Tackling Photonic Inverse Design with Machine Learning

Machine learning, as a study of algorithms that automate prediction and decision-making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data-driven approaches with research, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. It is becoming an indispensable tool in the fields of, for instance, quantum physics, organic chemistry, and medical imaging. Very recently, machine learning has been adopted in the research of photonics and optics as an alternative approach to address the inverse design problem. In this report, the fast advances of machine-learning-enabled photonic design strategies in the past few years are summarized. In particular, deep learning methods, a subset of machine learning algorithms, dealing with intractable high degrees-of-freedom structure design are focused upon.

Automated summary

Machine learning, as an algorithm study for automated prediction and decision-making based on complex data, has become an indispensable tool in artificial intelligence research, leading to significant progress in scientific research fields like quantum physics, organic chemistry, and medical imaging. Recently adopted in photonics and optics research, machine learning shows potential for addressing the inverse design problem and has been instrumental in advancing photonic design strategies in recent years, particularly focusing on deep learning methods for high degrees-of-freedom structure design.