A Unified Machine Learning Through Focus Resist 3-D Structure Model

Abstract

To ensure post OPC data quality, examination based on estimated resist contours at resist bottom alone is insufficient, reliable prediction of lithography performance within process window must rely on complete information of on-wafer resist 3D structures. In this regard, resist 3D structure model, in particular, the through focus resist 3D structure model, with full chip capability will be the ultimate model in demand. To develop machine learning resist 3D structure models,we have proposed the physics-based information encoding scheme, together with carefully chosen deep convolution neural network and model training strategies. Our proposed through focus resist 3D structure model is based on conditional U-net structure with first five eigen images as model’s main inputs and the focus setting as the conditional input. The average normalized cross correlation (NCC) or mean structure similarity index between ground truth and model predicted resist 3D structures can reach 0.92. With single GPU (Tesla M60), it takes 6.1ms for the model to produce resist 3D structure covering area of 1.8umx1.8 $\mu {\mathrm{ m}}$ . The model is fast enough and can be engineered for full chip implementation. The model can extend the capability of detecting lithography process window aware resist loss related hotspots.