Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management

TL;DR

This paper reviews recent advances in applying deep learning techniques to improve electronic thermal management, addressing limitations of traditional numerical methods and enhancing system efficiency and safety.

Contribution

It provides a comprehensive overview of how artificial intelligence, especially deep learning, is being integrated into electronic thermal management processes, highlighting current applications and future prospects.

Findings

Deep learning improves accuracy of thermal predictions.

AI-based methods outperform conventional numerical techniques.

Enhanced thermal management leads to better electronic device safety.

Abstract

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.