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In recent years, electronic packaging has evolved significantly to meet demands for higher performance, lower costs, and smaller designs. This shift has led to heterogeneous packaging, which integrates chips of varying stack heights and results in non-uniform heat flux and temperature distributions. These conditions pose substantial thermal management challenges, as they can create large temperature gradients, which increase thermal stress and potentially compromise chip reliability. This study explores single-phase liquid cooling for multi-chip modules (MCMs) through a comprehensive experimental and machine learning approach. It investigates the impact of chip spacing, height, fluid flow rate, fluid inlet location, and heat flux uniformity on chip temperature and the thermohydraulic performance of a commercial cold plate. Results show that increasing coolant flow from 1 LPM to 2 LPM decreased thermal resistance by 26 %, with heat losses remaining below 5 %. The left inlet configuration improved temperature uniformity compared to the right, though both yielded comparable thermal performance. Adjusting heater spacing impacted temperature distribution based on inlet position, and lowering one heater by 1 mm raised its temperatures by 15 ◦C due to increased thermal resistance from thermal interface material. A transient test demonstrated the cold plate’s quick response to power surges, in which there is only a 1 ◦C spike above steady state. Complementing these findings, an Artificial Neural Network (ANN) model was developed with optimized architecture specifically for the unique challenges of this study. The ANN model was rigorously validated using an independent dataset, achieving highly accurate temperature predictions (R2 = 0.99) within 2.5 % of experimental 

This study explores the latent thermal energy storage potential of an organic phase change material with porous copper foam and its applicability in electronic cooling under varying heat load conditions. The organic phase change material, n-eicosane, is known for its inherently low thermal conductivity of 0.15 W/mK, rendering it vulnerable during power spikes despite its abundant latent heat energy for phase transition from solid to liquid. Porous copper foams are often integrated into n-eicosane to enhance the composite’s thermal conductivity. However, the volume fraction of the phase change material in the porous foam that optimally improves the thermal performance can be dependent on the boundary condition, the cut-off temperature, and the thickness. A finite difference numerical model was developed and utilized to ascertain the energy consumption for the composite of n-eicosane with two kinds of porous copper foam with varying porosity under different heat rates, cut-off temperatures, and thickness. In addition, the results are compared with a metallic phase change material (gallium), a material chosen with a similar melting point but significantly high thermal conductivity and volumetric latent heat. For validation of the numerical model and to experimentally verify the effect of boundary condition (heat rate), experimental investigation was performed for n-eicosane and high porosity copper foam composite at varying heat rates to observe its melting and solidification behaviors during continuous operation until a cut-off temperature of 70 ◦C is reached. Experiments reveal that heat rate influences the amount of latent energy storage capability until a cutoff temperature is reached. For broad comparison, the numerical model was used to obtain the accessed energy and power density and generate thermal Ragone plots to compare and characterize pure gallium and n-eicosane - porous foam composite with varying volume fractions, cutoff temperature, and thickness under volumetric and gravimetric constraints. Overall, the proposed framework in the form of thermal Ragone plots effectively delineates the optimal points for various combinations of heat rate, cutoff point, and aspect ratio, affirming its utility for comprehensive design guidelines for PCM-based composites for electronic cooling applications 

The increasing prevalence of high-performance computing data centers necessitates the adoption of cutting-edge cooling technologies to ensure the safe and reliable operation of their powerful microprocessors. Two-phase cooling schemes are well-suited for high heat flux scenarios because of their high heat transfer coefficients and their ability to enhance chip temperature uniformity. In this study, we perform experimental characterization and deep learning driven optimization of a commercial two-phase cold plate. The initial working design of the cold plate comprises a fin height of 3mm, fin thickness of 0.1 mm, and a channel width of 0.1 mm.A dielectric coolant, Novec /HFE 7000, was impinged into microchannel fins through impinging jets. A copper block simulated an electronic chip with a surface area of 1˝ × 1˝. The experiment was conducted with three different coolant inlet temperatures of 25◦ C, 36◦ C, and 48◦ C with varying heat flux levels ranging from 7.5 to 73.5 W cm2. The effects of coolant inlet temperatures and flow rate on the thermo-hydraulic performance of the cold plate were explored. In two-phase flow, increasing coolant inlet temperature results in more nucleation sites and improved thermal performance consequently. Thermal resistance drops with flow rate in single-phase flow while it is not affected by flow rate in nucleate boiling region. An improvement in the design of the cold plate was carried out, with the goal of increasing the number of bubble sites and flow velocity at the root fins, by cutting the original fins and creating channels perpendicular to the original channels. Three design parameters, fin height, width of machined channels, and height of short fins preserved through machined channels, were defined. It was observed that widening the machined channels and cutting fins to some point can improve the thermal performance of the cold plate. However, removing fins excessively adversely affects the thermal performance of the cold plate because of loss of heat transfer surface area. Moreover, preserving the short fins through the machined channels decreases thermal resistance as they increase heat transfer surface area and nucleation sites. Furthermore, a deep learning-based compact model is demonstrated for the two-phase cold plate design in the specific range of geometry and flow conditions. The developed compact model is utilized to drive the single and multi-objective optimization to arrive at global optimal results. 

