thermal management

With the growing complexity of integrated circuits and heterogeneous microsystem integration, effective thermal management and intelligent design are crucial for preventing thermal failures. This study proposes an adaptive deep reinforcement learning-based (ADRL) optimization method for pin-fin microchannel heat sinks, enhancing iterative optimization efficiency with a hybrid neural network (HNN).

In three-dimensional integrated circuits, variations in power consumption across different regions lead to uneven temperature distribution, which can compromise system stability and reliability. While microchannels etched on the chip’s backside are commonly used for cooling, traditional designs provide a fixed cooling capacity and are often inefficient in targeting specific hotspots.

With the rapid advancement of 2.5D/3D heterogeneous integrated microsystems, the performance and fast intelligent design for thermal management are unprecedentedly required to address the electrical and mechanical reliability issues caused by thermal runaway.