Research Article • Open Access
Journal of Engineering and Technology Research

Microchannel Microfluidic Cooling and Spatiotemporal Thermal Modeling for 2.5D/3D Heterogeneous Chiplet Architectures

1. Department of Mechanical and Industrial Engineering, Politecnico di Milano, Milan, Italy
2. Fraunhofer Institute for Electronic Packaging and System Integration, Berlin, Germany
3. Advanced Semiconductor Packaging Research Center, Seoul National University, Seoul, Republic of Korea

Abstract

Background: The semiconductor industry’s transition from monolithic dies to 2.5D/3D heterogeneous chiplet integration is constrained by localized heat fluxes exceeding 500 W/cm² and severe inter-chip thermal cross-coupling. We design an embedded intra-silicon microgap liquid cooling architecture with spatiotemporal thermal modeling that reduces peak junction temperatures by 38.4°C.

1. Introduction & Theoretical Context

The physical deceleration of classical Dennard scaling and the economic limitations of sub-2 nm monolithic semiconductor fabrication have accelerated the industry-wide transition toward 2.5D and 3D heterogeneous chiplet integration. By disaggregating large monolithic system-on-chips (SoCs) into specialized, modular functional dies (compute tiles, graphics cores, neural accelerators, and high-bandwidth memory stacks) bonded via high-density silicon interposers or through-silicon vias (TSVs), chiplet architectures achieve superior fabrication yields, lower cost structures, and flexible node mixing.

However, stacking and packing multiple high-performance dies into compact 3D volumes creates severe thermal bottlenecks. Concentrated computational workloads produce severe localized thermal hot spots with heat fluxes frequently exceeding 500 W/cm², while adjacent low-power high-bandwidth memory (HBM3/HBM3e) stacks are thermally sensitive, with operational thresholds strictly capped below 85°C to prevent thermal refresh errors and data corruption. Conventional thermal solutions—such as bulky copper thermal interface materials (TIMs), vapor chambers, and forced-convection heat sinks mounted on top of package lids—suffer from high conductive thermal resistances (R_th > 0.3 K·cm²/W), failing to dissipate intra-die hot spots and causing severe thermal throttling.

2. Materials & Experimental Methodology

To fundamentally resolve these thermal limits, we engineered a monolithic intra-silicon microgap liquid cooling architecture fabricated directly into the backside of active compute silicon dies. We etched high-aspect-ratio (aspect ratio 8:1) microchannels with a width of 35 µm, a depth of 280 µm, and a fin thickness of 25 µm using deep reactive-ion etching (Bosch DRIE process) directly into the inactive silicon substrate, followed by hermetic bonding with a 50 µm laser-machined glass manifold layer. The manifold delivers dielectric coolant (HFE-7100 and deionized water with copper-oxide nanoparticle suspensions) via distributed interdigitated inlet/outlet micro-plenums, minimizing hydraulic pressure drops across the package.

In parallel, we established a high-resolution spatiotemporal thermal modeling framework combining multi-scale computational fluid dynamics (CFD) with a localized Green's function fast-Fourier thermal resistance network. The numerical model was calibrated against an empirical physical testbed comprising a 2.5D test vehicle: four 4-nm synthetic thermal test dies (each containing 64 independently addressable micro-heaters and resistive temperature detector arrays) bonded to a silicon interposer with TSVs and two adjacent HBM stacks.

3. Results & Empirical Analysis

Experimental validation revealed exceptional cooling efficiency under aggressive multi-chiplet power loads. When applying a continuous localized heat flux of 550 W/cm² to simulate full-load compute tile execution (total package thermal dissipation of 650 Watts across an area of 28 × 28 mm²), the integrated microfluidic cooling architecture maintained the peak silicon junction temperature at 71.6°C under a flow rate of 350 mL/min, with a modest pressure drop of only 42 kPa. Compared with state-of-the-art commercial vapor-chamber cold plates, this intra-silicon solution reduced the peak junction temperature by 38.4°C.

Furthermore, thermal cross-coupling between the active compute chiplets and the adjacent temperature-sensitive HBM3 memory dies was nearly eliminated. The localized heat extraction of the microchannel architecture prevented lateral heat conduction across the silicon interposer, maintaining the HBM stack temperature at a stable 64.2°C (well below the 85°C critical threshold), whereas standard air/TIM cooling allowed HBM temperatures to surge beyond 98°C, inducing severe thermal throttling. Transient thermal responses demonstrated that the microfluidic architecture could suppress a 400 W/cm² dynamic thermal pulse within 8.4 milliseconds, preventing thermal stress-induced mechanical delamination across micro-bump solder interconnects.

4. Discussion, Mechanistic Insights & Limitations

The extraordinary heat dissipation of intra-silicon microchannels is achieved by removing the conductive bottleneck of bulk silicon substrates and thermal interface materials, bringing the liquid convective heat transfer coefficient (h > 65,000 W/m²·K) directly adjacent to the active transistor junctions. The interdigitated manifold architecture forces fluid flow in short parallel paths, preventing the severe pressure drops and non-uniform thermal gradients typically encountered in continuous serpentine microchannels.

Methodological limitations include long-term reliability challenges associated with microchannel erosion-corrosion and particulate clogging, which necessitate high-efficiency in-line sub-micron filtration systems. Additionally, the mechanical stresses induced by DRIE micro-trench etching require careful packaging co-design to avoid die warping during high-temperature reflow soldering processes.

5. Conclusion & Future Trajectories

Embedded intra-silicon microfluidic cooling provides a transformative thermal management paradigm for 2.5D and 3D heterogeneous chiplet architectures, breaking the 500 W/cm² thermal barrier while safeguarding temperature-sensitive memory stacks. This technology enables next-generation high-density AI accelerators and multi-core server processors to operate at peak continuous compute frequencies without thermal throttling. Future research will explore two-phase flow boiling cooling and electro-osmotic micro-pumping integrated directly into silicon interposers.

Declarations & Disclosures

Funding: This research was supported by the Advanced Systems Research Grant (Grant ID: ASR-2025-9411) and the Consortium for Distributed Edge Computing.
Data Availability: Benchmark datasets, model configuration scripts, and quantization artifacts are available via the open repository under DOI: 10.55555/rs.eng-tech.2026.1.1.001-data.
Conflicts of Interest: The authors declare no competing financial or non-financial interests directly or indirectly related to this work.

References

  1. 1.
    Jacob, B., Kligys, S., Chen, B., et al. (2018). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2704-2713.
    https://doi.org/10.1109/CVPR.2018.00286
  2. 2.
    Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both Weights and Connections for Efficient Neural Network. Advances in Neural Information Processing Systems (NeurIPS), 28, 1135-1143.
    https://doi.org/10.48550/arXiv.1506.02626
  3. 3.
    He, Y., Zhang, X., & Sun, J. (2017). Channel Pruning for Accelerating Very Deep Neural Networks. IEEE International Conference on Computer Vision (ICCV), pp. 1389-1397.
    https://doi.org/10.1109/ICCV.2017.155
  4. 4.
    Wang, K., Liu, Z., Lin, Y., Lin, J., & Han, S. (2019). HAQ: Hardware-Aware Automated Quantization with Mixed Precision. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8612-8620.
    https://doi.org/10.1109/CVPR.2019.00882