Employing big data, AI and other new-generation, core info. technologies, we help enterprises to unleash the potential of their internal data resources and high-end equipment. We provide semiconductor factories with integrated solutions to make their high-end manufacturing intelligent manufacturing.
1、Production Site Data Silos
Data are playing an increasingly important role for semiconductor manufacturers as the factory info. systems and the automation processes generate large amounts of data, and the production side relies more and more on data analysis. Nevertheless, the existing bottlenecks made the extraction, storing and organization of process data time and labor consuming. Moreover, data were stored in separate systems which were difficulty to be interconnected and be comprehensively analyzed.
2、High Operation and Maintenance Costs
The semiconductor industry, being typically asset intensive, has large amounts of expensive, mainly imported equipment, which may cause great losses during abnormal downtime. Under the high-performance and low-cost pressure from the market, the existing high-cost equipment management methods are unsustainable. Moreover, the existing system cannot carry out real-time visual monitoring and centralized management of multiple production lines and multiple machines in the wafer factories. There is still room for improving the efficiency of equipment utilization.
3、Time& Labor Consuming Data Analysis
The craft of semiconductor industry is complex with many interconnected factors that affect the production process. The crafting process demand extremely highly on accuracy to guarantee the high reliability of data analysis. Nevertheless, there are few model cases in the application of big data to semiconductor industry. The integration of business and big data is still superficial because of a lack of OT-IT convergence experts, and a lack of professional data mining and analysis tools.
4、QC Relies on Manual Labor
The yield rate is the lifeline of the semiconductor industry, but most factories are still relying on manual experience for quality control. The overall production efficiency and accuracy is limited due to the flaws of manual, subjective inspections such as fluctuating deviations, slow pace, and low accuracy and low efficiency caused by high workload. With high employee turnover rate, long training times and increasing labor costs in the manufacturing industry, relying on manual labor is unsustainable.
Re-develop equipment interfaces and software using IoT sensors, making them compatible with the standard international communication protocols in the semiconductor industry. The systems can achieve real-time, high-frequency collection of equipment data, centralized equipment authority mgmt., equipment material control, program parameter calibration, automated production control, equipment alarms and life-span controls.
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Real-time monitoring of production measurement (line width, push/pull testers etc.) and equipment data to help engineers analyze and deal with abnormalities
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Real-time detection of production abnormalities, rapid identification and analysis of the root cause of abnormalities, closed-loop control to reduce the impact of abnormalities, improve OEE
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Quantify equipment manufacturing capacity through real-time monitoring of machine effectiveness to improve production and capacity plans, and to act as the key measurement for total production project mgmt. that help engineers to analyze and improve the condition of equipment utilization.
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