What is it about?
Accurate minimum operating voltage ($V_{min}$) 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 the data from a few additional chips is utilized for calibration. We demonstrate the superiority of the proposed method on industrial 16nm chip data.
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This page is a summary of: Data-Efficient Conformalized Interval Prediction of Minimum Operating Voltage Capturing Process Variations, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3649329.3657338.
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