AI-Era GPU Evolution and Foundry Technology Advancements: A Comprehensive Analysis
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1. Overview
As AI models grow rapidly in size and complexity, the importance of high-performance GPUs has skyrocketed. Originally designed mainly for gaming and graphics rendering, GPUs have now become the core platform for deep learning computations and High-Performance Computing (HPC).
For GPUs to evolve, foundry (Fab) technologies—responsible for the latest semiconductor process nodes—must also advance dramatically. In the sub-3nm era, various solutions to overcome the limits of ultra-fine processes (such as Gate-All-Around (GAA) transistors, 3D stacking, and advanced packaging) are being introduced.
This article provides a comprehensive overview of:
• Technical changes in GPUs for the AI era
• The competitive landscape among advanced foundries
• Global supply chain and market trends
• Future outlook for the semiconductor industry
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2. GPU Technology Evolution in the AI Era
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2.1 Growing AI Model Complexity and Shifts in GPU Architecture
• Rapid expansion of AI model parameters
Large-scale AI models, including massive language models like GPT, as well as models for image, speech, and autonomous driving, are growing exponentially in parameter count.
• AI-specific cores
NVIDIA’s Tensor Cores, specialized for matrix operations, are integrated into GPU architectures to significantly improve efficiency for AI workloads.
• Chiplet design
Instead of using a single, large die, chiplet-based architectures connect multiple smaller dies within the same package. This approach can simultaneously achieve higher yields and better performance.
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2.2 High-Bandwidth Memory (HBM) and 3D Stacking
• Adoption of HBM
High-performance GPUs employ HBM, which offers much greater bandwidth than traditional GDDR memory. This reduces data bottlenecks and speeds up training times.
• 3D stacking
By stacking memory in multiple layers and connecting them via Through-Silicon Vias (TSVs), data transfer rates and power efficiency are dramatically improved.
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2.3 Multi-GPU Configurations and Interconnects
• Multi-GPU collaboration
Training large-scale models often requires clusters of dozens to thousands of GPUs. Interconnect technologies like NVLink, InfiniBand, and Infinity Fabric are evolving to support these massive GPU clusters.
• Scale-out approach
Alongside improving single-GPU performance, the strategy of parallelizing multiple GPUs to distribute computational workloads has become a mainstream approach.
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3. Foundry Process Competition: The Sub-3nm Era
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3.1 Global Major Foundry Trends
• TSMC
Based in Taiwan, TSMC has secured leading 3nm process orders from key clients such as Apple, NVIDIA, and AMD.
• Samsung Electronics
Announced the world’s first 3nm process based on Gate-All-Around (GAAFET) technology, though initial yield concerns were reported.
• Intel (IFS)
Besides manufacturing its own chips, Intel is expanding its foundry services for external clients and has revealed a roadmap for 2nm (20A) and 1.8nm (18A) nodes.
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3.2 Extreme Ultraviolet (EUV) Lithography and Yield
• Introducing EUV
EUV helps reduce the number of multi-patterning steps, simplifying the fabrication process. However, it comes with high equipment costs and limited throughput.
• Yield challenges
At 3nm and beyond, transistor spacing becomes extremely tight, increasing the likelihood that even a tiny defect can compromise the entire chip. This makes yield management increasingly difficult.
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3.3 Transistor Structure Innovation: From FinFET to GAA
• FinFET limitations
Below 5nm, physical challenges such as threshold voltage leakage and quantum effects become more pronounced.
• Transition to GAAFET
By surrounding the channel on all sides, GAA structures greatly reduce current leakage. Samsung, for instance, adopts the MBCFET (Multi-Bridge Channel FET) variant of GAA.
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4. Technological and Physical Limitations, and Potential Alternatives
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4.1 Physical Limits to CMOS Scaling
• Quantum tunneling
As gate oxide layers approach atomic-scale thickness, current leakage due to tunneling becomes a major concern.
• Thermal management
Higher transistor density leads to extreme heat generation, necessitating innovative cooling methods and packaging solutions.
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4.2 Advanced Packaging and 3D Integration
• 2.5D & 3D packaging
By placing the GPU die and memory chips like HBM on a silicon interposer, overall system performance can be substantially boosted.
• Chip stacking
Efforts are underway to stack memory directly on top of logic dies or vertically stack multiple GPU cores, achieving ultra-high density on a single package.
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4.3 Post-CMOS Research
• Novel materials
Researchers are exploring next-generation semiconductor materials such as graphene, silicon carbide (SiC), and gallium nitride (GaN).
• Neuromorphic chips
Ongoing research aims to mimic the neural architecture of the human brain, targeting ultra-low power consumption.
