Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Single-mode lasing in ring-cavity surface-emitting lasers

Not Accessible

Your library or personal account may give you access


Subject of study. Distributed-feedback ring-cavity surface-emitting quantum-cascade lasers are studied. Aim of study. The aim is to realize stable single-mode emission in ring-cavity surface-emitting quantum-cascade lasers through a Bragg grating formed by direct-write ion beam lithography. Method. The selection of longitudinal whispering gallery modes owing to the fabrication of a second-order Bragg grating in the top waveguide cladding layers based on InP was implemented using direct-write ion beam lithography. Main results. The possibility of implementing stable single-mode emission in distributed-feedback ring-cavity surface-emitting quantum-cascade lasers with a Bragg grating formed by direct-write ion beam lithography was demonstrated. An increase in the etching depth of the grooves of the Bragg grating to 2.8 µm facilitated the implementation of stable single-mode emission at a temperature of 83 K. Single-mode emission was observed at a wavelength of 7.45 µm with a threshold current density of approximately 2kA/cm2. The maximum side mode suppression ratio was 25 dB. Practical significance. Single-mode distributed-feedback ring-cavity surface-emitting quantum-cascade lasers are promising for the creation of compact gas sensors, wherein a laser and a photodetector of the mid-infrared spectral range are monolithically integrated on one chip.

© 2024 Optica Publishing Group

PDF Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription

Select as filters

Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.