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Machine learning techniques have recently been applied to the problem of lithographic hotspot detection. It is widely believed that they are capable of identifying hotspot patterns unknown to the trained model. The quality of a machine learning method is conventionally measured by the accuracy rates determined from experiments employing random partitioning of benchmark samples into training and testing...
Printability of layout objects becomes increasingly dependent on neighboring shapes within a larger and larger context window. In this paper, we propose a two-level hotspot pattern classification methodology that examines both central and peripheral patterns. Accuracy and runtime enhancement techniques are proposed, making our detection methodology robust and efficient as a fast physical verification...
Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address...
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