Litho-aware redundant local-loop insertion framework with convolutional neural network

With the VLSI technology shrinking to 7nm and beyond, the Redundant Local Loop (RLL), also known as via pillar, becomes a promising candidate of redundant via insertion due to its compatibility with the unidirectional layout style. Existing RLL insertion approaches only leverage rule-based heuristics for manufacturing constraints, which can no longer obtain a large enough Process Window (PW) in advanced technology nodes. It is imperative to develop new techniques to optimize lithography process window while inserting RLL to achieve a good yield. In this paper, we propose a machine learning-based litho-aware RLL insertion framework. Conventional lithography simulation requires tremendous computational resources to evaluate the lithography quality accurately, which is not feasible for process window exploration. We formulate the lithography simulation as a regression task and develop a customized Conventional Neural Network (CNN) architecture to predict the Depth of Focus (DOF), a standard metric for evaluating process window. We propose a complete ow for litho-aware RLL insertion based on the CNN model for process window evaluation. The commercial lithography simulator evaluates the effectiveness of the proposed framework. Experimental results demonstrate that our lithography model can predict the DOF with high accuracy and generalize well on unseen patterns while achieving orders of magnitude speedup compared to conventional lithography simulation. Our litho-aware RLL insertion framework can effectively improve the lithography process window with comparable runtime and insertion rate compared to the state-of-the-art method.