Simulation Workflow Evaluation and Validation of Power Converter’s Electro Thermal Performance and Framework For Physics Based Prediction Model

A power MOSFET is a specific type of metal–oxide–semiconductor field-effect transistor (MOSFET) designed to handle significant power levels. Compared to the other power semiconductor devices, such as an insulated-gate bipolar transistor (IGBT) or a thyristor, its main advantages are high switching speed and good efficiency at low voltages. It shares with the IGBT an isolated gate that makes it easy to drive. Power converters are pivotal components in modern electronic systems, facilitating the transformation of electrical energy from one form to another. Among the crucial elements within these converters, Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) play a central role due to their efficiency and versatility. Understanding and optimizing the electro-thermal performance of MOSFETs in power converters is paramount for enhancing efficiency, reliability, and overall system performance. It delves into the fundamental principles governing MOSFET operation, emphasizing their pivotal role in power conversion circuits. Furthermore, it elucidates the intricate interplay between electrical and thermal characteristics in MOSFETs, highlighting the challenges and opportunities in achieving optimal performance. It discusses advanced techniques for thermal management and heat dissipation, crucial for mitigating thermal stresses and ensuring device longevity. Additionally, the study explores innovative approaches for enhancing MOSFET reliability and efficiency through optimized thermal design and material selection. Overall, this abstract provides valuable insights into the electro-thermal performance of MOSFETs in power converters and well-defined workflow process to identify the passage of heat flow from IC till heat sink. MOSFETs exhibit lower on-state resistance (RDS (on)) at lower temperatures. Effective thermal design ensures that MOSFETs operate at optimal temperatures, minimizing conduction losses and maximizing power conversion efficiency. Based on the available test data and simulation model, Training the AI model with algorithm based on detecting the Maximum temperature of each critical components. Providing the prediction and indication for each band by displaying the maximum temperature of the components. This physics-based prediction model helps not reduce overall time invested for power converters development