The chiplet economy rests on three fundamental pillars: deployment, innovation, and manufacturing, each presenting distinct opportunities and challenges.
www.eetasia.com, Jul. 02, 2025 –
The semiconductor industry stands at a crossroads. As we witness the explosive growth of artificial intelligence and the increasing complexity of computing demands, chiplet technology has emerged as a critical enabler of next-generation innovation. But for this technology to truly succeed, we must understand its economic foundations—the delicate balance between value creation and cost management that will determine whether chiplets become the industry standard or remain a niche solution.
The economic viability of any technology hinges on a simple equation: The unique value it creates must justify the costs required to make it succeed. For chiplet technology, this equation is particularly complex, involving an entire ecosystem of players from design to manufacturing and deployment.
I believe the chiplet economy rests on three fundamental pillars—deployment, innovation, and manufacturing, each presenting distinct opportunities and challenges.
The deployment imperative
The first pillar is commercial deployment—the widespread adoption of chiplet-based products across multiple use cases. Without broad deployment, there can be no economies of scale, and without scale, costs remain prohibitively high while value creation remains limited.
Today, we’re seeing promising signs. High-performance computing and AI products, particularly those destined for data centers, represent the primary market for chiplet technology. This makes sense. Data centers demand the kind of performance and power efficiency that chiplets enable, and they can amortize the premium costs associated with cutting-edge technology by using chiplets on advanced nodes only when and where they are needed.
True economic success requires expansion beyond this initial beachhead. The automotive sector presents the next logical step, especially as vehicles incorporate autonomous driving capabilities and sophisticated sensor arrays. Looking further ahead, augmented- and virtual-reality applications, robotics, humanoid systems, and other edge use cases yet to emerge represent massive opportunities. These use cases naturally align with chiplet advantages—they require integrated computing, memory, sensing, and communication functions, with robotics adding the complexity of movement and actuation.
The challenge lies in bridging the gap between early adoption in premium applications and mass deployment across diverse markets. This transition requires not just technical refinement but also cost reduction, reliability improvements, and security assurance over the entire product life cycle that only comes with scale.
The innovation engine
The second pillar involves the design and creation of chiplet products themselves. This is where the magic happens—where electronic design automation (EDA) companies and intellectual property (IP) providers are driving remarkable innovation. The result is a proliferation of designs with increasingly diverse functions, based on prevalidated components, translating to more new design starts and tapeouts each year.
This innovation funnel is largely self-sustaining, driven by competition among EDA and IP companies to provide the tools and building blocks that enable more sophisticated chiplet-based designs. The rapid pace of innovation in this space is opening endless possibilities that were unimaginable just a few years ago.
While this pillar appears to be thriving, it’s worth noting that innovation without corresponding advances in manufacturing and deployment can lead to a disconnect between what’s possible and what’s practical. The industry must ensure that the innovation pipeline remains aligned with manufacturing capabilities and market demands.
The manufacturing reality
The third pillar—manufacturing and testing—is where conceptual designs meet practical reality and where the final quality and cost equations are solved. This is arguably the most challenging pillar because it’s where all the complexities of chiplet technology converge.
The manufacturing challenge is multifaceted. Design complexity has increased dramatically, but so has manufacturing variability. Traditional approaches that relied on accounting for all variables during design time are no longer sufficient. The statistical nature of manufacturing processes, especially at advanced nodes, means that testing and quality assurance have become more critical than ever. Advanced packaging, while drastically opening design possibilities, poses additional challenges in the same context.
This is where data becomes invaluable. By leveraging comprehensive data from across the product lifecycle—from upstream design through downstream manufacturing and testing—and applying AI to build predictive models and, in some cases, leveraging generative and agentic AI, we can achieve better balance between quality and cost.
Adaptive tests, predictive binning, and predictive burn-in are just the beginning of what’s possible when we harness the power of manufacturing data.
The need for integration
Perhaps most important, success in the chiplet economy requires breaking down the silos between these three pillars. Too often, each segment of the industry optimizes for its own metrics without considering the broader ecosystem impact.
What we need is better integration—connections at the intersections where real value is created.