| 1. |
- Lim, Seung-Hyuk, et al.
(författare)
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Formation of a-plane facets in three-dimensional hexagonal GaN structures for photonic devices
- 2017
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Ingår i: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Control of the growth front in three-dimensional (3D) hexagonal GaN core structures is crucial for increased performance of light-emitting diodes (LEDs), and other photonic devices. This is due to the fact that InGaN layers formed on different growth facets in 3D structures exhibit various band gaps which originate from differences in the indium-incorporation efficiency, internal polarization, and growth rate. Here, a-plane {11 (2) over bar0} facets, which are rarely formed in hexagonal pyramid based growth, are intentionally fabricated using mask patterns and adjustment of the core growth conditions. Moreover, the growth area covered by these facets is modified by changing the growth time. The origin of the formation of a-plane {11 (2) over bar0} facets is also discussed. Furthermore, due to a growth condition transition from a 3D core structure to an InGaN multi-quantum well, a growth front transformation (i.e., a transformation of a-plane {11 (2) over bar0} facets to semi-polar {11 (2) over bar2} facets) is directly observed. Based on our understanding and control of this novel growth mechanism, we can achieve efficient broadband LEDs or photovoltaic cells.
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| 2. |
- Seo, Sangwon, et al.
(författare)
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Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning
- 2023
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:15, s. 13292-13302
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Tidskriftsartikel (refereegranskat)abstract
- In federated learning (FL), which clients and quantization levels are selected for the deep model parameters has a significant impact on learning time as well as learning accuracy. This is not a trivial issue because it is also significantly affected by factors, such as computational power, communication capacity, and data distribution. Considering these factors, we formulate a joint optimization problem for clustering and selecting clusters with quantization levels. Due to the high complexity of the formulated problem, we propose a situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. In this algorithm, the FL server first assembles clients into clusters to mitigate the impact of biased data distributions and determines the most suitable clusters and quantization levels based on their computing power and channel quality. Extensive simulation results show that SITUA-CQ can reduce the round time by up to 80.3% compared to conventional algorithms.
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