Improving library characterization with machine learning

Efficient and accurate library characterization is a critical step in full-chip or block-level design flows because it ensures that all library elements perform to specification under all intended operating conditions. However, traditional library characterization and validation have become increasingly expensive in terms of computation and engineering effort, due to complexity and the amount of characterized data. As characterization needs exceed the scalability of traditional methodologies, the risk of schedule delays, incomplete verification of characterized results, and re-spins due to chip failures increases.

This whitepaper describes innovative new approaches to accomplish fast and accurate library characterization and validation through mathematical modelling and machine learning. These methods accelerate characterization significantly, resulting in runtime speedup for production-accurate, full library characterization across all process, voltage, and temperatures (PVT), as well as almost-instant generation of additional PVTs.