What is the future of chip technology beyond Moore's Law?

The future involves gate-all-around (GAA) and complementary FET (CFET) structures, driven by advanced materials to overcome current scaling limits.

How does Gate-All-Around (GAA) technology improve computer chips?

GAA provides engineers with more precise control over electricity flow, enhancing performance and power efficiency.

Why are advanced materials essential for AI chips?

Advanced materials are crucial for tackling new manufacturing challenges introduced by GAA and enabling further chip miniaturization and performance gains.

Home / Technology / AI Era Needs New Chips: Gate-All-Around Explained

AI Era Needs New Chips: Gate-All-Around Explained

17 Jan

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Summary

Conventional chip scaling has reached its limits, driving innovation.
Gate-all-around (GAA) offers improved control and efficiency for AI chips.
Advanced materials are crucial for overcoming new manufacturing challenges.

AI Era Needs New Chips: Gate-All-Around Explained

The exponential growth of artificial intelligence is fundamentally reshaping the computer chip industry, demanding components that are faster, smaller, and more energy-efficient. Traditional scaling methods, once governed by Moore's Law, have reached their physical limitations. This has spurred the adoption of gate-all-around (GAA) transistor architecture, which offers superior control over electrical current and improved power efficiency.

While GAA addresses previous challenges, it introduces new manufacturing complexities. Issues such as dopant diffusion and surface roughness during silicon-germanium layer removal create resistance and performance variability. Chipmakers are now focusing on atomic-level engineering and advanced materials to mitigate these problems and unlock the full potential of GAA technology.

The industry is looking towards the 'angstrom era' and beyond, with innovations like complementary FETs (CFETs) and future 3D structures. These advancements, coupled with new materials, are essential for continued progress in the power, performance, area, and cost (PPAC) equation, enabling smarter and more sustainable computing for massive AI systems.