Energy Efficient Bio-Compatible Graphene Artificial Synaptic Transistors for Accurate Neuromorphic Computing

As the world becomes more interconnected and data-driven, effective deployment of data-intensive computation methods become more critical, driving the need for neuromorphic networks to overcome the memory wall bottleneck in von Neumann architectures. Previously proposed multi-weight synaptic devices often have drawbacks like nonlinear and asymmetric update response and low energy efficiency, which is detrimental to online supervised learning. Furthermore, most proposed neuromorphic devices, along with previously proposed graphene synapses, are stiff, rigid, and generally incompatible with biological tissue. In this work, we propose a graphene-based artificial synapse featuring highly linear and symmetric update response, superior energy efficiency, mechanical flexibility, and bio-compatibility as a platform for neuromorphics in biological interfaces.<br/>We fabricated mesoscale (few mm²) and microscale (few µm²) synaptic transistor devices by interfacing graphene with a Nafion membrane. The conductance of the device is controlled by applying a current pulse through the gate (Nafion) and the synaptic weight is read by applying voltage across the channel (graphene). Multiple different graphene structures are characterized and tested for long term potentiation, evaluating optimal write pulse duration and amplitude, write-read delay, switching energy, endurance, linearity, number of states, and temperature dependence. The devices were found to have high linearity and symmetry, important for implementation of backpropagation. In addition, they were found to have extremely low power updates, opening the possibility for online learning. CrossSim was used to evaluate neural network performance on the Fashion-MNIST clothing article dataset using a multilayer perceptron. Due to the unique synaptic update response, training performance was found to exceed that of an ideal linear software synapse. These results propose an artificial synapse that is bio-compatible, flexible, energy efficient, and tailored for learning applications, paving the way for a platform for neuromorphic interfacing with biological systems.