Thermal Management Solutions: Designing Heat Sinks for SOM and Edge Computing
Edge computing devices pack increasing processing power into smaller form factors, creating thermal challenges that directly impact performance and reliability. System-on-module designs amplify these challenges by concentrating high-power-density components into compact packages. Without proper thermal management solutions, even the most advanced processors throttle performance or fail prematurely.
This comprehensive guide walks you through heat sink design principles specifically for SOM and edge computing applications, combining theoretical foundations with practical implementation strategies used by Think Robotics in production deployments.
Understanding Thermal Challenges in Edge Computing
Modern edge computing hardware generates substantial heat in confined spaces. A typical AI accelerator SOM might dissipate 15 to 25 watts continuously during inference workloads, while high-performance computing modules can exceed 40 watts. This power density rivals desktop processors but must be cooled in much smaller enclosures, often without active cooling fans.
The thermal design challenge extends beyond simply removing heat. Edge devices often operate in uncontrolled environments where ambient temperatures reach 50 to 60 degrees Celsius. Industrial applications expose equipment to dust, vibration, and humidity, which render traditional cooling approaches ineffective. Many deployments require fanless operation for reliability and noise reduction.
SOM thermal design becomes particularly complex because the module manufacturer provides limited thermal interface options. The carrier board designer must create effective heat paths from the SOM to external heatsinks while managing thermal expansion mismatches and maintaining mechanical reliability.
According to research from the Massachusetts Institute of Technology on embedded thermal management, inadequate cooling reduces processor lifespan by 50 percent for every 10 degrees Celsius increase in junction temperature above specifications. Proper heat sink design directly impacts product reliability and total cost of ownership.
Thermal Fundamentals for Heat Sink Design
Effective heatsink design requires understanding three heat transfer mechanisms. Conduction carries heat from the processor through the thermal interface material to the heatsink base. Convection transfers heat from the heatsink fins to the surrounding air. Radiation contributes minimally at typical operating temperatures but becomes relevant at high temperatures.
The thermal resistance concept provides a framework for quantitative analysis. The total thermal resistance from the processor junction to the ambient air equals the sum of the individual resistances through each interface and material. Lower total resistance means lower operating temperatures for a given power dissipation.
Thermal resistance calculation begins with the processor's thermal specifications. If a processor dissipates 20 watts with a maximum junction temperature of 85 degrees Celsius in 40-degree ambient conditions, the total allowable thermal resistance is 2.25 degrees Celsius per watt. This budget must cover the thermal interface between the processor and the heatsink, as well as the heatsink itself.
Practical designs typically allocate 0.3 to 0.5 degrees per watt to the thermal interface materials, leaving 1.75 to 1.95 degrees per watt for the heatsink thermal resistance. This target drives all subsequent design decisions about material selection, fin geometry, and surface area.
Material Selection for Embedded System Cooling
Aluminum dominates heatsink applications due to its favorable combination of thermal conductivity, weight, cost, and machinability. Aluminum 6061 and 6063 alloys have thermal conductivities of 170-200 W/m · K, adequate for most applications while providing good mechanical properties.
Copper has a superior thermal conductivity of 400 W/m·K, nearly double that of aluminum. However, copper weighs three times as much as aluminum and costs significantly more. Copper makes sense for high-power-density applications where size constraints demand maximum thermal performance, regardless of cost.
The decision between copper and aluminum heatsinks often results in hybrid designs. A copper base plate contacts the heat source, quickly conducting heat across its surface. Aluminum fins extend from the copper base, providing a large surface area for convection at a lower cost and weight. This combination optimizes both conduction and convection efficiency.
Surface finish significantly impacts thermal performance. Anodized aluminum provides corrosion resistance but slightly reduces thermal conductivity at interfaces. Black anodizing improves radiative heat transfer, which is relevant for high-temperature applications. Chemical conversion coatings offer a compromise between protection and thermal performance.
The National Institute of Standards and Technology's research on thermal interface performance emphasizes that surface flatness matters more than material choice for contact resistance. A well-machined aluminum surface with proper thermal paste outperforms a rough copper surface.
