# Multi-layer 5D Optical Data Storage: Mathematical Modeling and Deep Learning-Based Reconstruction of Birefringent Parameters

## TL;DR

This paper introduces a mathematical model and a deep learning-based reconstruction method for 5D optical data storage, significantly improving accuracy in retrieving birefringent parameters from noisy measurements.

## Contribution

It develops a Jones matrix-based model for multi-layer 5D optical storage and proposes a deep learning algorithm for robust birefringence parameter reconstruction.

## Key findings

- Over an order-of-magnitude improvement in reconstruction accuracy
- Robust performance under noisy measurement conditions
- Model and algorithm integration into existing systems

## Abstract

Five-dimensional (5D) optical data storage has emerged as a promising technology for ultra-high-density, long-term data archiving. However, its practical realization is hindered by noise and interference during data readout. In this work, we develop a high-precision mathematical model for multi-layer 5D optical storage, grounded in the Jones matrix framework, to accurately capture polarization transformations induced by stacked birefringent nanostructures. Building on this model, we propose a 20-frame FiLM-conditioned U-Net algorithm to reconstruct birefringence parameters--specifically, slow-axis orientation and retardance magnitude-directly from measured intensity patterns. Trained on both ideal and noisy datasets, the network demonstrates robust reconstruction performance under challenging measurement conditions. Compared with conventional frame-based retrieval approaches, our method achieves over an order-of-magnitude improvement in reconstruction accuracy. The proposed model and algorithm can be readily integrated into existing 5D optical readout systems, offering both a solid theoretical foundation and practical tools for precise data recovery.