用旧工艺数据和硅里程计提升5纳米芯片最低工作电压预测精度
Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer Sensing
- 用16纳米遗留数据做迁移学习，缓解5纳米数据不足问题
- 引入芯片内硅里程计传感器数据，精准捕捉局部工艺波动
- 适合芯片制造与可靠性团队参考，尤其关注低功耗设计
准确预测芯片性能对半导体制造中的能效与可靠性至关重要。但在先进制程节点上，由于训练数据有限且工艺波动与最低工作电压（V_min）关系复杂，构建精确的V_min预测模型面临挑战。为此，我们提出一种新型迁移学习框架，利用丰富的16纳米工艺遗留数据，实现5纳米节点上的高精度V_min预测。本方法的关键创新在于融合了来自片上硅里程计传感器的输入特征，该特征可对局部工艺波动进行细粒度表征——这在5纳米节点中尤为关键，显著提升了预测准确性。
原文摘要 · Abstract (English)
Accurate prediction of chip performance is critical for ensuring energy efficiency and reliability in semiconductor manufacturing. However, developing minimum operating voltage ($V_{min}$) prediction models at advanced technology nodes is challenging due to limited training data and the complex relationship between process variations and $V_{min}$. To address these issues, we propose a novel transfer learning framework that leverages abundant legacy data from the 16nm technology node to enable accurate $V_{min}$ prediction at the advanced 5nm node. A key innovation of our approach is the integration of input features derived from on-chip silicon odometer sensor data, which provide fine-grained characterization of localized process variations -- an essential factor at the 5nm node -- resulting in significantly improved prediction accuracy.
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