Hi, LogicSynthPro,

That’s a great question! I’ve provided my answer below:

1. libMetric™ – Cell-Level PPA Modeling

• libMetric™ extracts timing, power, and area data from Liberty .lib files.
• It converts the data into structured JSON and uses lookup tables, interpolation, and least squares regression to build continuous models of PPA under varying conditions (e.g., different loads and transitions).
• These models are then used to drive higher-level analysis such as STA, power estimation, and area budgeting.

2. Copernic™ – Statistical and Yield-Aware Optimization

• Copernic™ creates large-scale virtual wafer datasets based on process variation models and pre-trained ML-based technology profiles.
• It uses statistical methods (e.g., feature density, binning, distribution fitting) to evaluate design sensitivity and estimate yield impact from design/process variations.
• These metrics help identify which design or cell-level parameters most strongly affect PPA/yield under process corners.

Overall, if you wanna optimize your design with process-aware PPA analysis, you can follow this flow:

A. Extract PPA: Use libMetric™ to extract cell-level PPA models.

B. Design Analysis: Plug these models into RTL-to-GDS flow (logic synthesis, STA, power analysis).

C. Variation Simulation: Use Copernic™ to simulate yield and performance under variation.

D. Adjustment: Based on analysis results, adjust design margins, cell choices, timing constraints, etc.

E. Repeat until both PPA and yield targets are met.