Automatic Data Extraction Based on Semiconductor Datasheet for Design Automation of Power Converters

Abstract

The application of artificial intelligence (AI) to design automation (DA) is a novel research area in power electronics, owing to the complexity of power converter design, power loss modeling on magnetic and semiconductors, and the large number of components to choose from. This paper presents a tool for extracting dynamic and complex nonlinear characteristics from semiconductor datasheets in order to improve the power loss estimation model, resulting in an optimal power converter design. An object recognition neural network, CenterNet, is trained to extract figures from data-sheets. The dynamic data is then extracted from figures utilizing Optical Character Recognition (OCR) and morphological image processing techniques. Finally, the acquired data is used to augment the dynamic properties of power switches and to develop a more accurate power loss model for use as input to design automation tools.

Pareto Optimised High-Frequency Planar Transformers for Electric Vehicle Chargers

“…Furthermore, the compatibility with Steinmetz's equation is validated, as the iGSE for a sinewave input yields the same result. Note that the core loss results are very sensitive to the Steinmetz parameters, which are extracted using the data extraction tool of [18], [19] and kept constant in all models.…”