After decades of perfecting the established way of computing, it is now evident that the fundamental logic of today’s computers will prevent them from ever reaching the efficiency of neural networks as found in nature. Neuromorphic hardware promises a leap forward by following the inherent working principles of biological neural networks. In very-large-scale integrated neuromorphic circuits incorporating an immense number of artificial neurons, the even much larger number of synapses poses the challenge of imitating especially the synaptic functionality in a most compact way. Over the last years, various memristive devices, i.e. devices with tunable resistance, have been proposed to represent the weight of a synapse, determining how well electrical spikes are transmitted from one neuron to another.
The NEURAMORPH project aimed to develop a simple and compact circuit element to modify the strength of synaptic connections between artificial neurons. The dynamics of electrical excitability intrinsic to the employed amorphous semiconductors should naturally be able to mimic features known from biological neural networks. For full control over the properties of these synaptic access elements, a fundamental understanding of the relaxation processes in such amorphous materials was imperative. To this end, in this project we performed physical experiments and computer simulations to elucidate the relationship between chemical composition, structural dynamics and changing electrical excitability.