Model-Driven Deep Learning Enables Speckle-Free Holography for 3D Parallel Nanofabrication

Holographic light fields offer a promising route toward high-throughput 3-dimensional (3D) nanofabrication. However, the fabrication uniformity remains limited by severe speckle noise present in many previously demonstrated hologram coding methods. Here, we address this challenge by developing a model-driven deep learning framework that enables speckle-free hologram generation with high uniformity. We demonstrate its practical feasibility for 3D nanofabrication using 2-photon lithography (TPL) and term this approach SMART HoloTPL. By establishing a polymerization model based on the broadband angular-spectrum method, self-supervised network training is guided to explore advanced hologram coding strategies without reliance on paired datasets. Tailored neural network architecture and loss functions are designed for high-uniformity hologram generation. A TPL fabrication platform powered by a femtosecond regenerative laser amplifier has been built. SMART HoloTPL achieves large-scale speckle-free 3D nanofabrication with a 120,000 voxels/s throughput and 120-nm resolution, addressing key challenges in fabrication rate and quality.