Neural network based prediction of stable equivalent series resistance in voltage regulator characterization

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)134-142
Number of pages9
JournalBulletin of Electrical Engineering and Informatics
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Mar 2018

Fingerprint

voltage regulators
Voltage regulators
Regulator
Voltage
Neural Networks
Neural networks
Series
Prediction
predictions
Tunnel
tunnels
output
Output
Tunnels
Resistance
New Product Development
Multilayer Neural Network
product development
Multilayer neural networks
Prediction Error

Keywords

  • Equivalent series resistance
  • Neural network
  • Output capacitor
  • Stable region
  • Voltage regulator

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

Cite this

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title = "Neural network based prediction of stable equivalent series resistance in voltage regulator characterization",
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.",
keywords = "Equivalent series resistance, Neural network, Output capacitor, Stable region, Voltage regulator",
author = "{Mohd Zaman}, {Mohd Hairi} and Mustafa, {Mohd. Marzuki} and {M A}, Hannan and Aini Hussain",
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AU - Mohd Zaman, Mohd Hairi

AU - Mustafa, Mohd. Marzuki

AU - M A, Hannan

AU - Hussain, Aini

PY - 2018/3/1

Y1 - 2018/3/1

N2 - 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.

AB - 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.

KW - Equivalent series resistance

KW - Neural network

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