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A perspective on the artificial intelligence's transformative role in advancing diffractive optics.
Khonina, S N; Kazanskiy, N L; Efimov, A R; Nikonorov, A V; Oseledets, I V; Skidanov, R V; Butt, M A.
Afiliação
  • Khonina SN; Samara National Research University, 443086 Samara, Russia.
  • Kazanskiy NL; Samara National Research University, 443086 Samara, Russia.
  • Efimov AR; Sber, Moscow, Russia.
  • Nikonorov AV; Samara National Research University, 443086 Samara, Russia.
  • Oseledets IV; Artificial Intelligence Research Institute (AIRI), Moscow, Russia.
  • Skidanov RV; Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia.
  • Butt MA; Samara National Research University, 443086 Samara, Russia.
iScience ; 27(7): 110270, 2024 Jul 19.
Article em En | MEDLINE | ID: mdl-39040075
ABSTRACT
Artificial intelligence (AI) is transforming diffractive optics development through its advanced capabilities in design optimization, pattern generation, fabrication enhancement, performance forecasting, and customization. Utilizing AI algorithms like machine learning, generative models, and transformers, researchers can analyze extensive datasets to refine the design of diffractive optical elements (DOEs) tailored to specific applications and performance requirements. AI-driven pattern generation methods enable the creation of intricate and efficient optical structures that manipulate light with exceptional precision. Furthermore, AI optimizes manufacturing processes by fine-tuning fabrication parameters, resulting in higher quality and productivity. AI models also simulate diffractive optics behavior, accelerating design iterations and facilitating rapid prototyping. This integration of AI into diffractive optics holds tremendous potential to revolutionize optical technology applications across diverse sectors, spanning from imaging and sensing to telecommunications and beyond.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IScience Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Federação Russa

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: IScience Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Federação Russa