Computational Meta-Optics Enable Ultrathin Cameras for Full-Color Imaging

Jul 21, 2022 High-quality cameras increasingly depend on extended systems of intricately optimized, aberration-correcting lenses. Unlike conventional lenses, metaoptics leverage subwavelength scatterers to replicate complex aspheres in an ultrathin, flat surface that is mass-manufacturable. A fundamental limitation of metaoptics, however, is their strong chromatic aberration. While some metaoptics are achromatic, these designs are fundamentally limited to small aperture, and narrow field of view. Though metaoptics are ill-suited for directly focusing broadband light, their subwavelength spatial resolution and inherent freeform capability enable them to transform light into different bases. These stable bases preserve image information and are used to computationally reconstruct a high-fidelity image. By optimally matching the metaoptics with the image processing routine, reductions in size and complexity are simultaneously realizable in cameras, producing high-quality images.

***This presentation premiered during the 2022 Vision Spectra Conference. For more information on Photonics Media conferences, visit events.photonics.com.

About the presenter:
Shane Colburn, Ph.D., is director of optical design at Tunoptix, a Seattle startup developing semiconductor-defined metaoptics combined with computational imaging software to build ultrathin, high-performance camera systems. He leads the development of Tunoptix’s proprietary design framework for developing optical systems using metaoptics. Colburn received his doctorate in electrical engineering and completed his postdoctoral studies at the University of Washington (UW), where he is an affiliate assistant professor in the Department of Electrical and Computer Engineering. His graduate research focused primarily on dielectric metasurfaces for computational imaging, emphasizing hybrid optical-digital systems that leverage the compact form factor offered by metasurfaces and the aberration mitigation capabilities of computational imaging. Additionally, he developed methods for reconfiguring metasurfaces, including novel architectures, electromechanical tuning, and phase-change material metasurfaces. He has received numerous recognitions, including the Vikram Jandhyala and Suja Vaidyanathan Endowed Innovation Award, the Paul C. Leach Fellowship, a National Science Foundation Graduate Research Fellowship Honorable Mention, an Amazon Catalyst Fellowship, the UW College of Engineering Student Research Award, and the Yang Research Award for Outstanding Doctoral Student.