Objective defocusing correction of collinear amplitude-modulated holographic data storage system based on deep learning

Yongkun Lin; Jianying Hao; Shenghui Ke; Haiyang Song; Hongjie Liu; Xiao Lin; Xiaodi Tan

doi:10.1117/12.3008318

20 September 2023 Objective defocusing correction of collinear amplitude-modulated holographic data storage system based on deep learning

Yongkun Lin, Jianying Hao, Shenghui Ke, Haiyang Song, Hongjie Liu, Xiao Lin, Xiaodi Tan

Author Affiliations +

Proceedings Volume 12606, Optical Manipulation and Structured Materials Conference; 1260604 (2023) https://doi.org/10.1117/12.3008318
Event: Optics and Photonics International Congress, 2023, Yokohama, Japan

Abstract

In recent years, optical holographic data storage system has gradually become a research hotspot and a strong competitor of big data storage due to its high data transfer rate, long storage life and high storage density. In the collinear amplitude modulated holographic data storage system, in order to improve the storage density, a high magnification objective lens is usually used as the recording lens to record the encoded data pages in the holographic storage medium. Therefore, when the objective lens is focused on the holographic storage medium, the accuracy and reliability of data recording and reading can be guaranteed. However, in the process of normal use of the system, environmental interference and other factors will inevitably lead to defocusing of the objective lens, which will result in high bit-error-rate (BER) and low signal-to-noise ratio (SNR) of the recorded and read coding information, affecting the accuracy and reliability of information reading. In this paper, we propose a collinear amplitude modulated holographic data storage system objective defocusing correction model using deep learning. Only a training model with defocusing distance of 100μm can be used to correct the defocusing of the objective lens with defocusing distance less than 100μm. The reconstructed BER is reduced to less than 1/10 of the original data, and the SNR is increased to more than 5 times of the original data. The reliability and accuracy of system record reading are improved.

Citation Download Citation

Yongkun Lin, Jianying Hao, Shenghui Ke, Haiyang Song, Hongjie Liu, Xiao Lin, and Xiaodi Tan "Objective defocusing correction of collinear amplitude-modulated holographic data storage system based on deep learning", Proc. SPIE 12606, Optical Manipulation and Structured Materials Conference, 1260604 (20 September 2023); https://doi.org/10.1117/12.3008318

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KEYWORDS

Data modeling

Signal to noise ratio

Deep learning

Holographic data storage systems

Holography

Data storage

Education and training

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