I‐Ting Wang

1.7k citations
44 papers · 1.3k · h-index 17

Impact in

Papers in

I‐Ting Wang

42 papers receiving 1.3k citations

Peers

I‐Ting Wang
Comparison fields: 5 of 53
  • Cellular and Molecular Neuroscience 499
  • Electrical and Electronic Engineering 1.2k
  • Polymers and Plastics 172
  • Cognitive Neuroscience 109
  • Artificial Intelligence 161
Replace Beom Jin Kim with:
Beom Jin Kim South Korea
Tianqing Wan Hong Kong
Jiaming Zhang China
Alessandro Fumarola Switzerland
Udayan Ganguly India
Woobin Lee United States
Mireia Bargalló González Spain
Guangdi Feng China
Sijie Ma Hong Kong
I‐Ting Wang relative to Beom Jin Kim South Korea Beom Jin Kim's profile →
Citations per field
00.5×20×40×60×73×
Beom Jin Kim · 1×
Citations per year

Countries citing papers authored by I‐Ting Wang

Since Specialization
Citations

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

Fields of papers citing papers by I‐Ting Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I‐Ting Wang. 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 I‐Ting Wang. The network helps show where I‐Ting Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside I‐Ting Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with I‐Ting Wang Line = papers co-authored together I‐Ting Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015177
2 2016167
3 2015160
4 2015117
5
Self-rectifying bipolar TaO x /TiO 2 RRAM with superior endurance over 10 12 cycles for 3D high-density storage-class memory
201385
6 201480
7 201474
8 201647
9 202146
10 201338
11 202136
12 201934
13 201729
14 202228
15 201624
16 201322
17 201317
18 201516
19 202114
20 202213

About I‐Ting Wang

I‐Ting Wang is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Materials Chemistry, Artificial Intelligence and Polymers and Plastics, having authored 44 papers that have together received 1.3k indexed citations. Recurring topics across this work include Ferroelectric and Negative Capacitance Devices (28 papers), Advanced Memory and Neural Computing (28 papers), Semiconductor materials and devices (15 papers), Neuroscience and Neural Engineering (10 papers), Transition Metal Oxide Nanomaterials (3 papers), Ferroelectric and Piezoelectric Materials (3 papers), Thin-Film Transistor Technologies (3 papers) and Photoreceptor and optogenetics research (3 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (499 citations), Electrical and Electronic Engineering (1.2k citations), Polymers and Plastics (172 citations), Cognitive Neuroscience (109 citations) and Artificial Intelligence (161 citations). I‐Ting Wang has collaborated with scholars based in Taiwan, Singapore and United States. Frequent co-authors include Tuo‐Hung Hou, Yufen Wang, Chung-Wei Hsu, Chih-Cheng Chang, Yu Cao, Shimeng Yu, Sarma Vrudhula, Pai-Yu Chen, Jae-sun Seo and Chun‐Li Lo. Their work appears in journals such as IEEE Transactions on Electron Devices, Nanotechnology, IEEE Electron Device Letters, Scientific Reports and Applied Physics Letters.

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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