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Lensless phase retrieval based on deep learning used in holographic data storage.

Jianying Hao, Xiao Lin, Yongkun Lin

|September 1, 2021

Summary

This study introduces a deep learning (DL) method for lensless phase retrieval in holographic data storage. This novel approach enhances storage density and reduces errors without iterative calculations, improving data transfer rates.

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Area of Science:

Optics and Photonics
Computer Science
Data Storage Technologies

Background:

Holographic data storage offers high density but faces challenges in phase retrieval.
Traditional iterative phase retrieval methods are computationally intensive and slow.

Purpose of the Study:

To develop a novel, efficient, and robust lensless phase retrieval method for holographic data storage.
To leverage deep learning for direct phase data prediction from intensity images.

Main Methods:

An end-to-end convolutional neural network (CNN) was trained to map phase-encoded data pages to their near-field diffraction intensity images.
The trained CNN model directly predicts unknown phase data pages from intensity images, eliminating the need for iterations.

Main Results:

The deep learning-based phase retrieval achieved higher storage density and a lower bit-error rate (BER).
The method demonstrated a higher data transfer rate compared to traditional iterative techniques.
The system showed robustness to environmental fluctuations and effective suppression of dynamic noise.

Conclusions:

Deep learning provides an effective, iterative-free solution for lensless phase retrieval in holographic data storage.
This method enhances key performance metrics, making holographic storage more practical and efficient.
The developed system is stable, robust, and suitable for real-world holographic data storage applications.