Robust occlusion-aware orbital angular momentum feature extraction via all-optical diffractive processing systems

As a brain-inspired optical computing architectures, diffractive optical neural networks (DONN) harness light's wave nature for high-speed, energy efficient and parallel information processing, enabling applications such as image classification and wavefront shaping. However, conventional spatially encoded DONNs struggle with robustness in complex and unpredictable environments, where occlusions and distortions degrade processing accuracy. To address these challenges, we propose a robust all-optical feature extraction framework based on orbital angular momentum (OAM). This approach converts optical information into target OAM modes using a diffractive processing framework trained via deep learning, enabling stable and efficient information representation in the OAM domain. Unlike conventional DONNs, our method maintains high performance across diverse and irregular occlusions without requiring network retraining. This self-adaptive occlusion immune operates with zero additional training samples, effectively enhancing optical computing tasks under dynamic and uncertain conditions. By fully utilizing the helical wavefront and orthogonality of OAM, our approach improves the robustness and scalability of DONNs, demonstrating superior performance in challenging optical environments. Our work paves the way for next-generation optical computing systems that can operate reliably in unpredictable and occlusion-rich environment, unlocking what we believe to be new possibilities for robust, real-time processing in a variety of applications.