The semiconductor design node shrinking requires tighter edge placement errors (EPE) budget. OPC error, as one major contributor of EPE budget, need to be reduced with better OPC model accuracy. In addition, the CD (Critical Dimension) shrinkage in advanced node heavily relies on the etch process. Therefore AEI (After Etch Inspection) metrology and modeling are important to provide accurate pattern correction and optimization. For nodes under 14nm, the etch bias (i.e. the bias between ADI (After Development Inspection) CD and AEI CD) could be -10 nm ~ -50 nm, with a strong loading and aspect-ratio dependency. Etch behavior in advanced node is very complicated and brings challenges to conventional rule based OPC correction. Therefore, accurate etch modeling becomes more and more important to make precise prediction of final complex shapes on wafer for OPC correction. In order to ensure the accuracy of etch modeling, high quality metrology is necessary to reduce random error and systematic measurement error. Moreover, CD gauges alone are not sufficient to capture all the effects of the etch process on different patterns. Edge placement (EP) gauges that accurately describe the contour shapes at various key positions are needed. In this work we used the AEI SEM images obtained from traditional CD-SEM flow, processed with ASML’s MXP (Metrology for eXtreme Performance) tool, and used the extracted CD gauges and massive EP gauges to train a deeplearning Newron Etch model. In the approach, MXP reduced the AEI metrology random errors and shape fitting measurement error and provides better pattern coverage with massive reliable CD and EP gauges, Newron Etch captures complex and unknown physical and chemical effects learned from wafer data. Results shows that MXP successfully extracted stable contour from AEI SEM for various pattern types. Three etch models are calibrated and compared: CD based EEB model (Effective Etch Bias), CD+EP based EEB model, and CD+EP based Newron etch model. CD based EEB model captures the major trend of the etch process. Including EP gauges helps EEB model with about 10% RMS reduction on prediction. Integration of MXP (CD+EP) and Newron Etch model gains about 45% prediction RMS reduction compared to baseline model. The good prediction of Newron Etch is also verified from wafer SEM overlay on complex-shape patterns. This result validates the effectiveness of ASML’s solution of deep learning etch model integration with MXP AEI’s massive wafer data extraction from etch process, and will help to provide accurate and reliable etch modeling for advanced node etch OPC correction in semiconductor manufacturing.
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.
For a better understanding of small-scale solar activities, the Chinese Large Solar Telescope (CLST) with a 1.8-m aperture was proposed in 2011. As the first open solar telescope in China, it has some technical challenges that need to be addressed (e.g., thermal controlling for the primary mirror, cooling for the heat stop, system assembly, etc.). To support the design of CLST, a prototype of an open solar telescope (POST) with a 600-mm aperture was designed and fabricated from 2014 to 2017. A series of experiments for technical verifications were carried out based on the POST. The design, integration, and experiments done with the POST are reviewed. The solar observation results during its first commissioning phase are also presented.