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1,611 results on '"Kim, Sungjun"'

157. Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system.

Author

Ju, Dongyeol, Ji, Hyeonseung, Lee, Jungwoo, and Kim, Sungjun

Subjects
ATOMIC layer deposition, MEMRISTORS, SHORT-term memory, PARALLEL processing, COMPUTER systems
Abstract

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between "0" and "1." [ABSTRACT FROM AUTHOR]

Published
2024
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199. Impact of HfO2 Dielectric Layer Placement in Hf0.5Zr0.5O2‐Based Ferroelectric Tunnel Junctions for Neuromorphic Applications.

Author

Kim, Juri, Park, Yongjin, Lee, Jungwoo, Lim, Eunjin, Lee, Jung‐Kyu, and Kim, Sungjun

Subjects
PATTERN recognition systems, LONG-term synaptic depression, DIELECTRICS, TUNNEL design & construction, STRAY currents, FERROELECTRICITY, LEAD alloys
Abstract

The use of Hf0.5Zr0.5O2 (HZO) films within hafnia‐based ferroelectric tunnel junctions (FTJ) presents a promising avenue for next‐generation non‐volatile memory devices. HZO exhibits excellent ferroelectric properties, ultra‐thinness, low power consumption, nondestructive readout, and compatibility with silicon devices. In this study, Mo/HZO/n+ Si devices are investigated, incorporating a 1 nm HfO2 dielectric layer at the top and bottom of the HZO ferroelectric layer. Comparing the FTJ device configurations, it is observed that the metal‐ferroelectric‐dielectric‐semiconductor (MFIS) outperforms the metal‐dielectric‐ferroelectric‐semiconductor (MIFS) in terms of ferroelectricity, displaying a high 2Pr value of ≈69 µC cm−2. Additionally, MFIS exhibits lower leakage current, higher tunneling electro‐resistance ratio, and a thin dead layer during short pulse switching, as confirmed through DC double sweeping of I−V characteristics. The modified half‐bias scheme demonstrates a maximum array size of 191 for MFIS, showcasing its superior performance over MIFS. Synaptic characteristics, including potentiation, depression, paired‐pulse facilitation, spike‐rate‐dependent plasticity, and excitatory postsynaptic current, are measured using MFIS, highlighting its outstanding ferroelectric properties. As a physical reservoir, the FTJ device implements 16 states of 4 bits in reservoir computing. Finally, pattern recognition using a deep learning neural network achieves high accuracy with using the Modified National Institute of Standards and Technology dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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200. Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review.

Author

Azad, Muhammad Muzammil, Kim, Sungjun, Cheon, Yu Bin, and Kim, Heung Soo

Subjects
*DEEP learning, *STRUCTURAL health monitoring, *COMPOSITE structures, *MACHINE learning, *DIGITAL transformation, *ARTIFICIAL intelligence
Abstract

Structural health monitoring (SHM) methods are essential to guarantee the safety and integrity of composite structures, which are extensively utilized in aerospace, automobile, marine, and infrastructure industry. The deterioration of composite structures is primarily caused by operational and environmental variability. To address this issue, artificial intelligence (AI) techniques are being integrated into the SHM systems to enhance the performance of composite structures via digital transformation and big data analysis. Therefore, the present article aims to provide a critical review of AI models, including machine learning, deep learning, and transfer learning, to preserve and sustain composite structures throughout their life. The article covers the complete SHM process for composite structures, including sensing technologies, data-preprocessing, feature extraction, and decision-making process. Thus, the health monitoring of composites is presented in consideration of modern AI techniques, accompanied by the identification of current challenges and potential future research directions. [ABSTRACT FROM AUTHOR]

Published
2024
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