Neuromorphic Photonic Devices and Applications

1st Edition - December 1, 2023
Editors: Min Gu, Elena Goi, Yangyundou Wang, Zhengfen Wan, Yibo Dong, Yuchao Zhang, Haoyi Yu
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 8 8 2 9 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 7 2 6 0 - 4

Neuromorphic Photonic Devices and Applications synthesizes in one volume the most critical advances in photonic neuromorphic models, photonic material platforms, and accele… Read more

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Neuromorphic Photonic Devices and Applications synthesizes in one volume the most critical advances in photonic neuromorphic models, photonic material platforms, and accelerators for neuromorphic computing. It discusses fields and applications that can leverage these new platforms.

A brief review of the historical development of the field is provided followed by a discussion of the emerging 2D photonic materials platforms and recent work in implementing neuromorphic models and 3D neuromorphic systems. The application of artificial intelligence such as neuromorphic models to inverse design neuromorphic materials and devices and predict performance challenges is discussed throughout. The book includes a comprehensive overview of the applications of neuromorphic photonic technologies and the challenges, opportunities, and future prospects facing the field.

Neuromorphic Photonic Devices and Applications is suitable for researchers and practitioners in academia and R&D in the multi-disciplinary field of photonics.

Cover image
Title page
Table of Contents
Copyright
List of contributors
About the editors
Preface
Section 1: Introduction
1. A brief introduction to the brain
Abstract
1.1 Why does the brain matter for studying artificial intelligence?
1.2 How can we learn from studying the brain?
1.3 A single neuron
1.4 Modeling
1.5 Neural circuits, network dynamics, and systems
References
2. Neuromorphic computing
Abstract
2.1 Artificial intelligence
2.2 Artificial intelligence applications
2.3 Artificial intelligence, machine learning, artificial neural networks, and deep leaning
2.4 Neuromorphic computing
2.5 Conclusions
References
3. Advanced neuromorphic models
Abstract
3.1 Types of machine learning
3.2 Traditional machine learning models
3.3 Deep learning models
3.4 Quantum machine learning
3.5 Conclusion
References
Section 2: Neuromorphic photonics
4. Neuromorphic photonics: development of the field
Abstract
4.1 Introduction
4.2 The rise of neuromorphic photonics
4.3 Implementation of neuromorphic photonics
4.4 Recent advances and applications of neuromorphic photonics
4.5 Chapter summary
References
5. Optoelectronic synapses for two-dimensional neuromorphic photonics
Abstract
5.1 Typical synaptic behaviors
5.2 Two-dimensional perovskite-based optoelectronic synapses
5.3 Two-dimensional oxide semiconductor-based optoelectronic synapses
5.4 Other two-dimensional optoelectronic synapses
5.5 Two-dimensional neuromorphic-computing applications
5.6 Summary and outlook
References
6. 2D neuromorphic photonics
Abstract
6.1 Introduction
6.2 Photonic neuromorphic computing: principle and architecture
6.3 Spike timing-dependent plasticity
6.4 Photonic spiking neural networks
6.5 Convolutional neural networks (CNNs)
6.6 Correlational neural networks
6.7 Summary
References
7. 3D neuromorphic photonics
Abstract
7.1 Diffractive and scattering optical elements
7.2 Diffractive neural computing
7.3 Scattering neural computing
7.4 Applications of 3D neuromorphic photonics
7.5 Discussion
Acknowledgments
References
8. Large-scale neuromorphic systems enabled by integrated photonics
Abstract
8.1 Introduction
8.2 Photonic weights and activation functions
8.3 Large-scale photonic integrated neuromorphic systems
8.4 Photonic training for neuromorphic systems
8.5 Toward neuromorphic photonic processors
References
9. Neuromorphic models applied to photonics
Abstract
9.1 Neuromorphic models applied to photonics
9.2 Machine learning for forward prediction
9.3 Machine learning for inverse design
9.4 Conclusion and outlook
References
Section 3: Applications
10. Photonic matrix computing accelerators
Abstract
10.1 Photonic matrix multiplication
10.2 Optical signal processing
10.3 Optical neural networks
10.4 Combinatorial optimization problems
10.5 Photonic recurrent Ising sampler
10.6 Conclusion
References
11. Deep optics
Abstract
11.1 Optics from the perspective of inverse problems
11.2 Deep learning in optics
11.3 Examples: deep lensless imaging and deep computer-generated holography
11.4 Discussions
References
12. Photonic neuromorphic processing for optical communications
Abstract
12.1 Optical communications
12.2 Intelligent processing for optical communications
12.3 Neuromorphic photonics for signal processing
12.4 Conclusion
References
Section 4: Perspective
13. Perspective on photonic neuromorphic computing
Abstract
13.1 Introduction
13.2 Current challenges in neuromorphic photonics
13.3 Emerging directions
13.4 Conclusion
References
Index

