FMSG: Cyber: 3D Printing of Holographic Optical Processors

Optical processors are computing devices that use light, rather than electricity, for sensing and processing images. Layers of gratings mimic the structure of neural networks, guide the paths of light through these layers via diffraction, and achieve low-latency and low-power computation. This technology provides a promising alternative to the existing computer vision methods, which are subject to delays caused by massive computations (billions of parameters) at high frame rates. This Future Manufacturing Seed Grant (FMSG) project will investigate the additive manufacturing of holographic optical processors and enable computer vision with minimal or no computational time. If successful, this project will generate new knowledge about high-resolution manufacturing, and lead to more affordable next-generation computing devices. The increased computation capability will potentially transform multiple fields, from artificial intelligence to quantum computing, cybersecurity to next-generation communications. This project will stimulate the interest in STEM education at the interface of future manufacturing and artificial intelligence, and contribute to workforce development in both areas. This project aims to establish a holographically-assisted Vat Photopolymerization (H-VPP) process, by combining high-throughput microscale VPP with the nanoscale holographic recording process. H-VPP fabricates billions of volumetric diffractive gratings, in place of interference-patterned refractive indices, allowing for high-resolution optical structures with a large number of layers to form complex optical pathways. Several fundamental research questions to be addressed include: (1) elucidating how transparent photocurable resins can be used for printing holographic devices, (2) investigating how nanoscale refractive index modulation can be achieved by adding extra laser beams to interfere with the projected mask-images, and (3) inversely designing the mask image patterns using the back-propagation algorithm in deep learning. This research will gain new knowledge about the relationship among the holographic process, optical structure, and processor performance. Though this project focuses on VPP, the resulting science will advance the understanding of other polymer-based high-resolution manufacturing processes. This Future Manufacturing award was supported by Division of Civil, Mechanical and Manufacturing Innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.