Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers

Abstract

We report a single-shot phase-detection method using deep learning in a holographic data-storage system. The error rate was experimentally confirmed to be reduced by up to three orders of magnitude compared with that in the conventional phase-determination algorithm by learning the light-intensity distribution around a target signal pixel. In addition, the output speed of a signal phase could be shortened by devising a network and arranging the fully connected layers in parallel. In our environment, the phase-output time of a single-pixel classification was approximately 18 times longer than that in our previous method, with the minimum-finding algorithm. However, it could be reduced to 1.7 times or less when 32 pixels were simultaneously classified. Therefore, the proposed method can significantly reduce the error rates and suppress the phase-output time to almost the same level as that in the previous method. Thus, our proposed method can be a promising phase-detection method for realizing a large-density data-storage system.