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Everyone talks about AI chips. NVIDIA, Apple Silicon, custom TPUs these names fill headlines constantly. But the performance of these processors depends on far more than silicon alone. Behind every advanced AI system is a network of specialized materials that support heat dissipation, electrical insulation, power conversion, packaging, and long-term reliability.
Aluminum nitride. Boron nitride. Diamond. Silicon carbide. These are not buzzwords. They are among the materials playing increasingly important roles in this ecosystem. Some are already widely used in thermal-management components, substrates, and power electronics, while others are emerging through advanced packaging and next-generation semiconductor research. For technical buyers, researchers, and materials engineers working in AI hardware, understanding these materials is becoming increasingly important.
For decades, silicon dominated chip design. It is affordable, well-understood, and relatively easy to process. But as AI workloads grow more intense, silicon is struggling to keep pace with what engineers demand from it.
The problem is not performance alone. It is heat. A modern GPU used for AI training can draw over 700 watts. Data centers running thousands of these chips face staggering thermal loads. Rising accelerator and rack power densities are pushing data centers toward direct-liquid cooling and more advanced package-level thermal-management strategies. Although silicon has a thermal conductivity of roughly 150 W/(m·K), the greater challenge lies in removing concentrated heat through the chip, packaging layers, thermal interfaces, and cooling system. As processors become more powerful and tightly integrated, these thermal bottlenecks increasingly limit performance and reliability.
These materials are more than laboratory curiosities. They are engineered for carefully controlled thermal, electrical, mechanical, and chemical properties, and they play established or emerging roles across semiconductor manufacturing, electronic packaging, thermal management, and data-center power systems. Each addresses a different limitation that silicon alone cannot solve.
Aluminum nitride does not get many headlines, yet it is an important substrate and thermal management ceramic for high-power electronic packages. Commercial AlN ceramics commonly provide thermal conductivity ranging from 140 to 180 W/mK, while some premium substrate grades reach 200 W/mK. Combining with near-perfect electrical insulation, AlN is one of the most important substrate materials in advanced semiconductor packaging today.
What makes it exceptional is its coefficient of thermal expansion, which is more compatible with silicon than many other ceramic substrate materials. This reduced thermal-expansion mismatch can limit mechanical stress during repeated heating and cooling, helping improve package reliability and reduce the risk of cracking, delamination, and connection failure. IBM Research published findings identifying that AlN bonding dramatically improves heat dissipation in backside power delivery networks, a promising alternative to conventional oxide bonding in next-generation chip architecture.
AlN can withstand demanding processing conditions and substantial temperature cycling, but its relevance to AI hardware is primarily its combination of heat conduction, electrical insulation, and dimensional stability—not operation near 1,000°C. These properties make it valuable for substrates, packages, heat spreaders, heaters, electrostatic chucks, and other components used in high-power electronics and semiconductor manufacturing.
Boron nitride earns its nickname for a reason. Its layered hexagonal structure mirrors graphite, but with one critical difference: BN is electrically insulating. That combination of high thermal conductivity (up to ~300 W/mK in some forms) and electrical insulation makes it uniquely valuable in electronics manufacturing.
In AI applications, hexagonal boron nitride (hBN) is drawing serious research attention as an atomically thin insulating, encapsulation, and interface material for two-dimensional devices. In addition, BN powders are actively used today in the following applications:
BN is also stable in air up to 900°C and in inert atmospheres up to 2,000°C. For manufacturing environments where temperature control is critical, that stability reduces material waste and improves production yield.
AI chips do not just need processing power. They also need electrical power. Lots of it. And delivering that power efficiently requires a different class of semiconductor material altogether.
Silicon carbide (SiC) and gallium nitride (GaN) are wide bandgap semiconductor materials increasingly used in the power-conversion infrastructure supporting AI servers and data center. Compared to silicon, they can operate at higher voltages, higher temperatures, and higher frequencies with significantly less energy loss. That efficiency gain translates directly into more reliable power delivery for AI data centers consuming tens of megawatts continuously.
Wolfspeed has highlighted their 300mm SiC wafer technology as a materials foundation intended for evaluation in next generation AI and high-performance computing packaging. Source: https://www.wolfspeed.com/knowledge-center/article/wolfspeeds-300-mm-silicon-carbide-technology-as-a-materials-foundation-for-next-generation-ai-and-hpc-advanced-packaging/
Navitas Semiconductor reports their GaN and SiC platform achieves 97.8% efficiency in hyperscale AI data center power systems. When a single data center consumes 50+ megawatts of power, even a 1% efficiency improvement means millions of dollars saved annually.
There is a material with a thermal conductivity far beyond most conventional electronic materials But until recently, its cost and manufacturing complexity kept it out of serious semiconductor conversations. That is changing fast.
Diamond, specifically synthetic diamond grown through chemical vapor deposition (CVD), is now one of the most actively researched materials in AI chip thermal management. At room temperature, single-crystal diamond reaches thermal conductivity above 2,000W/mK, with exceptional single-crystal grades reaching values of 2,200 to 2,400 W/mK. Isotopically enriched diamond exceeds 3,000 W/mK. In comparison, copper sits at roughly 400 W/mK, and Silicon at approximately 150 W/mK. Diamond has outclassed the competing materials by an order of magnitude.
