Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/10660
Title: Neural network based prediction of stable equivalent series resistance in voltage regulator characterization
Authors: Zaman, M.H.M. 
Mustafa, M.M. 
Hannan, M.A. 
Hussain, A. 
Issue Date: 2018
Abstract: High demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR. © 2018 Institute of Advanced Engineering and Science. All rights reserved.
DOI: 10.11591/eei.v7i1.857
Appears in Collections:CSIT Scholarly Publication

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