Francois Weisbuch

Enabling scanning electron microscope contour-based optical proximity correction models

Abstract. A scanning electron microscope (SEM) is the metrology tool used to accurately characterize very fine structures on wafers, usually by extracting one critical dimension (CD) per SEM image. This approach for optical proximity correction (OPC) modeling requires many measurements resulting in a lengthy cycle time for data collection, review, and cleaning, and faces reliability issues when dealing with critical two-dimensional (2-D) structures. An alternative to CD-based metrology is to use SEM image contours for OPC modeling. To calibrate OPC models with contours, reliable contours matched to traditional CD-SEM measurements are required along with a method to choose structure and site selections (number, type, and image space coverage) specific to a contour-based OPC model calibration. The potential of SEM contour model-based calibration is illustrated by comparing two contour-based models to reference models, one empirical model and a second rigorous simulation-based model. The contour-based models are as good as or better than a CD-based model with a significant advantage in the prediction of complex 2-D configurations with a reduced metrology work load.

Bringing SEM-contour based OPC to production

Calibrating an accurate OPC model usually requires a lot of one-dimensional CD-SEM measurements. A promising alternative is to use a SEM image contour approach but many challenges remain to implement this technique for production. In this work a specific flow is presented to get good and reliable contours well matched with traditional CDSEM measurements. Furthermore this work investigates the importance of site selection (number, type, image space coverage) for a successful contour-based OPC model. Finally the comparison of conventional and contour based models takes into account the calibration and verification performances of both models with a possible cross verification between model data sets. Specific advantages of contour based model are also discussed.

Improving contact layer patterning using SEM contour based etch model

The patterning of the contact layer is modulated by strong etch effects that are highly dependent on the geometry of the contacts. Such litho-etch biases need to be corrected to ensure a good pattern fidelity. But aggressive designs contain complex shapes that can hardly be compensated with etch bias table and are difficult to characterize with standard CD metrology. In this work we propose to implement a model based etch compensation method able to deal with any contact configuration. With the help of SEM contours, it was possible to get reliable 2D measurements particularly helpful to calibrate the etch model. The selections of calibration structures was optimized in combination with model form to achieve an overall errRMS of 3nm allowing the implementation of the model in production.

Measuring inter-layer edge placement error with SEM contours

For advanced technology nodes, the patterning of integrated circuits requires not only a very good control of critical dimensions but also a very accurate control of the alignment between layers. These two factors combine to define the metric of inter-layer edge placement error (EPE) that quantifies the quality of the pattern placement critical for yield. In this work, we consider the inter-layer EPE between a contact layer with respect to a poly layer measured with SEM contours. Inter-layer EPE was measured across wafer for various critical features to assess the importance of dimensional and overlay variability. Area of overlap between contact and poly as well as contact centroid distribution were considered to further characterize the interaction between poly and contact patterns.

Optimal structure sampling for etch model calibration

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one.

Contour-based etch modeling enablement: from pattern selection to final verification

Traditional CD-SEM metrology reaches its limits when measuring complex configurations (e.g. advanced node contact configurations). SEM extracted contours embody valuable information which is essential for building a robust etch prediction model [1, 2]. CDSEM recipe complexity, processing time and measurement robustness can be improved using contour based metrology. However, challenges for measurement pattern selection as well as final model verification arise. In this work, we present the full flow of implementing etch prediction models calibrated and verified with SEM contours into a manufacturing environment.

Pattern sampling for etch model calibration

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels –“internal, external, curvature, Gaussian, z_profile” – designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one. We also illustrate the usage of the specific kernel “z_profile” which adds a third dimension to the description of the resist profile.

Calibrating etch model with SEM contours

To ensure a high patterning quality, the etch effects have to be corrected within the OPC recipe in addition to the traditional lithographic effects. This requires the calibration of an accurate etch model and optimization of its implementation in the OPC flow. Using SEM contours is a promising approach to get numerous and highly reliable measurements especially for 2D structures for etch model calibration. A 28nm active layer was selected to calibrate and verify an etch model with 50 structures in total. We optimized the selection of the calibration structures as well as the model density. The implementation of the etch model to adjust the litho target layer allows a significant reduction of weak points. We also demonstrate that the etch model incorporated to the ORC recipe and run on large design can predict many hotspots.

Extending critical dimension measurement for optical microlithography with robust SEM image contours

Dimensions of fine and complex structures printed by microlithography inside a photoresist are inspected with the help of Scanning Electron Microscope generating high resolution top down greyscale digital images. The edges of the photoresist can be extracted from the images to produce contours that best exploit the relevant image information. Unfortunately, such contours are usually of bad quality and subject to edge detection errors due to the noise of the SEM image and the roughness of the photoresist. In this work, we introduce a new method to deal with contours to easily complete complex operations like smoothing of contours or robust averaging of multiple contours without the help of any reference contour layer. Our approach is to use level set to represent any 2D contour as a 3D surface. We demonstrate that we can easily smooth complex 2D contours of critical structures of around 50nm width. We also managed to generate very reliable contours by averaging multiple contours of low quality. Finally, the level set method was used to locally derive confidence about the determination of the average contours.

Assessing SEM contour based OPC models quality using rigorous simulation

OPC model of high quality relies on the accumulation of thousands of CD-SEM measurements with the drawback of long cycle time for data collection. Moreover regular CD measurements are not robust when dealing with critical bi-dimensional structures. In this paper, we propose to use SEM image contours for OPC model calibration and demonstrate the advantage in term of metrology work load. Two set of contours based on resist top and resist bottom measurements are extracted after lithography to generate simultaneously two OPC models. The performances of both models are evaluated with respect to rigorous S-Litho simulations. The model based on the resist bottom is very well matched with the rigorous simulation whereas the model based on resist top is not always following the rigorous simulation. It appears that resist thickness variations on specific hotspots is not compatible with the assumption of a simulated contour located in a single plane in resist.