Terahertz Pulse Engineering Using Diffractive Optical Networks

Deep learning is driving a new transformation in optics by providing non-intuitive solutions to a diverse set of problems. As a newly established deep-learning based physical design strategy, diffractive optical neural networks bridge deep learning and wave optics to all-optically implement different tasks including e.g., image classification. Here, we demonstrate a diffractive optical network with a small footprint that is trained to shape input pulses into various desired optical waveforms. The synthesis of different output pulses of interest was demonstrated at THz part of the electromagnetic spectrum using deep learning designed passive diffractive layers that are engineered to precisely control the amplitude and phase of each spectral component across a broad range of frequencies. These results constitute the first direct pulse shaping demonstration in terahertz spectrum without using any optical pump or optical-to-terahertz converters. Moreover, a lego-like physical transfer learning technique was utilized to demonstrate the modularity of this framework, by achieving pulse width tunability. A wide-range of applications in e.g., tele-communications, spectroscopy and ultra-fast imaging can benefit from this learning based diffractive pulse engineering framework.