Title: Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning,
Abstract: Layout hotpot detection is one of the main steps in modern VLSI design. A
typical hotspot detection flow is extremely time consuming due to the
computationally expensive mask optimization and lithographic simulation. Recent
researches try to facilitate the procedure with a reduced flow including
feature extraction, training set generation and hotspot detection, where
feature extraction methods and hotspot detection engines are deeply studied.
However, the performance of hotspot detectors relies highly on the quality of
reference layout libraries which are costly to obtain and usually predetermined
or randomly sampled in previous works. In this paper, we propose an active
learning-based layout pattern sampling and hotspot detection flow, which
simultaneously optimizes the machine learning model and the training set that
aims to achieve similar or better hotspot detection performance with much
smaller number of training instances. Experimental results show that our
proposed method can significantly reduce lithography simulation overhead while
attaining satisfactory detection accuracy on designs under both DUV and EUV
lithography technologies.