Cohort: 3

Members: Xu Tao, Wang Qi, Li Jingxiang, Cai Dan, Yan Jiaxi, Wang Yan

Problem: In solar cell manufacturing, millions of ultra-thin silicon wafers continuously move through highly automated production lines operating 24/7. While wafer-level traceability systems can record more than 1,800 process parameters for each individual cell, the data is typically distributed across multiple independent systems. When anomalies occur, engineers must manually correlate data across different platforms. Additionally, conventional traceability systems remain largely passive. As a result, corrective actions are delayed, yield performance declines, and manufacturing costs increase.

Approach: Based on a wafer-level full traceability data platform and AI-driven algorithms, the company developed a dual-link closed-loop collaborative system framework, enabling precise root cause identification down to abnormal process steps on specific equipment and continuous monitoring of real-time process parameter fluctuations and abnormalities detection through dynamic predictive models.

Result: Reduced root cause investigation from three days to three minutes, significantly improving problem response and corrective action efficiency, while increasing per-capita production capacity by 7%. The system also achieved a 0.5% improvement in overall solar cell yield and a 0.1% increase in cell conversion efficiency.