In this paper, we demonstrate the efficient use of contours extracted from photomask SEM images to characterize features with respect to placement, pattern fidelity, and uniformity. Absorber defects can be automatically detected and categorized. To assess the probability of mask defect printing, we show how an extracted mask image contour can be used as input for a rigorous lithography 3D resist process simulation to quickly estimate the severity and potential printing behavior in resist of a defect through dose and focus. The presented simulation results are validated by wafer data. The results of this work could provide guidelines for the mask making process and mask inspection.
Background: Well-designed test patterns are required to ensure a robust patterning involving mask, lithography and etch. They are expected to anticipate potential process challenges while representing well the layout diversity. Problem: We expect good test patterns to have a high design space coverage with minimal redundancy. Is it possible to get optimal design contents while keeping a small footprint and following the design rules? Approach: In this work, after defining specific optical and geometrical features and discretizing the pattern as a binary matrix, we propose to use the signature of the pattern in the feature space to assign a score measuring the usefulness of the pattern. The score is used as a cost function to drive an iterative optimization of the pattern shape based on a differential evolution algorithm. Conclusion: We demonstrated how to generate compact test patterns with high design diversity customized to specific applications that should help to anticipate, represent or monitor well process challenges.
The insertion of sub-resolution assist features as part of the optical proximity correction flow is one of the main elements of advanced resolution enhancement techniques. It needs to be ensured that there is no unintentional printing of such a sub-resolution assist feature in the photoresist since it would detract the product yield. To be able to prevent unintentional printings, a robust printing prediction simulation model is required. The calibration of such a model can be challenging since common CD-SEM measurements cannot be used to quantify such small sized printings at a poor contrast. The assessment of SEM images through human judgement is inefficient and somewhat subjective to errors. In this work, we propose an automated method to quantify SRAF printing through layout assisted SEM image analysis. The motivation is to get a fast and objective model calibration thanks to the automation and the absence of human judgement.
The control and the characterization of semiconductor very fine devices on a wafer are commonly performed by mean of a scanning electron microscope (SEM) to derive a critical dimension (CD) from a pair of parallel edges extracted from the images. However, this approach is often not very reliable when dealing with complex 2D patterns. An alternative is to use SEM contour technique to extract all the edges of the image. This method is more versatile and robust but before being implemented in a manufacturing environment, it must demonstrate that it can be matched well with traditional CD-SEM. Aim: The objective of this work is to present a method to evaluate and optimize the CD matching between a reference standard SEM-CD and SEM-Contours. Approach: After describing the metric used to assess the matching performance, we propose to screen some important influent parameters to give an evaluation of the best matching that we achieved with our experimental data. Results: After optimizing the matching calibration parameters and optimizing the selection of the best anchor pattern for the matching we could achieve a 3s-Total Measurement Uncertainty of 0.8 nm and 3.2 nm for 1D and 2D patterns. Conclusions: We established a method to achieve good matching performance that should facilitate the introduction of SEM contour in a manufacturing environment.
KEYWORDS: Calibration, Data modeling, Resolution enhancement technologies, Process modeling, Scanning electron microscopy, Data acquisition, Optical lithography, Metrology, Lithography, Computer simulations
Computational lithography applications for OPC/ RET utilize models that represent the lithographic process in simulations. The quality of OPC/ RET wafer results strongly depends on the quality of the model. Hence, achieving model quality and experimental match is the goal of the model calibration process where models are calibrated to experimental data. Ideally, the model would be calibrated and validated to a data set that completely covers the entire design space and all process conditions. A promising alternative to the traditionally applied SEM-CD-based model calibration is the calibration to pattern contours directly with benefits in design space coverage, reduced metrology effort and data preparation complexity. However, contour calibration also demands a new standard operating procedure for contour specific metrology, pattern design and calibration. Goal of this work is to develop and exercise a full contour-based calibration methodology. Firstly, we discuss preconditions for a successful calibration: good quality contour input data, predictive modeling of optics, mask topography and 3D resist and additional calibrator functionality to include aspects of alignment and pattern specific measurement confidence. Secondly, we assess pattern for their calibration-suitability using a metric for pattern information density. Experiments are performed to show the applicability of the metric and the potential to calibrate to a minimal set of patterns. A model calibrated to a well selected single 2.25 μm2 contour is predicting a large set of pattern contours, 3D resist characteristics and SEM-CD focus-exposure process windows.
The consideration of wafer topography effects in lithographic modeling of implant layers is mandatory for sub 32nm processes. The approximate assumption that both oxide- and resist thickness are independent of pattern design can lead to large model prediction errors and OPC correction failure. An implant lithography modeling flow based on rigorous models is presented that covers a) the STI stack formation and its etch–proximity effects, b) the resist spin-on and resulting thickness fluctuations, c) the image formation in the modeled stack and d) the chemical characterization of implant photoresist. This approach shows accuracy benefits and will be used to augment the existing OPC correction flow.
Background: The continuous scaling of integrated circuit requires not only a very good control of the device critical dimensions but also a very accurate control of the device overlay between layers to achieve satisfactory yields. These two critical factors can be combined to a single metric called interlayer edge placement error (EPEinterlayer) that quantifies the process margin necessary to keep a safe separation, extension, or overlap between the edges of a pattern in one layer with respect to another pattern in a second layer. Aim: The purpose of this work is to characterize with scanning electron microscopy (SEM), the EPEinterlayer and overlay variances of complex contact shapes relative to a poly layer to assess the contributions of the systematic and random EPEinterlayer. Approach: SEM images of a few etched patterns were recorded sequentially for both contact and poly features at the same locations on the wafer. Then, SEM contours were extracted, aligned, and overlapped to derive EPEinterlayer. One experiment was focusing on intrawafer EPEinterlayer characterization whereas another was studying more specifically intrafield overlay variations. For the latter experiment, systematic overlay errors were added to facilitate the comparison of the SEM-based method with respect to a reference image-based overlay (IBO) method. Results: The earliest direct metrology of EPEinterlayer and overlay in device enabled by this work shows a very high variability of EPEinterlayer and overlay errors across wafer and across field. Conclusions: By directly measuring the EPEinterlayer on devices that are not accessible by the standard method (optical IBO on OL structures), we showed the feasibility of this metrology and observed more dimensional variance in devices than recognized with IBO, thereby enabling better control of device pattern variation.
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.
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 represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.
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.
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.
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.
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.
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