Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on silicon-based asynchronous spiking neural networks and large crossbar-arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a parallel computation technology, capable of implementing compact and efficient artificial neural networks in hardware, remains a significant challenge. Organic electronic materials offer an attractive alternative to such systems and could provide neuromorphic devices with low-energy switching and excellent tunability, while being biocompatible and relatively inexpensive.
This talk describes state-of-the-art organic neuromorphic devices and provides an overview of the current challenges in the field and attempts to address them1. We demonstrate a novel concept based on an organic electrochemical transistor2 and show how some challenges in the field such as stability, linearity and state retention can be overcome3.
Furthermore, we investigate chemical doping mechanisms in the active material for improved material functionality and demonstrate that this device can be entirely fabricated on flexible substrates, introducing neuromorphic computing to large-area flexible electronics and opening up possibilities in brain-machine interfacing and adaptive learning of artificial organs.
1 van de Burgt et al. Nature Electronics, 2018
2 van de Burgt et al. Nature Materials, 2017
3 Keene et al. J Phys D, 2018