T. P. Chen

9.0k total citations
343 papers, 7.4k citations indexed

About

T. P. Chen is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Biomedical Engineering. According to data from OpenAlex, T. P. Chen has authored 343 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 286 papers in Electrical and Electronic Engineering, 141 papers in Materials Chemistry and 75 papers in Biomedical Engineering. Recurrent topics in T. P. Chen's work include Semiconductor materials and devices (150 papers), Silicon Nanostructures and Photoluminescence (82 papers) and Advanced Memory and Neural Computing (78 papers). T. P. Chen is often cited by papers focused on Semiconductor materials and devices (150 papers), Silicon Nanostructures and Photoluminescence (82 papers) and Advanced Memory and Neural Computing (78 papers). T. P. Chen collaborates with scholars based in Singapore, China and Hong Kong. T. P. Chen's co-authors include Pooi See Lee, Hui Ying Yang, Yang Liu, Ampere A. Tseng, A. Notargiacomo, S. Fung, Qi Yu, S. G. Hu, Sam Zhang and Kai Qian and has published in prestigious journals such as Advanced Materials, Nature Communications and Physical review. B, Condensed matter.

In The Last Decade

T. P. Chen

331 papers receiving 7.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T. P. Chen Singapore	42	5.6k	3.0k	1.6k	1.1k	963	343	7.4k
Joondong Kim South Korea	44	4.7k 0.9×	3.8k 1.3×	1.9k 1.1×	1.2k 1.1×	604 0.6×	316	7.3k
Byoung Hun Lee South Korea	49	7.4k 1.3×	5.2k 1.7×	1.6k 1.0×	756 0.7×	747 0.8×	313	9.6k
Kah‐Wee Ang Singapore	44	5.2k 0.9×	3.2k 1.1×	1.4k 0.9×	552 0.5×	998 1.0×	205	6.8k
David Wei Zhang China	45	6.3k 1.1×	4.2k 1.4×	1.4k 0.9×	1.0k 0.9×	455 0.5×	281	8.2k
Yoshio Nishi United States	58	10.1k 1.8×	4.5k 1.5×	1.9k 1.1×	1.2k 1.1×	1.7k 1.8×	344	12.2k
Xiangshui Miao China	47	6.9k 1.2×	5.3k 1.8×	857 0.5×	1.3k 1.1×	865 0.9×	305	9.0k
Kuijuan Jin China	44	4.0k 0.7×	4.5k 1.5×	1.1k 0.7×	3.1k 2.8×	785 0.8×	311	7.6k
Mario Lanza China	44	6.4k 1.2×	4.2k 1.4×	1.0k 0.6×	482 0.4×	668 0.7×	220	8.8k
Yong Zhao China	39	3.0k 0.5×	2.0k 0.7×	930 0.6×	1.4k 1.2×	405 0.4×	584	6.6k
Sung‐Yool Choi South Korea	55	6.9k 1.2×	5.2k 1.7×	3.1k 1.9×	1.4k 1.2×	630 0.7×	217	10.3k

Countries citing papers authored by T. P. Chen

Since Specialization

Citations

This map shows the geographic impact of T. P. Chen'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 T. P. Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. P. Chen more than expected).

Fields of papers citing papers by T. P. Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. P. Chen

This figure shows the co-authorship network connecting the top 25 collaborators of T. P. Chen. A scholar is included among the top collaborators of T. P. Chen 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 T. P. Chen. T. P. Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Yiwei, Yuqiao Chen, Hongyun Zhang, et al.. (2026). Data-Augmented Deep Learning for Downhole Depth Sensing and Validation. Sensors. 26(3). 775–775.

Huang, Jiajia, Han-hua Fang, T. P. Chen, et al.. (2025). Chitosan hydrogel catalyst shifts PMS activation towards a singlet oxygen-dominated non-radical pathway for efficient pollutant degradation. Chemical Engineering Journal. 525. 170553–170553.

