Abstract
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.
Abstract (translated)
准确预测芯片性能对于确保半导体制造中的能源效率和可靠性至关重要。然而，在先进工艺节点上开发最小操作电压($V_{min}$)的预测模型面临挑战，原因是可用训练数据有限以及加工变化与$V_{min}$之间复杂的关系。为解决这些问题，我们提出了一种新的迁移学习框架，利用来自16纳米技术节点的大量历史数据来实现5纳米先进节点上的准确$V_{min}$预测。我们的方法的一个关键创新点是整合了从芯片硅里程表传感器数据中提取的输入特征，这些特征能够精细地描述局部加工变化——这是在5纳米节点上至关重要的因素，从而显著提高了预测准确性。
URL
https://arxiv.org/abs/2509.00035
