Artificial nerve system that mimics the tactile perception ability of human skin which could be adopted in the realization of devices for a hyperconnected society, including prosthetic devices, interfaces for virtual reality, and smart sensors. The artificial nerve system consists of a pressure sensor, memory, which has the capability of analog operation, and a signal processing element. Although technological advances have been made in semiconductor process-based memory and transistors over the decades, sensors that detect external stimuli rely on micro-electromechanical systems, which are hard to miniaturize and integrate with high density. Our group presents a monolithic-three-dimensional integrated artificial nerve system consists of a self-powered piezoelectric pressure sensor and neuromorphic memory by using ferroelectric hafnium zirconium oxide (HfxZr1-xO2) films.

Artificial neurons are one of key components for high energy-efficient AI system. Recently, resistance based permissive neurons have been developed, but with limited reliability, bulky capacitors or additional reset circuits. In our laboratory, we propose an anti-ferroelectric field-effect transistor neurons based on the Hafnium-Zirconium oxide anti-ferroelectric film to overcome these challenges.

The number of nodes commonly used in sensory networks is rapidly increasing, which increases the bottlenecks that occur during data communication. To efficiently process large amounts of data and reduce power consumption for data processing, computational approaches that perform preprocessing in sensory networks must be developed. This approach can reduce redundant data movement between sensing and processing devices. Our group considers proximity sensors and intra-sensor computing, where the computational tasks are partially shifted to the sensory terminal.