Lin, Zhifang, T. P. Chen, Yang Yang, et al.. (2025). Disruption prediction on J-TEXT tokamak using ACO-BP-AdaBoost algorithm coupled with data augmentation. The European Physical Journal Special Topics. 234(13). 3427–3439.

Wang, Junjie, Li Guo, S. G. Hu, et al.. (2023). Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory. Sensors. 23(5). 2401–2401. 2 indexed citations

Wang, Junjie, Teng Zhang, S. G. Hu, et al.. (2023). Design and Implementation of a Hybrid, ADC/DAC-Free, Input-Sparsity-Aware, Precision Reconfigurable RRAM Processing-in-Memory Chip. IEEE Journal of Solid-State Circuits. 59(2). 595–604. 4 indexed citations

Wang, Junjie, et al.. (2023). An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning. IEEE Transactions on Biomedical Circuits and Systems. 17(1). 92–104. 14 indexed citations

Pan, Xinqiang, Junjie Wang, Chuangui Wu, et al.. (2023). Dual functional states of working memory realized by memristor-based neural network. Frontiers in Neuroscience. 17. 1192993–1192993.

Wang, Junjie, et al.. (2022). Epilepsy detection with artificial neural network based on as-fabricated neuromorphic chip platform. AIP Advances. 12(3). 9 indexed citations

Liu, Shuang, et al.. (2020). Spike‐driven gated recurrent neural network processor for electrocardiogram arrhythmias detection realised in 55‐nm CMOS technology. Electronics Letters. 56(23). 1230–1232. 8 indexed citations

10.

Tse, T.H., et al.. (2019). Steering Committee. 26–26. 1 indexed citations

11.

Qian, Kun, Shaogang Hu, Sheng Xu, et al.. (2019). Application of Deep Compression Technique in Spiking Neural Network Chip. IEEE Transactions on Biomedical Circuits and Systems. 14(2). 274–282. 11 indexed citations

12.

Zhang, Caizhi, T. P. Chen, Junjie Wang, et al.. (2019). Implementation of a Low Noise Amplifier With Self-Recovery Capability. IEEE Access. 7. 43076–43083. 6 indexed citations

13.

Qian, Kun, Jinping Wei, T. P. Chen, et al.. (2019). Design of a Neural Network-Based VCO With High Linearity and Wide Tuning Range. IEEE Access. 7. 60120–60125. 5 indexed citations

14.

Wang, Junjie, et al.. (2019). Winner-takes-all mechanism realized by memristive neural network. Applied Physics Letters. 115(24). 14 indexed citations

15.

Huang, Zhi Xiang, Ye Wang, Bo Liu, et al.. (2017). Unlocking the potential of SnS2: Transition metal catalyzed utilization of reversible conversion and alloying reactions. Scientific Reports. 7(1). 41015–41015. 40 indexed citations

16.

Hu, S. G., et al.. (2017). A MoS₂-based coplanar neuron transistor for logic applications. Nanotechnology. 28(21). 214001–214001. 14 indexed citations

17.

Chen, T. P., et al.. (2012). Size-suppressed dielectrics of Ge nanocrystals: skin-deep quantum entrapment. Nanoscale. 4(4). 1308–1308. 5 indexed citations

18.

Wong, Jen It, Hui Ying Yang, Hongxing Li, T. P. Chen, & Hong Jin Fan. (2011). Wavelength tunable electroluminescence from randomly assembled n-CdS_xSe_1−xnanowires/p⁺-SiC heterojunction. Nanoscale. 4(5). 1467–1470. 7 indexed citations

19.

Tseng, Ampere A., et al.. (2010). Profile Uniformity of Overlapped Oxide Dots Induced by Atomic Force Microscopy. Journal of Nanoscience and Nanotechnology. 10(7). 4390–4399. 2 indexed citations

20.

Ding, Lu, T. P. Chen, Yu Liu, C. Y. Ng, & S. Fung. (2005). An approach to optical-property profiling of a planar-waveguide structure of Si nanocrystals embedded in SiO₂. Nanotechnology. 16(11). 2657–2660. 2 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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