Learning to See and Compute through Multimode Fibers
Babak Rahmani, Uğur Teğin, Mustafa Yıldırım, İlker Oğuz, Damien Loterie, Eirini Kakkava, Navid Borhani, Demetri Psaltis, Christophe Moser · 2021
We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods. We also show the reverse, meaning that multimode fibers can be used as a computational tool that harnesses spatiotemporal nonlinear effects to perform end to end learning tasks with unprecedented speed and low power consumption.