권대웅 교수

We have prepared Al2O3 films by the atomic layer deposition technique using trimethylaluminum as the precursor for aluminum and O3, instead of commonly used H2O, as an oxidant. We show that even without any postdeposition annealing or any preventive layer between the Al2O3 film and Si substrate to suppress the formation of metallic clusters, the Al2O3 films prepared using O3 have significantly less amount of defect states like Al–Al and OH bonds compared with those prepared by H2O. The films sho

With the recently increasing prevalence of deep learning, both academia and industry exhibit substantial interest in neuromorphic computing, which mimics the functional and structural features of the human brain. To realize neuromorphic computing, an energy-efficient and reliable artificial synapse must be developed. In this study, the synaptic ferroelectric field-effect-transistor (FeFET) array is fabricated as a component of a neuromorphic convolutional neural network. Beyond the single transi

Gaseous pollutants, including nitrogen oxides, pose a severe threat to ecosystems and human health; therefore, developing reliable gas-sensing systems to detect them is becoming increasingly important. Among the various options, metal-oxide-based gas sensors have attracted attention due to their capability for real-time monitoring and large response. In particular, in the field of materials science, there has been extensive research into controlling the morphological properties of metal oxides.

In recent years, neuromorphic computing has been rapidly developed to overcome the limitations of von Neumann architecture. In this regard, the demand for high‐performance synaptic devices with high switching speeds, low power consumption, and multilevel conductance is increasing. Among the various synaptic devices, ferroelectric tunnel junctions (FTJs) are promising candidates. While previous studies have focused on improving reliability of FTJs to enhance the synaptic behavior, low‐frequency n

A ferroelectric thin‐film transistor (FeTFT)‐based synaptic device with an indium–gallium–zinc oxide (IGZO) channel and a metal–ferroelectric–metal–insulator–semiconductor (MFMIS) structure is reported. The fabricated FeTFT exhibits a highly linear conductance response (| α | = 0.21) with a large dynamic range ( G max / G min ≈ 53.2), although identical program pulses are applied to the device. In addition, because the inner metal layer of the FeTFTs has an MFMIS structure, the electric field is

We investigate the variability of a ferroelectric FET (FEFET) in program operation using low-frequency noise (LFN) spectroscopy. Contrary to the previous report, LFN characteristics of FEFETs differ significantly depending on the program [low threshold voltage (Vth)] or erase state [high Vth)] [Shin et al., IEEE Electron Device Lett. 43, 13 (2022)]. Furthermore, the 1/f noise variation of the FEFETs is much larger in the program state than that in the erase state. It is revealed that the change

Artificial neurons and synapses are crucial for efficiently implementing spiking neural networks (SNNs) in hardware. The distinct functional requirements of artificial neurons and synapses present significant challenges in the implementation of area- and energy-efficient SNNs. This study reports an all-ferroelectric SNN system through co-optimization of material properties and device configurations using wafer-scale atomic layer deposition. For the first time, a double-gate (DG) morphotropic pha

The effects of the grain size of Pt bottom electrodes on the ferroelectricity of hafnium zirconium oxide (HZO) were studied in terms of the orthorhombic phase transformation. HZO thin films were deposited by chemical solution deposition on the Pt bottom electrodes with various grain sizes which had been deposited by direct current sputtering. All the samples were crystallized by rapid thermal annealing at 700 °C to allow a phase transformation. The crystallographic phases were determined by graz

Reinforcement learning (RL), exhibiting outstanding performance in various fields, requires large amounts of data for high performance. While exploration techniques address this requirement, conventional exploration methods have limitations: complexity of hardware implementation and significant hardware burden. Herein, in‐memory RL systems leveraging intrinsic 1/ f noise of synaptic ferroelectric field‐effect‐transistors (FeFETs) for efficient exploration are proposed. The electrical characteris

The rapid advancement of artificial intelligence has enabled breakthroughs in diverse fields, including autonomous systems and medical diagnostics. However, conventional deterministic neural networks struggle to capture uncertainty, limiting their reliability when handling real-world data, which are often noisy, imbalanced, or scarce. Bayesian neural networks address this limitation by representing weights as probabilistic distributions, allowing for natural uncertainty quantification and improv