Organic neuromorphic electronics are inspired by a biological nervous system. Bio-inspired computing mimics learning and memory in a brain (i.e., the central nervous system), and bio-inspired soft robotics and nervous prosthetics mimics the neural signal transmission of afferent/efferent nerves (i.e., the peripheral nervous system). Synaptic decay time of nerves differ among biological organs, so the decay time of artificial synapses should be tuned for their specific uses in neuro-inspired electronics. However, controlling a synaptic decay constant in a fixed synaptic device geometry for broad applications was not been achieved in previous research of neuromorphic electronic devices despite the importance to achieve broad applications from neuromorphic computing to neuro-prosthetics. Here, we tailored the synaptic decay constant of organic synaptic transistors with fixed materials and devices structure rather than changing the form of presynaptic spikes, which enabled broad applications from neuromorphic computing to neuro-prosthetics. To achieve this, the relation between crystallinity of the polymer semiconductor film and the synaptic decay constant was revealed. The crystallinity of the polymer controlled electrochemical-doping kinetics and resultant synaptic behaviors of artificial synaptic transistors. In this way, we demonstrated not only long-term retention for learning and memory that is useful for neuromorphic computing in ion-gel gated organic synaptic transistor (IGOST) but also the short-term retention for fast synaptic transmission that is useful for emulating peripheral nerves such as sensory and motor nerve. To prove the feasibility of our approach in a two different ways, we first simulated pattern recognition on the MNIST dataset of handwritten digits using an IGOST with long-term retention due to increased crystallinity and then, developed artificial auditory sensory nerves that combines an IGOST with short term retention due to disordered chain morphology in a polymer semiconductor, with a triboelectric acoustic sensor. We expect that our approach will provide a universal strategy to realize wide neuromorphic electronic applications.
|Publication status||Published - 2019 Nov|
Bibliographical noteFunding Information:
D.-G. S. and Y. L. equally contributed to this work. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning) ( NRF-2016R1A3B1908431 , NRF2017R1A2B4009313 ). This work was also supported by the Center for Advanced Soft-Electronics funded by the Ministry of Science and ICT as Global Frontier Project ( 2013M3A6A5073175 ), and Creative-Pioneering Researchers Program through Seoul National University (SNU) .
© 2019 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Materials Science(all)
- Electrical and Electronic Engineering