Executive Interview with James Huang of AlChip

I had a chance sit down with James Huang, Director of Engineering at Alchip Technologies, to discuss the company’s recent multi-die packaging achievements and learn about Alchip’s next steps in pushing the boundaries of innovation for next generation AI ASIC.

James is acknowledged as a leading light in advanced node ASICs, based on his 25 years of SoC design and implementation experience. Prior to Alchip, he held key engineering and technical management positions at Simplex Solutions and Cadence Design Systems, Inc.

AI and HPC designs are pushing advanced packaging into the mainstream. From Alchip’s perspective, what is driving customer demand for TSMC CoWoS-based ASIC solutions now?

AI has fundamentally changed the design priorities for advanced silicon. A few years ago, advanced packaging was viewed as an optimization for a limited number of high-end applications. Today, it has become an architectural requirement for many AI and HPC designs.

The primary reason is that compute performance is no longer scaling fast enough on its own. Our customers tell us they need to combine multiple compute chiplets with HBM and high-speed I/O, while staying within practical limits for power, yield, and manufacturability. CoWoS provides a mature platform for achieving that level of integration.

Another important trend is that more companies, including hyperscalers and AI startups, are developing custom silicon. They are looking for differentiated architectures rather than off-the-shelf solutions. Advanced packaging is one of the key enablers of that differentiation.

For Alchip, this aligns closely with one of our core strengths: delivering complex custom ASICs through close collaboration with customers and ecosystem partners.

Where does Alchip see CoWoS fitting within the broader custom ASIC design flow, especially for AI accelerators, networking processors, and other high-performance designs?

We don’t see CoWoS as a packaging technology that is added at the end of a project. We see it as an integral part of the system architecture. For AI accelerators, networking processors, and HPC devices, packaging decisions influence many other aspects of the design, including die partitioning, floor planning, memory architecture, power delivery, thermal management, and verification.

That is why successful CoWoS programs require silicon and package co-design from the earliest planning stages. Our engineering teams work closely with foundry, packaging, IP, and EDA partners to ensure these decisions are made holistically, rather than sequentially.

Alchip has experience with both CoWoS-S and CoWoS-R. Can you summarize the company’s track record with these technologies and the types of customer programs they have supported?

Over the past several years, Alchip has participated in multiple advanced-node ASIC programs using both CoWoS-S and CoWoS-R technologies across AI, HPC, and networking applications.

While we can’t discuss customer-specific projects, these engagements have helped us build deep experience in silicon-package co-design, HBM integration, power integrity, thermal optimization, and manufacturing collaboration.

Each successful project strengthens our internal methodologies and increases our confidence in supporting increasingly complex heterogeneous integration platforms.

For readers who follow advanced packaging closely, how would you compare the design considerations for CoWoS-S versus CoWoS-R? Where does each technology tend to fit best?

These technologies address different optimization points:

CoWoS-S is based on a silicon interposer. It offers the highest interconnect density and is optimized for bandwidth-intensive applications, such as AI training accelerators with multiple HBM stacks.

CoWoS-R uses redistribution layers. It provides greater flexibility and cost advantages for designs that do not require ultra-high routing density.

Rather than viewing them as competing technologies, we see them as complementary options. The optimal choice depends on the device architecture, bandwidth requirements, package size, and cost objectives.

What are the most important front-end design decisions that influence success in a CoWoS-based ASIC program?

One of the most important lessons we have learned is that architectural decisions made early in the project have a disproportionate impact on overall program success.

These decisions include chiplet partitioning strategy, HBM organization, die size optimization, power budgeting, and package selection.

Each of these choices influences yield, manufacturability, verification complexity, and ultimately time-to-market.

Investing sufficient effort during the architecture phase can significantly reduce downstream design iterations.

What are the key architectural tradeoffs designers should evaluate when considering chiplet partitioning and memory integration?

There is no single optimal partitioning strategy.

Customers need to balance multiple considerations, including bandwidth versus latency, die size versus yield, process-node optimization, power efficiency, and package complexity.

