Optical proximity correction using bidirectional recurrent neural network (BRNN)

Abstract

Machine learning (ML) techniques have been applied for quick optical proximity correction (OPC) processing. A key limitation of previous ML-OPC approaches lies in the fact that a layout segment is corrected while the correction result for other segments is not reflected yet. Bidirectional recurrent neural network (BRNN) model is adopted in this paper to alleviate this problem. BRNN consists of multiple neural network instances, which are serially linked through hidden layer connections in both forward- A nd backward-directions. Each instance corresponds to one layout segment, so BRNN processing corrects a group of nearby segments together. Two key problems are identified and addressed: Mapping between layout segments and neural network instances, and network input features. In experiments, BRNN-OPC achieves 3.9nm average EPE for test M1 layout, which can be compared to 6.7nm average EPE from state-of-the-art ML-OPC method.