Two or three years ago, many server thermal design discussions still centered on CPUs, heat sink bases, and air-cooled systems. Today, GPU power in AI servers has moved into 800 W, 1000 W, or even higher platforms. Cold-plate liquid cooling, HBM stacking, CoWoS, Chiplet architectures, and large-size interposers are becoming normal in high-end computing systems. For thermal engineers, thermal interface materials are no longer auxiliary materials that simply transfer heat from the chip to the heat sink. They are key components that affect heat transfer, assembly, stress, reliability, and rework risk at the same time.
In the past, many projects selected TIMs by first checking thermal conductivity: 6 W, 8 W, 10 W, 12 W, 15 W, with higher numbers appearing safer. In AI server and liquid-cooled GPU applications, however, what often determines project success is not the initial thermal conductivity listed on a data sheet, but whether the interface can remain stable after hundreds of days or thousands of operating hours. Real customer problems are usually more specific: GPU temperature rising by 8 C after half a year, edge hot spots after thermal cycling, material migration toward the edge after cold-plate teardown, warpage in large packages, or early fatigue failure in BGA solder joints.
Although these problems appear scattered, they essentially point in two directions: long-term reliability and mechanical stress. In other words, TIM selection is moving from a competition around high thermal conductivity into a competition around system-level reliability. Engineers need to understand the material's role in the entire structure: it is both a thermal path and a stress path. It must reduce contact thermal resistance without transferring excessive stress to the die, substrate, PCB, and BGA solder joints.
Thermal conductivity is an intrinsic material property, but the temperature difference between the chip and the cold plate is determined by total thermal resistance. Total thermal resistance includes at least three parts: contact thermal resistance between the chip surface and the TIM, bulk thermal resistance of the TIM, and contact thermal resistance between the TIM and the cold plate. For highly filled materials, bulk thermal conductivity can be high, but if the material cannot fully wet the interface or fill microscopic roughness under assembly pressure, the actual contact thermal resistance may not be low.
This is even more obvious in large-size GPU applications. The die, lid, and cold-plate base are not ideal planes. Microscopic roughness, macroscopic flatness, assembly tolerance, and package warpage all affect the real contact area. A material rated at 12 W/m-K may only contact well in local regions if it has high modulus, poor flowability, or insufficient compression. A 6 W/m-K thermal gel may deliver more stable actual temperature performance if it fills the interface better and reduces the void ratio.
One common industry misconception is treating thermal conductivity as the end point of selection. Higher thermal conductivity usually means higher filler loading, which may make the material harder, more brittle, more difficult to compress, and more dependent on precise assembly pressure. In AI server projects, thermal engineers should pay more attention to actual bond line thickness, compression ratio, contact thermal resistance, and thermal resistance change after temperature cycling, rather than only comparing single-point thermal conductivity values on data sheets.
Traditional CPUs or power devices have relatively limited contact areas, and TIMs mainly need to handle local tolerance and surface roughness. AI GPUs are different. A single package has a much larger area, and HBM, interposers, substrates, and cold plates form a more complex structural stack. As area increases, flatness error becomes harder to control, and pressure differences between the center and edge regions become more obvious.
In this structure, the TIM must provide sufficient interface adaptability. Thermal pads must reach a reasonable compression ratio under specified pressure while avoiding excessive compression set over long-term compression. Thermal gels must form stable coverage after dispensing and avoid obvious pump-out under continuous pressure and thermal cycling. Thermal grease can provide low initial thermal resistance, but Pump-Out and Dry-Out risks must be validated carefully. Material selection is not about deciding whether pads, gels, or greases are universally better; it is about matching the material type to the current gap, pressure, flatness, rework method, and lifetime target.
When the interface gap exceeds 2 mm, simply pursuing higher thermal conductivity becomes less meaningful. Compression recovery, thickness stability, and aging resistance become more critical. When the gap is small, the surface is flat, and pressure is controllable, low-thermal-resistance grease or phase-change material may have advantages, but lifetime degradation must be confirmed through power cycling and thermal cycling. For liquid-cooled GPUs, the cold plate is rigid, installation pressure is high, and system maintenance cost is high. The material must not only look good in initial temperature tests; it must also maintain interface continuity after disassembly, vibration, and thermal cycling.
