Dabin Wu
Impact in
-
- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Semiconductor materials and devices
- CCD and CMOS Imaging Sensors
-
- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
Papers in
-
- Ferroelectric and Negative Capacitance Devices 4
- Advanced Memory and Neural Computing 4
- Semiconductor materials and devices 2
- Low-power high-performance VLSI design 1
- Virology 1
- HIV Research and Treatment 1
- Co-authors
- Bin Gao (5 shared papers)Huaqiang Wu (5 shared papers)Rajkumar Kubendran (2 shared papers)Wenqiang Zhang (2 shared papers)Priyanka Raina (2 shared papers)H.‐S. Philip Wong (2 shared papers)Stephen Deiss (2 shared papers)Siddharth Joshi (2 shared papers)
- Journals
- IEEE Transactions on Circuits and Systems I Regular Papers (1 paper)Nature (1 paper)Addictive Behaviors (1 paper)
- Partner nations
- ChinaUnited StatesUganda
In The Last Decade
Dabin Wu
6 papers receiving 684 citations
Dabin Wu's Hit Papers
Peers
Comparison fields: 5 of 51
- Electrical and Electronic Engineering 622
- Cellular and Molecular Neuroscience 167
- Hardware and Architecture 35
- Artificial Intelligence 143
- Cognitive Neuroscience 75
Countries citing papers authored by Dabin Wu
This map shows the geographic impact of Dabin Wu'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 Dabin Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dabin Wu more than expected).
Fields of papers citing papers by Dabin Wu
This network shows the impact of papers produced by Dabin Wu. 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 Dabin Wu. The network helps show where Dabin Wu may publish in the future.
Co-authors
The 20 scholars most cited alongside Dabin Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A compute-in-memory chip based on resistive random-access memory Hit paper breakdown → | 2022 | 567 |
| 2 | 2020 | 107 | |
| 3 | 2005 | 11 | |
| 4 | 2023 | 5 | |
| 5 | 2021 | 3 | |
| 6 | 2020 | 2 | |
| 7 | 2012 | 1 |
About Dabin Wu
Dabin Wu is a scholar working on Electrical and Electronic Engineering, Virology, Control and Systems Engineering, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 7 papers that have together received 696 indexed citations. Recurring topics across this work include Ferroelectric and Negative Capacitance Devices (4 papers), Advanced Memory and Neural Computing (4 papers), Semiconductor materials and devices (2 papers), Advanced Neural Network Applications (1 paper), Opioid Use Disorder Treatment (1 paper), HIV Research and Treatment (1 paper), Thermal Analysis in Power Transmission (1 paper) and Low-power high-performance VLSI design (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (622 citations), Cellular and Molecular Neuroscience (167 citations), Hardware and Architecture (35 citations), Artificial Intelligence (143 citations) and Cognitive Neuroscience (75 citations). Dabin Wu has collaborated with scholars based in China, United States and Uganda. Frequent co-authors include Bin Gao, Huaqiang Wu, Rajkumar Kubendran, Wenqiang Zhang, Priyanka Raina, H.‐S. Philip Wong, Stephen Deiss, Siddharth Joshi, Gert Cauwenberghs and Weier Wan. Their work appears in journals such as IEEE Transactions on Circuits and Systems I Regular Papers, Nature and Addictive Behaviors.
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.