Why 2.5D Systems Need Thermal Isolation, Not Just Better Floorplanning

As 2.5D systems bring GPUs, CPUs, HBM, and other chiplets closer together, thermal management stops being just a heatsink problem and becomes a system architecture problem. In Optimizing Thermal Performance in 2.5D Systems Using Embedded Isolators, the authors show that thermally sensitive chiplets such as HBM can be pushed past thermal trip points by nearby high-power compute dies, degrading effective bandwidth, increasing latency, and ultimately hurting workload performance. [1]

One of the paper’s most useful findings is that several intuitive fixes do not address the core issue. Changing TIM conductivity had very little effect on GPU-to-HBM thermal separation. Switching the interposer from silicon to lower-conductivity materials such as glass or FR-4 only modestly reduced HBM temperature. Even spreading chiplets farther apart delivered limited benefit until the spacing became so large that the interposer and package overhead became impractical. [1]

The reason is important: the dominant lateral heat path is not the interposer, but the thick copper heat spreader. Once the full thermal stack is modeled, the design implication becomes clear. To meaningfully reduce chiplet-to-chiplet thermal coupling, the heat spreader itself has to be partitioned. The paper proposes embedded low-conductivity thermal isolators inside the heat spreader to create separate thermal zones. In the more complex heterogeneous integration case, this increased thermal isolation between HBM and compute dies by up to 61%, and one isolator placement strategy reduced peak compute temperature by as much as 22.5% with only an 18% interposer increase. [1]

The performance implications are what make this especially compelling. The paper links thermal simulation with GPU and HBM performance modeling through a closed-loop workflow using Ansys Icepak, DRAMSim3, AccelSim, and AccelWattch. In the GPU+HBM example, the baseline case pushed HBM above its thermal trip points, while the isolator case pulled HBM below them. Under the paper’s more optimistic memory-bound assumptions, the resulting speedup reached 37%. [1]

Just as importantly, the paper is honest about tradeoffs. Protecting HBM with isolators can raise GPU temperature, so the benefit depends on workload type. Memory-bound workloads can gain significantly, while compute-bound workloads may see little benefit or even regress. That is exactly why thermal analysis cannot stop at temperature plots alone; it has to be tied to workload behavior and system performance. [1]

For the industry, the broader message is that thermal optimization in 2.5D systems must be treated as co-optimization. Package layout, heat spreader design, cooling architecture, workload sensitivity, and memory behavior all interact. The paper also points to future work around co-optimizing isolator placement with floorplanning, exploring other cooling approaches, and understanding reliability impacts from larger thermal gradients. [1]

Key Conclusions

- The main thermal coupling path in dense 2.5D systems is the heat spreader, not the interposer. [1]

- TIM tweaks and interposer material changes help only modestly. [1]

- Simple chiplet spreading is usually not a practical answer because the area penalty rises quickly before the thermal benefit becomes meaningful. [1]

- Embedded thermal isolators can protect HBM much more effectively than conventional floorplanning or spreading. [1]

- The real metric is not just temperature reduction, but performance preservation under thermal stress. [1]

- Benefits are workload-dependent, so thermal decisions should be tied to system-level performance models. [1]

How we can help you

At Anemoi Software, we help engineering teams move this kind of analysis earlier in the design cycle. Instead of waiting until package architecture is largely fixed, teams can evaluate chiplet placement, heat-spreader partitioning, cooling options, power-to-temperature behavior, and workload sensitivity up front. The goal is not just to generate thermal maps, but to understand which packaging choices preserve performance, reduce risk, and shorten iteration cycles.

- Early package architecture trade studies for 2.5D and heterogeneous systems

- Chiplet-to-chiplet thermal coupling analysis

- Comparison of floorplanning, spreading, isolators, and cooling options

- Power-temperature table generation for different package concepts

- Identification of HBM trip-point risk and throttling risk

- Tying thermal results to workload-level performance impact

- Studying temperature gradients and other reliability-sensitive hotspots

Reference

[1] George Karfakis, Myriam Bouzidi, Yunhyeok Im, Alexander Graening, Suresh K. Sitaraman, and Puneet Gupta, Optimizing Thermal Performance in 2.5D Systems Using Embedded Isolators, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, author’s accepted manuscript, DOI: 10.1109/JETCAS.2025.3595909.