The AI revolution has put an enormous strain on hardware. For decades, progress in computing was driven by making silicon transistors ever smaller (Moore’s Law), but today we are hitting fundamental limits. Chip designers face scaling bottlenecks, power limits, and density constraints as planar silicon chips approach their physical and economic limits. We’ve reached a point where simply shrinking transistors can lead to diminishing returns – chips run hotter and costlier for smaller speed gains. At the same time, AI workloads are exploding in size (today’s AI models can have billions of parameters), demanding exponentially more compute power. This mismatch has created a critical inflection: to continue the AI revolution, new hardware paradigms are required.
Traditional scaling is slowing down because of both physics and cost. Transistors channels can’t get much thinner or shorter without mobility degradation, quantum effects and leakage undermining them. Power density in top-of-the-line chips is so high that we’re at the cooling limits in many cases. Additionally, the cost to develop the next generation of silicon (e.g., 1.4 nm, 1 nm nodes) is skyrocketing, which is not sustainable for widespread adoption. This is where 3D integration comes in as a promising path forward. Instead of trying to cram everything onto a single flat slice of silicon, 3D integration proposes building “upwards.” By stacking chips or device layers vertically, we can achieve greater integration density and reduce the distances signals need to travel. This can significantly improve performance and energy efficiency. In essence, 3D integration extends Moore’s Law in a different dimension – literally the third dimension (height) – offering a way to keep improving computing capabilities without solely relying on smaller transistors in 2D.
CDimension’s 3D Approach: At CDimension, we are taking 3D integration to the next level with a monolithic approach. Unlike traditional 3D packaging (which might stack whole chips and connect them with micro-bumps or through-silicon vias, TSVs), we build multiple layers of circuitry as part of one unified chip. This means, for example, we can have a logic layer, a memory layer, and a power layer all connected by nanometer-scale or deep sub-micron-scale vertical interconnects within the same chip. The impact on AI hardware is profound. By bringing memory closer to logic, we cut down the communication delay and energy – data can flow 1000× faster and with far less power. By integrating power management on-chip, we reduce the 20–30% power loss that typically happens delivering power from a separate PMIC on a printed-circuit-board. And by using new materials and device structures, we can increase performance per layer beyond what silicon alone would allow. Essentially, our approach addresses the major bottlenecks (compute, memory, power) in a holistic way: if silicon integrated circuits can’t keep up integration in 2D plane, we add a new dimension and new materials to keep the improvements coming.
A New Era of Hardware for AI: The future of AI hardware will likely be defined by those who successfully transition to these advanced architectures. We foresee AI chips evolving from single-planar dies into multi-layered systems where CPUs, GPUs, NPUs (neural processing units), XPUs, memory arrays, and power circuits are all tightly integrated. This shift is comparable to the move from single-core to multi-core processors in the mid-2000s – only now we’re moving from single-layer to multi-layer chips. For AI applications, this could mean orders of magnitude improvements in performance and efficiency. Training a massive neural network that used to take days might be done in hours, or real-time AI inference on edge devices could become far more capable within a given power budget. Moreover, 3D integrated AI hardware can unlock new form factors – imagine powerful AI engines small enough to fit in a mobile device or IoT sensor because the integration is so high. In summary, breaking the silicon barrier with 3D integration and new materials is the path to sustaining AI’s exponential progress. At CDimension, we’re at the forefront of this movement, developing the technologies today that will become the standard for AI hardware tomorrow.