Detecting context sensitive hot spots in standard cell libraries

High performance lithographic hotspot detection using hierarchically refined machine learning

“…In [15], critical hotspot signature is proposed and extracted through certain special edge-based metrics. Although such edge-based extractions operate much faster compared with [13,14], its chip level application still faces similar issues such as scanning window coverage, etc.…”

“…Similarly in [14], SVM is employed for hotspot detection through extraction and classification of certain special layout density related metrics. As an improvement over [11,12], [13,14] demonstrate higher detection accuracy and lower classification noise, due to the high fidelity metrics introduced.…”

“…Similarly in [14], SVM is employed for hotspot detection through extraction and classification of certain special layout density related metrics. As an improvement over [11,12], [13,14] demonstrate higher detection accuracy and lower classification noise, due to the high fidelity metrics introduced. However, potential issues with such approaches lie in run time and detection coverage, since 2D transforms and density extractions can be expensive to perform, meanwhile detection windows for the layout images are hard to anchor for full chip level detections.…”

“…In order to be practically employed in modern IC physical design, a successful hotspot detection engine must demonstrate superior speed compared to full lithography simulation (> 100 CPUs running in the order of days) and DRC (tens of CPUs running for a few hours), as well as comparable performance to meet the real design and manufacturing requirements. Unfortunately under such situations, the hotspot evaluation models in [11][12][13][14][15] suffer from severe performance degradation, since real hotspot detection under real manufacturing conditions requires more than a straightforward model, but rather, multi-level detection models with hierarchies of performance refinement.…”

EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

“…(1) lithography simulations are very accurate but CPU intensive. (2) Machine learning techniques [7][8][9][10][11][12][13] with good noise suppression capability are still in need of further accuracy improvement. (3) Pattern matching techniques [14][15][16][17] that are very good at detecting pre-characterized hotspot patterns lack the capability to predict never-before-seen hotspots.…”