PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 4, 2026Light Science & Applications0 citationsOpen Access

Ultrahigh-Q integrated flame-hydrolysis-deposited germano-silicate resonators on silicon

View Full Paper
HCHao-Jing ChenKCKellan ColburnHHHanfei Hou

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract Optical fibres, owing to their ultra-low transmission loss, underpin global telecommunications. However, this remarkable low-loss performance has not been extended to integrated photonic devices, which are increasingly critical for data-intensive communications in the era of artificial intelligence (AI). Here, we translate the widely adopted mass-production process for fibre manufacturing—flame hydrolysis—to wafer-scale integrated photonics, and demonstrate ultrahigh- Q integrated microresonators. By leveraging high GeO 2 doping, the deposited germano-silicate (Ge:silica) films achieve full densification at moderate thermal budgets, while also allowing for a post-processing furnace-reflow technique that has the capability to both repair any etch-induced defects and enhance optical Q , leading to a high degree of process tolerance. When combined with deep-UV lithography, these films form microresonators exhibiting ultrahigh Q factors of up to 566 million at 1064 nm, corresponding to a waveguide propagation loss as low as 0.07 dB m −1 . Like their fibre counterparts, these devices exhibit a broad transmission window with Q factors surpassing 100 million demonstrated from the telecommunications band to the violet spectrum. Moreover, the width dependence of Q factor and a two-order-of-magnitude Q recovery enabled by the furnace reflow process are also demonstrated. This work extends high-quality, high-rate flame hydrolysis deposition (FHD) from optical fibre manufacturing to integrated photonics, establishing a scalable route towards fibre-level loss in photonic integrated circuits.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a220f614ae3d5108796f025https://doi.org/10.1038/s41377-026-02353-y
Ask AI
Helpful
Bookmark
Share
View Full Paper