Calibre SONR

Calibre SONR is a feature-based machine learning platform that transfers the layout and process information to features. Calibre SONR machine-learning models can be used for various full-chip applications, including hotspot prediction and analysis, pattern reduction, and coverage check.

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Key Features

Feature-vector driven full-chip ML platform

Calibre SONR, a feature-vector driven machine-learning platform, enables a broad range fab solutions and customizable, scalable and integrated design and fab interaction analytics such as care area generation, layout clustering and comparison. It is compatible with the full Calibre eco-system.

High performance and accuracy

Unique pattern clustering technology

Calibre SONR offers a unique clustering method for unsupervised or semi-supervised machine-learning models. Compared to the open source or other commercial clustering or grouping methods, Calibre SONR shows significant performance benefits for the full chip data level (billions) and maintains high accuracy.

A SONR tree representing the pattern similarity
Flexible and efficient

Powerful platform for feature collection

Calibre SONR converts all kinds of information to features through its powerful and flexible feature collection platform. Information such as geometry dimension, pattern density, lithography image intensity, as well as customized properties generated from other Calibre tools can all be converted into features with excellent scalability and memory consumption.

Pattern-level feature collection platform
Billion data analysis feasible

MLDB for fast full-chip data saving and querying

Calibre SONR uses a unique database format (MLDB) for data saving and querying. Compared to the traditional CSV and SQL saving, MLDB file size can be compressed to tens or even hundreds of times smaller by using the optimized algorithm. Querying information from the database is very fast (seconds) and can use multiple threads for further speed up. 

Calibre MLDB- The corner stone of Design to Fab ML applications

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