Phase-distribution-aware adaptive decision scheme to improve the reliability of holographic data storage

Strategy for Simple Control of High Performance PQ/PMMA Holographic Media

Holographic data storage technology is a cost-effective solution for long-term archival data storage. However, the development of suitable holographic recording materials remains a challenge. Among these materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) stands out due to its low cost and controllable thickness. Nevertheless, its limited photosensitivity and diffraction efficiency hinder its widespread application. In order to solve these problems, we put forward a kind of convenient and simple, low cost strategy, by adding plasticizer N,N-dimethylformamide (DMF) for preparation of DMF-PQ/PMMA photopolymer, avoid the use of complex compounds. The addition of DMF not only influences the thermal polymerization stage but also forms weak interactions with PQ during the photoreaction process, thereby enhancing the holographic performance of DMF-PQ/PMMA. Consequently, we achieved a remarkable 9.1-fold increase in photosensitivity (from ∼0.35 to 3.18 cm J-1), improved diffraction efficiency by 20% (from 65% to 80%), and reduced volume shrinkage by a factor of 8 (from 0.4% to 0.05%). Furthermore, utilizing a collinear holographic storage system with multiplexing shift at a scale of 5 μm resulted in an impressively low minimum bit error rate (BER) of only 0.36% (with an average BER of 1.4%), highlighting the fast processing capability and potential for low BER applications in holographic information storage using DMF-PQ/PMMA.

Phase retrieval based on deep learning with bandpass filtering in holographic data storage

A phase retrieval method based on deep learning with bandpass filtering in holographic data storage is proposed. The relationship between the known encoded data pages and their near-field diffraction intensity patterns is established by an end-to-end convolutional neural network, which is used to predict the unknown phase data page. We found the training efficiency of phase retrieval by deep learning is mainly determined by the edge details of the adjacent phase codes, which are the high-frequency components of the phase code. Therefore, we can attenuate the low-frequency components to reduce material consumption. Besides, we also filter out the high-order frequency over twice Nyquist size, which is redundant information with poor anti-noise performance. Compared with full-frequency recording, the consumption of storage media is reduced by 2.94 times, thus improving the storage density.

Fast phase error correction with reference beam-assisted LDPC coding for collinear holographic data storage

Low-density parity-check (LDPC) coding is a significant technique for ensuring data reliability in phase-modulated holographic data storage. To accelerate LDPC decoding, we design reference beam-assisted LDPC coding for 4-level phase-modulated holography. The reliability of a reference bit is higher than that of an information bit during decoding because reference data are known during recording and reading processes. By considering the reference data as prior information, the weight of the initial decoding information (i.e., log-likelihood ratio (LLR) information) of the reference bit is increased during LDPC decoding. The performance of the proposed method is evaluated through simulations and experiments. In the simulation, compared with the conventional LDPC code with a phase error rate (PER) of 0.019, the proposed method can reduce bit error rate (BER) by 38.8%, uncorrectable bit error rate (UBER) by 24.9%, decoding iteration time by 29.9%, the number of decoding iterations by 14.8%, and improve decoding success probability by 38.4% approximately. Experimental results demonstrate the superiority of the proposed reference beam-assisted LDPC coding. The developed method can significantly decrease the PER, BER, the number of decoding iterations, and decoding time by using the real captured images.

Decision-free downsampling method assisted via channel-transfer information to improve the reliability of holographic data storage systems

A decision-free downsampling method (DFDS) assisted by channel-transfer information for phase-modulated holographic data storage is proposed. DFDS is used to address the issue of the accumulation of decision errors induced by traditional downsampling. The issue degrades the downsampling accuracy. DFDS comprises two functional segments: acquiring the channel-transfer information offline and performing decision-free downsampling online. With the assistance of the channel-transfer information, DFDS uses Bayesian posterior probabilities instead of traditional decision results to avoid the accumulation of decision errors and achieve more accurate downsampling. The simulation and experimental results show that DFDS reduces the phase error rate, thereby improving the reliability of the holographic data storage system.