Data-Efficient Prediction of Minimum Operating Voltage via Inter- and Intra-Wafer Variation Alignment
<|reference_start|>Data-Efficient Prediction of Minimum Operating Voltage via Inter- and Intra-Wafer Variation Alignment: Predicting the minimum operating voltage ($V_{min}$) of chips stands as a crucial technique in enhancing the speed and reliability of manufacturing testing flow. However, existing $V_{min}$ prediction methods often overlook various sources of variations in both training and deployment phases. Notably, the neglect of wafer zone-to-zone (intra-wafer) variations and wafer-to-wafer (inter-wafer) variations, compounded by process variations, diminishes the accuracy, data efficiency, and reliability of $V_{min}$ predictors. To address this gap, we introduce a novel data-efficient $V_{min}$ prediction flow, termed restricted bias alignment (RBA), which incorporates a novel variation alignment technique. Our approach concurrently estimates inter- and intra-wafer variations. Furthermore, we propose utilizing class probe data to model inter-wafer variations for the first time. We empirically demonstrate RBA's effectiveness and data efficiency on an industrial 16nm automotive chip dataset.<|reference_end|>
@article{yin2024data-efficient,
    title={Data-Efficient Prediction of Minimum Operating Voltage via Inter- and
  Intra-Wafer Variation Alignment},
    author={Yuxuan Yin, Rebecca Chen, Chen He, Peng Li},
    journal={arXiv preprint arXiv:2408.06254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06254},
    primaryClass={eess.SY cs.SY}
}