Advancing photonic design and measurements with artificial intelligence

Abstract

Discovering novel, unconventional optical designs in combination with advanced machine-learning assisted data analysis techniques can uniquely enable new phenomena and breakthrough advances in many areas including imaging, sensing, energy, and quantum information technology. It demonstrated that compared to other inverse-design approaches that require extreme computation power to undertake a comprehensive search within a large parameter space, machine learning assisted topology optimization can expand the design space while improving the computational efficiency. This talk will highlight our most recent findings on 1) merging topology optimization with artificial-intelligence-assisted algorithms and 2) integrating machine-learning based analysis with photonic design and quantum optical measurements.