Validating Junction Temperature Estimation of Power Modules using Generative AI and Fiber Optic Sensing

Accurate junction temperature (Tj) estimation in switching power devices is crucial for the reliability and efficiency of power electronic systems. Traditional thermal models, based on analytical simulations, often fail to capture non-uniform heat distribution and transient behaviour during load changes, resulting in inaccurate estimations. To address this challenge, this paper proposes a generative AI approach which is trained from a linear time-invariant RC thermal network with minimal data inputs to form a temperature-dependent thermal (TDT) RC model. This model aims at capturing the temperature dependence of each layer in the stack-up of the power semiconductor dies, and the impact of thermal cross-talk in multi-die arrangements, as seen in high-power modules. Simulations are conducted to demonstrate the feasibility of the proposed approach, and the estimated Tj is compared with conventional RC thermal models. Additionally, to establish the accuracy of the proposed model in real-world scenarios, a validation methodology is presented, comparing the proposed model with fiber-optic temperature sensing for Tj estimation. A comparison of estimated temperature between the AI-based RC thermal model and experimental values show a relative error of 2.17% for the central die and 4.52% for the peripheral die, highlighting the capability of the proposed methodology to accurately capture non-uniform heat distribution and transient thermal behaviour. Moreover, the high estimation bandwidth of 20.03 Hz enables the model to track rapid temperature changes during transient events, ensuring accurate Tj prediction under dynamically varying power losses.