سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

A Comprehensive Review on the Optimization of Multilayer Optical Thin Films Using Artificial Intelligence and Deep Q-Learning

نویسندگان :

Mina Neghabi¹ Mehdi Zadsar²

1- Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad, Iran 2- Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad, Iran

کلمات کلیدی :

Artificial Intelligence،Deep Q-Learning،Inverse Design،Optical Thin Films،Reinforcement Learning

چکیده :

The optimization of multilayer optical thin films (MOTFs) plays a vital role in the development of advanced optical components used in a wide range of applications, including anti-reflection coatings, solar absorbers, and optical filters. Conventional optimization methods such as genetic algorithms and simulated annealing have limitations in handling high-dimensional, non-linear design spaces. In recent years, artificial intelligence (AI), particularly Deep Q-Learning (DQL), has emerged as a powerful tool for the intelligent design of MOTFs. This review article provides a comprehensive overview of classical and machine learning-based approaches, focusing on recent advances in reinforcement learning. We highlight key contributions, including the pioneering work by Jiang et al., and compare DQL with other AI techniques such as deep neural networks (DNNs) and hybrid methods. We also discuss current challenges, such as dataset generation and manufacturability, and outline future directions for integrating AI into industrial thin film design.