Neural Network Based Prediction of Stable Equivalent Series Resistance in Voltage Regulator Characterization

Mohd Hairi Mohd Zaman, M. M. Mustafa, M. A. Hannan, Aini Hussain


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.


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

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Bulletin of Electrical Engineering and Informatics (BEEI)
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