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

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. Moreover, straight microchannels spanning the entire system can result in overcooling in low-power areas and insufficient cooling in high-power regions.

This study presents a novel design of an adaptive jet impingement cooling structure that combines hydrogel, jet impingement heat sink (JIHS) and through silicon via (TSV) technology. The structure features vertical channels and utilizes the thermally induced deformation of hydrogel to achieve adaptive cooling. This design allows the cooler to be strategically placed at hotspots, dynamically adjusting microfluidic injection in response to temperature fluctuations. As a result, overcooling in low-power regions and inadequate cooling in hotspots are mitigated, improving thermal uniformity. Compared to conventional jet impingement heat sinks, the proposed adaptive jet impingement heat sink improves temperature uniformity by 12.21 %, reduces thermal spreading resistance by 13 %, and increases maximum total thermal resistance by only 3.08 %. The maximum pressure drop increases by just 1.28 kPa. Therefore, with the increasingly complex integrated microsystem architecture, the adaptive impingement jet heat sink has better comprehensive heat dissipation performance than the traditional impingement jet heat sink under complex heat distribution.

through silicon via (TSV) | MSc in Electronic Science and Technology

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

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