Addressing library characterization and verification challenges using ML

At advanced process nodes, Liberty or library (.lib) requirements are more demanding due to design complexities, increased number of corners required for timing signoff, and the need for statistical variation modeling. This results in an increase in size, complexity, and the number of .lib characterizations. Validation and verification of these complex and large .lib files is a challenging task and poses a significant threat to successful timing closure and even silicon failures if the .lib errors are not detected and fixed in time.

This white paper describes the use of machine learning (ML) techniques in the Siemens EDA Solido™ Characterization Suite that accelerates production quality .lib characterization and verification at advanced technology nodes. These ML techniques address some of the fundamental challenges with the demanding .lib requirements of modern technology nodes and their validation.

ML-enabled .lib production and verification with Solido Generator and Solido Analytics

The Solido Characterization Suite uses production-proven ML techniques to accelerate library
characterization and verification of standard cells,
memory and custom blocks. The two main components of the suite are Solido Generator and Solido
Analytics.

Solido Generator uses ML methods to accelerate the
overall library characterization process by instantly
generating libraries for additional PVT corners after
the initial characterization. Solido Generator uses
existing SPICE-characterized libraries as anchor data
to build ML models of the libraries and produce new
PVT libraries.

Prior to generating the additional PVTs, Solido
Generator analyzes the anchor corner set to determine the optimized set of libraries needed for additional PVT generation. Since the tool uses a set of
pre-characterized .libs, it eliminates the dependency
on SPICE netlists or subcircuits and the need to replicate characterization settings to match that of
the library vendor. Solido Generator runs about 100
times faster than traditional SPICE.

The ML-enabled methods in Solido Generator give
users the “best of both worlds” by generating production-accurate LVF .libs for additional PVT corners in a fraction of runtime compared to brute-force Monte Carlo or approximated Monte Carlo methods, while
retaining accuracy equivalent to its input anchor .libs.
Solido Analytics is an advanced library validation,
analysis, and debugging solution that includes not only fast, parallelized, and comprehensive static rule-based checks, but also employs an ML outlier detection tool that “learns” the expected characterized values in a
library and automatically detects errors like outliers or non-monotonic behaviors in the characterized data
that typically go undetected with other tools.