Joon Young Kwak

2.1k total citations
75 papers, 1.5k citations indexed

About

Joon Young Kwak is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Cellular and Molecular Neuroscience. According to data from OpenAlex, Joon Young Kwak has authored 75 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Electrical and Electronic Engineering, 35 papers in Materials Chemistry and 14 papers in Cellular and Molecular Neuroscience. Recurrent topics in Joon Young Kwak's work include Advanced Memory and Neural Computing (40 papers), 2D Materials and Applications (23 papers) and Ferroelectric and Negative Capacitance Devices (19 papers). Joon Young Kwak is often cited by papers focused on Advanced Memory and Neural Computing (40 papers), 2D Materials and Applications (23 papers) and Ferroelectric and Negative Capacitance Devices (19 papers). Joon Young Kwak collaborates with scholars based in South Korea, United States and Germany. Joon Young Kwak's co-authors include Hussain Alsalman, Michael G. Spencer, Jeonghyun Hwang, Jaewook Kim, Inho Kim, Hyun‐Cheol Song, YeonJoo Jeong, Jongkil Park, Suyoun Lee and Chong‐Yun Kang and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Nano Letters.

In The Last Decade

Joon Young Kwak

68 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joon Young Kwak South Korea 20 873 800 261 204 175 75 1.5k
Xuewei Feng Singapore 25 1.4k 1.6× 1.2k 1.4× 292 1.1× 303 1.5× 134 0.8× 57 2.1k
Sangheon Lee South Korea 25 1.4k 1.6× 643 0.8× 177 0.7× 308 1.5× 112 0.6× 93 1.9k
Il‐Suk Kang South Korea 17 710 0.8× 524 0.7× 613 2.3× 163 0.8× 106 0.6× 63 1.4k
Jasmin Aghassi‐Hagmann Germany 22 1.1k 1.3× 416 0.5× 397 1.5× 154 0.8× 194 1.1× 112 1.6k
Xiao Qiu Hong Kong 14 1.0k 1.2× 391 0.5× 318 1.2× 212 1.0× 108 0.6× 24 1.3k
Mei Yang China 24 897 1.0× 578 0.7× 188 0.7× 299 1.5× 391 2.2× 103 1.9k
Young‐Tae Kim South Korea 17 1.4k 1.6× 998 1.2× 379 1.5× 142 0.7× 118 0.7× 92 1.9k
Huizhong Zeng China 19 719 0.8× 678 0.8× 216 0.8× 96 0.5× 173 1.0× 66 1.3k
Jesse Tice United States 19 757 0.9× 604 0.8× 143 0.5× 119 0.6× 130 0.7× 43 1.2k

Countries citing papers authored by Joon Young Kwak

Since Specialization
Citations

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

Fields of papers citing papers by Joon Young Kwak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joon Young Kwak. 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 Joon Young Kwak. The network helps show where Joon Young Kwak may publish in the future.

