Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer Sensing

Yuxuan Yin, Boxun Xu, Peng Li

Abstract:

Accurate prediction of chip performance is critical
for ensuring energy efficiency and reliability in semiconductor
manufacturing. However, developing minimum operating voltage (Vmin) prediction models at advanced technology nodes
is challenging due to limited training data and the complex
relationship between process variations and Vmin. To address
these issues, we propose a novel transfer learning framework
that leverages abundant legacy data from the 16nm technology
node to enable accurate Vmin 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.