
- Publication Date: Nov. 16, 2022
- Vol. 4, Issue 6, 060501 (2022)
Kerr frequency combs have been attracting significant interest due to their rich physics and broad applications in metrology, microwave photonics, and telecommunications. In this review, we first introduce the fundamental physics, master equations, simulation methods, and dynamic process of Kerr frequency combs. We then analyze the most promising material platform for realizing Kerr frequency combs—silicon nitride on insulator (SNOI) in comparison with other material platforms. Moreover, we discuss the fabrication methods, process optimization as well as tuning and measurement schemes of SNOI-based Kerr frequency combs. Furthermore, we highlight several emerging applications of Kerr frequency combs in metrology, including spectroscopy, ranging, and timing. Finally, we summarize this review and envision the future development of chip-scale Kerr frequency combs from the viewpoint of theory, material platforms, and tuning methods.
.- Publication Date: Nov. 14, 2022
- Vol. 4, Issue 6, 064001 (2022)
- Publication Date: Dec. 21, 2022
- Vol. 4, Issue 6, 064002 (2022)
- Publication Date: Nov. 22, 2022
- Vol. 4, Issue 6, 066001 (2022)
Direct laser writing (DLW) enables arbitrary three-dimensional nanofabrication. However, the diffraction limit poses a major obstacle for realizing nanometer-scale features. Furthermore, it is challenging to improve the fabrication efficiency using the currently prevalent single-focal-spot systems, which cannot perform high-throughput lithography. To overcome these challenges, a parallel peripheral-photoinhibition lithography system with a sub-40-nm two-dimensional feature size and a sub-20-nm suspended line width was developed in our study, based on two-photon polymerization DLW. The lithography efficiency of the developed system is twice that of conventional systems for both uniform and complex structures. The proposed system facilitates the realization of portable DLW with a higher resolution and throughput.
.- Publication Date: Nov. 23, 2022
- Vol. 4, Issue 6, 066002 (2022)
- Publication Date: Dec. 27, 2022
- Vol. 4, Issue 6, 066003 (2022)
About the Cover
Optical diffraction tomography is a three-dimensional (3D) imaging technique that reconstructs the refractive index distribution of the sample using scattered lights from different illumination angles. Researchers from école Polytechnique Fédérale de Lausanne (EPFL) proposed a deep neural network to solve the optical scattering problem and used this neural network as a surrogate forward model in the iterative reconstruction of the optical diffraction tomography.