SEMATECH’s ATDF, QWiKS develop PPM system

November 6, 2007 – Quality Wise Knowledge Solutions (QWiKS) says it has developed a real-time visual predictive and preventative maintenance (PPM) system that more effectively monitors and maintains semiconductor process equipment.

Working with SEMATECH’s Advanced Technology Development Facility (ATDF), the two report no quality reduction in any of four vertical furnaces monitored over a nine-month period, resulting in $50K saved in maintenance costs. Scott Bisland, equipment support manager for ATDF, said in a statement that the ATDF expects to save about $15K this year for each of the four furnaces.

For the ATDF project, a condition-based maintenance portion of the system monitors furnace throttle valve positions and alerts manufacturing technicians to clean the furnace pump exhaust trap, avoiding unscheduled downtime due to process accumulation. For most of its tools ATDF uses a monitoring system requiring manual intervention to track tool performance, Bisland added, but the QWiKS system automatically extracts tool status from the system and displays that information on a graphical interface, enabling an equipment maintenance technician to respond immediately.

ATDF and QWiKS cited an ISMI study indicating 17% of a typical fab’s tool asset value gets allocated to tool maintenance for round-the-clock operations, translating into ~$680M/year for a fab with $4B in equipment assets. And on top of that, unscheduled downtime costs 4-7x more than routine maintenance.

Listing the system’s features, the company says that in addition to monitoring tool status and health, the QWiKS system also includes tools for managing and improving tools performance tracking. It also includes a failure mode effects analysis (FMEA) module to allow both process and equipment FMEAs to be implemented to ensure that the root causes of a failure are understood, addressed, and corrected. A FMEA pattern signature function can capture the tool and process parameters associated with the root causes of a failure. And equipment or process diagnostic rules can be generated for detecting early sign of tool or process failures, or pinpointing the root causes of tool failure to reduce equipment repair time.


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