Chinese Researchers Pioneer Diamond-Copper Composites to Conquer AI Overheating Crisis

As artificial intelligence systems push the boundaries of computational power, one of the most pressing challenges facing engineers and researchers is managing the intense heat generated by high-density chips. In data centers powering large language models and advanced machine learning workloads, overheating—often referred to as the 'thermal wall'—threatens to throttle performance, reduce hardware lifespan, and skyrocket energy consumption. Traditional copper heat sinks, while reliable, are reaching their limits as power densities climb beyond 1 kilowatt per square centimeter in next-generation processors.

Enter a groundbreaking innovation from Chinese scientists: a diamond-copper composite material designed specifically to combat this overheating crisis. By embedding high-thermal-conductivity diamonds within a copper matrix, researchers have created a heat dissipation solution that promises up to 80 percent improvement in cooling efficiency for AI data centers. This development not only addresses immediate hardware constraints but also positions China at the forefront of thermal management for high-performance computing.

The Thermal Wall in AI Hardware

The exponential growth of AI has led to chips with unprecedented transistor counts and power requirements. For instance, modern graphics processing units (GPUs) used in training massive neural networks can dissipate hundreds of watts per chip, generating localized hotspots exceeding 100 degrees Celsius. Without effective cooling, these temperatures degrade silicon performance via thermal throttling—where clock speeds are automatically reduced to prevent damage—and accelerate electromigration, a process where metal atoms in interconnects migrate under high current and heat, leading to failures.

Air cooling with fans, once standard, is obsolete for megawatt-scale clusters. Liquid cooling systems, which circulate coolants through microchannels or immersion baths, have become essential. However, even these struggle with the 'thermal wall,' where heat flux exceeds the material's ability to conduct it away quickly enough. Copper, with a thermal conductivity of about 400 watts per meter-kelvin (W/mK), has been the gold standard for heat spreaders and sinks due to its ductility, electrical conductivity, and cost-effectiveness. Yet, as AI workloads demand sustained peak performance, copper alone cannot keep pace.

Diamond's Superior Thermal Properties

Diamond stands out as nature's ultimate heat conductor, boasting a thermal conductivity of 2,000 to 2,400 W/mK in single-crystal form—five times that of copper and over ten times that of silicon. This stems from diamond's rigid carbon lattice, which enables phonons (quantized lattice vibrations carrying heat) to travel with minimal scattering. In polycrystalline or CVD-grown (chemical vapor deposition) forms used industrially, values still exceed 1,000 W/mK, far surpassing metals.

Challenges in using pure diamond include its brittleness, high fabrication cost, and poor electrical insulation for direct chip integration. Researchers have long explored diamond-copper composites, where diamond particles (typically 50-70 volume percent) are embedded in a copper matrix. The key lies in optimizing the interface: uncoated diamonds poorly bond with copper due to mismatched coefficients of thermal expansion (CTE)—diamond's 1.0 ppm/K versus copper's 17 ppm/K—leading to voids and low effective conductivity. Solutions involve coating diamonds with titanium, chromium, or tungsten carbide to promote wetting and reduce phonon scattering at boundaries.

NIMTE and CAS Lead the Charge

At the Ningbo Institute of Materials Technology and Engineering (NIMTE), under the Chinese Academy of Sciences (CAS), the functional carbon materials team has pioneered a diamond-copper composite tailored for AI applications. This institute, located in Zhejiang Province, specializes in advanced materials for energy and electronics, collaborating closely with universities like Zhejiang University and national labs.

Their material achieves over 1,000 W/mK thermal conductivity through innovative 3D network structuring of diamond particles within copper, minimizing interfacial resistance. Fabrication involves pressureless infiltration or spark plasma sintering (SPS), where molten copper flows around pre-arranged diamond arrays, followed by annealing to refine microstructures. This process ensures uniform heat spreading, crucial for irregular hotspots in AI GPUs.

Photo by Jorick Jing on Unsplash

Integration with Liquid Cooling: The C8000 V3.0 Solution

The composite's real-world debut is in Sugon's (Dawning Information Industry) C8000 V3.0 megawatt-scale phase-change immersion liquid cooling system. This cabinet supports over 900 kW per rack, using dielectric fluids that boil at low temperatures to absorb heat via latent heat of vaporization. Diamond-copper heat spreaders are embedded in high-heat-flux zones, channeling heat from chips to the coolant interface.

In tests, the setup reduced chip junction temperatures by up to 80 percent compared to standard copper, enabling 10 percent higher sustained performance without throttling. Deployed in a Zhengzhou AI computing node, it demonstrates scalability for national supercomputing hubs like those supporting China's 'Eastern Data, Western Compute' initiative.

Quantifiable Benefits and Benchmarks

• Thermal Conductivity: >1,000 W/mK, versus copper's 400 W/mK.
• Cooling Efficiency: 80% uplift in heat transfer from modules.
• Performance Gain: 10% boost in chip compute due to lower temperatures.
• Energy Savings: Reduced fan/pump power, potentially cutting data center power usage effectiveness (PUE) from 1.2 to below 1.1.
• Lifespan Extension: Lower thermal cycling reduces failure rates by 20-30%.

Comparisons with alternatives like graphene or boron nitride highlight diamond-copper's edge in cost-performance: while pure diamond sheets cost thousands per square cm, composites drop to hundreds, competitive with enhanced copper foams.

China's Strategic Push for AI Self-Sufficiency

China's AI ambitions—targeting 300 exaflops by 2025 under the 14th Five-Year Plan—rely on domestic hardware amid US export controls on advanced GPUs. Overheating exacerbates chip shortages, as throttled systems underperform. NIMTE's work aligns with national priorities, reducing import dependence on materials from Japan and the US (e.g., Element Six's CVD diamonds).

Collaborations with firms like Huawei and Sugon accelerate commercialization. Universities such as Tsinghua and Shanghai Jiao Tong contribute via joint labs, training materials engineers for the 'diamond economy.'

Global Context and Competition

Worldwide, AI cooling races intensify. US firms like Akash Systems grow diamond films directly on GaN chips, dropping temps 20°C. Taiwan's TSMC explores diamond interposers for 3D stacking. Europe's IMEC tests diamond microchannels. Yet China's scale—producing 90% of synthetic diamonds—gives manufacturing edge.

Challenges persist: scaling particle uniformity, cost (diamonds ~$50/g vs copper $0.01/g), and recycling. Ongoing R&D at CAS addresses these via AI-optimized sintering.

Photo by Peijia Li on Unsplash

Role of Higher Education in Materials Innovation

China's universities play pivotal roles. Zhejiang University's materials science program partners with NIMTE, training PhD students in composite fabrication. Peking University models phonon transport via density functional theory (DFT). This ecosystem—over 300 materials-focused universities—fuels breakthroughs, with 40% of global diamond R&D papers from Chinese institutions in 2025.

Implications extend to education: new curricula in thermal engineering for AI, internships at CAS labs, preparing 1 million specialists by 2030.

Future Outlook and Actionable Insights

By 2030, diamond-copper could standardize in 50% of hyperscale AI clusters, slashing global data center energy (3% of electricity) by 15%. For researchers: focus on hybrid diamond-graphene. Industry: pilot composites in edge AI. Policymakers: subsidize CVD diamond fabs.

This NIMTE-CAS advance exemplifies how targeted materials research propels AI frontiers, ensuring sustainable high-performance computing.Read the full SCMP report on the deployment.