Sung Yun Woo

608 total citations
43 papers, 440 citations indexed

About

Sung Yun Woo is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Sung Yun Woo has authored 43 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Electrical and Electronic Engineering, 14 papers in Cellular and Molecular Neuroscience and 12 papers in Cognitive Neuroscience. Recurrent topics in Sung Yun Woo's work include Advanced Memory and Neural Computing (32 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Semiconductor materials and devices (14 papers). Sung Yun Woo is often cited by papers focused on Advanced Memory and Neural Computing (32 papers), Ferroelectric and Negative Capacitance Devices (18 papers) and Semiconductor materials and devices (14 papers). Sung Yun Woo collaborates with scholars based in South Korea and United States. Sung Yun Woo's co-authors include Jong‐Ho Lee, Jong‐Ho Bae, Soochang Lee, Won-Mook Kang, Byung‐Gook Park, Chul-Heung Kim, Dongseok Kwon, Suhwan Lim, Jangsaeng Kim and Hyeongsu Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Science Advances.

In The Last Decade

Sung Yun Woo

39 papers receiving 426 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sung Yun Woo South Korea 13 425 140 129 97 22 43 440
Chul-Heung Kim South Korea 11 343 0.8× 122 0.9× 87 0.7× 82 0.8× 16 0.7× 21 359
Thomas Dalgaty France 13 400 0.9× 125 0.9× 142 1.1× 131 1.4× 32 1.5× 26 468
Cory Merkel United States 12 366 0.9× 108 0.8× 202 1.6× 123 1.3× 31 1.4× 47 439
Jaehyun Kim South Korea 8 309 0.7× 88 0.6× 100 0.8× 106 1.1× 43 2.0× 15 367
S. Bianchi Italy 11 353 0.8× 137 1.0× 106 0.8× 122 1.3× 15 0.7× 21 396
Alon Loeffler Australia 10 387 0.9× 128 0.9× 225 1.7× 244 2.5× 33 1.5× 16 444
Jangsaeng Kim South Korea 10 304 0.7× 76 0.5× 82 0.6× 50 0.5× 16 0.7× 44 328
Xiaolong Zou China 7 294 0.7× 167 1.2× 87 0.7× 146 1.5× 22 1.0× 12 384
Hanchan Song South Korea 12 456 1.1× 214 1.5× 84 0.7× 71 0.7× 15 0.7× 27 480
Jae Hyun In South Korea 9 288 0.7× 122 0.9× 60 0.5× 51 0.5× 37 1.7× 15 359

Countries citing papers authored by Sung Yun Woo

Since Specialization
Citations

This map shows the geographic impact of Sung Yun Woo'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 Sung Yun Woo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sung Yun Woo more than expected).

Fields of papers citing papers by Sung Yun Woo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sung Yun Woo. 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 Sung Yun Woo. The network helps show where Sung Yun Woo may publish in the future.

Co-authorship network of co-authors of Sung Yun Woo

This figure shows the co-authorship network connecting the top 25 collaborators of Sung Yun Woo. A scholar is included among the top collaborators of Sung Yun Woo 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 Sung Yun Woo. Sung Yun Woo 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.
Ko, Jonghyun, Jangsaeng Kim, Wonjun Shin, et al.. (2025). CMOS-compatible flash-gated thyristor–based neuromorphic module with small area and low energy consumption for in-memory computing. Science Advances. 11(29). eadt8227–eadt8227. 1 indexed citations
3.
Pak, Sangyeon, et al.. (2025). Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review. Biomimetics. 10(2). 121–121.
4.
Lee, Kyung Min, et al.. (2024). Lateral Migration‐based Flash‐like Synaptic Device for Hybrid Off‐chip/On‐chip Training. Advanced Electronic Materials. 10(4). 1 indexed citations
5.
Kim, Hyeongsu, et al.. (2022). Variation-Tolerant Capacitive Array for Binarized Neural Network. IEEE Electron Device Letters. 43(3). 478–481. 7 indexed citations
6.
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
7.
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
8.
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
9.
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
10.
Park, Minkyu, et al.. (2021). CMOS-Compatible Low-Power Gated Diode Synaptic Device for Hardware- Based Neural Network. IEEE Transactions on Electron Devices. 69(2). 832–837. 4 indexed citations
11.
Kim, Jangsaeng, Dongseok Kwon, Sung Yun Woo, et al.. (2021). On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm. Neural Computing and Applications. 33(15). 9391–9402. 8 indexed citations
12.
Woo, Sung Yun, Dongseok Kwon, Won-Mook Kang, et al.. (2020). Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems. IEEE Access. 8. 202639–202647. 18 indexed citations
13.
Bae, Jong‐Ho, Min-Woo Kwon, Jae Hwa Seo, et al.. (2019). Characterization of a Capacitorless DRAM Cell for Cryogenic Memory Applications. IEEE Electron Device Letters. 40(10). 1614–1617. 19 indexed citations
14.
Woo, Sung Yun, Jangsaeng Kim, Won-Mook Kang, et al.. (2019). Implementation of homeostasis functionality in neuron circuit using double-gate device for spiking neural network. Solid-State Electronics. 165. 107741–107741. 17 indexed citations
15.
Lee, Jong‐Ho, Sung Yun Woo, Sung‐Tae Lee, et al.. (2019). Review of candidate devices for neuromorphic applications. 22–27. 3 indexed citations
16.
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
17.
Kim, Chul-Heung, Soochang Lee, Sung Yun Woo, et al.. (2018). Demonstration of Unsupervised Learning With Spike-Timing-Dependent Plasticity Using a TFT-Type NOR Flash Memory Array. IEEE Transactions on Electron Devices. 65(5). 1774–1780. 52 indexed citations
18.
Woo, Sung Yun, Won-Mook Kang, Soochang Lee, et al.. (2018). A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit. Frontiers in Neuroscience. 12. 704–704. 31 indexed citations
19.
20.
Yoon, Young Jun, Sung Yun Woo, Jae Hwa Seo, et al.. (2012). Design optimization of vertical double-gate tunneling field-effect transistors. Journal of the Korean Physical Society. 61(10). 1679–1682. 5 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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