Bingjie Dang

1.5k total citations · 1 hit paper
33 papers, 1.2k citations indexed

About

Bingjie Dang is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Bingjie Dang has authored 33 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Electrical and Electronic Engineering, 13 papers in Cellular and Molecular Neuroscience and 9 papers in Artificial Intelligence. Recurrent topics in Bingjie Dang's work include Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Photoreceptor and optogenetics research (9 papers). Bingjie Dang is often cited by papers focused on Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and Photoreceptor and optogenetics research (9 papers). Bingjie Dang collaborates with scholars based in China, Germany and Singapore. Bingjie Dang's co-authors include Yuchao Yang, Keqin Liu, Ru Huang, Liying Xu, Teng Zhang, Zhen Yang, Caidie Cheng, Lin Bao, Hong Wang and Jing Sun and has published in prestigious journals such as Advanced Materials, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Bingjie Dang

30 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing

2022 377 citations Keqin Liu, Teng Zhang et al. Nature Electronics profile →

Peers

Bingjie Dang

EEE

CMN

Lin Bao China

Tianqing Wan China

Liying Xu China

Yijun Li China

Alessandro Fumarola Switzerland

Alessandro Bricalli Italy

I‐Ting Wang Taiwan

Beom Jin Kim South Korea

Sukru Burc Eryilmaz United States

Lin Bao China

Bingjie Dang

1.3k ×1.2

EEE

459 ×1.2

CMN

382 ×1.1

214 ×0.9

251 ×1.2

Citations per year, relative to Bingjie Dang Bingjie Dang (= 1×) peers Lin Bao

Countries citing papers authored by Bingjie Dang

Since Specialization

Citations

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

Fields of papers citing papers by Bingjie Dang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bingjie Dang. 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 Bingjie Dang. The network helps show where Bingjie Dang may publish in the future.

Co-authorship network of co-authors of Bingjie Dang

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

All Works

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20 of 20 papers shown

Dang, Bingjie, et al.. (2026). Spiking neural networks with fatigue spike-timing-dependent plasticity learning using hybrid memristor arrays. Nature Electronics. 9(2). 213–224.

Dang, Bingjie, Tao Zeng, Shu Shi, et al.. (2025). HZO/HSO Superlattice ReFET Array Integrating Optical Sensing for Neuromorphic Vision Computing. Advanced Materials. 37(50). e08889–e08889.

Liu, Keqin, Bingjie Dang, Teng Zhang, et al.. (2024). Tuning the ferroelectricity of Hf0.5Zr0.5O2 with alloy electrodes. Science China Information Sciences. 67(8). 1 indexed citations

Dang, Bingjie, et al.. (2024). Spatiotemporal audio feature extraction with dynamic memristor-based time-surface neurons. Science Advances. 10(14). 17 indexed citations

Yang, Zhen, Chang Liu, Yaoyu Tao, et al.. (2024). Fully Hardware Memristive Neuromorphic Computing Enabled by the Integration of Trainable Dendritic Neurons and High‐Density RRAM Chip. Advanced Functional Materials. 34(44). 13 indexed citations

Dang, Bingjie, Keqin Liu, Xulei Wu, et al.. (2023). One‐Phototransistor–One‐Memristor Array with High‐Linearity Light‐Tunable Weight for Optic Neuromorphic Computing (Adv. Mater. 37/2023). Advanced Materials. 35(37).

Yang, Zhen, Teng Zhang, Keqin Liu, et al.. (2023). Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion‐Modulated Memtransistors. SHILAP Revista de lepidopterología. 5(8). 12 indexed citations

Xu, Liying, Jiadi Zhu, Bing Chen, et al.. (2022). A distributed nanocluster based multi-agent evolutionary network. Nature Communications. 13(1). 4698–4698. 9 indexed citations

Liu, Keqin, Bingjie Dang, Teng Zhang, et al.. (2022). Multilayer Reservoir Computing Based on Ferroelectric α‐In₂Se₃ for Hierarchical Information Processing (Adv. Mater. 48/2022). Advanced Materials. 34(48). 7 indexed citations

10.

Cheng, Caidie, Yanghao Wang, Liying Xu, et al.. (2021). Artificial Astrocyte Memristor with Recoverable Linearity for Neuromorphic Computing. Advanced Electronic Materials. 8(8). 15 indexed citations

11.

Dang, Bingjie, et al.. (2021). Spike‐Enabled Audio Learning in Multilevel Synaptic Memristor Array‐Based Spiking Neural Network. SHILAP Revista de lepidopterología. 4(3). 35 indexed citations

12.

Wu, Quantan, Bingjie Dang, Congyan Lu, et al.. (2020). Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation. Nano Letters. 20(11). 8015–8023. 96 indexed citations

13.

Zhu, Jiadi, Bingjie Dang, Teng Zhang, et al.. (2020). TaOx synapse array based on ion profile engineering for high accuracy neuromorpic computing. 1–4. 2 indexed citations

14.

Liu, Keqin, Teng Zhang, Caidie Cheng, et al.. (2020). Highly Uniform Two‐Terminal Artificial Synapses Based on Polycrystalline Hf_0.5Zr_0.5O₂ for Sparsified Back Propagation Networks. Advanced Electronic Materials. 6(8). 10 indexed citations

15.

Dang, Bingjie, Lan Ma, Saisai Wang, et al.. (2020). Physically Transient Optic-Neural Synapse for Secure In-Sensor Computing. IEEE Electron Device Letters. 41(11). 1641–1644. 19 indexed citations

16.

Dang, Bingjie, Keqin Liu, Jiadi Zhu, et al.. (2019). Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing. APL Materials. 7(7). 43 indexed citations

17.

Dang, Bingjie, Jing Sun, Teng Zhang, et al.. (2019). Physically Transient True Random Number Generators Based on Paired Threshold Switches Enabling Monte Carlo Method Applications. IEEE Electron Device Letters. 40(7). 1096–1099. 31 indexed citations

18.

Dang, Bingjie, Quantan Wu, Jing Sun, et al.. (2019). Physically Transient Memristor Synapse Based on Embedding Magnesium Nanolayer in Oxide for Security Neuromorphic Electronics. IEEE Electron Device Letters. 40(8). 1265–1268. 22 indexed citations

19.

Dang, Bingjie, Quantan Wu, Fang Song, et al.. (2018). A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors. Nanoscale. 10(43). 20089–20095. 89 indexed citations

20.

Song, Fang, Hong Wang, Jing Sun, et al.. (2018). Solution-Processed Physically Transient Resistive Memory Based on Magnesium Oxide. IEEE Electron Device Letters. 40(2). 193–195. 24 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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