A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

• Published in: Nature materials 2017-04, Vol.16 (4), p.414-418
• Main Authors: van de Burgt, Yoeri, Lubberman, Ewout, Fuller, Elliot J., Keene, Scott T., Faria, Grégorio C., Agarwal, Sapan, Marinella, Matthew J., Alec Talin, A., Salleo, Alberto
• Format: Article
• Language: English
• Subjects: 140/146; 639/301/1005; 639/301/923; Biomaterials; Brain; CMOS; Computer simulation; Computers, Molecular; Condensed Matter Physics; Data processing; Electrochemical Techniques; Electrochemistry; Electronic systems; Energy efficiency; Humans; letter; Low voltage; Machine learning; MATERIALS SCIENCE; Memristors; Nanotechnology; Nerve Net; Neural networks; Neuromorphic computing; Optical and Electronic Materials; Pattern recognition; Resistance; Substrates; Switches; Volatility
• ISSN: 1476-1122; 1476-4660
• DOI: 10.1038/nmat4856

A neuromorphic device based on the stable electrochemical fine-tuning of the conductivity of an organic ionic/electronic conductor is realized. These devices show high linearity, low noise and extremely low switching voltage. The brain is capable of massively parallel information processing while consuming only ∼1–100 fJ per synaptic event 1 , 2 . Inspired by the efficiency of the brain, CMOS-based neural architectures 3 and memristors 4 , 5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems 6 , 7 . Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.