Dynamically Programmable Non-uniform THz Surfaces and Analog Neural-Network Accelerators through Inverse Design
Abstract
Spatio-temporal control, synthesis and detection of electromagnetic fields at deep sub-wavelength scales that is dynamically reconfigurable at THz frequencies can enable transformative changes in the field enabling a wide array of new applications across sensing, imaging and communication. In this proposal, we present basic research approaches to realize programmable THz surfaces on chip through inverse design methodologies that allow rapid manipulation of THz fields across frequency, angle of incidence and polarization at GHz speed. We will also show how such programmable surfaces can allow linear computation in the electromagnetic domain, and can act as rapid and efficient analog neural network accelerators for real-time decision making capabilities at the edge.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310176
Entities
People
- Kaushik Sengupta
Organizations
- Air Force Office of Scientific Research
- Trustees of Princeton University
- United States Air Force