What is the best solder paste to choose for AI chip packaging?

In AI chip packaging, the selection of suitable solder paste needs to take into account the chip power density, packaging process, reliability requirements and thermal performance.Based on industry technology trends and material characteristics, the following solder paste types are more advantageous:

I. Deepening analysis of alloy composition selection

Comparison of SAC305 and SAC405

SAC405: silver content is increased by 1%, mechanical strength is increased by about 15%, but the cost is increased by 20%-30%, which is suitable for scenarios with stringent reliability requirements (e.g., data centre GPUs).

SAC305: Balanced cost and performance, suitable for most AI chips, especially for devices that require long-term stable operation.

Alternative: SAC-X series (e.g. SAC-Q with trace Ni, Sb/Bi), optimised thermal fatigue resistance through alloying, cost between SAC305 and SAC405.

Low temperature solder paste (Sn-Bi) risk control

Brittleness improvement: Adding trace amounts of In (Indium) or Ag (Silver) can improve ductility, but the amount of addition (<3%) needs to be controlled to avoid melting point fluctuations.

Bottom filler adhesive: epoxy resin based adhesive is recommended, curing temperature should be lower than 120°C to avoid secondary thermal damage.

II. Particle Size (Type) Optimisation Suggestions

Type 4-Type 6 applicable scenarios

Type 4: Suitable for BGA and LGA packages with a pitch of 0.4-0.6mm, with a printing accuracy of ±10μm.

Type 5: Applicable to Flip Chip, μBump with pitch 0.3-0.4mm, printing accuracy ±10μm.

Type 6-8: Applicable to Flip Chip, μBump with pitch <0.3mm, printing accuracy ±5μm.

Trend: As the integration of AI chips increases, the demand for Type 6 and above will continue to grow.

New particle technology

Nanoscale Powder (Ultra-micro solder powder for T9 and T10 models): particle size <5μm, suitable for HBM stacking with pitch <20μm, but requires special equipment (e.g. laser transfer printing) support.

Sphericity control: Particle sphericity >95% reduces printing performance and improves solder joint consistency.

III. Technical Details of Flux Types

No-Clean Flux (No-Clean)

Activity level: It is recommended to choose M (medium activity) or L (low activity) to avoid corrosion of sensitive components by high activity fluxes.

Residue control: An ionic contamination test (e.g. IPC-TM-650 2.3.25) is required to ensure that the halide equivalent is <1.5 μg/cm².

Water Soluble Flux (OR)

Cleaning process: Deionised water + ultrasonic cleaning at 50-60°C for 3-5 minutes is recommended.

Environmental requirements: need to comply with RoHS 2.0 standards, avoid halogenated fluxes.

IV. Thermal performance optimisation technology path

Thermal conductivity enhancement technology

Metal nanoparticles: Cu nanoparticles (<50nm) can enhance thermal conductivity by 10%-15%, but the amount of addition (<1%) needs to be controlled to avoid fluctuations in melting point.

Composite material: SAC305+Al₂O₃ nanoparticles (<100nm) can achieve thermal conductivity of 60-80 W/m-K, which is suitable for high power AI chips.

Thermal fatigue resistant technology

Trace Ni addition: Ni content of 0.05%-0.3% inhibits IMC growth and extends solder joint life.

Alloying design: SAC-X series optimises thermal cycling performance by adjusting Ag/Cu ratio.

V. Key Considerations for Process Compatibility

Reflow temperature profile optimisation

Temperature range: Recommended peak temperature of 240-250°C for 60-90 seconds to ensure full fusion of solder joints.

Temperature rise rate: control at 2-3°C/s, to avoid thermal shock.

Advanced Packaging Technology Adaptation

Heterogeneous integration: recommend layered soldering (e.g. SAC305+Sn-Bi combination), high temperature soldering of the core chip first, then low temperature soldering of sensitive structures.

3D IC stacking: When multiple reflow is required, give priority to alloys with strong thermal decay resistance (e.g. SAC-X series) and optimise the temperature profile to reduce thermal damage.

VI. Practical application cases to supplement

High performance AI chips (e.g. NVIDIA/AMD)

Solution: SAC305 (Fitech FR209) + Type 4/5 + no-clean flux

Advantage: High silver content to resist thermal fatigue, fine particles to fit micro-pitch, less residue compatible with subsequent processes.

Verification: Passed temperature cycling test (1000 times, -40°C~125°C), solder joint void rate <5%.

In-vehicle/server AI chip

Solution: SAC405+high-activity flux

Advantage: Extreme temperature tolerance, strong wettability ensures reliable connection.

Verification: Perform drop test (1.5m height, 3 times on each of 6 sides), no solder joint cracking.

Consumer Electronics (Edge AI)

Solution: SnCu or Sn-Bi+Type 4

Advantage: Balance cost and performance, low-temperature process reduces thermal damage.

Verification: Shear force test (20N or more), solder joint strength up to standard.

VII. Validation Steps Additional Suggestions

Process Test

Printing test: Verify solder paste release, printing consistency (e.g. CTQ indicators: bridging rate <0.5%, slump rate <10%).

Reflow simulation: Analyse temperature distribution through thermal imaging to ensure uniformity.

Reliability testing

Temperature cycling: Perform 1000 cycles (-40°C~125°C) to monitor solder joint resistance changes.

Drop test: 1.5m height, 3 times on each of the 6 sides to check for solder joint cracks.

Shear test: Test the strength of the solder joint to ensure >20N.

Micro-analysis

SEM/EDS: Check IMC layer thickness (1-3μm), uniformity and voiding (<5%).

X-ray inspection: Analyse internal defects in the welded joint to ensure no voids or cracks.

VIII. Summary and recommendations

IX. Future Trends

Nanomaterial application: graphene, carbon nanotubes, etc. to enhance thermal conductivity and reduce thermal resistance.

Hybrid bonding compatibility: develop composite materials that simultaneously support Cu-Cu hybrid bonding and solder paste soldering.

AI-driven design: use machine learning to optimise solder paste composition and process parameters.

Through comprehensive consideration of alloy composition, particle size, solder type, thermal performance and process compatibility, the electrical and thermal performance and reliability of AI chips can be significantly improved to meet the demand for high computing power and low power consumption.