Lightning Talk 21: EDA with ML, Rule-Based, or Both?

Abstract

Machine learning (ML) has been effectively applied to many applications these days mainly because huge amount of data is available through internet. This is not the case in semiconductor industry, where data is not shared between companies or even inside a single organization. ML model, in particular complex one with many parameters, is in danger of overfit when data volume is small and becomes inefficient.Rule-based (or expert) system has been considered a part of AI (artificial intelligence), which also includes ML, and has been popular in many EDA applications. This paper tries to compare the two methods when data volume is high and low. The expectation is that ML is more efficient with high data volume and rule-based is less sensitive to the amount of data and so can be a better choice in low data volume. In addition, combining both methods in a way that rules are revised with some guidance from ML model is investigated so that rule-based method can be a good option in low data volume.Two example applications are considered: OPC refragmentation, which has been addressed through random forest classifier (RFC) ML model [1], and placement utilization in low aspect ratio design, where CNN has been applied [3] to identify utilization distribution over layout sub-regions.