Partitionable Diffractive Neural Networks for Multifunctional Optical Operations
Yudong Tian, Haifeng Xu, Yuqing Liu, Xiangyu Zhao, Jierong Cheng, and Chongzhao Wu

TL;DR
This paper introduces partitionable diffractive neural networks (PDNNs) that can be reconfigured by stacking independent submodules, enabling multifunctional optical operations without reconstructing the physical system.
Contribution
The authors propose a novel PDNN framework allowing flexible, multi-task optical neural networks through stacking independent submodules, enhancing reconfigurability and multifunctionality.
Findings
PDNNs enable multifunctional optical tasks by stacking submodules.
The framework offers high reconfigurability without physical reconstruction.
Demonstrates potential for integrated optical AI systems.
Abstract
Diffractive neural network (DNN), which can perform machine learning tasks based on the light propagation and diffraction, has recently emerged as a promising optical computing paradigm due to its high parallel processing speed and low power consumption nature. However, existing diffractive network architectures face challenges in implementing functional reconfiguration. Once a diffractive neural network is fabricated, its functionality is fixed. Deploying such systems for different tasks typically requires reconstructing the entire physical setup, which significantly compromises hardware efficiency in practical applications. In this work, we propose the multifunctional partitionable diffractive neural networks (PDNNs) that can generate networks with additional capabilities by stacking multiple sub-modules with independent functions in the horizontal direction. Each submodule functions…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Photonic and Optical Devices · Ferroelectric and Negative Capacitance Devices
