Photopolymers are special polymeric materials that can be irradiated with light to form polymer structures. It is widely used in the fields of 3D printing, anti-counterfeiting and information storage. In today's relatively mature holographic storage technology, high-density volume holographic data storage requires storage materials with a high response rate, high effective storage density and optical transparency, and high signal-to-noise ratio, etc. For this reason, storage materials have been studied in various fields. Phenanthrenequinone (PQ)/poly(methyl methacrylate) (PMMA) materials are a choice for holographic storage. By modifying the polymer substrate of the lamellar PQ/PMMA holographic polymeric material, it is possible to reduce the generation of bubbles during the preparation of the material to increase the usable area of the material, and at the same time, it is also possible to improve the photoreceptor sensitivity to a certain extent, to increase the read/write speed of the material and to analyze the causes of the phenomenon using the first nature principle calculations.
Phenanthraquinone-doped polymethylmethacrylate (PQ/PMMA) photopolymer is a promising material for holographic data storage, according to the negligible shrinkage, polarization sensitivity, and easy preparation. In this paper, we investigated the effect of thermal polymerization temperature and time of PQ/PMMA on the collinear holographic data storage system. By designing the baking temperatures 50℃ and 60℃ and baking times 4-20 hours each 2 hours during thermal polymerization. The information page storage and representation results show that under the baking temperature of 50℃ when the baking times were less than 8 hours, the material could not record the data page, and the bit error rate (BER) of the reconstructed data page was increased with the baking time extension. The material baked for 10 hours recorded data with the best results and reconstructed data pages with a minimum BER of 1.5%, when the baking time is 20 hours, the BER of the reconstructed data page was increased by about 12% compared to the baking time of 10 hours. When the baking temperature is 60℃, the data page BER was also increased with the baking time extension except for a very short baking time within 2.5 hours. We analyze the molecular weight of these materials that can be changed by controlling the baking temperature and time of thermal polymerization properly so that grating generation and readout efficiency can be changed. We believe the analysis is useful for the application of PQ/PMMA on collinear holographic data storage.
Exponential growth of data motivates scientists to develop novel optical data storage technologies with the merits of low cost, large capacity, long lifetime, and low power consumption, thus facilitates the development of low-carbon and environmentally friendly economy. Volume holographic data storage, as a promising new generation optical data storage technology, has attracted a tremendous amount of attention within the scientific community, and the lack of appropriate storage medium seriously hinder its large-scale commercial applications. PQ/PMMA photopolymers possess excellent characteristics of low cost, neglectable volume shrinkage and controllable thickness for holographic data storage, while poor holographic performance inhibits its direct applications. Herein, via simply introducing C60 nanoparticles in PQ/PMMA, we successfully synthesize photopolymer with a record-high diffraction efficiency and refractive index modulation, for the first time, reaching up to ~80% and ~1.13×10-4, respectively. More interestingly, C60 nanoparticles here dramatically enhance the intensity holography, but seriously suppress the polarized holographic one. Experimental characterizations and theoretical simulations demonstrate that polarization sensitive holographic performance and reduction of photoinduced anisotropy induced by C60 stem from the strong π-π non-bonding interactions between PQ photosensitizers and C60 molecules (supramolecular) but not chemical reactions. Moreover, C60-PQ/PMMA show a great potential for holographic data storage, exhibiting high chemical stability under extreme working conditions and the angle-multiplexing of 321 gratings and corresponding holographic images are successfully recorded and read on it.
Acknowledgment: This work was financially supported by the National Key R&D Program of China (Grant NO. 2018YFA0701800)
We used the amplitude coding method of 3:16, that is, in a 4 * 4 pixel matrix, only three pixels are in the on state, and the remaining pixels are in the off state. In the collinear amplitude holographic data storage system, U-Net full convolution neural network is used to denoise the amplitude coded image obtained by the detector. The experimental results show that the bit error rate can be reduced to less than 1% from 10% and the image signal-to-noise ratio can be increased by more than 5 times.
Phase retrieval is the key technique in phase-modulated holographic storage. In this paper, a deep convolutional neural network is proposed to directly retrieve phase data. Compared with the traditional non-interferometric phase retrieval method, this method has the advantages of fast retrieval speed and high reconstruction accuracy. In this paper, the influence of intensity image noise on retrieval results under different retrieved conditions is researched and analyzed. By establishing a simulation system that is in strict agreement with real experiments, the lensless spatial diffraction images are generated. By adding different proportions of random noise into the intensity images we get the training dataset. The convolutional neural network is trained by a training dataset and tested by a new noisy test dataset. Experimental results show that the phase retrieval method based on deep learning has a high tolerance for systematic errors and strong anti-noise performance.
In this paper, a phase retrieval method based on deep learning is proposed and applied to the phase-modulated holographic storage system. The phase-modulated holographic storage system has become a research hotspot because of its higher encoding rate and higher signal-to-noise ratio (SNR). Since the phase data cannot be detected directly by the detector, the intensity image is used to retrieve the phase. The traditional interferometric phase retrieval method is not suitable for the storage system because its optical system is complex and is easily affected by environmental disturbances. The non-interferometric phase modulation storage system uses iterative methods to solve the phase data, and the number of iterations will affect the data transmission rate in the holographic data system. In this paper, a simulated non-interferometric phase retrieval system based on deep learning is established, which uses a convolutional neural network to directly establish the relationship between phase and intensity images captured by CCD. The neural network is trained by learning the dataset of intensity images and phase data images. After training, the phase can be obtained by a single calculation, which greatly improves the data transmission speed. In the process of deep learning training, we introduced embedded data to improve the precision of phase reconstruction and reduce the bit error rate. According to our investigation, this is the first application of deep learning in phase retrieval of optical holographic storage.
The single-shot iterative Fourier transform algorithm as a common non-interferometric phase retrieval algorithm is very suitable for phase-modulated holographic data storage due to its fast, simple and stable properties. It retrieves the phase in the object domain iteratively from the intensity image in the Fourier domain captured by the detector. Because of the effects by complex noises of the experimental system, there is always an intensity image degradation which increases the phase decoding bit error rate. This paper proposed a denoising method based on end-to-end convolutional neural networks by learning the relationship between the captured intensity images and the simulation results to improve image quality significantly. Then the denoised intensity image was used in the phase retrieval. The experiment results showed that the bit error rate can be reduced by 6.7 times using the denoised image, which proved the feasibility of the neural network denoising method in the phase-modulated holographic data storage system.
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.