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Article Dans Une Revue Scientific Reports Année : 2016

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

Résumé

Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

Domaines

Matériaux
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Dates et versions

cea-01361933 , version 1 (07-09-2016)

Identifiants

Citer

Yu-Pu Lin, Christopher H. Bennett, Théo Cabaret, Damir Vodenicarevic, Djaafar Chabi, et al.. Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses. Scientific Reports, 2016, 6, pp.31932. ⟨10.1038/srep31932⟩. ⟨cea-01361933⟩
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