The electronics packaging community strongly believes that Moore’s law will continue for another few years due to recent technological efforts to build heterogeneously integrated packages. Heterogeneous integration (HI) can be at the chip level (a single chip with multiple hotspots), in multi-chip modules, or in vertically stacked three-dimensional (3D) integrated circuits. Flux values have increased exponentially with a simultaneous reduction in chip size and a significant increase in performance, leading to increased heat dissipation. The electronics industry and the academic research community have examined various solutions to tackle skyrocketing thermal-management challenges. Embedded cooling eliminates most sequential conduction resistance from the chip to the ambient, unlike separable cold plates/ heat sinks. Although embedding the cooling solution onto an electronic chip results in a high heat transfer potential, technological risks and complexity are still associated with the implementation of these technologies and with uncertainty regarding which technologies will be adopted. This manuscript discusses recent advances in embedded cooling, fluid selection considerations, and conventional, immersion, and additive manufacturing-based embedded cooling technologies. 

Abstract This work presents an approach to optimally designing a composite with thermal conductivity enhancers infiltrated with phase change material based on figure of merit (FOM) for thermal management of portable electronic devices. The FOM defines the balance between effective thermal conductivity and energy storage capacity. In this study, thermal conductivity enhancers are in the form of a honeycomb structure. Thermal conductivity enhancers are often used in conjunction with phase change material to enhance the conductivity of the composite medium. Under constrained heat sink volume, the higher volume fraction of thermal conductivity enhancers improves the effective thermal conductivity of the composite, while it reduces the amount of latent heat storage simultaneously. This work arrives at the optimal design of composite for electronic cooling by maximizing the FOM to resolve the stated tradeoff. In this study, the total volume of the composite and the interfacial heat transfer area between the phase change material and thermal conductivity enhancers are constrained for all design points. A benchmarked two-dimensional direct computational fluid dynamics model was employed to investigate the thermal performance of the phase change material and thermal conductivity enhancer composite. Furthermore, assuming conduction-dominated heat transfer in the composite, a simplified effective numerical model that solves the single energy equation with the effective properties of the phase change material and thermal conductivity enhancer has been developed. The effective properties like heat capacity can be obtained by volume averaging; however, effective thermal conductivity (required to calculate FOM) is unknown. The effective thermal conductivity of the composite is obtained by minimizing the error between the transient temperature gradient of direct and simplified model by iteratively varying the effective thermal conductivity. The FOM is maximized to find the optimal volume fraction for the present design. 

Miniaturization and high heat flux of power electronic devices have posed a colossal challenge for adequate thermal management. Conventional air-cooling solutions are inadequate for high-performance electronics. Liquid cooling is an alternative solution thanks to the higher specific heat and latent heat associated with the coolants. Liquid-cooled cold plates are typically manufactured by different approaches such as: skived, forged, extrusion, electrical discharge machining. When researchers are facing challenges at creating complex geometries in small spaces, 3D-printing can be a solution. In this paper, a 3D-printed cold plate was designed and characterized with water coolant. The printed metal fin structures were strong enough to undergo pressure from the fluid flow even at high flow rates and small fin structures. A copper block with top surface area of 1 inch by 1 inch was used to mimic a computer chip. Experimental data has good match with a simulation model which was built using commercial software 6SigmaET. Effects of geometry parameters and operating parameters were investigated. Fin diameter was varied from 0.3 mm to 0.5 mm and fin height was maintained at 2 mm. A special manifold was designed to maximize the surface contact area between coolant and metal surface and therefore minimize thermal resistance. The flow rate was varied from 0.75 L/min to 2 L/min and coolant inlet temperature was varied from 25 to 48 oC. It was observed that for the coolant inlet temperature 25 oC and aluminum cold plate, the junction temperature was kept below 63.2 oC at input power 350 W and pressure drop did not exceed 23 Kpa. Effects of metal materials used in 3D-printing on the thermal performance of the cold plate were also studied in detail.