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5. Market and Supply Chain Issues
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5.1 US-China Tensions and Export Controls
• Export restrictions on China
The U.S. has restricted the export of high-end GPUs and EUV equipment to China, causing significant shifts in the global semiconductor industry.
• China’s push for self-sufficiency
Despite efforts to develop 7nm and 14nm process technologies domestically, China still faces hurdles in achieving leading-edge nodes.
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5.2 Semiconductor Support Policies by Major Nations
• U.S. CHIPS Act
Through large-scale subsidies and tax incentives, the U.S. is actively attracting semiconductor manufacturing facilities to its shores.
• Europe and Japan
In pursuit of diversified semiconductor supply chains, initiatives like the EU Chips Act and Japan’s economic security policies are supporting advanced process development and R&D.
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5.3 Geographically Dispersed Foundry Production
• TSMC and Samsung investing in the U.S.
Building large-scale fabs in Arizona and Texas to stabilize supply chains and mitigate geopolitical risks.
• Intel and Europe
Intel is expanding its next-generation fabs in the U.S. and Germany, seeking to strengthen its presence in the global foundry market.
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6. Future Outlook: AI Demands, Intensifying Competition, and Emerging Paradigms
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6.1 AI Performance Requirements and Hardware Evolution
• GPU vs. ASIC
While GPUs excel in versatility and benefit from a robust software ecosystem, specialized AI chips like TPUs and NPUs offer advantages in power efficiency and speed.
• Beyond Moore’s Law
Even if scaling continues down to 2nm or 1.8nm, the era of performance doubling at the former Moore’s Law pace is nearing its end.
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6.2 Potential of Quantum Computing and Neuromorphic Chips
• Quantum computing
Although quantum computing promises revolutionary acceleration for specific algorithms, most experts agree it will take considerable time before it becomes widely commercialized.
• Neuromorphic chips
By emulating the human brain, these chips aim for ultra-low-power, high-efficiency operations. They could become a viable alternative for image or signal processing in the future.
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6.3 Hybrid and Heterogeneous Integration
• CPU + GPU + AI accelerator
In next-generation data centers, general-purpose CPUs, high-performance GPUs, and specialized ASICs will be packaged together to optimize AI workloads.
• Chiplet ecosystem
As standardized interfaces like UCIe become more prevalent, combining chiplets from multiple vendors to create customized SoCs is becoming increasingly feasible.
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7. Conclusion
The performance of GPUs, essential in the AI era, hinges on architectural innovations from design houses like NVIDIA and AMD, as well as advancements in foundry technologies by TSMC, Samsung, and Intel. In sub-3nm nodes, combining GAAFET, EUV lithography, and advanced packaging to boost transistor density and performance per watt grows increasingly complex.
At the same time, geopolitical factors—such as US-China tensions, regional diversification of manufacturing, and export controls—are reshaping the global semiconductor ecosystem and will significantly influence the future of the GPU market.
Over the next 5–10 years, AI models are forecasted to continue expanding, driving an urgent need for simultaneous progress in GPU architecture and foundry processes. Since Moore’s Law is no longer yielding exponential performance leaps, innovative solutions like chiplet-based designs, 3D stacking, and new transistor materials must be pursued in parallel.
Additionally, the rise of AI-specific ASICs (e.g., TPUs, NPUs), as well as quantum and neuromorphic computing, suggests that GPUs might not retain their dominance indefinitely. However, GPUs will likely remain at the core of the AI computing ecosystem, thanks to their robust software ecosystem, versatility, and developer-friendly environment.
Based on this analysis, professionals and policymakers in the global AI and semiconductor sectors should focus on:
1. Co-evolution of process and design
Recognize that GPU performance enhancements and stable advanced-node yields are interdependent.
2. Supply chain risk management
Mitigate geopolitical risks through regional diversification and strategic partnerships, bolstered by policy support.
3. Preparing for next-generation architectures
Maintain ongoing R&D investments for emerging AI chips and technologies (quantum, neuromorphic) that could challenge the GPU’s position.
By doing so, the industry can meet escalating AI computational demands and ensure the continuous growth of the global semiconductor ecosystem.
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(The information provided here is a synthesis of general technical and market data. It does not represent the official stance of any specific company or organization. For legal or policy decisions, please seek professional counsel.)
- TSMC and Samsung Face 3nm Challenges in the AI Era – Tom’s Hardware
- Can 3nm Technology Keep Up with AI and GPU Growth? – AnandTech
- The Future of 3nm Foundries Amid Rising AI and GPU Demands – ExtremeTech
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