On-Demand Controllable Photonic Processors and Networks
Abstract
We propose a novel paradigm for flat on-chip optoelectronics featuring on-demand controllable front-end photonic processors and networks. Such platforms are consisted of addressable and controllable nanoantennas building blocks allowing for feedback controlled response and multifunctionality. These building blocks can serve as self-generating sensors or front-end pre-processors to sensor networks with different types of sensory transduction enabling wide bandwidth and high-speed readout in data acquisition as well as minimal loss in communications. A robust inverse scattering design approach will be developed for complex wave shaping and optical signal processing by mapping desired nearfield and farfield patterns to the geometry and material composition of multiscale nanostructured elements. Different material features such as optical, electrical and thermal are exploited and linked at nanoscale to design tunable building blocks. Novel design paradigms will be introduced through time-modulation and incorporating nonlinear and gain materials. Advanced computational electromagnetic techniques will be implemented for modeling of such highly complex platforms with both temporal and spatial variations and different physical disciplines are bridged in a multiscale framework. Robust design rules will be established for creating and synthesizing a wide class of tunable platforms, to respond in real-time and manipulate space-time wave signatures to the interest. The routes for obtaining improved and novel functionalities will be identified. The proposed paradigm will provide solutions to several long-standing problems & will unlock several novel platforms such as multi-wavelength multifunctional photonic networks, optical interconnects with minimized insertion loss, non-reciprocal interfaces, mode multiplexers, tunable harmonic generators and mixers, tunable meta-sources and amplifiers, high-resolution real-time computational imaging and hybrid photonic/thermal platforms.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 11, 2018
- Source ID
- FA95501810354
Entities
People
- Hossein Mosallaei
Organizations
- Air Force Office of Scientific Research
- Northeastern University
- United States Air Force