Similarly, memory integration should be evaluated as part of the overall system architecture, rather than as an isolated component.

Our role is to evaluate these tradeoffs objectively and identify the solution that best fits the customer’s product goals.

What are the major power-delivery challenges in large CoWoS designs today?

Power density continues to increase rapidly, particularly in AI accelerators.
Today’s large multi-chip packages require careful coordination across silicon, package, and board design to maintain stable power delivery, minimize IR drop, and preserve signal integrity.

One trend we see clearly is that power delivery is becoming a system-level challenge, rather than only a chip-level challenge.

This reinforces the importance of cross-domain collaboration throughout the design process.

Thermal performance is another major design constraint. How early does thermal analysis begin, and what tradeoffs does it create in die placement, floor planning, package selection, and system-level design?

Thermal considerations should be addressed much earlier in the design process than many people think.

For advanced AI ASICs, thermal analysis starts during architectural planning because chiplet placement, high-bandwidth memory arrangement, power distribution, and package selection all strongly influence the cooling strategy. At this stage, engineers can evaluate how each architectural choice affects heat generation, heat movement through the package, and heat removal at the system level.

Waiting until physical implementation to address thermal issues often leads to costly redesigns.

Early thermal co-analysis enables more balanced tradeoffs among performance, manufacturability, reliability, and system-level cooling requirements.

What key verification challenges are unique to CoWoS designs?

Verification complexity increases significantly in heterogeneous multi-die systems compared with traditional single-die implementations.
In CoWoS-based designs, verification extends beyond silicon functionality. It must also account for interactions across die-to-die interfaces, package behavior, power delivery, thermal conditions, and system-level operating requirements.

The industry is moving toward more integrated verification methodologies that evaluate silicon and package behavior together, rather than independently.

Beyond design, successful CoWoS programs depend on manufacturing and supply-chain execution. What issues are most critical, particularly around capacity, yield, test strategy, and production ramp?

CoWoS programs require close coordination across multiple ecosystem partners, including foundry, packaging, memory, test, and assembly resources.
Key considerations include packaging capacity, HBM availability,manufacturing yield, test strategy, production scheduling, and ramp execution.

Successful execution depends not only on technical excellence, but also on disciplined program management across the entire supply chain.

This is an area where experienced ASIC service providers can create significant value for customers by helping align technical requirements, partner schedules, supply availability, and production milestones.

Without discussing customer-specific programs, what opportunities does Alchip see for CoWoS-L?

CoWoS-L represents another important step in heterogeneous integration.
As AI systems continue to scale, customers will need larger packages, higher interconnect density, and greater flexibility in integrating multiple functional chiplets.

We believe CoWoS-L will support new classes of AI and HPC systems that require higher levels of scalability than current packaging technologies can efficiently provide.

Alchip is actively preparing its design methodologies to support these future architectures.

Looking ahead, how does Alchip expect CoWoS-S, CoWoS-R, and CoWoS-L to evolve? As AI ASICs move to larger die, more chiplets, higher HBM capacity, and more demanding performance-per-watt targets, how should customers think about choosing among these options?

We expect CoWoS-S, CoWoS-R, and CoWoS-L to coexist because each addresses different design and market requirements.

Future AI ASICs will require more chiplets, higher HBM bandwidth and capacity, heterogeneous process technologies, more sophisticated power delivery, and stronger silicon-package co-optimization.

As these requirements increase, designers are unlikely to converge on a single packaging technology. Instead, they will select different CoWoS options based on their architecture, bandwidth requirements, package size, power objectives, manufacturability needs, schedule, and cost targets.

From Alchip’s perspective, the goal is not to promote one packaging technology over another. It is to help designers evaluate and implement the solution that delivers the best balance of performance, manufacturability, schedule, and total system cost.

Also Read:

Alchip Accelerates on AI ASIC Demand

AI Chip Design Moves Beyond Monolithic Silicon with Alchip 3DIC

Alchip’s Leadership in ASIC Innovation: Advancing Toward 2nm Semiconductor Technology

 

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