MOSFET Reliability Using Machine Learning

Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) are integral components in modern electronic systems. As devices shrink and operate under increasingly demanding conditions, ensuring their reliability has become a significant challenge. Machine learning (ML) techniques have emerged as powerful tools to model, predict, and improve the reliability of MOSFETs. This article explores the intersection of MOSFET reliability and machine learning, focusing on the methodologies, advancements, and challenges. In order to validate the design concepts, a power converter prototype was developed and manufactured. The Si power valves facilitate the operation at a switching frequency of 100 KHz. The utmost voltage that can be output is 2500 VDC, and the input supply voltage can vary from 243 to 297 V. This system employs the dynamic ON-resistance (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\triangle \text{RDS}$</tex> (on)) of the devices, which is real-time data. In order to validate the design concepts, a power converter prototype was developed and manufactured. The proposed approach exhibits exceptional accuracy, with a training accuracy exceeding 99 %. The linear regression model gives the R2 and RMSE values as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 7 1 7 1}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 0 0 7 9}$</tex>, respectively.