Galileo Galilei once quoted: “Measure what is measurable, and make measurable what is not so”. In silicon manufacturing R&D phase, it often happens that engineers would like to access some parameter values that are not easy, even impossible to measure. When looking at a CDSEM image, the parameters of interest seem easy to extract but in practice getting access to them in a robust and reliable way is not always simple. Developing a contour-based metrology tool coupling robust contour extraction with a comprehensive contour metrology environment could help to bridge this gap. In previous works, it has been shown that SEM images contain significant amounts of information that can be extracted and analyzed using efficient contour extraction and analysis toolboxes [1, 2]. Also, the concept of implementing remote contour-based metrology has been introduced. The present work continues to unveil what can be achieved with such solutions. For that, the example of implant layers’ process assumption will be explored. During this process step, counter doping problems can occur for example when the distance between layers deviates from nominal. Therefore, it is crucial for design rule control to measure some critical dimensions such as minimum distance between layers, corner rounding, slope, etc. However, given the characteristics of the different structures in the images, which may come from different layers and/or processes steps, the measurements are not straightforward to extract with standard CDSEM metrology algorithms. Moreover, recipes are complex to setup, measurements by themselves are not very stable, and usually an indirect determination of the key figure is performed. In this paper, we will show that multilayer contour-based metrology, mixing image contour and GDS layout, allows to overcome the previously mentioned difficulties, as well as to generate measurements that are not possible to be performed by using standard algorithms.

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Contour based metrology: “make measurable what is not so"

This paper introduces line roughness characterization non-straight patterns made of block copolymers (fingerprint patterns). Line Width Roughness have been determined using Power Spectral Density based on a special edge detection developed at CEA-LETI to extract edges contours. We investigated several process parameters impact on LWR such as the degree of polymerization of different BCPs and the impact of UV irradiation on the roughness of the PS block.

Block copolymer line roughness measurement via PSD: application to fingerprint samples

Development of new mass spectrometer : measure what is measurable, and make measurable what is not so

CD-SEM is one of the workhorse metrology tools for evaluating the patterning performances in the semiconductor industry. With the continuous shrink of device feature sizes, measurement uncertainty, of such devices should improve accordingly, including tool-to-tool matching. Great efforts are made in fabs to ensure matching between CD-SEMs. Basically, there are two approaches : tool matching based on statistical CD measurements and tool matching based on physical tool parameters. The first methods require long recipe run on each tool for data collection while the second methods require dedicated maintenance tool time. In parallel, the emergence of SEM contour metrology brought new opportunities, for example in OPC modeling, process assessment or overlay metrology. Recently, a step has been made to demonstrate that offline SEM contour metrology can be matched with standard CD-SEM. In this paper we evaluate the possibility to use SEM contour metrology to perform a remote CD matching on images coming from two different CD-SEM. A large set of patterns of various dimensions and shapes (both 1D and 2D) is imaged with two different CD-SEM after etch process step. For each image the contours are extracted using a remote software. A matching model is calibrated to match contour measurements of the same pattern imaged with the two CD-SEMs. The matching model can be calibrated using either one or both inline SEM measurement or using additional metrology data (typically AFM measurement or any reference metrology measurement). The matching model parameters include both parameters for the contour extraction calibration and measurement matching parameters. Two modeling approaches are compared : one matching model averaging multiple measurement matching sites and a multiple (per pattern type) contour calibrations/ measurement matching sites model. These different matching models are assessed using as main metrics the remaining CD difference between SEM contour measurements on images coming from the two CD-SEM (ΔCDmatching) and TMU analysis. We show that an overall ΔCDmatching variability of 0.6 nm and a TMU below 2 nm can be obtained with the dataset. We evaluated a strategy to achieve matching of two CD-SEM using a remote contour extraction solution and a matching model.

Can remote SEM contours be used to match various SEM tools in fabs?

Among metrology tools in the semi-conductor manufacturing, critical dimension scanning electron microscopes (CD-SEM) are the most broadly used, especially due to their high resolution, low destructivity, and high throughput. Contour metrology on CD-SEM images has become essential for characterization, modelling, and control of advanced lithography processes. In particular, OPC model’s accuracy can be highly improved using contours metrology. One of the issues when dealing with CD-SEM metrology is that the results are noise sensitive. Moreover, diminishing noise in CD-SEM acquisition leads to resist shrinkage due to exposure time increase. In addition, post-treatment of these shrinkage effects requires compensation algorithms such as artificial intelligence (AI)- driven algorithms, that are another contributor to the error budget of metrology systems. There is thus a need for an accurate, robust to noise, and purely deterministic edge detection algorithm. In this article, we evaluate the benefits of relying on a model-based contour extraction approach for performing measurements. This approach is applied onto both synthetic and experimental CD-SEM images with various patterns (mostly 2D) and noise levels to assess the influence of image integration (frame number) on the contour detection and CD measurement. We demonstrate that a model-based contour extraction algorithm is able to precisely characterize SEM-induced 2D resist shrinkage. We observe that this model-based approach is more robust to noise than standard algorithms by 21% on synthetic data and by 36% on experimental data. Another way of seeing it is, while keeping the same precision, a model-based contour extraction approach can significantly reduce the requested image frame number. The benefits of adopting this approach range from reducing the shrinkage effects to improving SEM image acquisition time. Eventually, no step of shrinkage modelling calibration nor AI-driven image post processing are needed which implies a gain on simplicity and avoids modelling errors.

