Brain-inspired polymer dendrite networks for morphology-dependent computing hardware

Abstract

Variability has always been a challenge to mitigate in electronics. This especially holds true for organic semiconductors, where reproducibility and long-term stability concerns hinder industrialization. By relying on a bio-inspired computing paradigm, we show that AC-electropolymerization is a powerful platform for the development of morphology-dependent computing hardware. Our findings reveal that electropolymerized polymer dendrite networks exhibit a complex relationship between structure and operation that allows them to implement nearly linear to nonlinear functions depending on the complexity of their structure. Moreover, dendritic networks can integrate a limitless number of inputs from their environment, for which their unique morphologies induce specific patterns in the dynamic encoding of the network's output. We demonstrate that this property can be used to our advantage in the context of in materio computing to discriminate between different spatiotemporal inputs. These results show how, due to its inherent stochasticity, electropolymerization is a pivotal technique for the bottom-up implementation of computationally powerful objects. We anticipate this study will help shifting the negative perception of variability in the material science community and promote the electropolymerization framework as a foundation for the development of a new generation of hardware defined by its topological richness.