3D Integration, Advanced Metrology Shape the Semiconductor Landscape

By Kai Beckmann, CEO Electronics and Member of the Executive Board of Merck KGaA, Darmstadt, (November 4, 2024)

The rapid evolution of AI has ushered in an era where semiconductors are more critical than ever. Behind every AI model and application, from training to deployment, lies a complex network of semiconductors that enable the processing power required to handle vast amounts of data. The surge in AI has driven the demand for advanced semiconductor chips, pushing the boundaries of chip design and manufacturing. To meet these demands, the semiconductor industry is increasingly turning to innovative solutions like 3D heterogeneous integration.

Breaking the von Neumann bottleneck

Traditionally, the semiconductor industry has followed Moore’s Law, which predicts a doubling of transistor counts on a microchip approximately every two years. This relentless pursuit of increasing computational power has driven the miniaturization of transistors for decades. 

However, as we approach the physical limits of shrinking transistors, the industry faces new challenges—particularly in optimizing the architecture of chips to manage the ever-growing data requirements of generative AI. One of the most significant challenges in modern chip design is the “memory wall” or “von Neumann bottleneck,” a limitation in the speed at which data can be transferred between the memory and logic units within a chip. This bottleneck becomes more pronounced as AI models grow in complexity and data sets expand, leading to inefficiencies in data movement that hinder overall performance.

To overcome this bottleneck, the semiconductor industry has embraced 3D heterogeneous integration—a technology that involves stacking memory and logic units vertically rather than placing them side by side. This vertical integration shortens data pathways, increasing energy efficiency and allowing for greater interconnect density—key factors in achieving the high bandwidths necessary for AI applications. By adopting this approach, the industry can bypass some of the physical limitations that have traditionally constrained chip performance.

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