Automated Extraction of Data from MOSFET Datasheets for Power Converter Design Automation

Power electronics design automation, implementing Artificial Intelligence (AI) to optimize the design of power converters, has emerged as a novel research topic given the complexity of power converter design, whose key challenges include power loss modeling across the enormous number of available components. This paper proposes a novel end-to-end AI-based tool for extracting nonlinear dynamic properties from semiconductor datasheets, which can enhance the power loss estimation model and accelerate the optimal design of power converters. Firstly, thousands of images from power transistor datasheets are collected and annotated to construct a training database. Then, CenterNet, a neural network for image object detection, is trained for figure segmentation from datasheets and key element detection from figures. Optical Character Recognition (OCR) and morphological image processing techniques are utilized to extract the specific dynamic data. The results illustrate that the customized tool for power transistor device datasheets in this paper can accurately extract the data, significantly reducing the time consumption for transistor data collection and its characteristic modeling work, promising pathways to streamline and optimize power electronics design. The tool has been published online and is actively being updated and improved via http://www.powerbrain.ai.