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.
Classical SEM metrology, CD-SEM, uses low data rate and extensive frame-averaging technique to achieve high-quality SEM imaging for high-precision metrology. The drawbacks include prolonged data collection time and larger photoresist shrinkage due to excess electron dosage. This paper will introduce a novel e-beam metrology system based on a high data rate, large probe current, and ultra-low noise electron optics design. At the same level of metrology precision, this high speed e-beam metrology system could significantly shorten data collection time and reduce electron dosage. In this work, the data collection speed is higher than 7,000 images per hr. Moreover, a novel large field of view (LFOV) capability at high resolution was enabled by an advanced electron deflection system design. The area coverage by LFOV is >100x larger than classical SEM. Superior metrology precision throughout the whole image has been achieved, and high quality metrology data could be extracted from full field. This new capability on metrology will further improve metrology data collection speed to support the need for large volume of metrology data from OPC model calibration of next generation technology. The shrinking EPE (Edge Placement Error) budget places more stringent requirement on OPC model accuracy, which is increasingly limited by metrology errors. In the current practice of metrology data collection and data processing to model calibration flow, CD-SEM throughput becomes a bottleneck that limits the amount of metrology measurements available for OPC model calibration, impacting pattern coverage and model accuracy especially for 2D pattern prediction. To address the trade-off in metrology sampling and model accuracy constrained by the cycle time requirement, this paper employs the high speed e-beam metrology system and a new computational software solution to take full advantage of the large volume data and significantly reduce both systematic and random metrology errors. The new computational software enables users to generate large quantity of highly accurate EP (Edge Placement) gauges and significantly improve design pattern coverage with up to 5X gain in model prediction accuracy on complex 2D patterns. Overall, this work showed >2x improvement in OPC model accuracy at a faster model turn-around time.
The extension of optical lithography to 7 nm node and beyond relies heavily on multiple litho-etch patterning technologies. The etch processes in multiple patterning often require progressively large bias differences between litho and etch as the target features become smaller. Moreover, since this litho-etch bias has strong pattern dependency, it must be taken into consideration during the Optical Proximity Correction (OPC) processes. Traditionally, two approaches are used to compensate etch biases: rule-based retargeting and model-based retargeting. The rule-based approach has a turn-around-time advantage but now has challenges meeting the increasingly tighter critical dimension (CD) requirements using a reasonable etch-bias table, especially for complex 2D patterns. Alternatively, model-based retargeting can meet these CD requirements by capturing the etch process physics with high accuracy, including the etch bias variability that arises from both patterning proximity effects and etch chamber non-uniformity. In the past, empirical terms have been used to approximate the etch bias due to pattern proximity effects but sometimes empirical models are known to have compromised model accuracy so a physical based approach is desired. This paper’s work will address the etch bias variability due to patterning proximity effects by using a physical approach based simplified chemical kinetics. It starts from a well calibrated After-Development-Inspection (ADI) model and the subsequent etch model is based on the ADI model contour. By assuming that plasma chemical species in the trenches are maintained in an equilibrium state, the plasma species act on the edges to induce etch bias. Methods are developed to evaluate plasma collision probability on trench edges for random layouts. Furthermore, the impact of resist materials on etch bias are treated with Arrhenius equation or as a second order reaction. Equations governing plasma collision probabilities on trench edges as a function of time are derived. An etch bias model can be calibrated based on those equations. Experimental results have shown that this physical approach to model etch bias is a promising direction to applications for full-chip etch proximity corrections.