Leveraging big data, AI, and new IT to maximize internal data and equipment potential, offering integrated solutions for semiconductor factories to advance from high-end to smart manufacturing.
1. Production site data silos
Semiconductor factories produce large data from info systems and automation. Data analysis is key for production, but data extraction, storage, and organization are bottlenecks, time-consuming. Data is siloed across systems, hard to integrate for analysis.
2. High equipment operation and maintenance costs
Semiconductor is asset-heavy with many expensive imported devices. Downtime losses are high. High management costs are unsustainable. Systems lack real-time monitoring of multiple lines and machines, efficiency can improve.
3. Data analysis is time-consuming and labor-intensive
Semiconductor processes are complex, need high precision. Data analysis must be reliable. But big data apps are rare, lack biz-tech integration, scarce OT/IT talent, no proper tools.
4. Quality relies on manual experience
Yield is critical, but quality checks often manual. Human detection has high variance, slow, affects accuracy. High turnover, training costs, labor costs rising, manual methods unsustainable.
