Low‐power electrochemically tunable graphene synapses for neuromorphic computing

Feng Xiong and his group developed an “artificial synapse” that does not process information like a digital computer but rather mimics the analog way the human brain completes tasks. 

For applications in neuromorphic computing, Xiong and his team focuses on the design of computational hardware inspired by the human brain and built graphene-based artificial synapses in a two-dimensional honeycomb configuration of carbon atoms. Graphene’s conductive properties allowed the researchers to finely tune its electrical conductance, which is the strength of the synaptic connection or the synaptic weight. The graphene synapse demonstrated excellent energy efficiency, just like biological synapses. 

According to Xiong, artificial neural networks based on the current CMOS (complementary metal-oxide semiconductor) technology will always have limited functionality in terms of energy efficiency, scalability, and packing density. “It is really important we develop new device concepts for synaptic electronics that are analog in nature, energy-efficient, scalable, and suitable for large-scale integrations,” he says. “Our graphene synapse seems to check all the boxes on these requirements so far.” 

With graphene’s inherent flexibility and excellent mechanical properties, these graphene-based neural networks can be employed in flexible and wearable electronics to enable computation at the “edge of the internet”—places where computing devices such as sensors make contact with the physical world.

Their work was published in the recent issue of the journal Advanced Materials (DOI:10.1002/adma.201802353). Other co-authors include Mohammad Sharbati (first author), Yanhao Du, Jorge Torres, Nolan Ardolino, and Minhee Yun.

Written by Matt Cichowicz.

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