Photonic-crystal surface-emitting lasers

Susumu Noda, Masahiro Yoshida, Takuya Inoue, Menaka De Zoysa, Kenji Ishizaki, Ryoichi Sakata
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Abstract

High-performance lasers are important to realize a range of applications including smart mobility and smart manufacturing, for example, through their uses in key technologies such as light detection and ranging (LiDAR) and laser processing. However, existing lasers have a number of performance limitations that hinder their practical use. For example, conventional semiconductor lasers are associated with low brightness and low functionality, even though they are compact and highly efficient. Conventional semiconductor lasers therefore require external optics and mechanical elements for reshaping and scanning of emitted beams, resulting in large, complicated systems for various practical uses. Furthermore, even with such external elements, the brightness of these lasers cannot be sufficiently increased for use in laser processing. Similarly, gas and solid-state lasers, while having high-brightness, are also large and complicated. Photonic-crystal surface-emitting lasers (PCSELs) boast both high brightness and high functionality while maintaining the merits of semiconductor lasers, and thus PCSELs are solutions to the issues of existing laser technologies. In this Review, we discuss recent progress of PCSELs towards high-brightness and high-functionality operations. We then elaborate on new trends such as short-pulse and short-wavelength operations as well as the combination with machine learning and quantum technologies. Finally, we outline future research directions of PCSELs with regard to various applications, including not only LiDAR and laser processing, as described above, but also communications, mobile technologies, and even aerospace and laser fusion. This Review surveys recent progress in photonic-crystal surface-emitting laser development and applications, including high-brightness, high-functionality, short-pulse and short-wavelength operations, and smart integration with machine learning.

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