Data accumulation without actionable insight
The semiconductor equipment R&D institute aimed to strengthen its in-house AI capabilities to enhance equipment performance and process reliability. Although it had accumulated extensive operational data, the team struggled to uncover meaningful correlations or identify datasets suitable for AI applications. Despite establishing a direction for AI development, the institute lacked the technical expertise and infrastructure to independently build and operate models. A shortage of skilled personnel further limited its ability to create a sustainable, data-driven R&D environment.
Establishing a real-time AI insight platform
A real-time data collection and integration system was developed to aggregate PLC equipment data from edge devices. The data was refined into a structure optimized for AI analysis, supporting the development of a machine learning model for early anomaly detection. The model identifies subtle abnormalities—such as leaks or drive unit irregularities—that are often undetectable through visual inspection. An intuitive dashboard visualizes equipment status and anomaly indicators in real time, enabling researchers to quickly interpret insights and take immediate action.
Enhancing equipment reliability and yield through AI
By predicting equipment failures and minimizing downtime, the AI platform improved operational uptime and process stability. Early detection of subtle anomalies reduced defect rates and process variation, increasing product yield and ensuring consistent production quality.
