A Reinforcement Learning Method for Optical Thin-Film Design

Anqing Jiang, Osamu Yoshie*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.

Original languageEnglish
Pages (from-to)95-101
Number of pages7
JournalIEICE Transactions on Electronics
Issue number2
Publication statusPublished - 2022 Feb 1


  • Neural combinatorial optimization
  • Optical thin-film design
  • Reinforcement learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


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