Ran Cheng

406 total citations
11 papers, 336 citations indexed

About

Ran Cheng is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Ran Cheng has authored 11 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Electrical and Electronic Engineering, 6 papers in Artificial Intelligence and 2 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Ran Cheng's work include Advanced Memory and Neural Computing (6 papers), Neural Networks and Reservoir Computing (5 papers) and Ferroelectric and Negative Capacitance Devices (3 papers). Ran Cheng is often cited by papers focused on Advanced Memory and Neural Computing (6 papers), Neural Networks and Reservoir Computing (5 papers) and Ferroelectric and Negative Capacitance Devices (3 papers). Ran Cheng collaborates with scholars based in United States, China and Qatar. Ran Cheng's co-authors include Guohai Liu, Wenxiang Zhao, Michael C. Hamilton, Huawei Zhou, Ying Xie, Liang Xu, Konstantin Nikolić, Khalid B. Mirza, Dilip Vasudevan and Christoph Kirst and has published in prestigious journals such as Journal of Applied Physics, IEEE Transactions on Industrial Electronics and Frontiers in Neuroscience.

In The Last Decade

Ran Cheng

11 papers receiving 334 citations

Peers

Ran Cheng
Andrei Vladimirescu France
J.W. Fattaruso United States
A. Shahani United States
Kushal Das Australia
Yuyao Huang China
Sarthak Das India
Johannes Kulick United States
Shawn A. Hall United States
Sumit Dutta United States
Xuan Hu United States
Andrei Vladimirescu France
Ran Cheng
Citations per year, relative to Ran Cheng Ran Cheng (= 1×) peers Andrei Vladimirescu

Countries citing papers authored by Ran Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Ran Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ran Cheng. 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 Ran Cheng. The network helps show where Ran Cheng may publish in the future.

Co-authorship network of co-authors of Ran Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Ran Cheng. A scholar is included among the top collaborators of Ran Cheng 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 Ran Cheng. Ran Cheng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Cheng, Ran, Dilip Vasudevan, & Christoph Kirst. (2024). Super-Tsetlin: Superconducting Tsetlin Machines. IEEE Transactions on Applied Superconductivity. 34(3). 1–12. 2 indexed citations
2.
Li, Hao, Han Cai, Ran Cheng, et al.. (2023). Transport Properties of NbN Thin Films Patterned With a Focused Helium Ion Beam. IEEE Transactions on Applied Superconductivity. 33(5). 1–4. 5 indexed citations
3.
Cheng, Ran, Christoph Kirst, & Dilip Vasudevan. (2023). Superconducting-Oscillatory Neural Network With Pixel Error Detection for Image Recognition. IEEE Transactions on Applied Superconductivity. 33(5). 1–7. 3 indexed citations
4.
Cheng, Ran, et al.. (2021). High-Speed and Low-Power Superconducting Neuromorphic Circuits Based on Quantum Phase-Slip Junctions. IEEE Transactions on Applied Superconductivity. 31(5). 1–8. 9 indexed citations
5.
Cheng, Ran, et al.. (2021). Toward Learning in Neuromorphic Circuits Based on Quantum Phase Slip Junctions. Frontiers in Neuroscience. 15. 765883–765883. 9 indexed citations
6.
Cheng, Ran, Khalid B. Mirza, & Konstantin Nikolić. (2020). Neuromorphic Robotic Platform with Visual Input, Processor and Actuator, Based on Spiking Neural Networks. Applied System Innovation. 3(2). 28–28. 9 indexed citations
7.
Cheng, Ran, et al.. (2019). Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions. IEEE Transactions on Applied Superconductivity. 29(5). 1–5. 26 indexed citations
8.
Cheng, Ran, et al.. (2018). Spiking neuron circuits using superconducting quantum phase-slip junctions. Journal of Applied Physics. 124(15). 44 indexed citations
9.
Cheng, Ran, Liangping Shen, Peng Xu, et al.. (2017). Electrical characteristics and density of states of thin-film transistors based on sol-gel derived ZnO channel layers with different annealing temperatures. Journal of Applied Physics. 123(16). 8 indexed citations
10.
Xu, Liang, et al.. (2016). Hybrid Stator Design of Fault-Tolerant Permanent-Magnet Vernier Machines for Direct-Drive Applications. IEEE Transactions on Industrial Electronics. 64(1). 179–190. 102 indexed citations
11.
Zhou, Huawei, Wenxiang Zhao, Guohai Liu, Ran Cheng, & Ying Xie. (2016). Remedial Field-Oriented Control of Five-Phase Fault-Tolerant Permanent-Magnet Motor by Using Reduced-Order Transformation Matrices. IEEE Transactions on Industrial Electronics. 64(1). 169–178. 119 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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2026