Abstract
Optical computing is being developed as a supplement or even potential replacement for semiconductor electronic computing. Neural networks computing may be particularly compatible with optics as it involves performing many parallel, nonlinear operations on a set of inputs to calculate a set of outputs [1]. This is like supercontinuum generation, where almost any subtle adjustment of the seed pulse can significantly change the pulse spectral or temporal shape. Here, we incorporate a shaped supercontinuum source into a neural network, performing standard classification and autoencoding tasks by measuring shaped supercontinuum spectra.
© 2023 IEEE
PDF ArticleMore Like This
Lauri Salmela, Coraline Lapre, John M. Dudley, and Goery Genty
STu4H.6 CLEO: Science and Innovations (CLEO:S&I) 2020
Y. C. Zhan, H. Zhang, H. Cai, D. P. Poenar, L. C. Kwek, and A. Q. Liu
JTh2A.22 CLEO: Applications and Technology (CLEO:A&T) 2023
Zhenyu Zhao, Shuang Zheng, and Weifeng Zhang
M3J.6 Optical Fiber Communication Conference (OFC) 2023