A Self-Formed Ag Nanostructure Based Neuromorphic Device Performing Arithmetic Computation and Area Integration: Influence of Presynaptic Pulsing Scheme on Mathematical Precision.

A material equivalent of a biosynapse is the key to neuromorphic architecture. Here we report a self-forming labyrinthine Ag nanostructure activated with a few pulses of 0.5 V, width and interval set at 50 ms, at current compliance () of 400 nA, serving as the active material for a highly stable device with programmable volatility.

Stretchable hierarchical metal wire networks for neuromorphic emulation of nociception and anti-nociception.

Among biomimetic technologies, the incorporation of sensory hardware holds exceptional utility in human-machine interfacing. In this context, devices receptive to nociception and emulating antinociception gain significance as part of pain management. Here we report, a stretchable two-terminal resistive neuromorphic device consisting of a hierarchical Ag microwire network formed using a crack templating protocol.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity.

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching ( ∼ 1-2 V) with an ultra-low energy requirement of ∼1.

Emulating learning behavior in a flexible device with self-formed Ag dewetted nanostructure as active element.

Neuromorphic devices are a promising alternative to the traditional von Neumann architecture. These devices have the potential to achieve high-speed, efficient, and low-power artificial intelligence. Flexibility is required in these devices so that they can bend and flex without causing damage to the underlying electronics.