Chin-I Su
Impact in
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Semiconductor materials and devices
- CCD and CMOS Imaging Sensors
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques
Papers in
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- Advanced Memory and Neural Computing 10
- Ferroelectric and Negative Capacitance Devices 9
- Semiconductor materials and devices 6
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- Neuroscience and Neural Engineering 2
- Co-authors
- Tsung-Yung Jonathan Chang (10 shared papers)Yu-Der Chih (10 shared papers)Win-San Khwa (6 shared papers)Je-Min Hung (6 shared papers)Chung‐Chuan Lo (6 shared papers)Meng‐Fan Chang (6 shared papers)Chih-Cheng Hsieh (6 shared papers)Kea‐Tiong Tang (6 shared papers)
- Journals
- IEEE Journal of Solid-State Circuits (2 papers)Nature Electronics (1 paper)Science (1 paper)2022 IEEE International Solid- State Circuits Conference (ISSCC) (1 paper)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Chin-I Su
10 papers receiving 504 citations
Peers
Comparison fields: 5 of 31
- Electrical and Electronic Engineering 460
- Hardware and Architecture 50
- Cellular and Molecular Neuroscience 83
- Computer Vision and Pattern Recognition 48
- Artificial Intelligence 74
Countries citing papers authored by Chin-I Su
This map shows the geographic impact of Chin-I Su'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 Chin-I Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chin-I Su more than expected).
Fields of papers citing papers by Chin-I Su
This network shows the impact of papers produced by Chin-I Su. 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 Chin-I Su. The network helps show where Chin-I Su may publish in the future.
Co-authors
The 25 scholars most cited alongside Chin-I Su, 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 | 2021 | 149 | |
| 2 | 2021 | 90 | |
| 3 | 2022 | 90 | |
| 4 | 2024 | 62 | |
| 5 | 2020 | 44 | |
| 6 | 2022 | 34 | |
| 7 | 2020 | 24 | |
| 8 | 2023 | 10 | |
| 9 | 2020 | 5 | |
| 10 | 2018 | 3 |
About Chin-I Su
Chin-I Su is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Theory and Mathematics, having authored 10 papers that have together received 511 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (10 papers), Ferroelectric and Negative Capacitance Devices (9 papers), Semiconductor materials and devices (6 papers), Neuroscience and Neural Engineering (2 papers), Quantum-Dot Cellular Automata (1 paper), Advanced Neural Network Applications (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (460 citations), Hardware and Architecture (50 citations), Cellular and Molecular Neuroscience (83 citations), Computer Vision and Pattern Recognition (48 citations) and Artificial Intelligence (74 citations). Chin-I Su has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Tsung-Yung Jonathan Chang, Yu-Der Chih, Win-San Khwa, Je-Min Hung, Chung‐Chuan Lo, Meng‐Fan Chang, Chih-Cheng Hsieh, Kea‐Tiong Tang, Ren-Shuo Liu and Yen-Hsiang Huang. Their work appears in journals such as IEEE Journal of Solid-State Circuits, Nature Electronics, Science and 2022 IEEE International Solid- State Circuits Conference (ISSCC).
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.