Presentation
Data-Efficient Conformalized Interval Prediction of Minimum Operating Voltage Capturing Process Variations
DescriptionAccurate minimum operating voltage (Vmin) prediction is a critical element in manufacturing tests. Conventional methods lack coverage guarantees in interval predictions. Conformal Prediction (CP), a distribution-free machine learning approach, excels in providing rigorous coverage guarantees for interval predictions. However, standard CP predictors may fail due to a lack of knowledge of process variations. We address this challenge by providing principled conformalized interval prediction in the presence of process variations with high data efficiency, where a few additional chips are utilized for calibration. We demonstrate the superiority of the proposed method on industrial 16nm chip data.
Event Type
Research Manuscript
TimeTuesday, June 2510:30am - 10:45am PDT
Location3010, 3rd Floor
EDA
Test, Validation and Silicon Lifecycle Management