Biocompatible Graphene Transistors for Multifunctional Neuromorphic Computing

Neuromorphic computing has emerged as an important field to reduce the energy impact of artificial neural networks (ANNs). Most neuromorphic devices, along with previously proposed graphene synapses, are stiff, rigid, and generally incompatible with biological tissue. We propose a transistor based on a graphene-Nafion interface featuring highly linear and symmetric update response, superior energy efficiency, mechanical flexibility, and biocompatibility as a platform for neuromorphics in biological interfaces.

We fabricated mesoscale (few mm²) and microscale (few µm²) synaptic transistor devices by interfacing graphene with a Nafion membrane. Multiple 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 extremely low power updates, and the synaptic updates were found to have useful algorithmic implications when applied to a crossbar simulator. To further evaluate the functionality of the device, we use dual-gate operation of the transistor in current mode to model alpha and Gaussian dendritic kernels. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. The results indicate that the graphene-Nafion device platform can provide multifunctional neuromorphic functionality, along with mechanical flexibility and biocompatibility.