Ionically gated transistors based on two-dimensional materials for neuromorphic computing

Abstract Neuromorphic computing is a low-power and energy efficient alternative to von Neumann computing that demands new materials and computing architectures. Two-dimensional (2D) van der Waals materials and ions are a particularly favorable pair for neuromorphic computing. The large surface to volume ratio of 2D layered materials makes them sensitive to the presence of ions, detected as orders of magnitude change in electrical resistance. Quantum confinement of 2D crystals limits carrier scattering and enhances mobility, which decreases power consumption. Moreover, the 2D crystal-ion pair can provide volatile and non-volatile responses in the same device, as well as dynamic synaptic properties, such as spike-timing dependent plasticity. These dynamic properties are particularly relevant because they mirror the mechanisms involved in biological learning and memory. In this perspective, we first summarize recent progress in the field, categorize 2D crystal-ion devices in terms of their mechanisms (either electrostatic or electrochemical), and highlight key synaptic functionalities these devices can replicate. We underscore the differences between artificial and biological synapses, and between devices meant to emulate biological functions versus those optimized for compatibility with digital artificial neural networks (ANNs). We note that the use of ionically gated transistors based on 2D crystals (2D IGTs) in ANNs has primarily focused on their non-volatile memory functions, rather than fully exploiting their dynamic synaptic properties. We assert that the energy-efficient operation of 2D IGTs, enabled by their high capacitance density and tunable ion dynamics, makes them particularly suited for low-power edge computing applications. Finally, our perspective is that realizing the full potential of 2D crystals and ions in neuromorphic systems will require bridging the gap between demonstrated synaptic functionalities and their practical implementations in neural networks.