[CPDP] CLEO-PR 2022 Post Deadline

[CPDP-05] A Machine Learning-based Approach to Model Highly-thermally Robust Metasurface Absorber

Sumbel Ijaz¹, Sadia Noureen¹, Bacha Rehman², Muhammad Zubair¹, Muhammad Qasim Mehmood¹, *Yehia Massoud³ (1. MicroNano Lab, Electrical Engineering Department, Information Technology University of the Punjab, Lahore 54600, Pakistan (Pakistan), 2. Department of Computer Science, Namal University, Mianwali, Punjab, Pakistan (Pakistan), 3. Innovative Technologies Laboratories (ITL), Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia (Saudi Arabia))

[Presentation Style] Online

For accelerating design procedure of compact and efficient on-chip nano-photonics and to aid computationally expensive, time-exhaustive state-of-the-art iterative simulation schemes, regression-based machine-learning models are demonstrated that predict the optical response and structural parameters of the meta-atoms