-time, neuromorphic computation.