With the advent of the big data era, massive data needs to be stored. Holographic storage has become a strong contender for the new generation of storage technology due to its advantages of multi-dimensional modulation and three-dimensional storage. Phenanthrenequinone-doped poly (methyl methacrylate) (PQ/PMMA) is a popular research for holographic storage materials due to the easy availability of raw materials, low cost and simple preparation process. At present, PQ/PMMA materials are basically made manually, but there are differences in the materials prepared by different people. Therefore, we designed an automated material preparation platform, which has more stable characteristics than manual preparation. On this basis, we investigated the effect of ambient temperature and humidity on the properties of holographic materials, and provided new suggestions and guidance for the research of holographic materials.
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
Displacement multiplexing can improve the storage density of collinear holographic data storage systems and is an essential multiplexing method. This article introduces the use of dark reaction phenomena in recording media to improve the displacement multiplexing effect of collinear holographic data storage systems, and achieves a multiplexing distance of 5 μm.
Research is to further increase the display size of the original 3D display system to achieve a better display effect. In order to achieve our goal, we adopted a new method to achieve large-area rotation display and at the same time reduce the noise generated by rotating parts during high-speed rotation. In this study, the relevant technology of magnetic levitation bearing is used for reference[1] and the magnetic bearing is mainly used to offset the gravity of the intermediate turntable and reduce the noise generated by friction and the brushless motor is used to improve the speed of the turntable, hoping to obtain better imaging effect. At the same time, high-precision sensors are used to read the rotation speed and rotation Angle of the magnetic levitation bearing.
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
In the past, we used hand-made holographic data storage materials for research and storage, but these materials have many problems, such as the experiment has a certain degree of non-repeatability, and the data storage system performance is unstable. This work uses an automated chamber to prepare photopolymers without manual operation. The automation room will complete all material fabrication processes except the initial weighing and pouring. Compared with hand-made materials, the automatically made materials have no bubbles due to the special mold design. The overall production process is carried out under the condition of constant temperature and humidity.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.