Designing Fin Geometry for Passive Cooling Solutions
Fin design balances competing requirements. More fins increase surface area for heat dissipation but reduce airflow between fins, degrading convection efficiency. Taller fins provide more surface area but increase thermal resistance along their length. Optimal geometry depends on available space, airflow conditions, and power dissipation requirements.
Natural convection applications, typical in fanless edge computing devices, require wider fin spacing than in forced-air designs. Natural convection generates weak airflow driven by density differences as air heats. Fin spacing below 8 to 10 millimeters restricts this airflow, reducing effectiveness. Spacing of 10 to 15 millimeters typically optimizes natural convection performance.
The fin height for natural convection should be 30 to 50 millimeters for horizontal fins or 50 to 80 millimeters for vertical fins in ideal orientations. Beyond these heights, thermal resistance through fin length outweighs additional surface area benefits. Fin thickness typically ranges from 1.5 to 3 millimeters, balancing manufacturing cost against thermal performance.
Forced-air cooling with fans or system-level airflow allows tighter fin spacing of 3 to 6 millimeters. Higher airflow velocity overcomes flow restriction, making dense fin arrays effective. However, dust accumulation in tight fin spacing creates maintenance issues for industrial deployments. Design must balance thermal performance with long-term reliability.
Think Robotics specializes in thermal design services that optimize heatsink geometry for specific application requirements, whether for fanless industrial computers or high-airflow server environments.
Thermal Interface Materials and Application
The interface between the processor and the heatsink creates significant thermal resistance despite its microscopic thickness. Surface imperfections create air gaps that insulate rather than conduct heat. Thermal interface materials fill these gaps, dramatically improving heat transfer.
Thermal paste remains the most common interface material for removable heatsinks. Modern thermal pastes achieve 3-8 W/m·K conductivity when appropriately applied. A thin layer of 50 to 100 micrometers provides optimal performance. Excess paste acts as insulation rather than improving conductivity. The key is filling surface imperfections without creating thick barriers.
Thermal pads offer easier assembly and rework compared to paste but provide slightly lower performance. Pads work well for lower power applications or where assembly repeatability matters more than absolute thermal performance. Pad thickness should match the gap between surfaces, typically 0.5 to 3 millimeters for SOM applications.
Phase change materials start as solid pads that liquefy at operating temperature, flowing to fill surface imperfections. This combines paste performance with pad convenience. However, phase change materials require careful temperature profiling during initial operation to ensure proper flow and surface wetting.
The thermal paste application technique significantly impacts results. Apply a small amount at the center of the processor surface, then allow the heatsink mounting pressure to spread the paste. Avoid pre-spreading the paste, as this can trap air bubbles. For large surfaces, a thin crosshatch pattern works better than center application.
Thermal Simulation and Validation
Computational fluid dynamics simulation predicts heatsink performance before manufacturing prototypes; modern thermal simulation software models conduction through materials, convection from surfaces, and radiation effects. Simulation identifies hot spots, validates design margins, and optimizes fin geometry.
The simulation workflow begins with accurate 3D models of all thermal components, including the processor, thermal interface, heatsink, and surrounding enclosure. Material properties must reflect actual parts, not handbook values. Boundary conditions define the ambient temperature, airflow (if present), and power dissipation.
Mesh generation divides the model into small elements for numerical analysis. Finer mesh provides more accurate results but requires longer computation time. Focus fine-mesh coverage on critical areas such as thermal interfaces and fin bases, and use a coarser mesh in less critical regions.
Solution accuracy depends on proper model setup. Common errors include incorrect thermal-conductivity values, oversimplified geometries that miss thermal bottlenecks, and unrealistic boundary conditions. Validate simulation results against known benchmark cases before trusting predictions for new designs.
Physical testing remains essential even with good simulation. Build prototypes with thermocouples embedded at critical locations, including the processor case, heatsink base, fin tips, and ambient air. Operate at worst-case power levels in heated chambers simulating maximum ambient conditions. Compare measured temperatures against simulation predictions and specifications.
Practical Design Process for SOM Applications
Designing heatsinks for system-on-module carrier boards follows a structured process. Start by gathering thermal specifications from the SOM datasheet, including maximum junction temperature, typical and maximum power dissipation, and package thermal resistance. Identify the thermal interface provided by the SOM, whether exposed die, metal lid, or thermal pad.
