An organic artificial synapse for low-energy neuromorphic computing

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• Yoeri van de Burgt (Eindhoven University of Technology)
• Tuesday 07 November 2017, 13:00-14:00
• Electrical Engineering Division Seminar Room, 9 J J Thomson Avenue.

If you have a question about this talk, please contact Yoeri van de Burgt.

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. Today these neural networks can not only translate languages and classify images but are also successfully implemented in recognizing diseases, identify jaywalkers, predict optimised routes and unlock your phone. 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. However, memristors demonstrated to date suffer from excessive write noise, write nonlinearity, and high write voltages. Reducing the noise and lowering the switching-voltage without compromising long-term data retention has proven difficult.

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 . Controlled ion injection into the bulk of the material facilitates modification of the conductance or synaptic weight in a near-analogue fashion over a wide range. 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 ENOD Es 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.

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