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Jeonghwan Song

Publications and source records attributed to Jeonghwan Song.

2 recordsLinked to original sources

A Heterogeneous 200 mm Silicon Nitride Photonics Platform for Visible-to-Near-Infrared Applications via Micro-Transfer Printing

The commercialization of next-generation technologies, including optical interconnects, quantum computing, AR/VR, and medical diagnostics, requires a low-loss photonic platform offering compact, multifunctional systems in the visible and near-infrared range. Although silicon nitride (SiN) is an excellent material due to its ultra-low loss and broad transparency window, integrating active components such as light sources, modulators and photodetectors from diverse material platforms in a scalable, reliable way remains challenging. Micro-transfer printing is an emerging wafer-scale heterogeneous integration technology that can be implemented as a back-end post-processing step without disrupting the primary in-line fabrication process. In this work, we present a dual LPCVD SiN layer platform fabricated in a 200 mm CMOS pilot line, that incorporates micro-transfer printing modules, allowing the integration of active components on well defined recesses. A hydrogenated amorphous silicon layer is also available to increase the versatility of the platform allowing for evanescently-coupled III-V lasers as well as other passive functionality in the near-infrared region. We report full wafer-scale measurements showing low optical SiN losses of 4 dB/cm and 0.23 dB/cm at a wavelength of 488 nm and 940 nm respectively. In addition, a transition loss of only 0.35 dB is obtained from the SiN to the a-Si:H layer, in good agreement with simulated values. Finally, to showcase more advanced functionality, GaAs-based gain sections are micro-transfer printed on several dies, achieving consistent die-to-die lasing at 970 nm with on-chip optical powers of approximately 1 mW. These results showcase the potential of the integrated photonics platform towards unlocking a wide range of new applications in the sub-1-$\mu$m spectral region.

physics.optics

Particle swarm optimization of a wind farm layout with active control of turbine yaws

Active yaw control (AYC) of wind turbines has been widely applied to increase the annual energy production (AEP) of a wind farm. AYC efficiency depends on the wind direction and the wind farm layout because an AYC method utilizes wake deflection by yawing wind turbines. Conventional optimization of a wind farm layout assumed that the swept areas of all wind turbines are aligned perpendicular to the wind direction, thereby allowing non-optimal utilization of an AYC method. Higher AEP can be obtained by joint optimization which considers an AYC method in the layout design stage. Joint optimization of the farm layout and AYC has been difficult due to the non-convexity of the problem and the computational inefficiency. In the present study, a particle swarm optimization based method is developed for joint optimization. The layout is optimized with simultaneous consideration for yaw angles for all wind velocities to obtain a globally optimal layout. A number of random initial particles consisting of the layout and yaw angles of wind turbines reduce the initial layout dependency on the optimized layout. To deal with the challenge of large-scale optimization, the adaptive granularity learning distributed particle swarm optimization algorithm is implemented. The improvement in AEP when using a jointly optimized layout compared to a conventionally optimized layout in a real wind farm is demonstrated using the present method.

math.OC