Research background:
Optical fibers are fundamental components in modern science and technology due to their inherent advantages, providing an efficient and secure medium for applications such as internet communication and big data transmission. Compared with single-mode fibers (SMFs), multimode fibers (MMFs) can support a much larger number of guided modes (~103 to ~104), offering the attractive advantage of high-capacity information and image transportation within the diameter of a hair. This capability has positioned MMFs as a critical tool in fields such as quantum information and micro-endoscopy.
However, MMFs pose a significant challenge: their highly scattering nature introduces severe modal dispersion during transmission, which significantly degrades the quality of transmitted information. Existing technologies, such as artificial neural networks (ANNs) and spatial light modulators (SLMs), have achieved limited success in reconstructing distorted images after MMF transmission. Despite these advancements, the direct optical transmission of undistorted images through MMFs using micron-scale integrated optical components has remained an elusive goal in optical research.
Innovation:
Addressing the longstanding challenges of multi-mode fiber (MMF) transmission, the research team led by Prof. Qiming Zhang and Associate Prof. Haoyi Yu from the School of Artificial Intelligence Science and Technology (SAIST) at the University of Shanghai for Science and Technology (USST) has introduced a groundbreaking solution. The team successfully integrated miniaturized multilayer optical diffractive neural networks (DN2s) onto the distal end of MMFs, enabling full-optical image transmission. Regarded as an ONN, the free-space diffractive neural networks (DN2s), have been proposed as more efficient ANN approaches based on deep learning to directly process the optical matrix multiplication at the speed of light, and realizing the high number of connectivity in ANNs, such as optical image classification, decryption and phase detection.
In this work, the researchers employed the 3D galvo-scanning two-photon nanolithographic (GS-TPN) fabrication approach to integrate a miniaturized DN2s with a footprint of 150 μm by 150 μm at the distal facet of a commercial 0.35 meter-long MMF. Operating within the visible wavelength range, the fiber-integrated miniaturized DN2s based on the multilayer diffractive elements optically infer the amplitude and phase information of speckle patterns, achieving image reconstruction and transmission directly through the optical fiber.
The system demonstrated exceptional performance in imaging handwritten digits, achieving a minimum image reconstruction feature size of approximately 4.90 μm for real-time input images of 65 μm by 65 μm, with the average optical intensity contrast of about 4% and the diffraction efficiency of around 35% per layer. Moreover, the platform demonstrates a transfer learning characteristic. In transmitting 31 HeLa cell images not included in the training dataset, the system maintained high-quality optical image reconstruction, underscoring the potential of integrating miniaturized DN²s with MMFs as an unprecedented micron-scale optical inference platform. This innovation paves the way for multifunctional compact photonic systems.
Summary and Outlook:
This study successfully achieved direct optical image transmission through MMFs by integrating multilayer optical diffractive neural networks (DN2s) with MMFs. Leveraging the exceptional computational capabilities of diffractive neural networks, the system holds promise for future applications in compact photonic systems, enabling broader functional extensions. The integration of miniaturized DN²s at the fiber facet provides a novel micrometer-scale platform for advancing MMF-based technologies in compact photonic systems, such as rigid endoscopes, MMF signal transmission, mode sorting, and short-range quantum optical interconnects. Furthermore, this approach can extend to various fiber systems, including single-mode, gradient-index, and disordered fibers.
