The semiconductor manufacturing roadmap which generally follows Moore’s law requires smaller and smaller EPE (Edge Placement Error), and this places stricter requirements on OPC model accuracy, which is mainly limited by metrology errors, pattern coverage and model form. Current metrology errors are mainly related to SEM image noise and measurement difficulty in complex 2D patterns. And traditional model form improvement by adding empirical terms for PEB (Post Exposure Bake), NTD (Negative Tone Development) and PRS (Physical Resist Shrinkage) effects still cannot meet the accuracy spec because other physical and chemical effects are uncaptured. Fitting these effects also requires comprehensive pattern coverage during model calibration. Solely improving model form may overfit the metrology error, which is risky, while solely improving metrology ignores existing model errors: both factors are troublesome for OPC. In this paper, a new metrology (MXP, naming for Metrology of Extreme Performance) and deep learning (Newron, naming for a Deep Convolutional Neural Network model form) integrated solution is proposed, where MXP decreases the metrology errors and provides good pattern coverage with high-volume reliable CD and EP (Edge Placement) gauges, and Newron captures remaining complex physical and chemical effects embedded in high-volume gauges beyond the traditional model. This solution shows overall ~30% prediction accuracy improvement compared to baseline metrology and FEM+ (Focus Exposure Matrix) model flow in N14 NTD process, predicts SEM shape of critical weak points more accurately.
With semiconductor technology progressing beyond 5nm node, there is tremendous pressure on computational lithography to achieve both accuracy and speed. One very promising technique to accomplish this mission is to take full advantage of the maturing machine learning techniques based on neural network architecture. Some success has been achieved using convolution neural network (CNN) to obtain inverse lithography technology (ILT) solution with significantly less computational time. In general, CNN architecture consists of feature extraction layers and nonlinear mapping function construction layers. To train a CNN model requires a large amount of data and computational resource. To maintain certain intrinsic symmetries of imaging behavior, the feature extraction layers must be carefully engineered using weight sharing techniques or using well balanced training samples of different orientations, otherwise, feature extraction part will be skewed. It is therefore very desired to have a scheme that can obtain optimal feature vector for machine learning based computational lithography automatically without the need of feature extraction layers in CNN. In this paper, we will make an attempt to describe such a scheme and present our test results on machine learning based OPC and ILT solution. It should be understood that machine learning based computational lithography solutions do not possess the capability to replace conventional OPC or ILT completely due to its lack of required accuracy. However, it can provide an initial solution that is close enough to final OPC solution or ILT solution, therefore fast OPC and fast ILT can be realized.