Print Email Facebook Twitter High efficiency, highly integrated DC-DC converter for 48V data centers with standard CMOS and GaN devices Title High efficiency, highly integrated DC-DC converter for 48V data centers with standard CMOS and GaN devices Author HUA, YUAN (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Electronic Instrumentation) Contributor Du, S. (mentor) Serdijn, W.A. (graduation committee) Manzaneque Garcia, T. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Date 2022-08-26 Abstract This thesis presents a 48V-to-1V 10-level dual inductor hybrid converter (DIHC) containing 11 on-chip switches and an off-chip Gallium Nitride (GaN) switch. Thanks to the 10-level Dickson switched-capacitor (SC) circuit, most of the voltage stress will be taken over by off-chip capacitors, which reduces the voltage stress of each switch to 4.8 V and takes full advantage of the voltage pressure on the 5-V on-chip transistors. This proposed structure is implemented in a 0.18-μm BCD process to convert 48-V input to 1-V output with up to 18-A current load. The post-layout simulations show that a peak power efficiency of 90.6% can be achieved at 5.2-A loading and the power density is about 2093 W/in3 considering the power stage volume.This thesis also proposes a 48V/3V multi-resonant DC-DC converter for data center applications, consisting of a 3Φ-SC stage and a 4-to-1 series-parallel stage. Thanks to the multi-phase resonant operation mode, the converter uses fewer components to achieve the same voltage conversion ratio as the conventional two-phase SC converters, and can further improve the efficiency by realising soft-charging. This topology is simulated in cadence spectre, and achieves a peak efficiency of 96.94%, and 95.0% full load efficiency at 30-A load. Subject hybrid dc-dc converter10-level48V-to-1Vswitched capacitorGaN switch5-V on-chipresonantmulti-phasevoltage conversion ratiosoft-charging To reference this document use: http://resolver.tudelft.nl/uuid:cfd99eaf-b2c4-428e-983d-87e5c0318d13 Part of collection Student theses Document type master thesis Rights © 2022 YUAN HUA Files PDF Master_thesis_Yuan_Hua.pdf 4.92 MB Close viewer /islandora/object/uuid:cfd99eaf-b2c4-428e-983d-87e5c0318d13/datastream/OBJ/view