Chaotic-cavity surface-emitting lasers for optical wireless communication and low-speckle illumination

Omar Alkhazragi, Ming Dong, Liang Chen, Meiwei Kong, Georgian Melinte, Dong Liang, Tien Khee Ng, Junping Zhang, Hakan Bagci, Boon S. Ooi

Research output: Contribution to journalArticlepeer-review


Though necessary and advantageous in many fields, the high coherence of lasers is detrimental to their performance in certain applications, including illumination, imaging, and projection. This is due to the formation of coherence artifacts, commonly known as speckles, resulting from the interference of randomly scattering spatially coherent photons. It is possible to resolve this issue by increasing the number of mutually incoherent modes emitted from the laser. In vertical-cavity surface-emitting lasers (VCSELs), this can be performed by designing them to have chaotic cavities. This paves the way toward their use in simultaneous illumination and communication scenarios. Herein, we show that chaotic-cavity broad-area VCSELs can achieve significantly broader modulation bandwidths (up to 5 GHz) and higher data rates (up to 12.6 GB/s) compared to other low-coherence light sources, with a lower speckle contrast. We further report a novel technique for lowering the speckle contrast by carefully designing the AC signal used for communication. We show that the apparent spatial coherence is dramatically decreased by inserting a short chirp signal between symbols. Using this method with a chaotic-cavity VCSEL, the number of apparent modes can be up to 450, compared to 88 modes measured from a conventional broad-area VCSEL (a fivefold increase). In light of the recent advances in visible-light VCSELs, this work shows the potential of low-coherence surface-emitting lasers (LCSELs) in simultaneous illumination and optical wireless communication systems since they combine the high speed of lasers with the excellent illumination properties of light-emitting diodes.
Original languageEnglish (US)
JournalAPL Photonics
Issue number8
StatePublished - Aug 14 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-08-24
Acknowledged KAUST grant number(s): BAS/1/1614-01-01, GEN/1/6607-01-01, KCR/1/2081-01-01, KCR/1/4114-01-01), ORA-2022-5313, RGC/3/4275-01-01
Acknowledgements: Huawei Technologies Co., Ltd. (Grant No. YBN2020085017); King Abdullah University of Science and Technology (Grant Nos. BAS/1/1614-01-01, RGC/3/4275-01-01, GEN/1/6607-01-01, KCR/1/2081-01-01, and KCR/1/4114-01-01). T.K.N. and B.S.O. acknowledge funding from King Abdullah University of Science and Technology (KAUST) Research Funding (KRF) under Award No. ORA-2022-5313.


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