Photonics for artificial intelligence and neuromorphic computing

1.0k indexed citations

Abstract

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About

This paper, published in 2021, received 1.0k indexed citations. Written by Bhavin J. Shastri, Alexander N. Tait, Thomas Ferreira de Lima, Wolfram H. P. Pernice, C. David Wright and Paul R. Prucnal covering the research area of Artificial Intelligence and Electrical and Electronic Engineering. It is primarily cited by scholars working on Electrical and Electronic Engineering (938 citations), Artificial Intelligence (810 citations) and Atomic and Molecular Physics, and Optics (157 citations). Published in Oxford University Research Archive (ORA) (University of Oxford).

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Countries where authors are citing Photonics for artificial intelligence and neuromorphic computing

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This map shows the geographic impact of Photonics for artificial intelligence and neuromorphic computing. 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 Photonics for artificial intelligence and neuromorphic computing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Photonics for artificial intelligence and neuromorphic computing more than expected).

Fields of papers citing Photonics for artificial intelligence and neuromorphic computing

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Photonics for artificial intelligence and neuromorphic computing. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Photonics for artificial intelligence and neuromorphic computing.

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.

This paper is also available at doi.org/w11600678.

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