As the computing power density of AI chips has exceeded 500W/cm², traditional materials face multiple challenges such as packaging failure and heat dissipation bottlenecks under extreme working conditions. With excellent dielectric properties, thermal management capabilities, and precision processing adaptability, silicone rubber and silicone oil have become the "material vanguards" in AI manufacturing. From chip-level packaging to liquid cooling systems, from sensor protection to flexible circuits, they are solving key problems in large-scale production of AI hardware through molecular-level innovation.

一、Material Challenges in AI Chip Manufacturing

（一）Packaging Dilemmas under Extreme Conditions

AI chips with 7nm or smaller processes operate at temperatures exceeding 150°C, accompanied by high-frequency electromagnetic interference, pushing traditional packaging materials to face three major bottlenecks:

Thermal stress failure: The thermal expansion coefficient of ordinary epoxy resin packaging mismatches with silicon chips, causing micro-cracks in 90% of cases during cycles from -40°C to 125°C.

Surge in dielectric loss: Traditional materials have a dielectric loss tangent > 0.05 in frequency bands above 10GHz, increasing signal delay in AI chips by 15%.

Inadequate heat dissipation efficiency: Conventional packaging materials have thermal conductivity < 1W/m・K, failing to dissipate heat from high-computing-power chips in time.

Silicone rubber and silicone oil break through these limits via molecular design: The thermal expansion coefficient of silicone rubber can be adjusted to 20-50ppm/°C, narrowing the gap with silicon chips (3ppm/°C) by 40%; silicone oil maintains dielectric loss < 0.001 at 100GHz, making it an ideal medium for high-frequency signal transmission.

（二）Precision Processing Demands of Advanced Packaging

3D stacked chip packaging requires materials with nanoscale filling capability, which silicone rubber achieves through special processes:

Low-viscosity potting: The viscosity of addition-cure silicone rubber can be reduced to below 500cSt, capable of filling micro-channels narrower than 0.1mm.

Stress-free curing: Room-temperature vulcanizing silicone rubber has a curing shrinkage rate < 0.1%, avoiding mechanical stress on precision chips.

Surface flatness: The surface roughness of silicone rubber packaging layers is Ra<1μm, meeting optical inspection requirements for advanced packaging.

二、Silicone Rubber: Precision Packaging and Structural Protection for AI Chips

（一）Technological Breakthroughs in Chip-Level Packaging

Silicone rubber dedicated to AI chips achieves performance leaps through triple molecular optimization:

Crosslinking network regulation: Using a platinum catalysis system improves the uniformity of crosslinking density in silicone rubber to ±5%. After an AI server's GPU packaging adopted this material, thermal resistance decreased by 35%.

Ceramic filler composite: Silicone rubber added with boron nitride nanosheets has a thermal conductivity of 5W/m・K while maintaining volume resistivity > 1×10^15Ω・cm.

Interface modification technology: The peel strength between silicone rubber and copper lead frames reaches 5N/mm, with strength retention > 90% after aging for 1,000 hours in an 85°C/85% RH environment.

In the liquid-cooled packaging of NVIDIA A100 chips, silicone rubber seals successfully passed 1,000 thermal cycles (-40°C to 150°C) testing, improving packaging reliability to 99.99%.

（二）Environmental Protection for AI Sensors

Silicone rubber addresses multiple challenges in AI sensors:

Weather resistance protection: The silicone rubber protective cover for outdoor AI cameras has a yellowing index < 5 after 5,000 hours of UV aging testing.

Shock buffering: Silicone rubber added with hollow glass microspheres has an impact absorption capacity of 100J/m². After a self-driving car's lidar used this material, its vibration resistance increased by 4 times.

Anti-condensation design: Hydrophobically modified silicone rubber has a surface contact angle > 110°, suppressing condensation during sudden temperature changes to ensure sensor signal stability.

三、Silicone Oil: Thermal Management and Fluid Wisdom in AI Systems

（一）Heat Dissipation Revolution of Immersion Liquid Cooling

Silicone oil demonstrates unique advantages in immersion liquid cooling for AI data centers:

Wide-temperature stability: Perfluorinated silicone oil has a viscosity change rate < 20% in the range of -60°C to 200°C. After a supercomputing center adopted this silicone oil, the PUE value dropped to 1.08.

High reliability: Silicone oil has volume resistivity > 1×10^16Ω・cm and breakdown voltage > 50kV, ensuring electrical safety under high voltage.

Low toxicity advantage: RoHS-compliant silicone oil has a biological toxicity index (BTI) < 5, meeting environmental protection requirements for data centers.

（二）Precision Lubrication for AI Equipment

Silicone oil plays a key role in precision components like AI robot joints:

Long-life lubrication: Hydroxyl-terminated silicone oil has an oxidation induction period > 5,000 hours. After an AI robotic arm used this silicone oil, the maintenance cycle extended from 3 months to 2 years.

Low volatility: Phenyl silicone oil has a volatile content < 0.1%, preventing contamination of precision optical components in cleanroom environments.

Dynamic response: Silicone oil added with magnetorheological particles can adjust viscosity in real time under magnetic fields, suitable for adaptive damping systems in AI equipment.

四、Future Material Innovation Directions in AI Manufacturing

（一）R&D of Intelligent Response Packaging Materials

Researchers are developing temperature-stress dual-response silicone rubber:

Self-healing crosslinking network: Silicone rubber with disulfide bonds can achieve 80% damage repair through heating (80°C) when micro-cracks occur.

Signal sensing function: Embedding carbon nanotubes in silicone rubber makes the material's resistance change rate linearly related to thermal stress, enabling real-time monitoring of packaging status.

（二）Performance Breakthroughs of AI-Dedicated Silicone Oil

Through molecular design optimization, new silicone oils achieve performance leaps in AI scenarios:

Ultra-low dielectric loss: Fluorinated polysiloxane has a dielectric constant as low as 2.2 at 100GHz, meeting the high-frequency requirements of AI chips in the 6G era.

Application of quantum tunneling effect: Silicone oil added with nano-silver wires can conduct instantly under high shear rates, used for overcurrent protection in AI equipment.

（三）AI-Material Collaborative Design

Cross-innovation between machine learning and materials science is emerging:

Data-driven R&D: Using neural networks to predict the formulation-performance relationship of silicone rubber, an enterprise shortened the new material R&D cycle from 18 months to 6 months via this method.

Intelligent processing systems: Machine vision-based precision dispensing technology for silicone rubber achieves ±5μm coating accuracy, suitable for 3D IC packaging.

Digital twin models: Establishing virtual simulation platforms for silicone oil cooling systems to predict AI equipment performance under extreme conditions in advance.

From nanoscale chip packaging to megawatt-level data centers, silicone rubber and silicone oil are driving the AI manufacturing revolution through material innovation. They are not only the "escorts" for increased computing power density but also the "catalysts" for miniaturization and low power consumption of AI hardware. As AI technology evolves toward general intelligence, these silicon-based materials will create more miracles in frontier fields such as quantum computing chips, brain-computer interface hardware, and autonomous robots—providing key material support for large-scale AI applications and helping human intelligent civilization advance to new heights.



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