Chengyi Feng | 电子与技术硕士

Adaptive Deep Reinforcement Learning Optimization Design Process for Hybrid Pin-Fin Microchannel Heat Sink Based on Hybrid Neural Network Acceleration

Wed, 01 Jan 2025 00:00:00 +0000

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). A hybrid pin-fin microchannel model is established, with simulation-generated datasets capture temperature and pressure drop performance under various parameters. The proposed HNN significantly improves the prediction accuracy under small sample conditions. Compared with deep neural network and convolutional neural network, the root mean square error, mean absolute error, relative error, and comprehensive standard deviation are reduced by 92.6%, 93.7%, 92.5%, 97.2% and 96.4%, 93.7%, 90%, 92.3% respectively, effectively mapping structural parameters to performance indicators. Using the proximal policy optimization algorithm in the ADRL framework, this method optimizes the hybrid heat sink layout under non-uniform heat flow conditions, reducing the maximum temperature of high-power heat sources by 5.34%, the maximum temperature of low-power heat sources by 10.01%, and the pressure drop by 53.05%.

Multiobjective Deep Reinforcement Learning Driven Collaborative Optimization of TSV-Based Microchannel and PDN for 3-D ICs

Wed, 01 Jan 2025 00:00:00 +0000

This study introduces a multiobjective deep reinforcement learning (MODRL) framework for the concurrent thermal-hydraulic optimization of the through-silicon-via (TSV) microchannel heat sink (MCHS) embedded in 3-D integrated circuits (3-D ICs) power delivery network (PDN). By exploiting the inherent structural synergy between TSVs and pin-fin MCHS, the proposed method enhances thermal management in high-density 3-D ICs. The framework integrates deep reinforcement learning (RL) with multiobjective optimization and computational fluid dynamics (CFD) simulations, enabling an efficient exploration of the high-dimensional design space to resolve tradeoffs between thermal efficacy and fluidic resistance. Relative to baseline, the optimized design achieves a reduction in maximum chip temperature of up to 3.3% while concurrently lowering the overall pressure drop by 17.2%. Impedance analysis further validates the design’s superiority, showing that the optimized TSV geometry effectively suppresses high-frequency peak impedance. Compared with standard deep reinforcement learning (SDRL) and genetic algorithm (GA), MODRL converges faster by 57.1% and 62.5%, respectively, showing stronger convergence. These results highlight the advantages of the MODRL intelligent optimization framework in design speed and its great potential in driving the development of next-generation 3-D integrated circuits, especially in applications requiring high power density and high reliability.

Smart cooling: Hydrogel-enhanced adaptive jet impingement utilizing through silicon via for integrated microsystems

Wed, 01 Jan 2025 00:00:00 +0000

Hybrid neural network based multi-objective optimal design of hybrid pin-fin microchannel heatsink for integrated microsystems

Mon, 01 Jan 2024 00:00:00 +0000