A wafer goes through hundreds of manufacturing processes. If an abnormality is discovered during electrical testing, engineers have to track the equipment, temperature, power and measurement records station by station. As chips enter the second nanometer and angstrom process generations, the cost of such after-the-fact repairs becomes increasingly high. TSMC CIO Lin Hongda unveiled the "AI Brain" blueprint in early September, proposing to transform decades of accumulated process knowledge, standard operating procedures and engineering experience into domain AI capabilities. What is noteworthy about this development is not just the addition of a few more AI digital engineers to the fab, but also the possible changes in the way the semiconductor industry creates value from design, manufacturing to packaging and testing.
Taiwan's semiconductor industry has long established global competitive advantages through professional division of labor. IC design, wafer manufacturing, and packaging and testing each have their own expertise, and each pursues efficiency and yield. But customers want more than just a wafer that meets specifications, but one that can be delivered on time, with reliable performance and at a reasonable cost. When defects discovered on the manufacturing side can be fed back into the design in advance, and heat dissipation, power supply, and reliability limitations on the packaging side can also be taken into consideration at the early stage of design, there is an opportunity to reduce re-production and repeated verification. This is exactly the value stream thinking emphasized by lean management. When wafers and products flow forward, quality information and engineering experience are fed back upstream. The contribution of AI is that problems that used to take weeks to track down by different departments can be discovered and dealt with earlier.
TSMC has applied AI to wafer measurement analysis, mask pattern inspection and process optimization, and has cooperated with Huida to accelerate computing and improve computing lithography and defect detection. The AI equipment experts, yield analysts and shift assistants proposed by Chief Information Officer Lin Hongda show that in the future engineers will be able to delegate AI to handle large amounts of data analysis work, while they themselves focus on abnormal judgment and key decisions. If advanced packaging technologies such as CoWoS, SoIC and high-bandwidth memory are further connected in series, chip design can consider system heat dissipation, interconnection and reliability earlier. Semiconductor competition will gradually extend from the yield rate of a single process to the delivery time, performance and total cost of each good product of the entire system.
This change will test the moat of Taiwan's semiconductor industry. In order to reduce their reliance on general-purpose GPUs, large cloud service providers are actively investing in customized chip design. If Taiwan's IC design service industry only relies on accepting orders and delivering designs, it may not be able to maintain its irreplaceable position in the long term. The truly rare condition in Taiwan is the engineering experience accumulated through long-term cooperation between design companies, advanced wafer fabs and packaging and testing industries. TSMC’s nearly forty years of process parameter correction, exception handling, and cross-factory production management knowledge can be obtained without purchasing an AI model. If these experiences can be turned into verifiable digital knowledge services, it will not only help shorten the yield learning time of new factories, but also strengthen global production coordination.
Therefore, the government’s policy on promoting semiconductor and AI industries should begin to pay attention to this value stream reorganization. The Ministry of Economic Affairs and the National Science Council should assist IC design, equipment, materials and packaging and testing industries to establish common data interfaces, defect classification and reliability verification mechanisms, and develop Taiwan's own industrial AI tools and engineering knowledge services on the premise of protecting business secrets and customers' intellectual property rights. Measuring effectiveness should not only look at how much computing power is invested and how many AI models are introduced, but also how much the time from design to mass production is shortened, how much heavy work is reduced, and how much energy each good product consumes. The industry direction revealed by TSMC’s AI brain is to transform Taiwan’s decades of manufacturing wisdom into sustainable accumulated competitiveness. If Taiwan can master the knowledge feedback and verification capabilities between design, manufacturing and packaging, it can transform from the world's most important chip manufacturing base to become the core of the semiconductor industry with value distribution influence.
