Brain-Inspired Polymer Dendrite Networks for Morphology-Dependent Computing Hardware

Process variation is always a challenge to mitigate in electronics. This especially holds true for organic semiconductors, where reproducibility concerns hinder industrialization. Challenging this concept, it shows AC-electropolymerization to be a powerful platform for the development of morphology-dependent computing hardware, thanks precisely to its intrinsic stochasticity. The findings reveal that electropolymerized polymer dendrite networks exhibit a complex structure-operation relationship that allows to implement nearly linear to nonlinear functions. Moreover, dendritic networks can integrate a limitless number of inputs from their environment, which can be used to the advantage in the context of in materio computing to discriminate between different spatiotemporal inputs. These results position electropolymerization as a pivotal technique for the bottom-up implementation of computationally powerful objects. This study anticipates this study to 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.