Abstract

We present and experimentally evaluate the use of transfer learning to address experimental data scarcity when training neural network (NN) models for Mach–Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pretraining the model using synthetic data generated from a less accurate analytical model and fine-tuning it with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve <1 dB root-mean-square error on the 3×3 matrix weights implemented by a photonic chip while using only $25{\% }$ of the available data.

© 2023 Optica Publishing Group

Model and data-centric machine learning algorithms to address data scarcity for failure identification

Lareb Zar Khan, João Pedro, Nelson Costa, Andrea Sgambelluri, Antonio Napoli, and Nicola Sambo
J. Opt. Commun. Netw. 16(3) 369-381 (2024)

Object-independent wavefront sensing method based on an unsupervised learning model for overcoming aberrations in optical systems

Xinlan Ge, Licheng Zhu, Zeyu Gao, Ning Wang, Hongwei Ye, Shuai Wang, and Ping Yang
Opt. Lett. 48(17) 4476-4479 (2023)

Parallel photonic acceleration processor for matrix–matrix multiplication

Ying Huang, Hengsong Yue, Wei Ma, Yiyuan Zhang, Yao Xiao, Yong Tang, He Tang, and Tao Chu
Opt. Lett. 48(12) 3231-3234 (2023)

Reconfigurable matrix multiplier with on-site reinforcement learning

Zhedong Wang, Min Chen, Chao Qian, Zhixiang Fan, Huaping Wang, and Hongsheng Chen
Opt. Lett. 47(22) 5897-5900 (2022)

DeepSCI: scalable speckle correlation imaging using physics-enhanced deep learning

Zhiwei Tang, Fei Wang, ZhenFeng Fu, Shanshan Zheng, Ying Jin, and Guohai Situ
Opt. Lett. 48(9) 2285-2288 (2023)