Multiscale Thermal Investigations of High Thermal Conductivity Heat Spreaders for Advanced Microelectronic Packages

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has led to an unprecedented demand for high-performance computing systems. These systems, often composed of densely packed microelectronic components, generate significant amounts of heat during operation. As AI workloads become more complex and data-intensive—particularly in applications such as deep learning, autonomous systems, and edge computing—the thermal management of microelectronic packages has emerged as a critical design challenge. Efficient heat dissipation is essential not only for maintaining device reliability and performance but also for enabling the scalability of AI hardware architectures. In this context, thermal analysis has transitioned from a supporting role to a central pillar in the design and optimization of next-generation AI hardware. Traditional thermal solutions are struggling to keep pace with the heat fluxes generated by this advanced microelectronic. In response, novel materials and multiscale thermal modelling is emerging to address these challenges. Among these materials, diamond-based heat spreaders have appeared as a promising solution. The integration of synthetic diamond as a heat spreader material offers significant advantages in managing localized hotspots and improving overall thermal uniformity. However, the integration of diamond heat spreaders into microelectronic compounds presents several challenges. One of the most critical for thermal management is the interface thermal resistance related to the bonding of the heat spreader into the silicon. To identify the thermal resistance of the bonding interface requires multiscale thermal investigations from nanoscale to macroscale. This interface thermal resistance will determine the reduced thermal conductivity that compromise the potential gains in heat spreading.

The focus of this study is to investigate the integration of heat spreader materials into microelectronic devices as an advanced thermal management solution for mitigating localized heat source. The research will combine:

1. Multiscale Thermal Modelling:
• Predict thermal resistance across different bonding interfaces at micro/nano level.
• Integrate the results of interface thermal resistance values into package-level finite element modelling (FEM).
• Explore the impact of heat spreader parameters such as bonding conditions, thickness, layout, and power map distribution with multiple hotspot sizes.
2. Experimental Validation
• Use dedicated thermal test vehicles with integrated heat spreaders.
• Improve transient thermal measurement methodologies to capture hotspot responses at lower time steps.
3. Advanced Thermal Characterization
• Employ Time-Domain Thermoreflectance (TDTR) to measure thermal contact resistance across heat spreader interfaces.
• Correlate experimental data with simulation results to refine models and validate design strategies.

The goal of this project is to investigate the bonding interface thermal resistance in a multiscale thermal modelling approach with the aim of enabling integration strategies that preserve heat spreaders material capabilities in advanced microelectronic systems.

Timeline:

• Year 1: Literature review, selection of data for heat spreader materials, initial bonding interface thermal resistance simulations at micro/nano scale.
• Year 2: Refine micro/nano scale simulations. Created a parametric package-level finite element model with input of bonding interface thermal resistance. Thermal analysis and data interpretation.
• Year 3: Sample preparation, electrical measurement and validation.
• Year 4: Data analysis, refinement of models, optimization and dissertation writing.