A Review of the Optimisation of Photopolymer Materials for Holographic Data Storage

Photopolymers are very interesting as optically sensitive recording media due to the fact that they are inexpensive, self-processing materials with the ability to capture low-loss, high-fidelity volume recordings of 3D illuminating patterns. We have prepared this paper in part in order to enable the recognition of outstanding issues, which limit in particular the data storage capacity in holographic data storage media. In an attempt to further develop the data storage capacity and quality of the information stored, that is, the material sensitivity and resolution, a deeper understanding of such materials in order to improve them has become ever more crucial. In this paper a brief review of the optimisation of photopolymer materials for holographic data storage (HDS) applications is described. The key contributions of each work examined and many of the suggestions made for the improvement of the different photopolymer material discussed are presented.

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High dynamic range holographic data storage media

10.1117/12.2188794 ◽

2015 ◽

Cited By ~ 5

Author(s):

Fred Askham ◽

Mark R. Ayres ◽

Adam C. Urness

Keyword(s):

Data Storage ◽

Dynamic Range ◽

High Dynamic Range ◽

Holographic Data Storage ◽

Storage Media ◽

High Dynamic

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Multilevel Optical Data Storage in Eu2+/Ho3+ doped Ba2SiO4 Phosphor with Linear Mapping between Ultraviolet Excitation and Thermoluminescence/Photostimulated Luminescence Response

Journal of Materials Chemistry C ◽

10.1039/d1tc05254c ◽

2021 ◽

Author(s):

Mingliang Pan ◽

Yi Zhong ◽

Hui Lin ◽

Hongran Bao ◽

Lulu Zheng ◽

...

Keyword(s):

Data Storage ◽

Storage Capacity ◽

Optical Storage ◽

Linear Mapping ◽

Optical Data Storage ◽

Optical Data ◽

Photostimulated Luminescence ◽

Persistent Luminescence ◽

Ultraviolet Excitation ◽

Storage Media

Persistent luminescence phosphors are regarded as one of the promising candidates for optical storage media. However, most optical storages using phosphors can only realize single-bit-data recording, limiting the storage capacity....

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Non-Volatile Holographic Data Storage Media based on Dye-Doped Thermoplastic

2006 Optical Data Storage Topical Meeting ◽

10.1109/ods.2006.1632767 ◽

2006 ◽

Author(s):

C. Erben ◽

Xiaolei Shi ◽

E. Boden ◽

K.L. Longley ◽

B. Lawrence ◽

...

Keyword(s):

Data Storage ◽

Holographic Data Storage ◽

Storage Media

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Storage scaling management model

Information and Control Systems ◽

10.31799/1684-8853-2020-5-43-49 ◽

2020 ◽

pp. 43-49

Author(s):

Boris Sovetov ◽

Tatiana Tatarnikova ◽

Ekaterina Poymanova

Keyword(s):

Machine Learning ◽

Data Storage ◽

Hierarchical Structure ◽

Storage Capacity ◽

Storage System ◽

Capacity Management ◽

Learning Methods ◽

Machine Learning Methods ◽

Storage Media ◽

The Hierarchical Structure

Introduction: The implementation of data storage process requires timely scaling of the infrastructure to accommodate the data received for storage. Given the rapid accumulation of data, new models of storage capacity management are needed, which should take into account the hierarchical structure of the data storage, various requirements for file storage and restrictions on the storage media size. Purpose: To propose a model for timely scaling of the storage infrastructure based on predictive estimates of the moment when the data storage media is fully filled. Results: A model of storage capacity management is presented, based on the analysis of storage system state patterns. A pattern is a matrix each cell of which reflects the filling state of the storage medium at an appropriate level in the hierarchical structure of the storage system. A matrix cell is characterized by the real, limit, and maximum values of its carrier capacity. To solve the scaling problem for a data storage system means to predict the moments when the limit capacity and maximum capacity of the data carrier are reached. The difference between the predictive estimatesis the time which the administrator has to connect extra media. It is proposed to calculate the values of the predictive estimates programmatically, using machine learning methods. It is shown that when making a short-term prediction, machine learning methods have lower accuracy than ARIMA, an integrated model of autoregression and moving average. However, when making a long-term forecast, machine learning methods provide results commensurate with those from ARIMA. Practical relevance: The proposed model is necessary for timely allocation of storage capacity for incoming data. The implementation of this model at the storage input allows you to automate the process of connecting media, which helps prevent the loss of data entering the system.

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