Artificial intelligence has reshaped the semiconductor industry, driving an endless chase for better performance and efficiency. But as transistor scaling slows and Moore’s Law fades, the gains from smaller nodes are running into a wall. Now, packaging is where the real action is.
In this new phase, performance breakthroughs aren’t being won by shrinking transistors — but instead by innovating on how those transistors are connected. Advanced packaging techniques are allowing chipmakers to stack components, shorten data pathways, and being to tackle the issues associated with memory bottlenecks. The implications for AI could be massive.
The evolution of AI chip packaging
For decades, progress in semiconductor performance came from shrinking transistors and fitting more onto a single silicon die — called 2D integration. That approach drove computing power for decades, but the benefits are fading as costs rise and yields drop. Engineers are now rethinking chip design entirely, moving beyond the flat, two-dimensional model to architectures that connect multiple chips more efficiently. This shift marks the start of the advanced packaging era, where how transistors are connected matters as much as how small they are.
In 2.5D packaging, multiple dies sit side by side on a silicon interposer, dramatically boosting bandwidth by placing memory and compute close together. Today’s AI accelerators leverage 2.5D packaging designs, including the likes of Nvidia’s H100 and AMD’s MI300. But even this design has limits, like heat, cost, and physical distance between components. That’s where 3D packaging comes in.
The rise of 3D packaging
If 2.5D designs marked the beginning of the advanced packaging revolution, 3D packaging represents its next great leap. Instead of arranging multiple chips side by side, 3D integration stacks them vertically, connecting layers through technologies like through-silicon vias (TSVs) and hybrid bonding. This approach drastically shortens the distance between logic and memory, reducing latency and boosting bandwidth — two factors that are critical for AI workloads that move enormous volumes of data between processing cores and memory.
The benefits extend beyond raw speed too. By bringing compute and memory closer together, 3D packaging improves energy efficiency and allows designers to pack more functionality into a smaller footprint. It’s already paying off in products like AMD’s 3D V-Cache CPUs, which stack additional cache directly on top of the processor to deliver dramatic performance gains without increasing power draw, and Intel’s Foveros technology, which lets the company mix and match chiplets built on different process nodes.
The transition to 3D isn’t without challenges. Stacking chips introduces thermal and yield complexities that make 3D designs more difficult and expensive to produce at scale. However, as bonding techniques mature and manufacturing processes become more efficient, 3D packaging is moving from high-end computing into broader markets. For AI accelerators, it’s fast becoming the defining technology of the decade.
“With increased levels of silicon integration also comes increased total power and, more importantly, the need for high density power delivery,” said Eelco Bergman, Chief Business Officer at Saras Micro Devices, in an interview with RCR Tech. “High-current delivery in a limited space, along with the associated thermal management, have become key focus areas for the industry. With these new high-performance accelerators, there’s just not enough real estate on the package or system board for all the components that are needed for power conversion. We’re seeing current levels upwards of 1,000 to 2,000 amps, making it critical to deliver that power efficiently with minimal losses. This requires a rethink of the power delivery network and the package itself.”
Addressing memory bottlenecks
As AI models scale into the trillions of parameters, the speed and efficiency of data movement have become the defining limits of performance. Traditional architectures separate logic and memory, forcing data to travel longer distances across interconnects and leading to latency, bandwidth, and power challenges. This “memory wall” has emerged as one of the most critical bottlenecks in AI chip design, especially as training and inference workloads increasingly rely on high-bandwidth memory (HBM) and massive parallelism.
Advanced packaging, particularly 3D integration, offers a direct path around these constraints. By stacking memory directly on top of compute dies or positioning them within microns of each other through hybrid bonding, designers can achieve dramatically higher data throughput and lower energy consumption. This is why modern AI accelerators all rely on tightly coupled HBM stacks connected through advanced interposers or 3D structures. This helps minimize latency, reduce thermal resistance, and enable sustained bandwidth.
“For edge devices, we are seeing system-in-package (SiP) designs integrate AI accelerators with application processors and memory for larger models as a very cost-effective approach,” said Jonathan Tapson, Chief Development Officer for BrainChip, in an interview with RCR Tech. “Advanced packaging technologies have been more successfully deployed in data centers, where the integration of high-speed data movement and computation is essential, and where SerDes cores can outnumber compute cores in a system design.”
Packaging and AI
The race to push AI performance beyond the limits of silicon has brought some of the world’s biggest chipmakers into new territory. Qualcomm, for example, recently took the wraps off of its first data center AI chips, in the form of the AI200 and AI250 chips. These processors use multi-die integration and high-bandwidth interconnects to link compute, memory, and networking elements Qualcomm says this architecture delivers massive efficiency gains while reducing latency.
Intel, meanwhile, continues to advance its Foveros and EMIB packaging technologies, linking chiplets built on different process nodes within a single package to improve efficiency and modularity. The company has also been a driving force behind interconnect standardization, contributing to initiatives such as the Universal Chiplet Interconnect Express (UCIe) consortium — an open specification that allows chiplets from different vendors to communicate over a common interface.
“Standardization of interconnect for AI co-processors or accelerators remains a major challenge. Most AI accelerators utilize PCIe or similar serial high-speed interfaces, but broader adoption of native interfaces like OCI is needed to create an ecosystem of truly plug-and-play accelerators,” continued Tapson.
And on the manufacturing front, TSMC’s SoIC X platform represents one of the most advanced 3D integration ecosystems yet. It enables direct copper-to-copper hybrid bonding between stacked logic and HBM dies, cutting interconnect lengths to near zero while supporting power delivery and thermal management at previously unattainable densities. Combined with CoWoS-L for 2.5D designs, SoIC X is already being adopted by leading AI accelerator vendors looking to push bandwidth and efficiency even further.
“Advanced packaging introduces complexity across the supply chain, especially when moving toward heterogeneous integration. You’re trying to bring together die from multiple sources, logic from one fab, memory from another, and then bring all of that together into one package,” continued Bergman. “That’s a completely different model than the monolithic chip design. It requires tight coordination with OSATs, substrate manufacturers, and material suppliers.”
Conclusion
As AI pushes deeper into every facet of computing, the role of packaging has shifted from an engineering afterthought to a defining force in chip design. No longer just about protecting silicon, packaging now determines how efficiently data moves, how much power is consumed, and how far performance can scale.