Flexible In–Ga–Zn–N–O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain–computer interfaces

Abstract

Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain–computer interfaces (BCIs). Herein, we report the development of flexible In–Ga–Zn–N–O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.