KEYWORDS: Phase reconstruction, Holography, Data storage, Image processing, Phase shift keying, Image compression, Diffraction, Deep learning, Photonics, Education and training
Holographic data storage systems are candidates for information recording due to their large storage capacity and high transmission rate. In this paper, a phase modulation holographic storage technology with three-grayscale encoding is proposed and implemented. According to the experimental results, if two phase codes in the three-grayscale encoding are relatively close, the performance of phase reconstruction would be degenerated.
Now the era of big data has arrived, and there is an urgent need for high storage capacity storage solutions to store large amounts of data. As a new generation of storage technology, holographic optical storage has the advantages of large data storage capacity, fast transmission speed, read-write parallelism and so on. The storage material for holographic storage should have the characteristics of fast response, high signal-to-noise ratio, high diffraction efficiency and high stability. Phenanthrenequinone-doped poly (methyl methacrylate) (PQ/PMMA) photopolymer is a common storage material, which has the advantages of high diffraction efficiency, inexpensive and simple preparation. Currently, PQ/PMMA is mainly prepared manually. The reproducibility of the preparation process faces challenge due to human errors. Therefore, we designed an automatic PQ/PMMA preparation device, which can effectively eliminate the differences caused by human factors. We have verified through experiments that materials prepared automatically have better stability than those prepared manually. Among the prepared single sheet materials by automatic preparation device, we measured that the difference in diffraction efficiency at different positions is within 10%. The automated experimental platform provides assistance for the stable preparation of materials
With the rapid development of information technology, the amount of data has shown explosive growth. The traditional magnetic storage and optical storage can no longer gradually meet the needs of data storage. Holographic data storage breaks through the mode of two-dimensional data storage and stores data in the form of three-dimensional volume, which can improve the data storage density by one dimension and bring ultra-fast data transfer rate at the same time. However, to promise holographic data storage work well, the servo system should be used in practice to avoid the effect of vibration.
KEYWORDS: Data storage, Holography, Deep learning, Tunable filters, Phase retrieval, Education and training, Optical filters, Linear filtering, Data modeling, Signal to noise ratio
Holographic data storage is a powerful potential technology to solve the problem of mass data long-term storage. To increase the storage capacity, the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout. In this talk, we propose a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem is decomposed into two backward operators denoted by two convolutional neural networks to demodulate amplitude and phase respectively. The stable and simple complex amplitude demodulation and strong anti-noise performance from the deep learning provide an important guarantee for the practicality of holographic data storage.
KEYWORDS: Deep learning, Crosstalk, Spatial light modulators, Phase retrieval, Phase reconstruction, Diffraction, Data storage, Near field diffraction, Image restoration, Photonics
In the holographic data storage system, we can use deep learning method to learn the relationship between phase patterns and their near-field diffraction intensity images. In the practice, pixel crosstalk always exists. We found the pixel crosstalk between adjacent variable phase pixels was benefit for quick and accurate phase retrieval based on deep learning. We validated our idea by the simulation of adding phase disturbance between pixels on the spatial light modulator.
Compared with traditional iterative methods, deep learning phase reconstruction has lower bit error rate and higher data transfer rate. We found the efficiency of training mainly was from the edges of the phase patterns due to their stronger intensity changes between adjacent phase distribution. According to this characteristic, we proposed a method to only record and use the high frequency component of the phase patterns and to do the deep learning training. This method can improve the storage density due to reducing the material consumption.
The phase retrieval method based on deep learning can be used to solve the iterative problem in holographic data storage. The key of the deep learning method is to build the relationship between the phase data pages and the corresponding near-field diffraction intensity patterns. However, to build the correct relationship, thousands of samples of the training dataset are usually required. In this paper, according to the coding characteristics of phase data pages, we proposed an image segmentation method to greatly reduce the number of original training dataset. The innovation proposed by this new method lies in the special segmentation of the original samples to expand the number of samples.
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