The imbalance in heat power generated by various types of chips poses an obstacle to the reliability and performance of heterogeneous integration(HI)packaging technology,leading to excessive cooling that reduces the s...The imbalance in heat power generated by various types of chips poses an obstacle to the reliability and performance of heterogeneous integration(HI)packaging technology,leading to excessive cooling that reduces the system’s energy efficiency.We propose a framework to optimize the impinging nozzle arrangement for energyefficient uniform jet cooling of HI packages.This framework utilizes a convolutional neural network(CNN)-based surrogate model that learns nozzle arrangements and heating scenarios to predict the temperature nonuniformity of the package.The potential optimal designs predicted by the CNN are used for re-training through an experimentally validated numerical analysis model.Combined with this active learning approach,the proposed hierarchical exploration algorithm accelerates optimization by gradually scaling the design options.The optimization results showed an increase in cooling uniformity by up to 39.5%,while the cooling COP improved by up to 200%across the investigated flow rate range(3–8 L/min).The optimized designs were experimentally validated with a maximum error of 4.34%in average thermal resistance.Our framework achieved up to 45.7%data savings compared to the random sampling-based approach.Along with a discussion on applying the CNN model to untrained conditions to further enhance optimization efficiency,our work represents a novel approach to broadly address the rapidly evolving diverse heating scenarios of HI,contributing to improved cooling energy efficiency in data centers and enhanced reliability of high-performance processors.展开更多
基金supported by the Basic Science Research Program(Grant No.2022R1A2C3011684)of the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT,Koreathe International Cooperative R&D Program(Grant No.RS-2024-00436521)+1 种基金the Korea Institute of Energy Technology Evaluation and Planning(KETEP)funded by the Ministry of Trade,Industry&Energy,Koreasupported by Samsung Electronics Co.,Ltd.Research Program.
文摘The imbalance in heat power generated by various types of chips poses an obstacle to the reliability and performance of heterogeneous integration(HI)packaging technology,leading to excessive cooling that reduces the system’s energy efficiency.We propose a framework to optimize the impinging nozzle arrangement for energyefficient uniform jet cooling of HI packages.This framework utilizes a convolutional neural network(CNN)-based surrogate model that learns nozzle arrangements and heating scenarios to predict the temperature nonuniformity of the package.The potential optimal designs predicted by the CNN are used for re-training through an experimentally validated numerical analysis model.Combined with this active learning approach,the proposed hierarchical exploration algorithm accelerates optimization by gradually scaling the design options.The optimization results showed an increase in cooling uniformity by up to 39.5%,while the cooling COP improved by up to 200%across the investigated flow rate range(3–8 L/min).The optimized designs were experimentally validated with a maximum error of 4.34%in average thermal resistance.Our framework achieved up to 45.7%data savings compared to the random sampling-based approach.Along with a discussion on applying the CNN model to untrained conditions to further enhance optimization efficiency,our work represents a novel approach to broadly address the rapidly evolving diverse heating scenarios of HI,contributing to improved cooling energy efficiency in data centers and enhanced reliability of high-performance processors.
