Smart down-sampling using Calibre SONR

Using machine learning, the Calibre SONR tool performs smart down-sampling of patterns on wafers, and chooses the most representative patterns for different applications during semiconductor manufacturing. Users can also choose a range of needed representative patterns on which to tune their models. A comparison between the Calibre SONR and typical off-the-shelf down-sampling and clustering techniques is performed, demonstrating that the Calibre SONR tool not only gives better coverage to unique patterns, but also handles larger data sets.

Rehab Kotb Ali, Le Hong, "Smart down-sampling using SONR," Proc. SPIE 12052, DTCO and Computational Patterning, 120520N (26 May 2022); https://doi.org/10.1117/12.2614609

The Calibre SONR tool uses machine learning to Implement a smart pattern down-sampling process that selects the most representative wafer patterns during semiconductor manufacturing.

The post-design tapeout semiconductor manufacturing process requires multiple compact model-driven steps. Test pattern design is critical for producing stable, predictable compact models. Model calibration is usually performed on a test bank, which is a relatively small set of patterns expected to be included in this node.

After model calibration, checking model coverage and detecting hotspots at the full-chip level is performed. A full-chip design typically has 10s of millions of patterns—checking all of them would exceed the memory and runtime capacity of the compact model calibration flow. Down-sampling representative patterns from the real design is frequently used to boost model coverage and detect hotspots. Patterns chosen in the down-sampling process should cover all the different types of patterns in the full chip. Grouping similar hotspots together for root cause analysis can enhance yield by recalibrating the models to boost model coverage, or by adding new DFM rules to prevent hotspots.

In this presentation based on their SPIE paper, Rehab Kotb-Ali and Le Hong present a smart and efficient methodology to select the most representative patterns on the wafer.  The Calibre SONR tool is a complete machine learning platform that uses the design test chip and process information such as multi-layer interactions, OPC, and lithography and etching parameters to calculate needed features. It performs supervised, semi-supervised or unsupervised clustering, depending on the applications. The Calibre SONR tool has a machine learning database that handles large data sets efficiently with low memory and run time requirements. This platform can be used in multiple applications, including pattern reduction, pattern coverage, model coverage, layout comparison, hotspot detection, and defect classification.