On 16 December 2025, NCCU’s International Doctoral Program on Asia-Pacific Studies (IDAS) hosted a guest lecture by Dr. Jefferey Chiu (Co-Founder and CEO of AnsForce; Adjunct Professor at TIIPM, NCCU School of Business) titled “Semiconductor Industry and Challenge to Taiwan’s Technology Development.” The lecture explained, in technical but accessible terms, why semiconductor progress is the enabling foundation of the current AI surge. Dr. Chiu stressed that contemporary advances in AI are tightly constrained by hardware realities such as compute availability, energy efficiency, thermal stability, and manufacturable performance at scale rather than by algorithms alone. Using recent TSMC- and Nvidia-related forum materials as illustrative anchors, he connected the engineering logic of leading-edge fabrication to the broader issue of technological capability, arguing that TSMC’s strategic importance derives not from abstract narratives, but from accumulated process integration capacity and yield reliability at extreme dimensions.
A central theme was that modern chipmaking has moved beyond the simplistic story of “smaller transistors.” Dr. Chiu framed leading-edge manufacturing as a systems discipline combining process technology, device architecture, materials engineering, and increasingly advanced packaging. At the frontier, production is executed at near-atomic tolerances, where minor defects, contamination, or overlay misalignment can sharply reduce yield and erase theoretical performance gains. In this sense, technological leadership is inseparable from the ability to translate design intent into stable mass production. Dr. Chiu argued that this is why semiconductor competitiveness is so concentrated globally: only a small set of firms can coordinate the full stack of tooling, materials, metrology, and process control required for high-volume production at the newest nodes.
The technical core of the talk focused on TSMC’s leading logic roadmap N3 (3nm), N2 (2nm), and A16 (1.6nm-class) and the architectural transitions that make each node relevant for AI-era computing. Dr. Chiu explained that N3 remains based on FinFET technology, a 3D transistor structure designed to improve gate control and suppress leakage relative to planar transistors. He emphasized that N3’s significance is not limited to nominal feature size: it is strategically valuable because it can be manufactured at scale with high reliability, supporting the production of today’s demanding AI accelerators where power-performance balance and yield predictability are decisive. In his account, N3 demonstrates how a mature, deployable node can remain central to frontier computing even when newer architectures are being developed, because scale production and consistency are prerequisites for real-world AI capacity.
He described N2 as a more substantial inflection point because it introduces Gate-All-Around (GAA) nanosheet transistors for mass production. Dr. Chiu framed the transition as a response to the physics limits of continued FinFET scaling: as dimensions shrink further, maintaining electrostatic control and acceptable leakage becomes increasingly difficult. GAA nanosheet designs, by surrounding the channel more effectively and enabling tunable channel geometries through stacked nanosheets, can restore control and efficiency improvements needed for the next generation of AI chips. Importantly, he emphasized that N2’s promise is inseparable from manufacturing complexity: nanosheet architectures raise integration demands across materials, etch processes, variability control, and metrology, meaning their strategic value depends on whether they can be produced reliably, not simply demonstrated.
Furthermore, A16 was presented as addressing an additional bottleneck that becomes acute as AI accelerators grow denser and more power-hungry. He summarized the node progression as a constraint-resolution sequence: FinFET-based N3 supports reliable high-yield scaling; GAA nanosheets at N2 restore leakage control and efficiency when FinFET approaches saturation; and A16-class power-delivery innovations mitigate electrical bottlenecks that would otherwise cap performance growth in dense AI hardware.
Across these discussions, Dr. Chiu repeatedly underscored a broader conclusion: semiconductors enable AI’s expansion by sustaining performance-per-watt gains through architectural reinvention even as classical scaling assumptions weaken.
The Q&A then widened the lens to Taiwan’s position in global supply chains and the way TSMC’s overseas investments and partnerships such as U.S. fab expansion and ecosystem coordination reflect shifting supply-chain strategies amid intensifying geopolitical competition. In sum, the lecture demonstrated that understanding chip-level innovation and manufacturability is essential for analyzing contemporary technological power, particularly in an AI era where compute, yield, and supply-chain resilience increasingly shape both economic outcomes and security-relevant capability.
