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Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. 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. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks. 

The rate at which graphene is used in different fields of science and engineering has only increased over the past decade and shows no indication of saturating. At the same time, the most common source of high-quality graphene is through chemical vapor deposition (CVD) growth on copper foils with subsequent wet transfer steps that bring environmental problems and technical challenges due to the compliance of copper foils. To overcome these issues, thin copper films deposited on silicon wafers have been used, but the high temperatures required for graphene growth can cause dewetting of the copper film and consequent challenges in obtaining uniform growth. In this work, we explore sapphire as a substrate for the direct growth of graphene without any metal catalyst at conventional metal CVD temperatures. First, we found that annealing the substrate prior to growth was a crucial step to improve the quality of graphene that can be grown directly on such substrates. The graphene grown on annealed sapphire was uniformly bilayer and had some of the lowest Raman D/G ratios found in the literature. In addition, dry transfer experiments have been performed that have provided a direct measure of the adhesion energy, strength, and range of interactions at the sapphire/graphene interface. The adhesion energy of graphene to sapphire is lower than that of graphene grown on copper, but the strength of the graphene–sapphire interaction is higher. The quality of the several centimeter scale transfer was evaluated using Raman, SEM, and AFM as well as fracture mechanics concepts. Based on the evaluation of the electrical characteristics of the graphene synthesized in this work, this work has implications for several potential electronic applications. 

Abstract CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm²switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.