Solido ML Characterization Suite
Fast, Accurate Library Characterization Tools Powered by Machine Learning
- Significantly reduces standard cell, memory, and I/O characterization time and resources
- Delivers production-accurate Liberty models—tune to work across all Liberty data types, such as timing, power, noise, waveform, and statistical data
- Machine Learning algorithms efficiently and accurately model the characterization space
- Real-time cross-validation techniques are then applied to determine model error and fit
- Proven in production customer environments and compatible with all characterization flows
Reduces library characterization time by 30 to 70%
Solido ML Characterization Suite Predictor uses machine learning to accelerate characterization of standard cells, memory, and I/O. Predictor accurately generates Liberty models at new conditions from existing Liberty data at different PVT conditions, Vt families, supplies, channel lengths, model revisions, and more. It significantly reduces up-front characterization time as well as turnaround to generate library models, and works with NLDM, CCS, CCSN, waveforms, ECSM, AOCV, LVF, and more.
Generates Monte Carlo accurate statistical timing models, >1000x faster than Monte Carlo
Solido ML Characterization Suite Statistical Characterizer uses machine learning technology to accelerate statistical characterization of standard cells, memory, and I/O. It delivers true 3-sigma LVF/AOCV/POCV values with Monte Carlo and SPICE accuracy, and handles non-Gaussian distributions. It adaptively selects simulations to meet accuracy requirements, minimizing runtime for all cells, corners, arcs, and slew-load combinations.
For fast, accurate, statistical characterization, Statistical Characterizer and Predictor can be used in combination. Use Statistical Characterizer on anchor corners to quickly add Monte Carlo accurate LVF, and then use Predictor to create and expand remaining corners, including statistical characterization data, generating savings of more than 50% without compromising accuracy.