DRC-Based Hotspot Detection Considering Edge Tolerance and Incomplete Specification

Abstract

To improve the yield in current manufacturing processes, the problematic layout configurations, so-called process-hotspots, should be detected and replaced with yield friendly patterns. A hotspot pattern with edge tolerances and incomplete specifications, where edges may vary in a certain range and any layout configurations may exist in its ambit regions, can sufficiently and generally represent a process-hotspot. This type of hotspots, however, cannot be efficiently or correctly detected by using the state-of-the-art string-matching-based method. In this paper, we present an accurate and efficient DRC-based hotspot detection framework to handle hotspot patterns with edge tolerances and incomplete specifications. Unlike existing DRC-based work, which handles only completely specified patterns, we extract critical design rules to represent all possible topologies of hotspot patterns with edge tolerances and incomplete specifications. We further order these rules to iteratively reduce the search regions of a layout during design rule checking. Then, we apply longest common subsequence and linear scan to locate all hotspots accurately and efficiently. Compared with the state-of-the-art work, experimental results show that our approach can reach promising success rates with significant speedups.