Published Paper Details:

The trend in miniaturization and enhanced functional performance of integrated circuits and power electronics and photonics has amplified the generated thermal energy in these devices making thermal management a bottleneck for further advancement in these fields. A range of geometries of Sequence structures are developed and examined to address this challenge. However, the numerous form factors and dimension of hierarchical structures in addition to cost and time-consuming synthesis and test procedures make it unfeasible to explore bountiful variations of hierarchical geometries through experimental methods. Here, we introduce a general Artificial Intelligence (AI) platform to address this challenge and guide discovery of hierarchical structures for extreme thermal management of high-performance 2 photonics/electronics. The AI platform is based on Random Forest (RF) algorithm, a robust AI method, and was trained using a large collected experimental data set corresponding to thin film evaporation in various forms of Sequence structures. Four geometrical dimensions of the hierarchical structures and two dimensionless numbers governing heat transfer and fluid dynamics in these structures were used as independent variables to predict heat flux in these structures. The trained model's performance was analyzed using statistical metrics and showed an excellent prediction of heat flux for all the structures with various working fluids. The performance of predictive AI platform was further validated by two independent studies of different research groups. This predictive platform provides a foundation for rational discovery of Sequence structures and working fluids to address the ongoing challenge of thermal management in broad spectrum of technologies including electronics, hypersonic aviation and electric vehicles.