Model-based contour extraction: an enabler for very low-frame SEM images metrology

2D curvilinear patterns are more and more present in the lithography landscape. For the related devices, the line edge roughness (LER) is, as well as for lines and spaces, a critical figure of merit. In this article we propose to use a dedicated edge detection algorithm to measure LER of 2D curvilinear patterns on CD-SEM images. We present an original method to validate the algorithm, in the context of roughness measurement. It is based on the generation of realistic synthetic CD-SEM images with programmed roughness and a precise PSD analysis flow. We show excellent correlation (average R2 = 0.988) between the input roughness parameters and the measured parameters for both 1D and 2D synthetic images. Using synthetic images for different number of frames, the contour extraction sensitivity to noise is also explored. Finally, the methodology is successfully applied to experimental CD-SEM images for two classes of applications : photonic devices and DSA fingerprint patterns.

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Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

In the effort of continuing improving patterning strategies for increasing circuit density while reducing dimensions, several challenges regarding patterning fidelity emerge. In recent years, stochastic effects had their relative importance increased, and therefore the need for closely monitoring those effects is also increasing [1]. Among other stochastic effects, within-feature roughness is significant as it can impact circuit electrical behavior, decreasing time and power performance, and even lead to failures. The workhorse method of the industry for measuring roughness is based on top-down CD-SEM (Critical Dimension Scanning Electron Microscope) image. In recent years, methods have been proposed as a way to improve and standardize the roughness measurement [2, 3]. Those methods rely on the obtention of the power spectral density (PSD) from the detected edges of the features in SEM images, in order to determine their roughness. However, one important aspect is the impact of the CD-SEM image acquisition conditions on the limitation of the observed PSD. As the acquisition parameters changes, different frequencies may be more or less observable in a SEM image, potentially leading to errors in the metrology evaluation [4].

The goal of this study is to first, present the impact of the CD-SEM image acquisition conditions in the roughness measurement, and, second, propose a method to determine the validity domain of the roughness measurements as a function of the acquisition conditions. 

The proposed method relies on a compact SEM image model. For each acquisition condition, the model is calibrated based on experimental SEM images, from several design samples. Using this calibrated model, synthetic SEM images are generated with a known sample, including its programmed roughness signature (input-PSD), which can be a white noise (defined by a constant PSD). The next step relies on a robust-to-noise edge detection algorithm [5], which is then used to compute the PSD by applying the method proposed in [4]. As the input-PSD is known, it is possible to compute the transfer function of the acquisition system [6], for each of the evaluated acquisition conditions. We call ‘limit-PSD’ the transfer function which may be considered as the signature of the acquisition conditions in the frequency domain. It can be seen as a low-pass filter and it defines the validity domain of the roughness measurements. For each input-PSD (simulated or experimental), if it is ‘below’ the limit-PSD (within the low-pass filter), the measurement is within the validity domain. If the resulting measurement reaches the limit-PSD, it is not possible to know if some roughness information was lost due to the acquisition conditions. Such relationship is illustrated in Figure 1, presenting one case where the input-PSD is inside the validity domain and a second case where the actual roughness is underestimated.

Thanks to the produced results, the information of different acquisition conditions and detected roughness can be obtained and stored in what we call condition tables. For each acquisition condition, the limits of frequency range and roughness parameters range (ξ, H) of the measurable roughness are stored (Figure 2). These condition tables are very useful for assisting metrology specialists in choosing the most suitable acquisition conditions for the CD-SEM, if prior knowledge about the expected roughness is available, or in accounting for the validity domain of the roughness measurement. 

As a final step, the proposed method is applied to experimental data – an example of observed PSD over an experimental SEM image is shown in Figure 3. The experimental dataset is composed of SEM images acquired under different conditions (notably a large variation in landing energy – 300eV to 4000eV), and different sample materials (post-etch and post-litho samples). The obtained results confirmed the applicability of the proposed method in a real environment, and will be fully demonstrated in the final work.

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Jessy Bustos

Papers

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

Determining the validity domain of roughness measurements as a function of CD-SEM acquisition conditions