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In the era of nanotechnology, variability detection and control in the manufacturing process is vital in delivering products at high yields and at optimal cost. At Intel, DFM techniques are utilized to combat variability at all stages of product delivery-design, tape-out, mask generation, fab processing and assembly. Statistical process control (SPC), advanced process control (APC) and fault detection and classification (FDC) are examples of key capabilities used in the fab for variability detection, containment and correction on processing equipment and manufacturing process. This paper will focus on adaptive metrology sampling (AMS) and its role in variability detection and control during fab processing. AMS includes lot, wafer and field level metrology sampling strategies that varies the amount of sampling based on previous observations which include fab events and/or process control feedback. Adaptive sampling if implemented appropriately will improve precision in excursion detection, reduce detection time, minimize the quantity of silicon at risk and yield losses from such excursions. This paper will illustrate key strategies and techniques that should be considered for a successful AMS implementation. This includes details on system architecture, data inputs, sample material selection rules, and metrology capacity management strategy.