Min Gu

Min Gu is Executive Chancellor of the University Council and Distinguished Professor of University of Shanghai for Science and Technology. He was Distinguished Professor and Associate Deputy Vice-Chancellor at RMIT University and a Laureate Fellow of the Australian Research Council. He is an author of four standard reference books and has over 500 publications in nano- and biophotonics. He is an elected Fellow of the Australian Academy of Science and the Australian Academy of Technological Sciences and Engineering as well as Foreign Fellow of the Chinese Academy of Engineering. He was awarded the Einstein Professorship, the W. H. (Beattie) Steel Medal, the Ian Wark Medal, the Boas Medal, the Victoria Prize for Science and Innovation and the 2019 Dennis Gabor Award of SPIE.

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, P.R. China

Elena Goi

Elena Goi received her B.S. and M.S. in physics from the University of Trieste. In 2018 she was awarded her Ph.D. from RMIT University, Australia, under the supervision of Prof. Min Gu. She is now an early career researcher at USST, Shanghai, China. Her current research includes the development and implementation of hybrid three-dimensional (3D) nanolithographic techniques that enable the study of novel 3D optical materials and architectures for optical neural networks.

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, P.R. China

Yangyundou Wang

Dr Wang received her B.S. in Biomedical Engineering from Changzhi Medical College and M.S. in Communication and Information Engineering from Northwestern Polytechnical University. In 2017 she was received her Ph.D. in Theoretical Physics from Free University, Amsterdam, under the supervision of Prof. Emil Wolf and Prof. Taco D. Visser. She is now an early career researcher at USST, Shanghai, China. Her current research includes the development of nanoscopic imaging techniques and its implementation in neuroscience for the development of nanophotonics chip

Affiliations and expertise

Hangzhou Institute of Technology, Xidian University, Hangzhou, P.R. China; School of Optoelectronic Engineering, Xidian University, Xi’an, P.R. China

Zhengfen Wan

Dr. Wan received his B.S. degree in Physics from Xinjiang University and M.S. degree in Physics from Zhejiang University in 2008 and 2010, respectively. After graduated from Zhejiang University, he worked about 6 years as engineer in the industry field of semiconductor devices. He pursued his Ph.D. at Griffith University, Australia, since 2016. After submitted the thesis in 2019, he got the research fellow position in Griffith University. In 2020, he received the Doctor’s degree and joined Prof. Min Gu’s research group as postdoctor at University of Shanghai for Science and Technology. Editor # 6:

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, P.R. China

Yibo Dong

Dr Dong received his B.S. in electronics from the University of Shanghai for Science and Technology. In 2020 he earned Ph.D. degree in electronics from Beijing University of Technology with a research focus on graphene optoelectronic devices and graphene growth. He joined the Centre for Artificial-Intelligence Nanophotonics established by Prof. Min Gu in summer 2020. His current research interest is to develop novel optical diffraction neural network based on two dimensional materials enabled by laser direct writing.

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, P.R. China

Yuchao Zhang

Dr Zhang received him B.S. in optical information from Beijing University of Posts and Telecommunications, China. And in 2014 he was awarded the Ph.D. from Institute of Microelectronics, Chinese Academy of Sciences, China. He is now a researcher at USST, Shanghai, China. Him current research is using nano-photonics including metasurface and metamaterials to construct optical neural networks.

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, P.R. China

Haoyi Yu

Dr Haoyi Yu received his M.S. in condensed matter physics from Nankai University, China. In 2019 he earned Ph.D. degree in nanophotonics from Royal Melbourne Institute of Technology University, Australia, with a research focus on neuron-inspired nanophotonics. He joined the Centre for Artificial-Intelligence Nanophotonics established by Prof. Min Gu in March 2021. His current research interest is to develop novel nanophotonics system by laser direct writing.

Affiliations and expertise

Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, P.R. China