Many TIM qualifications focus only on Day 1 performance: initial thermal resistance, initial temperature, initial pressure drop, and initial assembly state. The issue is that AI server customers truly care about how much performance remains by Day 1000. Data centers usually operate 24/7, with lifetimes designed for three to five years. GPU loads frequently switch between training, inference, scheduling, and idle states, making temperature cycling and power cycling unavoidable.
A common engineering scenario is that a material performs well in initial tests, with only about 3 C temperature difference between the GPU and cold plate. After a period of operation, the temperature difference gradually expands to 8 C, 12 C, or even higher. At this point, the bulk thermal conductivity of the material may not have changed significantly; the real failure is at the interface. Once voids, migration, drying, hardening, or pressure release occur, contact thermal resistance rises quickly, and local hot spots appear earlier than changes in average temperature.
Pump-Out is one of the most common and easily underestimated failure modes in high-power GPUs. The GPU repeatedly switches between idle and full load, and its temperature may rise from 40 C to above 90 C and then fall again. The material continuously experiences thermal expansion, contraction, and shear deformation. As cycle count increases, TIMs with low viscosity or insufficient shear resistance gradually migrate toward the edge. Large-size GPUs face higher risk because larger dimensions create larger thermal expansion displacement and higher shear strain in the material.
Liquid cooling does not automatically reduce TIM reliability risk. Many people assume that lower liquid-cooling temperature always improves material lifetime, but in actual systems, cold plates are usually more rigid, installation pressure is higher, and GPU power is also higher. The TIM simultaneously experiences continuous compression, thermal-cycling shear, and high heat flux density. Some grease products have very low initial thermal resistance, yet may migrate more easily during long-term service, causing local material loss. For high-value GPUs, the cost of this type of failure is far higher than the price difference between materials.
Dry-Out is another hidden risk, mainly appearing in some greases or low-crosslinking systems. Typical behaviors include silicone oil volatilization, oil-powder separation, filler aggregation, material hardening, and reduced interface wettability. Dry-Out may not be fully exposed in short-term high-temperature tests, but it can accumulate gradually under long-term temperature, pressure, and air exposure. The final result is not that the material completely disappears, but that interface contact deteriorates and the thermal resistance curve begins to rise.
Therefore, AI server TIM validation should move from single-point performance testing to lifetime-curve validation. In addition to thermal conductivity and initial thermal resistance, engineers should focus on thermal cycling tests, high-temperature aging tests, power cycling tests, interface integrity after thermal shock, material distribution after teardown, pump-out distance, thickness change, and thermal resistance retention. The most valuable data is not how low the initial thermal resistance is, but how it changes after 1000, 2000, or 3000 hours.
Mechanical stress is still under-discussed in the TIM industry, but it will become increasingly important over the next few years. In the past, server CPU power was about 200 W, and package area and system heat flux were relatively manageable, so the mechanical influence of TIMs was often overlooked. Today, high-end GPU power approaches or exceeds 1000 W, and the package is valuable, large, and structurally complex. If package damage occurs because of material stiffness, assembly pressure, or warpage, the loss is no longer the replacement of a single thermal pad; it may involve an entire accelerator card, server, or rack downtime risk.
A TIM not only conducts heat; it also transmits stress. The GPU, TIM, and cold plate form a complete stress chain. Pressure generated by cold-plate fastening is transmitted through the TIM to the die, substrate, PCB, and BGA solder joints. If the TIM is too hard, local pressure cannot be released effectively, and the chip edge, substrate corner, and outer rings of the solder joint array may experience higher stress. For CoWoS, Chiplet, HBM stacking, and large-size interposer structures, this stress can more easily combine with package warpage, CTE mismatch, and thermal-cycle fatigue.
Higher thermal conductivity usually means higher filler loading. To achieve 10 W, 12 W, 15 W, or even higher conductivity, material systems often need more alumina, boron nitride, magnesium oxide, or spherical fillers. As filler content increases, the material may become harder, more brittle, harder to compress, and less compliant in shear. Although bulk thermal conductivity improves, mechanical risk can increase at the same time.
A counterintuitive point is that a 12 W material is not necessarily safer than a 6 W material. In some projects, the thermal resistance reduction provided by a high-conductivity material may be only a few percent, while compression stress, assembly difficulty, and long-term fatigue risk increase significantly. For a 1000 W-class GPU, if the temperature benefit is limited but BGA fatigue, chip-edge hot spots, or package warpage risk increases substantially, that material is not the better selection.
