A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

Abstract

Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (2D) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models.

Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

“…TIEs are a one-shot phase recovery method; however, a minimum of two diffraction patterns must be captured time-sequentially or using multiple image sensors. Recently, digital deep neural networks (DNNs) have been used as classifiers and equalizers and for the defocus correction and and super-resolution of readout data pages [10,11,[23][24][25][26][27][28][29][30][31][32][33]. For example, Ref.…”

“…[31] applied a DNN to self-referential holographic data storage, Ref. [32] used a DNN as an equalizer, and Ref. [33] corrected the defocus error of data pages using a DNN.…”

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