Neuromorphic computing is a brain-inspired computing paradigm that aims to construct
efficient, low-power, and adaptive computing systems by emulating the information …

Recently, neuromorphic computing has been proposed to overcome the drawbacks of the
current von Neumann computing architecture. Especially, spiking neural network (SNN) has …

The concept of neuromorphic devices, aiming to process large amounts of information in
parallel, at low power, high speed, and high efficiency, is to mimic the functions of human …

We conducted research to create reverse synapse plasticity using metal oxide
semiconductor-based field-effect transistors. Specifically, we used IGZO as the channel, Al 2 …

We propose that the post-deposition oxidation of the IGZO surface is essential for improving
the interface quality, with Al2O3 prepared by atomic layer deposition (ALD) employing a …

In the human brain, attention plays a crucial role in encoding information into memory.
Therefore, focused attention during encoding enhances the likelihood of information being …

Synaptic transistors have been proposed to implement neuron activation functions of neural
networks (NNs). While promising to enable compact, fast, inexpensive, and energy-efficient …

In this paper, we conducted a simulation of an indium-gallium-zinc oxide (IGZO)-based
neuromorphic system and proposed layer-by-layer membrane capacitor (Cmem) …

Neuromorphic computing is a rapidly emerging technology that can overcome the limitations
of von Neumann-type architecture-based computing systems, offering the potential for …

Nowadays, the researcher has an eye on enhancing the performance of resistive random-
access memory (RRAM) and exploring new demands and applications, particularly in the …