Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Learning
This addresses the time-consuming hotspot detection problem for VLSI designers, representing an incremental improvement over existing methods.
The paper tackles the problem of costly layout hotspot detection in VLSI design by proposing an active learning-based method that simultaneously optimizes the machine learning model and training set, achieving satisfactory detection accuracy with significantly reduced lithography simulation overhead.
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.