Deliang Fan
- Hardware and Architecture top 1%
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- Advanced Memory and Neural Computing 116
- Ferroelectric and Negative Capacitance Devices 79
- Semiconductor materials and devices 22
- Artificial Intelligence top 1%
- Adversarial Robustness in Machine Learning 19
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- Advanced Neural Network Applications 39
- Signal Processing top 5%
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- Magnetic properties of thin films 24
- Quantum and electron transport phenomena 15
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- Advanced Data Storage Technologies 16
- Co-authors
- Zhezhi HeShaahin AngiziAdnan Siraj RakinKaushik RoyMrigank SharadKarthik YogendraRonald F. DeMaraFarhana Parveen
- Journals
- SHILAP Revista de lepidopterología (2 papers)Bioinformatics (1 paper)Journal of Applied Physics (1 paper)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Deliang Fan
166 papers receiving 3.2k citations
Peers
Comparison fields: 5 of 87
- Hardware and Architecture 498
- Electrical and Electronic Engineering 2.3k
- Artificial Intelligence 1.2k
- Computer Vision and Pattern Recognition 538
- Signal Processing 164
Countries citing papers authored by Deliang Fan
This map shows the geographic impact of Deliang Fan'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 Deliang Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deliang Fan more than expected).
Fields of papers citing papers by Deliang Fan
This network shows the impact of papers produced by Deliang Fan. 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 Deliang Fan. The network helps show where Deliang Fan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Deliang Fan, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 6 | |
| 5 | 2023 | 0 | |
| 6 | 2022 | 5 | |
| 7 | 2021 | 24 | |
| 8 | 2020 | 2 | |
| 9 | 2020 | 20 | |
| 10 | 2019 | 74 | |
| 11 | 2018 | 25 | |
| 12 | 2018 | 29 | |
| 13 | 2018 | 8 | |
| 14 | 2018 | 14 | |
| 15 | DIMA: A Depthwise CNN In-Memory Accelerator | 2018 | 5 |
| 16 | 2017 | 43 | |
| 17 | 2017 | 4 | |
| 18 | 2016 | 9 | |
| 19 | BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES | 2015 | 1 |
| 20 | 2013 | 2 |
About Deliang Fan
Deliang Fan is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 179 papers that have together received 3.2k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (116 papers), Ferroelectric and Negative Capacitance Devices (79 papers), Advanced Neural Network Applications (39 papers), Magnetic properties of thin films (24 papers), Semiconductor materials and devices (22 papers), Adversarial Robustness in Machine Learning (19 papers), Advanced Data Storage Technologies (16 papers) and Quantum and electron transport phenomena (15 papers). The work is most often cited by research in Hardware and Architecture (498 citations), Electrical and Electronic Engineering (2.3k citations) and Artificial Intelligence (1.2k citations). Deliang Fan has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Zhezhi He, Shaahin Angizi, Adnan Siraj Rakin, Kaushik Roy, Mrigank Sharad, Karthik Yogendra, Ronald F. DeMara, Farhana Parveen, Rickard Ewetz and Anand Raghunathan. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and Journal of Applied Physics.
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.