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The optical reservoir computer: a new approach to a programmable integrated optics system based on an artificial neural network

Phang, Sendy; Sewell, Phillip D.; Vukovic, Ana; Benson, Trevor M.

Authors

Phillip D. Sewell

ANA VUKOVIC ANA.VUKOVIC@NOTTINGHAM.AC.UK
Professor of Electromagnetic Applications

Trevor M. Benson



Contributors

Giancarlo C. Righini
Editor

Maurizio Ferrari
Editor

Abstract

In this chapter, we briefly reviewed some developments in the field of integrated optics since Miller first introduced this concept, noting some approaches to optimise structures based on functional performance criteria. Chapter Contents: • 12.1 Introduction • 12.2 Some applications of genetic algorithms in integrated optics design • 12.3 Functional integrated optics powered by a reservoir computer • 12.3.1 Introduction to an algorithmic reservoir computer • 12.3.2 An optical reservoir computer as a temporal signal discriminator • 12.3.3 Chaotic cavity as a reservoir computer kernel • 12.3.4 ORC training and validation • 12.4 Conclusions • References.

Citation

Phang, S., Sewell, P. D., Vukovic, A., & Benson, T. M. (2020). The optical reservoir computer: a new approach to a programmable integrated optics system based on an artificial neural network. In G. C. Righini, & M. Ferrari (Eds.), Integrated Optics Volume 2: Characterization, devices and applications (361-380). Institution of Engineering and Technology (IET). https://doi.org/10.1049/pbcs077g_ch12

Online Publication Date Dec 31, 2020
Publication Date Dec 31, 2020
Deposit Date May 18, 2022
Publisher Institution of Engineering and Technology (IET)
Pages 361-380
Book Title Integrated Optics Volume 2: Characterization, devices and applications
Chapter Number 12
ISBN 9781839533433
DOI https://doi.org/10.1049/pbcs077g_ch12
Public URL https://nottingham-repository.worktribe.com/output/8132533
Publisher URL https://digital-library.theiet.org/content/books/10.1049/pbcs077g_ch12
Contract Date Dec 31, 2020