Leveraging next-generation information technologies such as big data and artificial intelligence, we fully tap into internal data resources and the potential of high-end equipment to provide integrated solutions for semiconductor manufacturing factories, enabling the transition from high-end manufacturing to high-end intelligent manufacturing.
1. Data Silos in Production Sites
Semiconductor factories generate vast amounts of data from information systems and automated processes. Production decisions increasingly rely on data analysis, making data's role more critical. However, bottlenecks in data extraction, storage, and organization persist, making data collection and organization time-consuming and labor-intensive. Moreover, data exists independently across systems, making it difficult to integrate and perform comprehensive analysis.
2. High Equipment Maintenance Costs
The semiconductor industry is a typical capital-intensive sector with a large number of expensive, mainly imported equipment. Unexpected downtime results in significant losses. Under market pressures for high performance and low cost, existing high-cost equipment management methods are unsustainable. Additionally, current systems struggle with real-time visual monitoring and centralized management of multiple production lines and machines in fabs, leaving room for improving equipment utilization efficiency.
3. Time-Consuming Data Analysis
Semiconductor processes are complex with numerous influencing factors and intricate interrelationships, requiring extremely high precision in process control. This demands highly reliable and interpretable data analysis results. However, there are few big data application examples in the semiconductor industry, lacking deep integration of business and big data technology. Talent with combined OT and IT skills is scarce, and professional data mining and analysis tools are lacking.
4. Quality Reliance on Manual Experience
Yield is the 'lifeline' of the semiconductor industry, yet most factories still rely on manual experience for quality monitoring. Subjective human inspection leads to large quality deviations, slow detection speeds, and high labor intensity affecting accuracy and efficiency, limiting overall production efficiency and precision. High employee turnover, long training times, and rising labor costs make reliance on manual methods unsustainable.
