Taming Prolonged Ionic Drift-Diffusion Dynamics for Brain-Inspired Computation

Recent advances in neural network-based computing have enabled human-like information processing in areas such as image classification and voice recognition. However, many neural networks run on conventional computers that operate at GHz clock frequency and consume considerable power compared to biological neural networks, such as human brains, which work with a much slower spiking rate. Although many electronic devices aiming to emulate the energy efficiency of biological neural networks have been explored, achieving long timescales while maintaining scalability remains an important challenge. In this study, a field-effect transistor based on the oxide semiconductor strontium titanate (SrTiO₃) achieves leaky integration on a long timescale by leveraging the drift-diffusion of oxygen vacancies in this material. Experimental analysis and finite-element model simulations reveal the mechanism behind the leaky integration of the SrTiO₃ transistor. With a timescale in the order of one second, which is close to that of biological neuron activity, this transistor is a promising component for biomimicking neuromorphic computing.