Severin Sidler

3.4k total citations · 3 hit papers
11 papers, 2.6k citations indexed

About

Severin Sidler is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Severin Sidler has authored 11 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 7 papers in Artificial Intelligence and 3 papers in Cognitive Neuroscience. Recurrent topics in Severin Sidler's work include Advanced Memory and Neural Computing (11 papers), Ferroelectric and Negative Capacitance Devices (8 papers) and Neural Networks and Reservoir Computing (4 papers). Severin Sidler is often cited by papers focused on Advanced Memory and Neural Computing (11 papers), Ferroelectric and Negative Capacitance Devices (8 papers) and Neural Networks and Reservoir Computing (4 papers). Severin Sidler collaborates with scholars based in Switzerland, United States and South Korea. Severin Sidler's co-authors include Pritish Narayanan, Geoffrey W. Burr, R. M. Shelby, Irem Boybat, Carmelo di Nolfo, Hyunsang Hwang, Kumar Virwani, Junwoo Jang, Yusuf Leblebici and Kibong Moon and has published in prestigious journals such as Nature, IEEE Transactions on Electron Devices and IEEE Transactions on Circuits & Systems II Express Briefs.

In The Last Decade

Severin Sidler

11 papers receiving 2.6k citations

Hit Papers

Neuromorphic computing using non-volatile memory 2015 2026 2018 2022 2016 2018 2015 250 500 750
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.

  1. Neuromorphic computing using non-volatile memory
    2016 818 citations Geoffrey W. Burr, R. M. Shelby et al. Advances in Physics X profile →
  2. Equivalent-accuracy accelerated neural-network training using analogue memory
    2018 770 citations Stefano Ambrogio, Pritish Narayanan et al. Nature profile →
  3. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element
    2015 741 citations Geoffrey W. Burr, R. M. Shelby et al. IEEE Transactions on Electron Devices profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Severin Sidler Switzerland 9 2.5k 837 611 385 342 11 2.6k
Noraica Dávila United States 17 2.8k 1.1× 1.1k 1.3× 509 0.8× 426 1.1× 266 0.8× 23 2.9k
Farshad Merrikh‐Bayat Iran 16 2.5k 1.0× 1.2k 1.4× 593 1.0× 435 1.1× 181 0.5× 45 3.1k
Teng Zhang China 20 2.4k 0.9× 968 1.2× 570 0.9× 473 1.2× 340 1.0× 59 2.6k
Christian Gamrat France 18 2.2k 0.9× 1.0k 1.2× 452 0.7× 639 1.7× 187 0.5× 36 2.3k
Peng Huang China 29 3.1k 1.2× 918 1.1× 329 0.5× 234 0.6× 363 1.1× 166 3.2k
Seung Hwan Lee United States 17 2.4k 0.9× 606 0.7× 983 1.6× 625 1.6× 184 0.5× 36 2.6k
Olivier Bichler France 19 2.2k 0.9× 1.0k 1.2× 456 0.7× 725 1.9× 181 0.5× 43 2.3k
S. R. Nandakumar United States 15 1.5k 0.6× 458 0.5× 427 0.7× 225 0.6× 294 0.9× 26 1.6k
Carmelo di Nolfo United States 8 2.6k 1.0× 744 0.9× 844 1.4× 673 1.7× 233 0.7× 10 2.9k
Junwoo Jang South Korea 16 1.8k 0.7× 666 0.8× 370 0.6× 268 0.7× 149 0.4× 26 1.8k

Countries citing papers authored by Severin Sidler

Since Specialization
Citations

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

Fields of papers citing papers by Severin Sidler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Severin Sidler

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

All Works

11 of 11 papers shown
1.
Ambrogio, Stefano, Pritish Narayanan, Hsinyu Tsai, et al.. (2018). Equivalent-accuracy accelerated neural-network training using analogue memory. Nature. 558(7708). 60–67. 770 indexed citations breakdown →
2.
Moon, Kibong, Alessandro Fumarola, Severin Sidler, et al.. (2017). Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part I: Al/Mo/Pr0.7Ca0.3MnO3 Material Improvements and Device Measurements. IEEE Journal of the Electron Devices Society. 6. 146–155. 61 indexed citations
3.
Fumarola, Alessandro, Severin Sidler, Kibong Moon, et al.. (2017). Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part II: Impact of Al/Mo/Pr0.7Ca0.3MnO3 Device Characteristics on Neural Network Training Accuracy. IEEE Journal of the Electron Devices Society. 6. 169–178. 22 indexed citations
4.
Woźniak, Stanisław, Angeliki Pantazi, Severin Sidler, et al.. (2017). Neuromorphic Architecture With 1M Memristive Synapses for Detection of Weakly Correlated Inputs. IEEE Transactions on Circuits & Systems II Express Briefs. 64(11). 1342–1346. 7 indexed citations
5.
Boybat, Irem, Carmelo di Nolfo, Stefano Ambrogio, et al.. (2017). Improved Deep Neural Network Hardware-Accelerators Based on Non-Volatile-Memory: The Local Gains Technique. 1. 1–8. 10 indexed citations
6.
Fumarola, Alessandro, Pritish Narayanan, Severin Sidler, et al.. (2016). Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–8. 22 indexed citations
7.
Burr, Geoffrey W., R. M. Shelby, Abu Sebastian, et al.. (2016). Neuromorphic computing using non-volatile memory. Advances in Physics X. 2(1). 89–124. 818 indexed citations breakdown →
8.
Sidler, Severin, Irem Boybat, R. M. Shelby, et al.. (2016). Large-scale neural networks implemented with Non-Volatile Memory as the synaptic weight element: Impact of conductance response. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 440–443. 36 indexed citations
9.
Burr, Geoffrey W., R. M. Shelby, Severin Sidler, et al.. (2015). Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element. IEEE Transactions on Electron Devices. 62(11). 3498–3507. 741 indexed citations breakdown →
10.
Boybat, Irem, Severin Sidler, Carmelo di Nolfo, et al.. (2015). PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
11.
Burr, Geoffrey W., Pritish Narayanan, R. M. Shelby, et al.. (2015). Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power). Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4.4.1–4.4.4. 124 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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