CCS&M seminar - Yoeri van de Burgt: An organic artificial synapse for low-energy neuromorphic computing

The widely anticipated end to Moore’s law and the growing demand for low power computing systems capable of learning, image recognition and real-time analysis of large streams of unstructured data has spurred intense interest in neural algorithms for brain-inspired computing.

Inspired by the efficiency of the brain, CMOS-based neural architectures were developed for low-power pattern recognition and machine learning. Still, the volatility, high supply voltages, and number of transistors required per synapse significantly complicate the path for CMOS-based architectures to achieve the extreme interconnectivity, information density, and energy efficiency of the brain. Alternatively, two-terminal tunable resistance elements (memristors) based on filament forming metal oxides (FFMOs) or phase change memory (PCM) materials have been demonstrated to function as non-volatile memory that can emulate synaptic functions, but they require high voltages and currents due to the inherent physics of their switching mechanism.

Here we present an organic neuromorphic device operating with a fundamentally different mechanism from existing memristors, based on the electrochemical doping of a conjugated polymer. The device switches at low energy and voltage, displays >500 distinct, non-volatile conductance states and achieves high classification accuracy when implemented in neural network simulations.

We also demonstrate that plastic ENODEs can be entirely fabricated on flexible substrates, introducing neuromorphic computing to large-area flexible electronics and opening up possibilities in brain-machine interfacing, adaptive learning of artificial organs, such as “smart skins”, and laying the foundation for 3D manufacturing of highly interconnected device networks.