Dongseok Kwon

1.6k total citations
80 papers, 1.1k citations indexed

About

Dongseok Kwon is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Dongseok Kwon has authored 80 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Electrical and Electronic Engineering, 20 papers in Artificial Intelligence and 14 papers in Cellular and Molecular Neuroscience. Recurrent topics in Dongseok Kwon's work include Advanced Memory and Neural Computing (59 papers), Ferroelectric and Negative Capacitance Devices (48 papers) and Semiconductor materials and devices (24 papers). Dongseok Kwon is often cited by papers focused on Advanced Memory and Neural Computing (59 papers), Ferroelectric and Negative Capacitance Devices (48 papers) and Semiconductor materials and devices (24 papers). Dongseok Kwon collaborates with scholars based in South Korea, United States and Canada. Dongseok Kwon's co-authors include Jong‐Ho Lee, Jong‐Ho Bae, Byung‐Gook Park, Wonjun Shin, Suhwan Lim, Sung‐Tae Lee, Hyeongsu Kim, Ryun‐Han Koo, Jae‐Joon Kim and Daewoong Kwon and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Applied Physics Letters.

In The Last Decade

Dongseok Kwon

79 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dongseok Kwon South Korea 20 1.0k 225 173 164 151 80 1.1k
Jong‐Ho Bae South Korea 24 1.8k 1.8× 236 1.0× 253 1.5× 337 2.1× 406 2.7× 136 1.9k
Spyros Stathopoulos United Kingdom 14 863 0.8× 81 0.4× 392 2.3× 87 0.5× 107 0.7× 59 946
Sijie Ma Hong Kong 9 570 0.6× 141 0.6× 179 1.0× 93 0.6× 123 0.8× 12 654
Yong Lim South Korea 11 1.0k 1.0× 125 0.6× 268 1.5× 397 2.4× 52 0.3× 29 1.1k
Joanna Symonowicz United Kingdom 7 772 0.8× 188 0.8× 200 1.2× 141 0.9× 306 2.0× 9 944
Furqan Zahoor India 13 1.0k 1.0× 72 0.3× 201 1.2× 118 0.7× 198 1.3× 37 1.1k
Suhwan Lim South Korea 15 663 0.6× 144 0.6× 140 0.8× 58 0.4× 44 0.3× 54 714
Danian Dong China 12 684 0.7× 119 0.5× 130 0.8× 59 0.4× 275 1.8× 54 845

Countries citing papers authored by Dongseok Kwon

Since Specialization
Citations

This map shows the geographic impact of Dongseok Kwon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dongseok Kwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongseok Kwon more than expected).

Fields of papers citing papers by Dongseok Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dongseok Kwon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dongseok Kwon. The network helps show where Dongseok Kwon may publish in the future.

Co-authorship network of co-authors of Dongseok Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Dongseok Kwon. A scholar is included among the top collaborators of Dongseok Kwon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dongseok Kwon. Dongseok Kwon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Kim, Jangsaeng, Wonjun Shin, Soochang Lee, et al.. (2024). Demonstration of In‐Memory Biosignal Analysis: Novel High‐Density and Low‐Power 3D Flash Memory Array for Arrhythmia Detection. Advanced Science. 11(26). e2308460–e2308460. 15 indexed citations
3.
Ko, Jonghyun, Dongseok Kwon, Jeonghyun Kim, et al.. (2024). SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices. SHILAP Revista de lepidopterología. 6(4). 2 indexed citations
4.
Shin, Wonjun, et al.. (2024). Robust 1/f Noise Unaffected by Program/Erase Cycling-Induced Damage in Ferroelectric Schottky Barrier FETs. IEEE Electron Device Letters. 45(9). 1645–1648. 3 indexed citations
5.
Kwon, Dongseok, et al.. (2024). FPIA: Field-Programmable Ising Arrays with In-Memory Computing. 1–6. 2 indexed citations
6.
Kim, Haesung, Ha Neul Lee, Minkyu Park, et al.. (2024). Exploration of structural influences on the ferroelectric switching characteristics of ferroelectric thin-film transistors. Nanoscale. 16(42). 19856–19864. 2 indexed citations
7.
Shin, Wonjun, Ryun‐Han Koo, Dongseok Kwon, et al.. (2023). Self‐Curable Synaptic Ferroelectric FET Arrays for Neuromorphic Convolutional Neural Network. Advanced Science. 10(15). e2207661–e2207661. 36 indexed citations
8.
Shin, Wonjun, Kyung Kyu Min, Jong‐Ho Bae, et al.. (2023). 1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network. SHILAP Revista de lepidopterología. 5(6). 18 indexed citations
9.
Kwon, Dongseok, Eun Chan Park, Wonjun Shin, et al.. (2023). Analog Synaptic Devices Based on IGZO Thin‐Film Transistors with a Metal–Ferroelectric–Metal–Insulator–Semiconductor Structure for High‐Performance Neuromorphic Systems. SHILAP Revista de lepidopterología. 5(12). 13 indexed citations
10.
Jung, Gyuweon, Seongbin Hong, Yujeong Jeong, et al.. (2023). Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection. Advanced Science. 10(30). e2302506–e2302506. 21 indexed citations
11.
Kwon, Dongseok, et al.. (2022). On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding. IEEE Access. 10. 31263–31272. 2 indexed citations
12.
Kwon, Dongseok, et al.. (2022). Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding. IEEE Access. 10. 24444–24455. 19 indexed citations
13.
Kwon, Dongseok, Soochang Lee, Minkyu Park, et al.. (2021). 3-D AND-Type Flash Memory Architecture With High-κ Gate Dielectric for High-Density Synaptic Devices. IEEE Transactions on Electron Devices. 68(8). 3801–3806. 25 indexed citations
14.
Lee, Soochang, Sung Yun Woo, Dongseok Kwon, et al.. (2021). Spiking Neural Networks With Time-to-First-Spike Coding Using TFT-Type Synaptic Device Model. IEEE Access. 9. 78098–78107. 10 indexed citations
15.
Kwon, Dongseok, Gyuweon Jung, Wonjun Shin, et al.. (2021). Efficient fusion of spiking neural networks and FET-type gas sensors for a fast and reliable artificial olfactory system. Sensors and Actuators B Chemical. 345. 130419–130419. 32 indexed citations
16.
Kwon, Dongseok, et al.. (2021). Direct Gradient Calculation: Simple and Variation‐Tolerant On‐Chip Training Method for Neural Networks. SHILAP Revista de lepidopterología. 3(8). 5 indexed citations
17.
Kang, Won-Mook, Dongseok Kwon, Sung Yun Woo, et al.. (2021). Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment. IEEE Access. 9. 73121–73132. 11 indexed citations
18.
Kwon, Dongseok, et al.. (2021). Direct Gradient Calculation: Simple and Variation‐Tolerant On‐Chip Training Method for Neural Networks. Advanced Intelligent Systems. 3(8). 1 indexed citations
19.
Shin, Wonjun, Kyung Kyu Min, Jong‐Ho Bae, et al.. (2021). Comprehensive and accurate analysis of the working principle in ferroelectric tunnel junctions using low-frequency noise spectroscopy. Nanoscale. 14(6). 2177–2185. 43 indexed citations
20.
Kim, Chul-Heung, Suhwan Lim, Sung Yun Woo, et al.. (2018). Emerging memory technologies for neuromorphic computing. Nanotechnology. 30(3). 32001–32001. 70 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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