Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications.

The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems.

Improvement of Resistance Change Memory Characteristics in Ferroelectric and Antiferroelectric (like) Parallel Structures.

Recently, considerable attention has been paid to the development of advanced technologies such as artificial intelligence (AI) and big data, and high-density, high-speed storage devices are being extensively studied to realize the technology. Ferroelectrics are promising non-volatile memory materials because of their ability to maintain polarization, even when an external electric field is removed. Recently, it has been reported that HfO thin films compatible with complementary metal-oxide-semiconductor (CMOS) processes exhibit ferroelectricity even at a thickness of less than 10 nm.

Synaptic Characteristic of Hafnia-Based Ferroelectric Tunnel Junction Device for Neuromorphic Computing Application.

Owing to the 4th Industrial Revolution, the amount of unstructured data, such as voice and video data, is rapidly increasing. Brain-inspired neuromorphic computing is a new computing method that can efficiently and parallelly process rapidly increasing data. Among artificial neural networks that mimic the structure of the brain, the spiking neural network (SNN) is a network that imitates the information-processing method of biological neural networks.