Djaafar Chabi

540 total citations
16 papers, 340 citations indexed

About

Djaafar Chabi is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Djaafar Chabi has authored 16 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 11 papers in Cellular and Molecular Neuroscience and 3 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Djaafar Chabi's work include Advanced Memory and Neural Computing (15 papers), Neuroscience and Neural Engineering (10 papers) and Ferroelectric and Negative Capacitance Devices (5 papers). Djaafar Chabi is often cited by papers focused on Advanced Memory and Neural Computing (15 papers), Neuroscience and Neural Engineering (10 papers) and Ferroelectric and Negative Capacitance Devices (5 papers). Djaafar Chabi collaborates with scholars based in France, China and Italy. Djaafar Chabi's co-authors include Jacques‐Olivier Klein, Weisheng Zhao, Claude Chappert, Damien Querlioz, Zhaohao Wang, Christopher H. Bennett, Yue Zhang, Vincent Derycke, Erya Deng and Bruno Jousselme and has published in prestigious journals such as Scientific Reports, Nanotechnology and IEEE Transactions on Nuclear Science.

In The Last Decade

Djaafar Chabi

15 papers receiving 328 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Djaafar Chabi France	10	320	117	61	53	48	16	340
Á. Salamon Italy	8	261 0.8×	111 0.9×	32 0.5×	126 2.4×	32 0.7×	42	377
Stanley Rogers United States	8	331 1.0×	199 1.7×	25 0.4×	84 1.6×	21 0.4×	25	367
Hyungwoo Lee South Korea	4	364 1.1×	63 0.5×	94 1.5×	30 0.6×	91 1.9×	12	435
Mark B. Ritter United States	12	340 1.1×	66 0.6×	21 0.3×	39 0.7×	36 0.8×	24	363
Hassen Aziza France	12	494 1.5×	99 0.8×	31 0.5×	17 0.3×	35 0.7×	45	521
Hui-Yao Kao Taiwan	7	515 1.6×	83 0.7×	32 0.5×	16 0.3×	88 1.8×	7	552
Arne Heittmann Germany	9	237 0.7×	70 0.6×	15 0.2×	29 0.5×	62 1.3×	35	273
David Russell Hughart United States	13	626 2.0×	135 1.2×	31 0.5×	31 0.6×	57 1.2×	53	665
Boyoung Seo South Korea	3	362 1.1×	44 0.4×	97 1.6×	19 0.4×	90 1.9×	4	418
Miguel Ángel Lastras-Montaño United States	10	349 1.1×	138 1.2×	14 0.2×	21 0.4×	34 0.7×	24	363

Countries citing papers authored by Djaafar Chabi

Since Specialization

Citations

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

Fields of papers citing papers by Djaafar Chabi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Djaafar Chabi

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

All Works

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16 of 16 papers shown

1.

Lin, Yu‐Pu, Christopher H. Bennett, Damir Vodenicarevic, et al.. (2016). Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses. Scientific Reports. 6(1). 31932–31932. 46 indexed citations

2.

Lin, Yu‐Pu, Christopher H. Bennett, Damir Vodenicarevic, et al.. (2016). Electro-grafted Organic Memristors as Synapses: Spike Timing-Dependent Plasticity and Supervised Function Learning. HAL (Le Centre pour la Communication Scientifique Directe).

3.

Chabi, Djaafar, Zhaohao Wang, Christopher H. Bennett, Jacques‐Olivier Klein, & Weisheng Zhao. (2015). Ultrahigh Density Memristor Neural Crossbar for On-Chip Supervised Learning. IEEE Transactions on Nanotechnology. 14(6). 954–962. 39 indexed citations

4.

Bennett, Christopher H., Djaafar Chabi, Bruno Jousselme, et al.. (2015). Supervised learning with organic memristor devices and prospects for neural crossbar arrays. HAL (Le Centre pour la Communication Scientifique Directe). 58. 181–186. 8 indexed citations

5.

Chabi, Djaafar, Weisheng Zhao, Damien Querlioz, & Jacques‐Olivier Klein. (2015). On-Chip Universal Supervised Learning Methods for Neuro-Inspired Block of Memristive Nanodevices. ACM Journal on Emerging Technologies in Computing Systems. 11(4). 1–20. 8 indexed citations

6.

Chabi, Djaafar, Weisheng Zhao, Jacques‐Olivier Klein, & Claude Chappert. (2014). Design and Analysis of Radiation Hardened Sensing Circuits for Spin Transfer Torque Magnetic Memory and Logic. IEEE Transactions on Nuclear Science. 61(6). 3258–3264. 15 indexed citations

7.

Chabi, Djaafar, Damien Querlioz, Weisheng Zhao, & Jacques‐Olivier Klein. (2014). Robust learning approach for neuro-inspired nanoscale crossbar architecture. ACM Journal on Emerging Technologies in Computing Systems. 10(1). 1–20. 26 indexed citations

8.

Chabi, Djaafar, Zhaohao Wang, Weisheng Zhao, & Jacques‐Olivier Klein. (2014). On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron. 7–12. 14 indexed citations

9.

Kang, Wang, Weisheng Zhao, Zhaohao Wang, et al.. (2014). An overview of spin-based integrated circuits. 676–683. 20 indexed citations

10.

Chabi, Djaafar, Zhaohao Wang, Weisheng Zhao, & Jacques‐Olivier Klein. (2014). On-chip supervised learning rule for ultra high density neural crossbar using memristor for synapse and neuron. 7–12. 6 indexed citations

11.

Chabi, Djaafar, Weisheng Zhao, Erya Deng, et al.. (2014). Ultra Low Power Magnetic Flip-Flop Based on Checkpointing/Power Gating and Self-Enable Mechanisms. IEEE Transactions on Circuits and Systems I Regular Papers. 61(6). 1755–1765. 80 indexed citations

12.

Chabi, Djaafar, et al.. (2013). Neuromorphic function learning with carbon nanotube based synapses. Nanotechnology. 24(38). 384013–384013. 31 indexed citations

13.

Chabi, Djaafar, Weisheng Zhao, Yue Zhang, Jacques‐Olivier Klein, & Claude Chappert. (2013). Low power magnetic flip-flop based on checkpointing and self-enable mechanism. 1–4. 2 indexed citations

14.

Zhao, Weisheng, Damien Querlioz, Jacques‐Olivier Klein, Djaafar Chabi, & Claude Chappert. (2012). Nanodevice-based novel computing paradigms and the neuromorphic approach. HAL (Le Centre pour la Communication Scientifique Directe). 2509–2512. 24 indexed citations

15.

Agnus, Guillaume, Weisheng Zhao, Cristell Maneux, et al.. (2011). Design and Modeling of a Neuro-Inspired Learning Circuit Using Nanotube-Based Memory Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 58(9). 2172–2181. 13 indexed citations

16.

Chabi, Djaafar & Jacques‐Olivier Klein. (2010). Hight fault tolerance in neural crossbar. 1–6. 8 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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