CoWoS and Advanced Packaging: How Chip Architecture Shapes Data Center Design

Updated December 11, 2025

December 2025 Update: TSMC demonstrating direct-to-silicon liquid cooling on CoWoS achieving 0.055°C/W thermal resistance at 2.6kW+ TDP on 3,300mm² interposers. NVIDIA securing 70%+ of TSMC CoWoS-L capacity for 2025. Blackwell GPU volumes increasing 20%+ quarterly toward 2M+ annual units. Advanced packaging becoming primary constraint in AI accelerator supply.

TSMC demonstrated Direct-to-Silicon Liquid Cooling integrated onto its CoWoS platform at the 2025 IEEE ECTC conference, achieving junction-to-ambient thermal resistance of 0.055 °C/W at 40 ml/s coolant flow—nearly 15% better than lidded liquid cooling with thermal interface materials.¹ The demonstration validated sustained operation above 2.6 kW TDP on a massive 3,300 mm² interposer supporting multiple logic dies and HBM stacks. Advanced packaging technology has evolved from a semiconductor manufacturing concern to a primary driver of data center power and cooling architecture.

NVIDIA secured over 70% of TSMC's CoWoS-L advanced packaging capacity for 2025, with Blackwell architecture GPU shipment volumes increasing more than 20% each quarter toward annual volumes exceeding 2 million units.² The capacity allocation reflects how advanced packaging has become the critical constraint in AI accelerator supply. Data center operators planning infrastructure investments must understand how packaging technology affects the systems they deploy, from power delivery requirements through cooling demands to physical form factors.

Understanding advanced packaging

Advanced packaging integrates multiple silicon dies into unified packages that function as single chips, enabling capabilities impossible with monolithic designs.

CoWoS technology explained

CoWoS (Chip-on-Wafer-on-Substrate) combines multiple dies on a silicon interposer, which then bonds to a package substrate.³ The silicon interposer features high-density metal interconnects and through-silicon vias (TSVs), providing ultra-high-bandwidth, low-latency data communication between dies. The result delivers improved power efficiency, thermal performance, and compact footprint critical for AI, HPC, and cloud workloads.

Unlike traditional single-chip packages, CoWoS enables heterogeneous integration combining SoCs, GPUs, and HBM memory stacks in a single package.³ The integration eliminates the bandwidth and latency penalties of communicating across package boundaries. Memory bandwidth that limits AI performance increases dramatically when HBM stacks sit millimeters from compute dies rather than across a PCB.

CoWoS variants

NVIDIA adopted CoWoS-L advanced packaging, integrating a redistribution layer (RDL) with a partial silicon interposer (LSI).² CoWoS-L enhances chip size and area by increasing transistor density, enabling stacking of more high-bandwidth memory. Compared to CoWoS-S and CoWoS-R technologies, CoWoS-L offers superior performance, higher yield, and better cost efficiency.

CoWoS-S (silicon interposer) uses a full silicon interposer spanning all dies. The approach provides the finest interconnect pitch but constrains package size to silicon interposer manufacturing limits. Current CoWoS-S packages reach approximately 2.5 reticle sizes.

CoWoS-R (RDL interposer) replaces the silicon interposer with an organic redistribution layer, reducing cost at the expense of interconnect density. The technology suits applications requiring large packages where full silicon interposers become prohibitively expensive.

Competitive technologies

Intel's EMIB (Embedded Multi-die Interconnect Bridge) connects chiplets using tiny silicon bridges embedded directly in the package substrate, eliminating the need for a large silicon interposer.⁴ The approach reduces both cost and thermal complexity compared to full interposer solutions. EMIB suits designs where dies communicate in pairs rather than requiring full mesh connectivity.

Intel's Foveros technology vertically stacks dies using through-silicon vias or direct copper bonding.⁴ The 3D stacking offers high interconnect density and heterogeneous node integration at the cost of more stringent thermal and yield considerations. Thermal management becomes especially challenging when heat-generating dies stack vertically.

TSMC's CoWoS-L remains the primary option for high-performance AI GPUs and HBM-heavy accelerators despite competitive alternatives.⁴ The technology's production maturity and proven performance at AI accelerator power levels make it the default choice for leading-edge designs.

Thermal implications

Advanced packaging concentrates heat generation in ways that challenge traditional cooling approaches.

Power density challenges

A 3,300 mm² CoWoS package dissipating 2.6+ kW represents power densities requiring sophisticated cooling beyond air cooling capabilities.¹ The power concentrates in compute dies that occupy a fraction of total package area, creating thermal hotspots that average package power density understates.

HBM stacks surrounding compute dies generate additional heat while requiring temperature control to maintain memory reliability. HBM specifications limit operating temperatures more strictly than logic dies tolerate. Cooling designs must address both peak logic die temperatures and distributed HBM thermal requirements.

The progression from 300W GPUs to 700W+ current generation and anticipated 1000W+ next-generation packages drives fundamental changes in data center thermal architecture. Air cooling approaches that handled previous generations cannot scale to current power levels without unacceptable acoustic or energy penalties.

