Presentation
ML-Based Sigma Av–aware Switch Cell Optimization
DescriptionIn advanced technology nodes, timing pessimism caused by power integrity variation has become a critical challenge for sign-off closure. In particular, Sigma AV reported by RedHawk analysis introduces excessive timing margins under non-uniform power density conditions. Conventional switch cell selection assumes uniform power density and applies a single switch cell across the design, failing to capture post-placement current variation.
This work presents a machine learning–based, Sigma AV–aware switch cell optimization methodology. Instead of directly predicting Sigma AV, the proposed approach learns per–switch-cell current behavior extracted from RedHawk analysis using a gradient-boosted decision tree model. Current-critical switch cell instances are identified and selectively optimized using mixed switch cells.
Experimental results demonstrate a clear reduction in Sigma AV variation and the associated Sigma AV–induced slack shift. As a result, worst-case timing slack at p1 endpoints is consistently improved under sign-off conditions, confirming the effectiveness and practicality of the proposed approach.
This work presents a machine learning–based, Sigma AV–aware switch cell optimization methodology. Instead of directly predicting Sigma AV, the proposed approach learns per–switch-cell current behavior extracted from RedHawk analysis using a gradient-boosted decision tree model. Current-critical switch cell instances are identified and selectively optimized using mixed switch cells.
Experimental results demonstrate a clear reduction in Sigma AV variation and the associated Sigma AV–induced slack shift. As a result, worst-case timing slack at p1 endpoints is consistently improved under sign-off conditions, confirming the effectiveness and practicality of the proposed approach.
Event Type
Engineering Poster Gladiator
TimeTuesday, July 285:25pm - 5:34pm PDT
LocationDAC Pavilion, Exhibit Floor