Co-authorship network of co-authors of Joon Young Kwak

This figure shows the co-authorship network connecting the top 25 collaborators of Joon Young Kwak. A scholar is included among the top collaborators of Joon Young Kwak 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 Joon Young Kwak. Joon Young Kwak 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.
Kim, Min-Jee, Hyung‐Min Lee, YeonJoo Jeong, & Joon Young Kwak. (2025). Emerging Neuromorphic Devices-Compatible SNN Hardware With Adaptive STDP and Validation Using Novel CMOS Neuron-Synapse Circuits. IEEE Access. 13. 173739–173751.
2.
3.
Islam, Md Mobaidul, et al.. (2024). Transistors with ferroelectric ZrXAl1−XOY crystallized by ZnO growth for multi-level memory and neuromorphic computing. Communications Materials. 5(1). 6 indexed citations
4.
Park, Sang‐Joon, et al.. (2024). Realization of Extremely High-Gain and Low-Power in nMOS Inverter Based on Monolayer WS2 Transistor Operating in Subthreshold Regime. ACS Nano. 18(34). 22965–22977. 4 indexed citations
5.
6.
Jeong, Dong Geun, Eunpyo Park, Gichang Noh, et al.. (2024). Grain boundary control for high-reliability HfO2-based RRAM. Chaos Solitons & Fractals. 183. 114956–114956. 9 indexed citations
7.
Noh, Gichang, Eunpyo Park, Min Jee Kim, et al.. (2023). Hardware Implementation of Network Connectivity Relationships Using 2D hBN‐Based Artificial Neuron and Synaptic Devices. Advanced Functional Materials. 34(10). 32 indexed citations
8.
Jeong, YeonJoo, Jaewook Kim, Suyoun Lee, et al.. (2023). Multifilamentary switching of Cu/SiOx memristive devices with a Ge-implanted a-Si underlayer for analog synaptic devices. NPG Asia Materials. 15(1). 4 indexed citations
9.
Park, Eunpyo, Gichang Noh, In Soo Kim, et al.. (2022). A pentagonal 2D layered PdSe2-based synaptic device with a graphene floating gate. Journal of Materials Chemistry C. 10(43). 16536–16545. 11 indexed citations
10.
Kim, Tae-Yoon, Suyoun Lee, Jong‐Keuk Park, et al.. (2022). SPICE Study of STDP Characteristics in a Drift and Diffusive Memristor-Based Synapse for Neuromorphic Computing. IEEE Access. 10. 6381–6392.
11.
Park, Jongkil, YeonJoo Jeong, Jaewook Kim, et al.. (2022). High Dynamic Range Digital Neuron Core With Time-Embedded Floating-Point Arithmetic. IEEE Transactions on Circuits and Systems I Regular Papers. 70(1). 290–301. 2 indexed citations
12.
Jeong, YeonJoo, Jaewook Kim, Suyoun Lee, et al.. (2022). Emulating the short-term plasticity of a biological synapse with a ruthenium complex-based organic mixed ionic–electronic conductor. Materials Advances. 3(6). 2827–2837. 21 indexed citations
13.
Jeong, YeonJoo, Joon Young Kwak, Jongkil Park, et al.. (2022). A Poisson Process Generator Based on Multiple Thermal Noise Amplifiers for Parallel Stochastic Simulation of Biochemical Reactions. Electronics. 11(7). 1039–1039. 1 indexed citations
14.
Dhakal, Krishna P., Eunpyo Park, Gichang Noh, et al.. (2022). Gas‐Phase Alkali Metal‐Assisted MOCVD Growth of 2D Transition Metal Dichalcogenides for Large‐Scale Precise Nucleation Control. Small. 18(20). e2106368–e2106368. 37 indexed citations
15.
Kang, Minsoo, Han Beom Jeong, Han Beom Jeong, et al.. (2021). Low-Temperature and High-Quality Growth of Bi2O2Se Layered Semiconductors via Cracking Metal–Organic Chemical Vapor Deposition. ACS Nano. 15(5). 8715–8723. 55 indexed citations
16.
Kim, Tae-Yoon, Jaewook Kim, Joon Young Kwak, et al.. (2021). Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update. Frontiers in Computational Neuroscience. 15. 646125–646125. 62 indexed citations
17.
Jeong, YeonJoo, Jaewook Kim, Suyoun Lee, et al.. (2021). Ion beam-assisted solid phase epitaxy of SiGe and its application for analog memristors. Journal of Alloys and Compounds. 884. 161086–161086. 7 indexed citations
18.
Alsalman, Hussain, et al.. (2020). Self-heating controlled current–voltage and noise characteristics in graphene. Journal of Physics D Applied Physics. 54(18). 185101–185101. 2 indexed citations
19.
Park, Soo‐Jin, Jonghan Song, YeonJoo Jeong, et al.. (2020). Enhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation. NPG Asia Materials. 12(1). 23 indexed citations
20.
Park, Jaehong, et al.. (2019). Optical control of the layer degree of freedom through Wannier–Stark states in polar 3R MoS 2. Journal of Physics Condensed Matter. 31(31). 315502–315502. 6 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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