Wei-Chen Wei

4.2k total citations
27 papers, 2.0k citations indexed

About

Wei-Chen Wei is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Wei-Chen Wei has authored 27 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 6 papers in Cellular and Molecular Neuroscience and 5 papers in Artificial Intelligence. Recurrent topics in Wei-Chen Wei's work include Advanced Memory and Neural Computing (14 papers), Ferroelectric and Negative Capacitance Devices (8 papers) and CCD and CMOS Imaging Sensors (6 papers). Wei-Chen Wei is often cited by papers focused on Advanced Memory and Neural Computing (14 papers), Ferroelectric and Negative Capacitance Devices (8 papers) and CCD and CMOS Imaging Sensors (6 papers). Wei-Chen Wei collaborates with scholars based in China, Taiwan and United States. Wei-Chen Wei's co-authors include Zhijun Li, Wenxing Chen, Yuen Wu, Yunteng Qu, Kea‐Tiong Tang, Meng‐Fan Chang, Ren-Shuo Liu, Chih-Cheng Hsieh, Yen-Cheng Chiu and Juncai Dong and has published in prestigious journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and ACS Catalysis.

In The Last Decade

Wei-Chen Wei

27 papers receiving 1.9k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Wei-Chen Wei China 17 1.4k 687 398 170 157 27 2.0k
Xia Sheng China 25 1.6k 1.2× 918 1.3× 1.2k 3.1× 287 1.7× 172 1.1× 90 2.7k
Xiaoxin Xu China 33 2.5k 1.8× 478 0.7× 1.1k 2.7× 229 1.3× 588 3.7× 143 3.0k
Yujie Huang China 18 498 0.4× 529 0.8× 433 1.1× 40 0.2× 29 0.2× 77 1.1k
Jianhao Huang China 15 508 0.4× 312 0.5× 316 0.8× 61 0.4× 66 0.4× 44 1.1k
In-Chul Hwang South Korea 25 1.2k 0.9× 298 0.4× 312 0.8× 97 0.6× 16 0.1× 90 1.9k
Joohee Lee South Korea 16 757 0.6× 420 0.6× 725 1.8× 62 0.4× 51 0.3× 29 1.5k
Maguy Abi Jaoudé United Arab Emirates 22 670 0.5× 221 0.3× 541 1.4× 25 0.1× 182 1.2× 64 1.6k
Won Ho Choi South Korea 15 473 0.3× 237 0.3× 157 0.4× 42 0.2× 13 0.1× 51 773
Yu‐Hsuan Lin Taiwan 15 573 0.4× 131 0.2× 215 0.5× 52 0.3× 154 1.0× 71 841

Countries citing papers authored by Wei-Chen Wei

Since Specialization
Citations

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

Fields of papers citing papers by Wei-Chen Wei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei-Chen Wei. 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 Wei-Chen Wei. The network helps show where Wei-Chen Wei may publish in the future.