Advanced packaging further amplifies this issue. CoWoS, HBM stacking, and large-size interposers continuously increase package area, making the overall structure thinner, more complex, and more sensitive to flatness and stress. Package warpage causes uneven TIM compression: the center may be under-compressed, while the edge may be over-compressed. Some regions may show higher thermal resistance, while others experience stress concentration. Thermal management is no longer only about high temperature; it is about the coupling of temperature fields, stress fields, and deformation.
BGA solder joint fatigue risk is also increasing. Server failure is not always caused by chip overheating; sometimes solder joints gradually crack under thermal cycling and mechanical stress. Influencing factors include CTE mismatch, package warpage, cold-plate stiffness, fastening pressure, TIM modulus, and thermal cycle amplitude. Such problems often require thousands of hours to emerge, making them difficult to detect in short-term laboratory tests. Therefore, next-generation TIM selection cannot look only at thermal conductivity and thermal resistance. Modulus, compression stress, shear compliance, rebound, and long-term mechanical stability must also be included in evaluation.
Thermal pads offer controlled thickness, simple assembly, and good reworkability. They are suitable for structures with larger gaps and tolerances. The risk is that high-conductivity pads may have higher modulus; if compression is insufficient, contact thermal resistance may be high, and long-term compression may cause inadequate shape recovery. For large-size GPUs, pads are more suitable for auxiliary thermal paths or locations with larger gaps. They should be evaluated carefully in the main die area with high heat flux density, especially regarding pressure distribution.
Thermal gels are dispensable, automation-friendly, and capable of filling complex gaps. They can often adapt to interface tolerance better than high-hardness pads. For liquid-cooled servers, power modules, communication equipment, and some large-size packages, gels have engineering value in reducing contact thermal resistance and releasing local stress. Their risks include the need to control dispensing amount, slump profile, curing or pre-forming stability, and long-term pump-out, oil bleeding, and thickness retention.
Thermal grease offers low initial thermal resistance and good wettability. It is suitable for small gaps, uniform pressure, and flat surfaces. The risks are Pump-Out and Dry-Out, especially under high power cycling, large-area chips, and high-pressure cold plates. Grease should not be judged only by initial temperature performance; material distribution and thermal resistance drift after multiple thermal cycles must be observed.
Phase-change materials and low-modulus composites are also worth attention. They may improve wetting and contact state within certain temperature ranges, and may maintain interface stability more easily than traditional grease. However, phase-change temperature, flow boundary, rework method, and long-term reliability still need validation under real operating conditions. For the main thermal path in AI servers, no material can be declared optimal independent of structure. The correct approach is to evaluate material, pressure, gap, surface roughness, cold-plate flatness, package warpage, and lifetime target together.
For AI server and liquid-cooled GPU projects, TIM selection should be evaluated across five dimensions. First, determine the thermal path and heat flux density. The main die, HBM, VRM, optical module, and auxiliary chips have different heat flux levels, so the material strategy should not be identical. Second, confirm the real interface conditions, including bond line thickness, flatness, roughness, pressure window, and assembly tolerance. Third, evaluate lifetime requirements, including operating temperature, number of thermal cycles, maintenance cycle, and disassembly or rework method. Fourth, establish thermal-mechanical coupled testing that measures not only temperature difference, but also compression stress, modulus, warpage sensitivity, and thermal resistance retention. Fifth, balance cost and risk. In high-value GPU applications, material cost is usually not the largest variable; downtime, rework, and failure risk are the core costs.
A common historical priority order was thermal conductivity, thermal resistance, and cost. In future AI server and liquid-cooled cold plate applications, a more reasonable order may be contact thermal resistance, long-term reliability, modulus and compression stress, interface adaptability, thermal conductivity, and cost. Thermal conductivity remains important, but it is no longer the only metric, nor is higher always better.
Selection should also pay attention to four specific test actions. First, run initial thermal resistance versus assembly pressure curves to determine whether the material is stable under different pressures. Second, run thermal resistance curves after thermal cycling to observe whether significant drift occurs. Third, perform teardown analysis to check whether the material has migrated, formed voids, dried, cracked, or locally disappeared. Fourth, perform system-level temperature distribution analysis and focus on edge hot spots rather than only average temperature.
If the project is in an early development stage, it is better not to test only the highest-conductivity model. Instead, prepare candidate materials with low modulus, high reliability, different viscosities, or different compression ratios. Using DOE to compare thermal resistance, stress, and lifetime degradation can prevent discovering material mismatch only after mass production begins.