Calculate the required heatsink thermal resistance using the junction temperature limits, the expected ambient temperature, and the power dissipation. Add margin for aging, dust accumulation, and manufacturing tolerances. A design targeting 70 percent of the maximum junction temperature provides a good reliability margin.
Select heatsink material and rough geometry based on space constraints and cooling approach. For natural convection in a 60 millimeter height constraint, start with vertical fin arrays 40 to 50 millimeters tall with 10 to 12 millimeter spacing. Size the base to match or slightly exceed the processor footprint.
Calculate the theoretical thermal resistance using natural-convection heat-transfer correlations. Compare against requirements and iterate on fin height, spacing, and base thickness until meeting targets. This analytical phase narrows the design space before detailed simulation.
Model the design in thermal simulation software, including an accurate representation of the SOM, carrier board, and enclosure. Run simulations at maximum power and ambient temperature. If results exceed temperature limits, increase surface area by adding fins, increasing height, or expanding base size.
Think Robotics offers prototyping services that accelerate this design cycle, producing machined aluminum prototypes for thermal validation before committing to production tooling.
Manufacturing Considerations and Cost Optimization
Heatsink manufacturing method dramatically impacts cost and performance. Extrusion works well for long, uniform fin arrays and offers the lowest price for moderate to high volumes. However, extrusion limits fin complexity and requires fins to be parallel to the extrusion direction.
CNC machining creates complex geometries, including crossed fins, variable spacing, and integrated mounting features. Machining suits low to medium volumes and prototyping. Material waste and machining time make this approach expensive for high-volume production.
Die casting allows complex three-dimensional fin geometries at high volumes. Tooling costs are substantial, but per-part costs drop significantly above 10,000 units. Die-cast aluminum can incorporate mounting bosses, alignment features, and complex curves that are impossible with extrusion.
Bonded fin construction attaches separately formed fins to a base plate. This allows optimal material selection, using copper bases with aluminum fins. Assembly costs limit bonded designs to applications where performance justifies the expense.
Surface area-to-volume ratio determines material efficiency. Maximizing this ratio improves performance for a given material cost. However, complex geometries that maximize the ratio often increase manufacturing cost. Balance thermal performance against production economics based on volume and application requirements.
Advanced Techniques for Edge Computing Cooling
Heat pipes extend thermal design capability beyond simple conduction—sealed tubes containing a working fluid transport heat with extremely low thermal resistance. Heat pipes move heat from concentrated sources to larger heatsink areas, enabling better heat spreading before convection.
Vapor chambers function like flat heat pipes, spreading heat across two-dimensional planes. A vapor chamber base plate under a processor distributes heat evenly to all heatsink fins, improving performance compared to solid bases, where distant fins remain cooler.
Two-phase cooling using a liquid coolant circulated through cold plates achieves the highest performance but adds complexity, cost, and potential failure modes. Reserve two-phase cooling for applications where air cooling cannot meet requirements, typically above 100 watts in compact spaces.
Thermal design increasingly uses computational optimization algorithms. These tools iterate thousands of geometry variations, using simulation to evaluate each and converging on optimal designs that human designers might miss. Optimization handles complex multivariable problems, such as minimizing mass while meeting temperature limits.
Conclusion
Effective heat sink design combines thermal physics fundamentals with practical engineering judgment about manufacturing, cost, and reliability. Edge computing and SOM applications present unique challenges due to high power density, compact packaging, and often harsh operating environments.
Success requires a systematic approach that starts with precise thermal requirements, careful material selection, optimized fin geometry, proper thermal interface implementation, and validation through both simulation and testing. Each application presents unique constraints that shape design decisions.
Think Robotics specializes in thermal management solutions for demanding embedded applications. Our thermal design services combine analytical modeling, CFD simulation, and practical manufacturing experience to deliver heatsinks that meet performance targets while controlling cost and ensuring reliability. Whether you need natural convection cooling for fanless edge devices or high-performance forced air designs, we develop solutions optimized for your specific requirements.
The convergence of higher processing power and smaller form factors will continue pushing thermal design capabilities. Engineers who master these fundamentals and stay current with advancing materials and techniques will enable the next generation of edge computing devices that bring AI and advanced processing to increasingly challenging deployment environments.