Co-authorship network of co-authors of Wei-Chen Wei

This figure shows the co-authorship network connecting the top 25 collaborators of Wei-Chen Wei. A scholar is included among the top collaborators of Wei-Chen Wei based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wei-Chen Wei. Wei-Chen Wei is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Zhiyu, et al.. (2024). PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro With In Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout. IEEE Journal of Solid-State Circuits. 60(1). 308–320. 5 indexed citations
2.
Wei, Wei-Chen, Huajie Ze, & Zijie Qiu. (2023). Reticular sensing materials with aggregation-induced emission characteristics. TrAC Trends in Analytical Chemistry. 161. 116997–116997. 17 indexed citations
3.
Wei, Wei-Chen. (2023). Hofmeister Effects Shine in Nanoscience. Advanced Science. 10(22). e2302057–e2302057. 50 indexed citations
4.
Gao, Feng, et al.. (2023). Polymeric nanomaterials with aggregation-induced emission characteristics. Materials Chemistry Frontiers. 7(20). 4768–4781. 4 indexed citations
5.
Wei, Wei-Chen & Zijie Qiu. (2022). Diagnostics and theranostics of central nervous system diseases based on aggregation-induced emission luminogens. Biosensors and Bioelectronics. 217. 114670–114670. 20 indexed citations
6.
Wei, Wei-Chen. (2022). Single-atom nanozymes towards central nervous system diseases. Nano Research. 16(4). 5121–5139. 10 indexed citations
7.
Wei, Wei-Chen, et al.. (2022). Ion‐specific effects in confined nanochannels and neural network. SHILAP Revista de lepidopterología. 4(2). 4 indexed citations
8.
Xu, Can, Wei-Chen Wei, & Yong He. (2022). Enhanced hydrogen separation performance of Linde Type-A zeolite molecular sieving membrane by cesium ion exchange. Materials Letters. 324. 132680–132680. 14 indexed citations
9.
Wei, Wei-Chen, et al.. (2021). Graphene-Based Electrode Materials for Neural Activity Detection. Materials. 14(20). 6170–6170. 19 indexed citations
10.
Xue, Cheng-Xin, Ting-Wei Chang, Hui-Yao Kao, et al.. (2020). 15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices. 244–246. 180 indexed citations
11.
Wei, Wei-Chen, Chuan-Jia Jhang, Yiren Chen, et al.. (2020). A Relaxed Quantization Training Method for Hardware Limitations of Resistive Random Access Memory (ReRAM)-Based Computing-in-Memory. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 6(1). 45–52. 10 indexed citations
12.
Chiu, Yen-Cheng, Zhixiao Zhang, Jiajing Chen, et al.. (2020). A 4-Kb 1-to-8-bit Configurable 6T SRAM-Based Computation-in-Memory Unit-Macro for CNN-Based AI Edge Processors. IEEE Journal of Solid-State Circuits. 55(10). 2790–2801. 65 indexed citations
13.
Si, Xin, Jiajing Chen, Yung-Ning Tu, et al.. (2019). A Twin-8T SRAM Computation-in-Memory Unit-Macro for Multibit CNN-Based AI Edge Processors. IEEE Journal of Solid-State Circuits. 55(1). 189–202. 159 indexed citations
14.
Si, Xin, Jiajing Chen, Yung-Ning Tu, et al.. (2019). 24.5 A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning. 396–398. 203 indexed citations
15.
Yang, Zhengkun, Yu Wang, Mengzhao Zhu, et al.. (2019). Boosting Oxygen Reduction Catalysis with Fe–N4 Sites Decorated Porous Carbons toward Fuel Cells. ACS Catalysis. 9(3). 2158–2163. 345 indexed citations
16.
Xue, Cheng-Xin, Wei-Hao Chen, Jiafang Li, et al.. (2019). Embedded 1-Mb ReRAM-Based Computing-in- Memory Macro With Multibit Input and Weight for CNN-Based AI Edge Processors. IEEE Journal of Solid-State Circuits. 55(1). 203–215. 84 indexed citations
17.
Tang, Kea‐Tiong, Wei-Chen Wei, Tzu-Hsiang Hsu, et al.. (2019). Considerations of Integrating Computing-In-Memory and Processing-In-Sensor into Convolutional Neural Network Accelerators for Low-Power Edge Devices. T166–T167. 17 indexed citations
18.
Yang, Jian, Zongyang Qiu, Changming Zhao, et al.. (2018). In Situ Thermal Atomization To Convert Supported Nickel Nanoparticles into Surface‐Bound Nickel Single‐Atom Catalysts. Angewandte Chemie. 130(43). 14291–14296. 43 indexed citations
19.
Yang, Jian, Zongyang Qiu, Changming Zhao, et al.. (2018). In Situ Thermal Atomization To Convert Supported Nickel Nanoparticles into Surface‐Bound Nickel Single‐Atom Catalysts. Angewandte Chemie International Edition. 57(43). 14095–14100. 391 indexed citations
20.
Li, Pinyi, Ren-Shuo Liu, Meng‐Fan Chang, et al.. (2018). A Neuromorphic Computing System for Bitwise Neural Networks Based on ReRAM Synaptic Array. 113. 1–4. 3 indexed citations

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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