Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing

TLDR

Brain‑inspired neuromorphic computing promises massive parallelism and low power, but current CMOS‑based digital emulations are energy intensive and memristor devices suffer from nonlinearities and write noise. The study introduces an electrochemical graphene synapse that modulates conductance via Li‑ion concentration between graphene layers. The synapse operates by reversibly changing graphene conductance through Li‑ion intercalation between its layers. The device achieves <500 fJ per switching, >250 nonvolatile states, excellent endurance and retention, linear symmetric resistance, and demonstrates key neuronal functions (excitatory/inhibitory synapses, LTP/LTD, STDP) with repeatability, while scaling studies indicate energy‑efficient, fast operation.

Abstract

Abstract Brain‐inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal–oxide–semiconductor devices (with “0” and “1” states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (&lt;500 fJ per switching event), analog tunability (&gt;250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long‐term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two‐dimensional synapse is scalable in terms of switching energy and speed.