Researchers have developed a new holographic data storage method that records and retrieves information in three dimensions by combining three key properties of light — amplitude, phase and polarization. By using all three together, the approach allows much more data to be stored within the same space, offering a potential solution to the growing global demand for data storage.
Traditional storage systems write data onto flat surfaces such as hard drives or optical discs. In contrast, holographic data storage embeds information throughout the volume of a material using laser light. This creates multiple overlapping light patterns within the same space, which significantly increases storage capacity and enables faster data transfer.
“In conventional holographic data storage, data encoding typically uses one light dimension such as amplitude or phase alone, or, at most, combines two of these dimensions,” said research team leader Xiaodi Tan from Fujian Normal University in China. “Based on the principle of polarization holography, we used a deep learning architecture known as a convolutional neural network model to enable the use of polarization as an independent information dimension.”
The research, published in Optica, Optica Publishing Group’s journal for high-impact research, shows that this new technique can increase how much information is stored while also making it easier to retrieve.
“With further development and commercialization, this type of multidimensional holographic data storage could enable smaller data centers and more efficient large-scale archival storage, while also enhancing data processing and transmission efficiency,” said Tan. “It could also contribute to safer data transmission, optical encryption and advanced imaging.”
Using Polarization to Expand Data Encoding
In holographic storage, information is saved as image-like data pages created by laser light patterns. Encoding converts digital data into these pages, while decoding translates them back into usable information.
Although light has multiple properties that could be used to carry more data, combining them effectively has been difficult in practice. To overcome this, the researchers refined a method called tensor-based polarization holography, which preserves the polarization state of light during reconstruction. This makes polarization a dependable channel for storing additional information.
Building on this work, the team created a 3D modulation encoding strategy. By adjusting the intensity and phase of two perpendicular polarization states and applying a double-phase hologram technique, they enabled a single phase-only spatial light modulator to encode amplitude, phase and polarization together in the optical field.
AI Decoding of Multidimensional Light Data
Decoding this combined information is challenging because standard sensors only measure light intensity (amplitude) and cannot directly detect phase or polarization. To address this, the researchers used tensor-polarization holography theory along with a convolutional neural network to recover all three types of data from diffraction intensity images.
The neural network is trained using two complementary diffraction images, one captured with a vertical polarizer and one without. By analyzing these images, the model learns to identify patterns linked to amplitude, phase and polarization. This allows it to reconstruct all three simultaneously, improving storage density and boosting data transmission speed.
Toward Faster and Higher-Capacity Data Storage
After confirming the concept, the researchers built a compact system capable of recording and reconstructing the encoded optical field within a polarization-sensitive material. During testing, intensity images were analyzed to detect signatures related to amplitude, phase and polarization. These were then used as inputs for the neural network, enabling full 3D reconstruction using only intensity-based measurements.
“Overall, our results showed that multidimensional joint encoding substantially increased the information carried by a single holographic data page, thereby improving storage capacity,” said Tan. “In addition, neural network synchronous decoding reduced the need for complex measurements and step-by-step reconstruction, supporting more efficient readout and decoding. This could enable a practical route toward high-capacity, high-throughput holographic data storage.”
Next Steps for Real-World Applications
The researchers emphasize that the system is still in the research stage and requires further development before it can be used commercially. Future work will focus on increasing the gray levels used in encoding to expand capacity even further, as well as improving the long-term stability, uniformity and repeatability of the recording materials.
They also plan to integrate this method with volumetric holographic multiplexing techniques, which could allow multiple pages and channels of data to be stored at once. Strengthening the integration between optical hardware and decoding algorithms will be essential for achieving faster and more reliable data retrieval under real-world conditions.