Print Email Facebook Twitter A Dynamically Reconfigurable Recursive Switched-Capacitor DC-DC Converter with Adaptive Load Ability Enhancement Title A Dynamically Reconfigurable Recursive Switched-Capacitor DC-DC Converter with Adaptive Load Ability Enhancement Author Lu, Qi (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Du, S. (mentor) van der Meijs, N.P. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Date 2022-07-27 Abstract Multiple voltage conversion ratio (VCR) recursive switched-capacitor (SC) DC-DCconverters, based on a number of basic 2:1 converters, are widely used for on-chip power supplies due to their flexible VCRs for higher energy efficiency. However, conventional multi-VCR SC converters usually have one or more 2:1 converters unused for some VCRs, which results in lower power density and chip area wastage. This paper presents a new recursive DC-DC converter system, which is able to dynamically reconfigure the connection of all on-chip 2:1 converter cells so that the unused converters in conventional designs can be re-used in this new architecture for increasing the load-driving capacity,power density and power efficiency.To validate the design, a 4-bit-input 15-ratio system was designed and fabricated in a 180-nm BCD process, which can support a maximum load current of 0.71mA and achieve a peak power efficiency of 93.1% with 105.3μA/mm^2 chip power density from a 2V input power supply. The measurement results show that the load-driving capacity can become 6.826×, 2.236× and 2.175× larger than the conventional topology when the VCR is 1/2, 1/4 and 3/4, respectively. In addition, the power efficiency under these specific VCRs can also be improved considerably. Subject DC-DC convertersswitched-capacitorrecursive switched capacitor (RSC)fully integratedmultiple voltage conversion ratio (VCR) To reference this document use: http://resolver.tudelft.nl/uuid:f109f488-539c-4448-b498-885ff5c34075 Embargo date 2023-01-01 Part of collection Student theses Document type master thesis Rights © 2022 Qi Lu Files PDF Master_Thesis_Qi_Lu_5376939.pdf 5.2 MB Close viewer /islandora/object/uuid:f109f488-539c-4448-b498-885ff5c34075/datastream/OBJ/view