Many suppliers in the market still compete on higher thermal conductivity: 12 W, 15 W, or 20 W. From a system engineering perspective, however, the core competitive point of next-generation high-value TIMs may no longer be simply increasing conductivity, but achieving low stress, high reliability, and long-term interface stability.
For 1000 W-class GPUs, an ideal TIM needs several characteristics at the same time: adaptability to large-size packages, low contact thermal resistance, no obvious Pump-Out under thermal cycling, no Dry-Out under long-term high temperature and pressure, low compression stress that does not amplify package warpage and BGA fatigue, compatibility with automated dispensing or stable assembly, and controllable process behavior after rework disassembly.
This also means competition among material suppliers will change. In the past, it was enough to provide thermal conductivity, hardness, thickness, and dielectric strength parameters. In the future, suppliers will need to provide reliability curves closer to real customer conditions, pressure-thermal resistance curves, interface photos after thermal cycling, pump-out evaluation, long-term aging data, and structural adaptation recommendations. Customers purchasing thermal materials will increasingly care whether suppliers understand server architecture, liquid-cooling assembly, advanced packaging, and system reliability, rather than only whether they can offer a lower unit price.
For thermal management material suppliers such as ZNIM, professional value should not stop at material supply. It should be reflected in helping customers define selection boundaries: which scenarios are suitable for thermal pads, which are suitable for thermal gels, which can use thermal grease, which require special Pump-Out validation, and which should prioritize lower mechanical stress. True solution capability comes from understanding failure mechanisms, process windows, and long-term reliability.
TIM selection for large-size high-power chips can no longer be handled with the traditional logic of higher thermal conductivity being always better. AI servers, liquid-cooled cold plates, CoWoS, Chiplet architectures, and HBM stacking are jointly pushing TIMs into a new stage. Materials must not only conduct heat, but also adapt to interfaces, resist thermal cycling, control Pump-Out and Dry-Out, and reduce mechanical risk to packages and BGA solder joints as much as possible.
What engineers should truly watch is not a few degrees of Day 1 temperature difference, but whether Day 1000 thermal resistance remains stable. It is not whether the data sheet lists 2 W/m-K more conductivity, but whether high filler loading and high modulus introduce higher stress. It is not whether a single-point test passes, but whether the thermal path remains continuous throughout the whole system lifetime.
Over the next few years, the most important direction in the AI server TIM market may not be which supplier first reaches higher thermal conductivity, but which supplier can provide low-stress, high-reliability, manufacturable interface materials under high heat flux density, large-size packaging, and liquid-cooling systems. For thermal design engineers and procurement decision makers, rebuilding the selection framework around contact thermal resistance, long-term reliability, and mechanical stress will be closer to the essence of project success. Ultimately, TIM selection for large-size high-power chips tests how well the material and system work together.
Because actual temperature difference is determined by total thermal resistance. Contact thermal resistance, interface gap, pressure, surface roughness, and long-term reliability all affect results. A high-conductivity material that is too hard or cannot fully conform to the interface may perform worse than a lower-conductivity material with lower contact thermal resistance.
Not necessarily. A 12 W material may reduce bulk thermal resistance, but it may also have higher filler loading, higher modulus, and greater compression stress. In large-size GPUs or advanced packages, added mechanical stress may offset thermal benefits and even create reliability risks.
Common risks include Pump-Out, Dry-Out, compression set, interface voids, edge material loss, and thermal resistance drift. Temperature cycling and cold-plate pressure in high-power GPUs amplify these problems.
No. Liquid cooling can reduce the cooling-side temperature, but liquid-cooled cold plates are usually more rigid, assembly pressure is higher, and GPU power is also higher. The TIM may still experience continuous compression and thermal-cycling shear, so dedicated validation is required.
CoWoS, Chiplet, HBM, and large-size interposers increase package area, CTE-matching complexity, and warpage risk. If the TIM is too stiff or unevenly compressed, it may cause local hot spots and BGA fatigue.
Recommended tests include initial thermal resistance, pressure-thermal resistance curves, thermal cycling, power cycling, high-temperature aging, post-teardown material distribution, pump-out distance, Dry-Out condition, and thermal resistance retention.
It cannot be judged absolutely. Thermal gel has advantages in low stress, automated dispensing, and complex gap adaptation, but migration, oil bleeding, and thermal resistance after cycling still need validation. The final choice depends on interface gap, pressure, surface roughness, operating temperature, electrical insulation requirements, long-term reliability, and cost targets.