Direct liquid cooling integration

TSMC's Direct-to-Silicon Liquid Cooling embeds microfluidic channels directly into silicon structure, bypassing thermal interface materials for near-zero thermal impedance.¹ The Si-Integrated Micro Cooler fusion-bonds to the chip's backside, creating intimate thermal contact that TIM-based approaches cannot match.

The technology enables sustained operation at power levels that would overwhelm lidded packages with external cold plates. Data centers deploying next-generation AI accelerators may require this level of thermal integration rather than retrofitting existing cooling to higher power loads.

Integration at the package level shifts cooling responsibility toward semiconductor manufacturers and system vendors rather than data center operators. Organizations specifying AI infrastructure should understand which thermal solutions their chosen systems employ and what facility requirements those solutions impose.

Facility cooling requirements

Liquid cooling at chip level still requires heat rejection at facility level. The thermal load moves from chip to coolant loop to data center cooling infrastructure. Facility designs must accommodate coolant distribution, heat exchangers, and ultimate heat rejection regardless of how efficiently chips couple to coolant.

High-density racks enabled by advanced packaging may concentrate 100+ kW in single rack positions. The concentration creates localized cooling demands that row-based or room-based approaches struggle to address. Rear-door heat exchangers, in-row cooling units, or direct-to-chip liquid cooling infrastructure becomes necessary.

Water supply and treatment requirements increase with liquid cooling deployment. Coolant quality affects both thermal performance and equipment longevity. Data centers must either provision water treatment or specify closed-loop systems that minimize water quality dependencies.

Power delivery considerations

Advanced packages require power delivery systems matching increased current demands and tighter voltage regulation requirements.

Voltage regulator placement

High-current delivery to advanced packages benefits from voltage regulators positioned near the package. The short distance reduces resistive losses and improves transient response when power demand changes rapidly. Board designs increasingly place VRMs immediately adjacent to GPU packages.

Current levels reaching hundreds of amperes at sub-1V voltages create challenging power distribution requirements. PCB layer counts and copper weights increase to carry current without excessive loss or temperature rise. Board design complexity and cost increase alongside package power.

Power delivery network (PDN) design affects both steady-state efficiency and transient stability. AI workloads exhibit rapid power transitions as batch computations start and complete. The PDN must supply current surges without voltage droops that cause errors.

Facility power infrastructure

Data center power infrastructure must accommodate both total power increases and power density increases. A rack requiring 100 kW needs electrical infrastructure few facilities provision by default. Busway capacity, PDU ratings, and branch circuit counts all require validation against actual deployment plans.

Power efficiency at facility level affects total cost of ownership significantly. Advanced packages achieving better performance per watt reduce cooling loads alongside compute costs. However, the benefit realizes only if facility infrastructure operates efficiently across the relevant power range.

Backup power systems face new challenges from high-density AI infrastructure. UPS and generator capacity must match peak facility load while providing runtime adequate for graceful shutdown. The capital cost of backup power scales with protected load, increasing infrastructure investment.

Physical form factors

Advanced packaging affects physical form factors throughout the system hierarchy.

Package dimensions

Interposer size constraints limit how many dies and HBM stacks fit in a single package. Current CoWoS packages span multiple reticle sizes, approaching limits of manufacturing equipment. Package size growth enables more capability per package but challenges socket and board designs.

Package height increases with HBM stack count. Each HBM stack adds vertical dimension that socket and heatsink designs must accommodate. System designs balancing package count against height constraints make different tradeoffs than previous generations.

Ball grid array (BGA) patterns for advanced packages include thousands of connections for power, signal, and ground. Socket designs must reliably contact all connections while allowing package removal for service. The mechanical engineering of high-pin-count sockets affects system serviceability.

Board and system design

Motherboard designs for advanced packages dedicate substantial area to power delivery, memory channels, and high-speed interconnects. The board real estate required per package may limit how many packages fit on a single board. System designs choose between fewer large packages or more smaller packages based on workload requirements.

Server form factors evolve to accommodate advanced package requirements. Height constraints in standard 1U and 2U form factors conflict with cooling solutions for high-power packages. Purpose-built AI server designs prioritize thermal performance over rack density.

Rack power density increases as packages grow more capable within constant form factors. Facilities designed for 10-15 kW per rack find AI infrastructure requiring 50-100+ kW per rack. The mismatch between installed infrastructure and deployment requirements creates costly retrofit situations.

Supply chain implications

Advanced packaging capacity constraints affect AI infrastructure availability and planning horizons.

Capacity allocation

TSMC plans to expand eight CoWoS facilities in the short term, including facilities at ChiaYi Science Park and acquired Innolux locations.⁵ Semiconductor equipment suppliers confirm that TSMC and non-TSMC players including ASE, Amkor, and UMC are accelerating advanced packaging capacity expansion. The expansions address demand that currently exceeds supply.

Major customers like NVIDIA secure capacity allocations well in advance, with current booking extending into 2026-27.⁶ Organizations depending on AI accelerators should understand that their vendors' packaging allocation affects delivery timelines as much as chip manufacturing capacity.

