Photonics-Computing-Survey

Journal

Nature Photonics（NP)

Advances in Optics and Photonics (AOP)

Light: Science & Applications (LSA)

Laser & Photonics Reviews (LPR)

Advanced Photonics (LPR)

Optica

Opto-Electronic advances (OEA)

Opto-Electronic Sciences (OES)

Advanced Optical Materials (AOM)

Nanophotonics

PhotonicX

ACS Photonics

APL Photonics

eLight

Light: Advanced Manufacturing (LAM)

IEEE Journal Of Selected Topics In Quantum Electronics (JSTQE)

Physics Review Applied (PRA)

Journal of Lightwave Technology (JLT)

Optics Letters (OL)

Optics Express (OE)

Applied Physics Letters (APL)

IEEE Photonics Technology Letters (PTL)

Optics Communications (OC)

Apllied Optics (AO)

Conference

Conference on Laser and Electro-Optics (CLEO)

Optical Fiber Communication (OFC)

Groups

Marin Soljacic
MIT-Photonics and Modern Electro-Magnetics Group
[Homepage] [Google Scholar]

David A B Miller
Stanford University
[Homepage] [Google Scholar]

Olav Solgaard
Stanford University-Solgaard Lab
[Homepage] [Google Scholar]

Harish Bhaskaran
Oxford University-Professor of Applied Nanomaterials
[Google Scholar]

Xin Lin
Tsinghua University
[Homepage] [Google Scholar]

Business

Lightelligence
[Homepage]

曦智科技
[Homepage]

Optalysys
[Homepage]

Fathom Computing
[Homepage]

Review

Single-chip microprocessor that communicates directly using light.
C Sun, MT Wade, Y Lee, JS Orcutt, L Alloatti, et al.
Nature, 2015.

Optical computing.
J Touch, AH Badawy, VJ Sorger.
Nanophotonics, 2017.

The challenges of modern computing and new opportunities for optics.
C Li, X Zhang, J Li, T Fang, X Dong.
PhotoniX, 2021.

Photonic-Electronic Integrated Circuits for High-Performance Computing and AI Accelerator.
S Ning, H Zhu, C Feng, J Gu, Z Jiang, Z Ying, J Midkiff, S Jain, MH Hlaing, DZ Pan, RT Chen.
arXiv:2403.14806, 2024.

Analog Optical Computing (AOC)

Traditional AOC

Analog optical computing.
DR Solli, B Jalali.
Nature Photonics, 2015.

Wave physics as an analog recurrent neural network.
TW Hughes, IAD Williamson, et al.
Science Advanced, 2018.

Inverse-designed metastructures that solve equations.
NM Estakhri, B Edwards, N Engheta.
Science, 2019.

Analogue computing with metamaterials.
F Zangeneh-Nejad, et al.
Nature Reviews Materials, 2021.

A single inverse-designed photonic structure that performs parallel computing.
M Camacho, B Edwards, N Engheta.
Nature Communications, 2021.

Analog Optical Computing for Artificial Intelligence.
J Wu, X Lin, Y Guo, J Liu, L Fang, S Jiao, Q Dai.
Engineering, 2021.

Optical Neural Networks (ONNs)

Review

Inference in artificial intelligence with deep optics and photonics.
G Wetzstein, A Ozcan, S Gigan, S Fan, D Englund, et al.
Nature, 2020.

Light in AI: Toward Efficient Neurocomputing with Optical Neural Networks-A Tutorial.
J Gu, C Feng, H Zhu, RT Chen, et al.
IEEE Transactions on Circuits and Systems II: Express Briefs, 2022.

Integrated ONNs

Deep learning with coherent nanophotonic circuits.
Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljacic.
Nature Photonics, 2017, 11(7), 441–446.