Undoped diamond also provides high electrical resistivity, allowing it to spread heat without creating an electrically conductive pathway. This combination makes diamond especially promising for heat spreaders and thermal layers positioned close to semiconductor hot spots. However, the performance of an actual device depends not only on the diamond itself but also on the bonding layer and thermal resistance at the diamond–semiconductor interface.
To understand the scale of the challenge, a high-performance multi-GPU AI server can consume well over 10 kW of power, most of which ultimately becomes heat that must be removed. Multiply that across thousands of racks in a hyperscale data center and the thermal challenge becomes the central design problem not a secondary concern.
Excessive temperature and repeated thermal cycling can shorten hardware life by accelerating electromigration in metal interconnects, contributing to solder-joint fatigue, and increasing stress within semiconductor packages. Effective thermal management is therefore one of the major constraints in the design of high-performance AI hardware.
Advanced materials help create more efficient pathways for moving heat away from concentrated hot spots. AlN may be used in electrically insulating substrates, heat spreaders, and other thermal components, while BN powders and platelets are commonly incorporated into thermal-interface materials, adhesives, encapsulants, and polymer composites. Material quality is important because oxygen-related impurities, lattice defects, residual porosity, and secondary phases can significantly reduce the thermal conductivity of AlN ceramics.
Beyond silicon, next-generation AI chips rely on aluminum nitride (AlN), boron nitride (BN), silicon carbide (SiC), gallium nitride (GaN), and diamond. Each material addresses a specific challenge whether thermal management, electrical insulation, or power conversion efficiency. The combination of these materials determines the overall performance and reliability of the chip system.
Silicon has a thermal conductivity of approximately 150 W/mK. As chip power densities increase with AI workloads, silicon substrates alone cannot dissipate heat fast enough to prevent performance degradation. The greater challenge lies in removing concentrated heat through the chip, packaging layers, thermal interfaces, and cooling system. As processors become more powerful and tightly integrated, these thermal bottlenecks increasingly limit performance and reliability.
Hexagonal boron nitride (hBN) is a layered ceramic material with exceptional electrical insulation properties and high in-plane thermal conductivity. The material can be exfoliated into atomically thin monolayers or few-layer nanosheets, in which form considered a two-dimensional material. Researchers are actively exploring it as an encapsulation layer, insulating substrate, interface material, and potential gate dielectric in next-generation transistors, particularly for devices built on 2D semiconductor materials. Beyond transistor research, boron nitride powders, coatings, and sintered components are used in selected semiconductor-manufacturing and high-temperature processing applications because of their electrical insulation, chemical stability, and resistance to elevated temperatures. What is a wide bandgap semiconductor and why does AI hardware need them?
Wide bandgap semiconductors like silicon carbide (SiC) and gallium nitride (GaN) have larger energy gaps than standard silicon. This property allows them to operate at higher voltages, temperatures, and switching frequencies with far less energy loss. For AI data centers drawing tens of megawatts of power, SiC and GaN-based power electronics deliver efficiency gains that translate into major cost savings and reduced thermal loads.
Boron nitride serves multiple roles in AI chip manufacturing. As a thermal interface material, it transfers heat away from active devices. As a high-temperature coating, it protects processing equipment during sintering and deposition. In advanced research, hexagonal BN is being evaluated as a dielectric layer in experimental 2D transistors that could eventually replace or supplement conventional silicon gate oxides.
AlN substrates are used across several high-performance sectors including semiconductor packaging, microwave and RF components, LED mounting boards, automotive power modules, and high-power transistors. All of these sectors are experiencing growth driven directly by AI hardware demand, making AlN one of the most strategically important materials in the current technology supply chain.
Diamond offers the highest thermal conductivity of any known bulk material ranging from 2,200 to 2,400 W/mK in high quality single-crystal form and exceeding 3,000 W/mK for isotopically enriched grades. CVD-grown synthetic diamond is being increasingly adopted as a heat spreader, substrate, and thermally conductive layer positioned close to high-power semiconductor devices. Researchers are also exploring diamond for inter-tier thermal management in 3D-stacked architectures and as a component in advanced packaging composites. Although cost, wafer size, bonding quality, and interfacial thermal resistance remain significant challenges, rising AI-chip power densities are increasing interest in diamond-based thermal-management technologies. .
The materials discussed in this article often require careful selection because performance depends on more than material name alone. Purity, composition, particle characteristics, density, thermal conductivity, electrical properties, dimensions, surface finish, and manufacturing method can all affect suitability for semiconductor and AI-hardware applications. The appropriate specification also varies depending on whether the material will be used as a powder, substrate, heat spreader, insulating component, coating, or structural ceramic part.
AdValue Technology supplies advanced ceramic powders and finished components which are widely used for thermal management, electrical insulation, semiconductor processing, and related research and production applications. The team at AdValue Technology can help you identify exactly what your project requires and connect you with the right material in the right specification.
Explore the full product catalog at AdValue Technology or reach out directly to discuss your specific application. The right material makes the difference and finding it starts here.
