Project Description:

Optical metasurfaces are planar transparent substrates with custom-designed, nanoscale features that selectively modulate incident light with respect to direction, wavelength, and polarization. When coupled with photodetectors and appropriate post-capture processing, they provide a means to create computational imagers and sensors that are exceptionally small and have distinctive capabilities. We are developing a framework in TensorFlow that renders physically-accurate images induced by metasurface optical systems, named D-Flat. This framework is fully differentiable with respect to metasurface shape and post-capture computational parameters and allows both to be optimized with respect to almost any measure of sensor performance. D-Flat enables simulation of millimeter to centimeter diameter metasurfaces on commodity computers, and it is modular in the sense of accommodating a variety of wave optics models for scattering at the metasurface and for propagation to photosensors.

This project includes creating use cases of the D-Flat framework to increase its accessibilities and potentially developing new functionalities for D-Flat.

Requirements:

• Ongoing Purdue undergrad or master's students

• Fluent in TensorFlow and Python

• Comfortable in web development

• Has background in computer vision