Training of photonic neural networks through in situ backpropagation and gradient measurement.
T. W. Hughes, M. Minkov, Y. Shi, and S. Fan.
Optica, 2018, 5(7), 864–871. [Github]

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks.
I. A. Williamson, T. W. Hughes, M. Minkov, B. Bartlett, S. Pai, and S. Fan.
IEEE Journal of Selected Topics in Quantum Electronics, 2020, 26(1):7700412. [Github]

Matrix Optimization on Universal Unitary Photonic Devices.
S. Pai, B. Bartlett, O. Solgaard, D.A.B. Miller.
Physical Review Applied, 2019, 11(6):064044. [Github]

Parallel Programming of an Arbitrary Feedforward Photonic Network.
S. Pai, I.A.D. Williamson, T.W. Hughes, O. Solgaard, S. Fan, D.A.B. Miller.
IEEE Journal of Selected Topics in Quantum Electronics, 2020, 26(1):1-12. [Github]

Towards Hardware-Efficient Optical Neural Networks: Beyond FFT Architecture via Joint Learnability.
J Gu, Z Zhao, C Feng, Z Ying, M Liu, RT Chen, DZ Pan.
TCAD, 2021.

Efficient On-Chip Training of Optical Neural Networks Using Genetic Algorithm.
H Zhang, J Thompson, M Gu, XD Jiang, H Cai, et al.
ACS Photonics, 2021.

Parallel convolutional processing using an integrated photonic tensor core.
J Feldmann, N Youngblood, M Karpov, H Gehring, X Li, et al.
Nature, 2021.

11 TOPS photonic convolutional accelerator for optical neural networks.
X Xu, M Tan, B Corcoran, J Wu, A Boes, TG Nguyen, et al.
Nature, 2021.

Quantifying power use in silicon photonic neural networks.
AN Tait.
arXiv:2108.04819, 2021.

L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization.
J Gu, H Zhu, C Feng, Z Jiang, RT Chen, DZ Pan.
NeurIPS, 2021. [Github]

ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores.
J Gu, H Zhu, C Feng, Z Jiang, M Liu, S Zhang, and et al.
arXiv:2112.08703, 2021.

An on-chip photonic deep neural network for image classification.
F Ashtiani, AJ Geers, F Aflatouni.
Nature, 2022.

Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence.
Z Xu, T Zhou, M Ma, CC Deng, Q Dai, L Fang.
Science, 2024.

Fully forward mode training for optical neural networks.
Z Xue, T Zhou, Z Xu, S Yu, Q Dai, L Fang.
Nature, 2024.

Diffractive ONNs

All optical machine learning using diffractive deep neural networks.
X Lin, Y Rivenson, NT Yardimci, M Veli, Y Luo, et al.
Science, 2018.

In situ optical backpropagation training of diffractive optical neural networks.
T Zhou, L Fang, T Yan, J Wu, Y Li, J Fan, et al.
Photonics Research, 2020.

Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit.
T Zhou, X Lin, J Wu, Y Chen, H Xie, Y Li, J Fan, H Wu, et al.
Nature Photonics, 2021, 15(5):367-373.

Express Wavenet: A lower parameter optical neural network with random shift wavelet pattern.
Y Chen, N Feng, B Hong, MS Tong, GP Wang, et al.
Optics Communications, 2021.

Digital Optical Computing (DOC)

Logic computing

All-optical logic gates based on nanoscale plasmonic slot waveguides.
Y Fu, X Hu, C Lu, S Yue, H Yang, Q Gong.
Nano letters, 2012.

Applications

Slover

Neural combinatorial optimization with reinforcement learning.
I Bello, H Pham, QV Le, M Norouzi, S Bengio.
ArXiv, 2016.

Heuristic recurrent algorithms for photonic Ising machines.
C Roques-Carmes, Y Shen, C Zanoci, et al.
Nature Communications, 2020.

Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks.
F Cai, S Kumar, T Van Vaerenbergh, X Sheng, et al.
Nature Electronics, 2020.

Accelerating recurrent Ising machines in photonic integrated circuits.
M Prabhu, C Roques-Carmes, Y Shen, N Harris, L Jing, et al.
Optica, 2020.

Physics successfully implements Lagrange multiplier optimization.
SK Vadlamani, TP Xiao, et al.
PNAS, 2020.

Communications

General-purpose programmable photonic processor for advanced radiofrequency applications.
D Pérez-López, A Gutierrez, D Sánchez, et al.
Nature Communications, 2024.

Photonic Computing to Accelerate Data Processing in Wireless Communications.
M Salmani, A Eshaghi, E Luan, S Saha.
Optics Express, 2021.