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Over the past two years the Predictive Maintenance (PdM) capability in semiconductor manufacturing has migrated from Proof-of-Concept (PoC) and univariate Fault Detection (FD) extrapolation mechanisms to fab-wide solutions that are (1) robust to typical process and equipment disturbances, (2) extensible so as to provide solution approaches that are portable across instances of a tool type and across...
Matching tools running identical processes is particularly critical for users migrating to more advanced nodes. Sustaining a fleet of tools to a matched state can reduce yield losses and yield variability, allow for greater routing flexibility in the fab, identify and control process inefficiencies, and reduce time for root cause analysis of yield issues. The matching process is multi-dimensional,...
Predictive maintenance (PdM) is cited by the ITRS as a critical technology to incorporate into production over the next five years to reduce unscheduled downtime and cycle time, maintain high quality, and reduce cost. Equipment Health monitoring (EHM) is a companion to PdM that provides a tracking indication of equipment health. The industry needs to deploy and assess PdM and EHM capabilities to determine...
Predictive Maintenance (PdM) systems use process and equipment state information to predict when a tool or a particular component in a tool might need maintenance. PdM systems can be realized cost-effectively by leveraging Advanced Process Control (APC) technologies and infrastructure. APC data collection infrastructure can provide the state information necessary for prediction. APC fault detection...
Applied Materials is working with customers to assess new ways to identify opportunities to reduce resources usage (energy, process chemicals/gasses) by existing semiconductor process tools. Through six customer pilot projects we have identified potential average savings of $22.5K per year, for the CMP and CVD process tool examined, by using a consultative service approach to business and financial...
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