Tokiyoshi Matsuda
- Polymers and Plastics top 5%
- Transition Metal Oxide Nanomaterials 19
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- Thin-Film Transistor Technologies 61
- Advanced Memory and Neural Computing 18
- Semiconductor materials and devices 15
- CCD and CMOS Imaging Sensors 14
- Gas Sensing Nanomaterials and Sensors 7
- Materials Chemistry top 5%
- ZnO doping and properties 37
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- Ga2O3 and related materials 15
Tokiyoshi Matsuda
87 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 69
- Polymers and Plastics 308
- Electrical and Electronic Engineering 1.1k
- Materials Chemistry 852
- Electronic, Optical and Magnetic Materials 209
- Bioengineering 23
Countries citing papers authored by Tokiyoshi Matsuda
This map shows the geographic impact of Tokiyoshi Matsuda'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 Tokiyoshi Matsuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tokiyoshi Matsuda more than expected).
Fields of papers citing papers by Tokiyoshi Matsuda
This network shows the impact of papers produced by Tokiyoshi Matsuda. 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 Tokiyoshi Matsuda. The network helps show where Tokiyoshi Matsuda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tokiyoshi Matsuda, 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 | 2024 | 0 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 1 | |
| 7 | 2023 | 3 | |
| 8 | 2019 | 14 | |
| 9 | 2019 | 9 | |
| 10 | Reading accuracy improvement of flatpanel imager by liquid-crystal micro lens | 2017 | 1 |
| 11 | 2017 | 61 | |
| 12 | Simplification of Processing Elements in Cellular Neural Networks - Working Confirmation Using Circuit Simulation. | 2016 | 1 |
| 13 | 2016 | 2 | |
| 14 | 2015 | 1 | |
| 15 | 2014 | 6 | |
| 16 | 2014 | 4 | |
| 17 | Stacked Organic Image Sensor with Zinc-oxide TFTs as Signal Readout Circuit | 2008 | 3 |
| 18 | 2007 | 42 | |
| 19 | 2004 | 17 | |
| 20 | 2004 | 21 |
About Tokiyoshi Matsuda
Tokiyoshi Matsuda is a scholar working on Polymers and Plastics, Electrical and Electronic Engineering and Materials Chemistry, having authored 94 papers that have together received 1.3k indexed citations. Recurring topics across this work include Thin-Film Transistor Technologies (61 papers), ZnO doping and properties (37 papers), Transition Metal Oxide Nanomaterials (19 papers), Advanced Memory and Neural Computing (18 papers), Ga2O3 and related materials (15 papers), Semiconductor materials and devices (15 papers), CCD and CMOS Imaging Sensors (14 papers) and Gas Sensing Nanomaterials and Sensors (7 papers). The work is most often cited by research in Polymers and Plastics (308 citations), Electrical and Electronic Engineering (1.1k citations) and Materials Chemistry (852 citations). Tokiyoshi Matsuda has collaborated with scholars based in Japan, United Kingdom and Azerbaijan. Frequent co-authors include Mamoru Furuta, Takahiro Hiramatsu, Hiroshi Furuta, Takashi Hirao, Mutsumi Kimura, Takashi Hirao, Hitoshi Hokari, Hiromitsu Ishii, Chaoyang Li and Chao Li. Their work appears in journals such as Applied Physics Letters, Journal of Applied Physics and Scientific Reports.
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.