MODELING THERMAL BEHAVIOR IN HIGH-POWER SEMICONDUCTOR DEVICES USING THE MODIFIED OHM’S LAW

Abstract

This paper addresses the challenge of thermal management in high-power semiconductor devices, where increasing power densities and complex operating environments demand more accurate thermal prediction methods. Traditional approaches often rely on simplified models that do not account for the crucial factor of temperature-dependent resistance variations. This limitation leads to inaccurate device temperature predictions, potentially compromising device reliability. This work proposes a novel approach for thermal management by introducing the first empirical application of a Modified Ohm’s Law. This modified law incorporates an exponential term to account for the non-linear relationship between temperature, current, and resistance. The paper demonstrates through simulations and empirical validation that the Modified Ohm’s Law offers a more accurate representation of thermal behavior compared to the standard version. This translates to more precise predictions of device temperature, especially during periods of rapid temperature changes. The validation process goes beyond simply establishing the Modified Ohm’s Law. It provides valuable insights into the thermal dynamics of the device, allowing for the refinement of simulation parameters used to assess various cooling strategies. These strategies include simulating different heat sink geometries and materials, modifying airflow rates over the device’s surface, and exploring the impact of Thermal Interface Materials (TIMs) between the device and the heat sink. By incorporating these elements, the simulations provide a more comprehensive picture of the device’s thermal behavior under various operating conditions and cooling configurations. Ultimately, this paper not only advances the theoretical understanding of thermal management but also offers practical benefits. Through enabling more accurate thermal predictions, the Modified Ohm’s Law model paves the way for informed decision-making in device design and optimization.