Deep inverse photonic design: A tutorial

Yang Deng, Simiao Ren, Jordan Malof, Willie J. Padilla

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Deep learning (DL) has been used to design deep neural networks (DNNs) which have recently been applied to solving inverse problems in artificial electromagnetic materials (AEMs). Although inverse problems are often ill-posed, and therefore are difficult to solve, deep inverse models (DIMs) have achieved impressive results often surpassing capabilities possible with other approaches. We overview the process of deep inverse learning applied to AEM problems, including the building of data sets, design of a forward model, and comparison of inverse approaches including limitations. We conclude by detailing some important outstanding issues of deep inverse design of AEMs, and present an outlook of this exciting field.

Original languageEnglish
Article number101070
JournalPhotonics and Nanostructures - Fundamentals and Applications
StatePublished - Dec 2022


  • Deep learning
  • Inverse
  • Photonics


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