Yasushi Hotta

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.

The interface properties of strongly correlated electron system/band semiconductor junctions were investigated in the La_1−xSr_xVO₃/p-Si(100) structure. Spectroscopic observations show that the electronic structure of the interface is typical of insulator/semiconductor junctions for x ≤ 0.2 and Schottky junctions for x ≥ 0.25. For the forward current density–bias voltage (J–V) characteristics, energy barriers that were otherwise unexplainable by spectroscopy were estimated from the thermionic emission model fitting of the J–V characteristics, indicating that the injected carriers from Si to La_1−xSr_xVO₃ can also feel the correlation force of Coulomb repulsion at the interface.