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Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Ruotong Wang, Junhe Zhou

|June 11, 2026

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Parallel subnetwork-filtered diffractive deep neural networks (PSF-D2NNs) improve hardware implementation by enhancing robustness against diffractive plane misalignment. This optical computing approach offers improved tolerance in both simulations and experiments.

Area of Science:

Optical computing
Deep learning hardware
Photonic devices

Background:

Diffractive deep neural networks (D2NNs) offer low latency and power consumption for optical computing.
Hardware implementation of D2NNs is hindered by challenges in aligning multiple diffractive planes.
System errors and misalignment reduce the practical deployability of D2NNs.

Purpose of the Study:

To propose a novel parallel subnetwork-filtered diffractive deep neural networks (PSF-D2NNs) architecture.
To enhance the robustness of D2NNs against multi-plane misalignment and experimental errors.
To improve the practical feasibility of D2NNs in hardware implementations.

Main Methods:

Introduced a dataset with random misalignment errors for modulation planes.

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Last Updated: Jun 12, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Employed phase filtering factors to achieve modulation phase smoothing.

Developed and simulated the PSF-D2NNs architecture.

Main Results:

PSF-D2NNs demonstrated enhanced robustness against misalignment in simulations and experiments.
Simulated misalignment tolerance reached 26 pixels (MSE: 5.441×10⁻⁵, SSIM: 0.7741).
Experimental misalignment tolerance exceeded 13 pixels (MSE: 3.350×10⁻⁶, SSIM: 0.9760).

Conclusions:

The proposed PSF-D2NNs architecture significantly improves tolerance to multi-plane misalignment.
PSF-D2NNs offer a viable solution for robust optical computing hardware.
The findings pave the way for more reliable and practical diffractive deep neural network implementations.