Charles Mackin

2.0k total citations
37 papers, 1.2k citations indexed

About

Charles Mackin is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Charles Mackin has authored 37 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Electrical and Electronic Engineering, 16 papers in Materials Chemistry and 10 papers in Artificial Intelligence. Recurrent topics in Charles Mackin's work include Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (21 papers) and Graphene research and applications (7 papers). Charles Mackin is often cited by papers focused on Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (21 papers) and Graphene research and applications (7 papers). Charles Mackin collaborates with scholars based in United States, Switzerland and Japan. Charles Mackin's co-authors include Tomás Palacios, Jing Kong, Xu Zhang, Yi Song, Xinming Li, Hongwei Zhu, Wenjing Fang, Hsinyu Tsai, Pritish Narayanan and Geoffrey W. Burr and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Nano Letters.

In The Last Decade

Charles Mackin

37 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles Mackin United States 18 758 564 388 142 141 37 1.2k
Qing Wan China 16 796 1.1× 509 0.9× 350 0.9× 89 0.6× 160 1.1× 46 1.2k
Chang‐Ki Baek South Korea 24 1.3k 1.7× 508 0.9× 662 1.7× 34 0.2× 142 1.0× 112 1.8k
Beiju Huang China 20 1.1k 1.4× 373 0.7× 277 0.7× 85 0.6× 244 1.7× 109 1.4k
Hossein Taghinejad United States 17 471 0.6× 454 0.8× 494 1.3× 41 0.3× 192 1.4× 37 1.1k
Mohammad Taghinejad United States 22 521 0.7× 351 0.6× 667 1.7× 55 0.4× 336 2.4× 37 1.4k
Andrea Fasoli United Kingdom 20 631 0.8× 438 0.8× 471 1.2× 68 0.5× 195 1.4× 46 957
Wenhui Yi China 20 219 0.3× 463 0.8× 425 1.1× 187 1.3× 89 0.6× 74 1.2k
Chuantong Cheng China 17 644 0.8× 247 0.4× 195 0.5× 76 0.5× 146 1.0× 62 942
Sichao Du China 15 657 0.9× 751 1.3× 357 0.9× 76 0.5× 174 1.2× 34 1.3k
Xiao Fu China 18 846 1.1× 806 1.4× 151 0.4× 82 0.6× 89 0.6× 51 1.3k

Countries citing papers authored by Charles Mackin

Since Specialization
Citations

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

Fields of papers citing papers by Charles Mackin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Mackin

This figure shows the co-authorship network connecting the top 25 collaborators of Charles Mackin. A scholar is included among the top collaborators of Charles Mackin 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 Charles Mackin. Charles Mackin 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.
Ambrogio, Stefano, Pritish Narayanan, Charles Mackin, et al.. (2025). Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip. Nature Communications. 16(1). 8661–8661. 1 indexed citations
2.
Chen, An, Stefano Ambrogio, Pritish Narayanan, et al.. (2024). (Invited) Emerging Nonvolatile Memories for Analog Neuromorphic Computing. ECS Meeting Abstracts. MA2024-01(21). 1293–1293. 1 indexed citations
3.
Mackin, Charles, et al.. (2024). Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization. 1–10. 2 indexed citations
4.
Vijayaraghavan, Prashanth, et al.. (2024). VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation. 1–6. 4 indexed citations
5.
Gallo, Manuel Le, Corey Lammie, Julian Büchel, et al.. (2023). Using the IBM analog in-memory hardware acceleration kit for neural network training and inference. SHILAP Revista de lepidopterología. 1(4). 27 indexed citations
6.
Rasch, Malte J., Charles Mackin, Manuel Le Gallo, et al.. (2023). Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. Nature Communications. 14(1). 5282–5282. 77 indexed citations
7.
Li, Ning, Charles Mackin, An Chen, et al.. (2023). Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference. Advanced Electronic Materials. 9(6). 5 indexed citations
8.
Mackin, Charles, Malte J. Rasch, An Chen, et al.. (2022). Optimised weight programming for analogue memory-based deep neural networks. Nature Communications. 13(1). 3765–3765. 34 indexed citations
9.
Xue, Mantian, Charles Mackin, Wei‐Hung Weng, et al.. (2022). Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing. Nature Communications. 13(1). 5064–5064. 100 indexed citations
10.
Tsai, Hsinyu, An Chen, Malte J. Rasch, et al.. (2021). Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. Frontiers in Computational Neuroscience. 15. 675741–675741. 16 indexed citations
11.
Mackin, Charles, Andrea Fasoli, Mantian Xue, et al.. (2020). Chemical sensor systems based on 2D and thin film materials. 2D Materials. 7(2). 22002–22002. 46 indexed citations
12.
Mackin, Charles, Pritish Narayanan, Stefano Ambrogio, et al.. (2020). Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges. 1–10. 4 indexed citations
13.
Ambrogio, Stefano, Pritish Narayanan, Hsinyu Tsai, et al.. (2020). Inference of Deep Neural Networks with Analog Memory Devices. 29. 119–120. 1 indexed citations
14.
Mackin, Charles, et al.. (2019). Accelerating Deep Neural Networks with Analog Memory Devices. Bulletin of the American Physical Society. 2019. 1 indexed citations
15.
Narayanan, Pritish, Kohji Hosokawa, Charles Mackin, et al.. (2019). AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed. IBM Journal of Research and Development. 63(6). 8:1–8:14. 39 indexed citations
16.
Mackin, Charles, Vera Schroeder, Amaia Zurutuza, et al.. (2018). Chemiresistive Graphene Sensors for Ammonia Detection. ACS Applied Materials & Interfaces. 10(18). 16169–16176. 113 indexed citations
17.
Mackin, Charles, Elaine McVay, & Tomás Palacios. (2018). Frequency Response of Graphene Electrolyte-Gated Field-Effect Transistors. Sensors. 18(2). 494–494. 21 indexed citations
18.
Mackin, Charles, et al.. (2018). Intravenous Amiodarone and Sotalol Impair Contractility and Cardiac Output, but Procainamide Does Not: A Langendorff Study. Journal of Cardiovascular Pharmacology and Therapeutics. 24(3). 288–297. 11 indexed citations
19.
Mackin, Charles, Lucas H. Hess, Allen Hsu, et al.. (2014). A Current–Voltage Model for Graphene Electrolyte-Gated Field-Effect Transistors. IEEE Transactions on Electron Devices. 61(12). 3971–3977. 35 indexed citations
20.
Tarbox, James, Molly P. Keppel, Charles Mackin, et al.. (2014). Elevated Double Negative T Cells in Pediatric Autoimmunity. Journal of Clinical Immunology. 34(5). 594–599. 37 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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Rankless by CCL
2026