| Full text | |
| Author(s): |
van de Burgt, Yoeri
;
Lubberman, Ewout
;
Fuller, Elliot J.
;
Keene, Scott T.
;
Faria, Gregorio C.
;
Agarwal, Sapan
;
Marinella, Matthew J.
;
Talin, A. Alec
;
Salleo, Alberto
Total Authors: 9
|
| Document type: | Journal article |
| Source: | NATURE MATERIALS; v. 16, n. 4, p. 414+, APR 2017. |
| Web of Science Citations: | 225 |
| Abstract | |
The brain is capable of massively parallel information processing while consuming only similar to 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 (<10 pJ for 10(3) mu m(2) devices), displays >500 distinct, non-volatile conductance states within a similar to 1V 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. (AU) | |
| FAPESP's process: | 13/21034-0 - Organic bioelectronics: from materials characterization to devices development |
| Grantee: | Gregório Couto Faria |
| Support Opportunities: | Scholarships abroad - Research |