Supply constraints create pricing pressure favoring customers with scale and established vendor relationships. Smaller deployments may face longer lead times or premium pricing compared to hyperscale buyers with allocation commitments.

Multi-source considerations

Packaging technology differences between TSMC, Intel, and Samsung affect chip designs and system architectures. Components designed for one packaging technology may not easily migrate to alternatives. Organizations evaluating AI infrastructure should consider supply chain resilience alongside performance specifications.

OSAT (Outsourced Semiconductor Assembly and Test) providers offer packaging services that may provide alternatives to foundry-captive packaging. The ecosystem provides capacity beyond leading foundry offerings, potentially improving supply resilience.

Long-term infrastructure planning should account for packaging technology evolution. Current CoWoS implementations will evolve, and designs optimized for today's packaging may not take full advantage of future capabilities. Architecture decisions that maintain upgrade paths provide flexibility as technology evolves.

Data center planning guidance

Understanding advanced packaging implications improves infrastructure planning for AI deployments.

Requirements assessment

Organizations planning AI infrastructure should assess requirements across power, cooling, and physical dimensions. Specifications based on previous-generation systems may substantially underestimate current requirements. Vendor engagement early in planning identifies requirements that catalog specifications may not fully convey.

Introl's network of 550 field engineers support organizations planning and deploying AI infrastructure accounting for advanced packaging requirements.⁷ The company ranked #14 on the 2025 Inc. 5000 with 9,594% three-year growth, reflecting demand for professional infrastructure services.⁸

Facility readiness

Facility assessments should evaluate power and cooling headroom for anticipated deployments. Marginal facilities may require upgrades before AI infrastructure deployment. The lead time for facility modifications often exceeds equipment procurement timelines.

Deployments across 257 global locations require consistent facility assessment practices regardless of geography.⁹ Introl manages deployments reaching 100,000 GPUs with over 40,000 miles of fiber optic network infrastructure, providing operational scale for organizations deploying AI across distributed facilities.¹⁰

Technology monitoring

Advanced packaging technology continues evolving rapidly. Organizations should monitor technology developments affecting future infrastructure requirements. Planning that assumes static technology misses both opportunities and challenges that evolution creates.

Decision framework: infrastructure planning for advanced packaging

Facility Readiness Assessment:

GPU Generation	Package TDP	Cooling Requirement	Power/Rack	Minimum Facility Spec
Ampere (A100)	400W	Air or liquid	20-40 kW	Standard enterprise DC
Hopper (H100)	700W	Liquid recommended	40-80 kW	Liquid-ready DC
Blackwell (B200)	1000W+	Direct liquid	80-150 kW	Purpose-built AI DC
Blackwell Ultra	1500W+	Direct-to-silicon	150-300 kW	Next-gen AI DC

Cooling Technology Selection:

If Your Deployment Is...	Choose	Rationale
<40kW per rack	Air or rear-door heat exchangers	Standard infrastructure sufficient
40-80kW per rack	Cold plate liquid cooling	Retrofit existing facilities
80-150kW per rack	Direct-to-chip liquid cooling	Purpose-built required
>150kW per rack	Direct-to-silicon liquid cooling	Wait for TSMC integration or specialized vendors

Supply Chain Planning:

Timeframe	Action	Considerations
Now	Assess current facility capacity	Power, cooling, physical space
6-12 months	Engage GPU vendors on allocation	Lead times extend 12-24+ months
12-24 months	Plan facility upgrades	Cooling retrofits, power expansion
24+ months	Consider purpose-built facilities	Greenfield for next-gen GPUs

Key takeaways

For facilities engineers: - TSMC demonstrated 0.055°C/W thermal resistance with direct-to-silicon cooling—15% better than lidded - 3,300mm² interposers at 2.6kW+ TDP require liquid cooling—air cooling insufficient - HBM stacks have stricter temperature limits than logic dies—cooling must address both - Coolant quality affects equipment longevity—plan water treatment or closed-loop systems

For infrastructure architects: - NVIDIA secured 70% of TSMC's 2025 CoWoS-L capacity—supply constrained through 2027 - 100kW+ racks require electrical infrastructure most facilities don't provision by default - Package size approaching multi-reticle limits—socket/heatsink designs must accommodate - CXL memory pooling emerging for capacity expansion—consider compatibility in new deployments

For strategic planners: - Packaging, not chip manufacturing, is now the supply constraint for AI accelerators - TSMC expanding 8 CoWoS facilities—but demand exceeds supply through 2026-27 - Multi-source packaging strategies may improve resilience—Intel EMIB, OSAT providers - Facility decisions now must plan for 10-year horizon of increasing power density

The relationship between chip architecture and data center design will only strengthen as power levels continue increasing. Organizations that understand this relationship make better infrastructure investments than those treating chips as interchangeable commodities. Advanced packaging has become data center infrastructure.

References

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SEO Description (155 characters): Understand how CoWoS and advanced chip packaging affect data center infrastructure. Cover thermal design, power delivery, and facility requirements for AI GPUs.

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