Chul-Heung Kim

453 total citations
21 papers, 359 citations indexed

About

Chul-Heung Kim is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Chul-Heung Kim has authored 21 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Electrical and Electronic Engineering, 10 papers in Cellular and Molecular Neuroscience and 8 papers in Cognitive Neuroscience. Recurrent topics in Chul-Heung Kim's work include Advanced Memory and Neural Computing (19 papers), Neuroscience and Neural Engineering (10 papers) and Neural dynamics and brain function (8 papers). Chul-Heung Kim is often cited by papers focused on Advanced Memory and Neural Computing (19 papers), Neuroscience and Neural Engineering (10 papers) and Neural dynamics and brain function (8 papers). Chul-Heung Kim collaborates with scholars based in South Korea and United States. Chul-Heung Kim's co-authors include Jong‐Ho Lee, Jong‐Ho Bae, Soochang Lee, Sung Yun Woo, Suhwan Lim, Won-Mook Kang, Byung‐Gook Park, Dongseok Kwon, Jangsaeng Kim and Sung‐Tae Lee and has published in prestigious journals such as IEEE Transactions on Electron Devices, Nanotechnology and Neurocomputing.

In The Last Decade

Chul-Heung Kim

21 papers receiving 347 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chul-Heung Kim South Korea 11 343 122 87 82 16 21 359
Sung Yun Woo South Korea 13 425 1.2× 140 1.1× 129 1.5× 97 1.2× 17 1.1× 43 440
Jangsaeng Kim South Korea 10 304 0.9× 76 0.6× 82 0.9× 50 0.6× 20 1.3× 44 328
Thomas Dalgaty France 13 400 1.2× 125 1.0× 142 1.6× 131 1.6× 27 1.7× 26 468
Jaehyun Kim South Korea 8 309 0.9× 88 0.7× 100 1.1× 106 1.3× 40 2.5× 15 367
S. Bianchi Italy 11 353 1.0× 137 1.1× 106 1.2× 122 1.5× 26 1.6× 21 396
Cory Merkel United States 12 366 1.1× 108 0.9× 202 2.3× 123 1.5× 25 1.6× 47 439
Yann Beilliard Canada 9 325 0.9× 88 0.7× 60 0.7× 34 0.4× 29 1.8× 28 352
Xiaolong Zou China 7 294 0.9× 167 1.4× 87 1.0× 146 1.8× 40 2.5× 12 384
Jae Hyun In South Korea 9 288 0.8× 122 1.0× 60 0.7× 51 0.6× 21 1.3× 15 359
Alexander H. Hsia United States 7 419 1.2× 117 1.0× 107 1.2× 38 0.5× 57 3.6× 11 460

Countries citing papers authored by Chul-Heung Kim

Since Specialization
Citations

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

Fields of papers citing papers by Chul-Heung Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chul-Heung Kim. 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 Chul-Heung Kim. The network helps show where Chul-Heung Kim may publish in the future.

Co-authorship network of co-authors of Chul-Heung Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Chul-Heung Kim. A scholar is included among the top collaborators of Chul-Heung Kim 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 Chul-Heung Kim. Chul-Heung Kim 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, Jangsaeng, Soochang Lee, Chul-Heung Kim, Byung-Gook Park, & Jong‐Ho Lee. (2022). Analog synaptic devices applied to spiking neural networks for reinforcement learning applications. Semiconductor Science and Technology. 37(7). 75002–75002. 2 indexed citations
2.
Kim, Chul-Heung, et al.. (2022). A novel methodology for neural compact modeling based on knowledge transfer. Solid-State Electronics. 198. 108450–108450. 9 indexed citations
3.
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
4.
Kim, Jangsaeng, Dongseok Kwon, Sung Yun Woo, et al.. (2020). Hardware-based spiking neural network architecture using simplified backpropagation algorithm and homeostasis functionality. Neurocomputing. 428. 153–165. 16 indexed citations
5.
Kim, Chul-Heung, et al.. (2019). Unsupervised online learning of temporal information in spiking neural network using thin-film transistor-type NOR flash memory devices. Nanotechnology. 30(43). 435206–435206. 10 indexed citations
6.
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
7.
Kim, Jangsaeng, Chul-Heung Kim, Sung Yun Woo, et al.. (2019). Initial synaptic weight distribution for fast learning speed and high recognition rate in STDP-based spiking neural network. Solid-State Electronics. 165. 107742–107742. 8 indexed citations
8.
9.
Woo, Sung Yun, Suhwan Lim, Sung‐Tae Lee, et al.. (2019). Synaptic device using a floating fin-body MOSFET with memory functionality for neural network. Solid-State Electronics. 156. 23–27. 5 indexed citations
10.
Woo, Sung Yun, Won-Mook Kang, Jangsaeng Kim, et al.. (2019). Analyzation of Positive Feedback device with Steep Subthreshold Swing Characteristics in 14 nm FinFET Technology. 404–406. 1 indexed citations
11.
Kim, Chul-Heung, et al.. (2019). Grayscale Image Recognition Using Spike-Rate-Based Online Learning and Threshold Adjustment of Neurons in a Thin-Film Transistor-Type NOR Flash Memory Array. Journal of Nanoscience and Nanotechnology. 19(10). 6055–6060. 2 indexed citations
12.
Lim, Suhwan, Dongseok Kwon, Sung‐Tae Lee, et al.. (2019). Highly Reliable Inference System of Neural Networks Using Gated Schottky Diodes. IEEE Journal of the Electron Devices Society. 7. 522–528. 15 indexed citations
13.
Lee, Soochang, et al.. (2019). Unsupervised Online Learning With Multiple Postsynaptic Neurons Based on Spike-Timing-Dependent Plasticity Using a Thin-Film Transistor-Type NOR Flash Memory Array. Journal of Nanoscience and Nanotechnology. 19(10). 6050–6054. 8 indexed citations
14.
15.
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
16.
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
17.
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
18.
Lee, Sung‐Tae, Suhwan Lim, Jong‐Ho Bae, et al.. (2018). Neuromorphic Technology Based on Charge Storage Memory Devices. 169–170. 19 indexed citations
19.
Kwon, Dongseok, Suhwan Lim, Jong‐Ho Bae, et al.. (2018). Adaptive Weight Quantization Method for Nonlinear Synaptic Devices. IEEE Transactions on Electron Devices. 66(1). 395–401. 34 indexed citations
20.
Kim, Chul-Heung, et al.. (2015). GIDL Characteristics in Gated-Diode Memory String and Its Application to Current-Steering Digital-to-Analog Conversion. IEEE Transactions on Electron Devices. 62(10). 3272–3277. 4 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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