For CMOS image sensors fabrication, deep trenches are commonly incorporated in the device to isolate the individual pixel one another within the pixel matrix. These etched structures typically exhibit a high aspect ratio of 1:20 and controlling such narrow and deep object is a challenge for inline metrology. In a manufacturing environment, the preferred method for trench height measurements remains the optical scatterometry (OCD) technique as being very sensitive and reliable. Still, it requires time and resources for model construction and validation. It appears then that an analysis of its predicted sensitivity could be a valuable pre-step before starting any activity on large periodical objects where OCD sensitivity can reach its limits. In this study, we tested this approach for deep trench structures with CD dimension in the range of 100nm to 1400nm and the depth from 100 nm to 5 μm. The periodicity (pitch) was fixed at CD*2. At first, 3D Mueller scatterometry signatures were modelled selecting spectroscopic ellipsometry acquisition configurations according to industrial most common ones. Thanks to an optimized RCWA (Rigorous Coupled Wave Analysis) code developed inhouse, calculation durations were reduced enough to allow massive data generation. By implementing a sensitivity analysis approach that uses Sobol coefficients, the sensitivity of the OCD metrology technique is here evaluated for each CD and depth values. More particulary, it will be illustrated by a CD range of ±10% of 350nm and with the depth of the trench varying from 100nm up to 5μm. As a results, a sensitivity frontier can be estimated at around 3μm, a critical depth value above which OCD in the given configuration is no more sensitive to the metrics determination. Such observation will be further discussed by analysis of convergence evaluation.

Since 2010, a lot of progress was done concerning Small Angle X-ray Scattering (SAXS) extraction capabilities for dimensional control of line gratings. In this paper, we summarize the general methodology, including transmission (T-)SAXS experimental setup, and extract the line shape and roughness in line gratings. We describe step by step the full data treatment to extract the pitch, critical dimension (CD), line shape and roughness, focusing on the Line Width Roughness (LWR) extraction through a Power Spectral Density (PSD) analysis. The reliability of the extraction by T-SAXS is evaluated thanks to different extractions, comparison to TEM cross-section and analyses of count statistic in case of line roughness.

In the domain of advanced patterning, and especially at lithography steps achieve very small sizes becomes more and more crucial. This induces measurement challenges and thus requiring the development of new, precise and robust metrology techniques. To overcome the limited constraints of different techniques, one of the most promising approaches is hybrid metrology. It consists in gathering several metrology techniques to measure all the geometrical parameters which are processed them by an algorithm (mainly machine learning algorithm). This work stands out by using for deep learning a multi-branch neural network to increase the precision of predicts. With a particular attention made to the dataset generation and specific settings for each branch, we developed the potential of this approach which increase the precision of predicts.

Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to improve metrology at nanoscale characterization by hybridization of several techniques. Namely, optical critical dimension (OCD) or scatterometry, CD–Scanning electron microscopy (CDSEM), CD–Atomic force microscopy (CDAFM) and CD–Small angle x-rays scattering (CDSAXS). With virtual data of tabular–type generated by modelling, the ANN is able to predict the geometrical parameters compared to true measured values with high accuracies and detect irregularities in input data.

The capabilities of Small Angle X-ray Scattering (SAXS) for dimensional control of line gratings are reviewed. We first introduce different experimental methodologies used to extract the pitch, the critical dimension (CD) and the side-wall angle (SWA) of line gratings. A special focus is done on line roughness extraction. We already demonstrated that the SAXS technique has the sensitivity to measure line roughness amplitude below 1 nm on a set of line gratings designed with a controlled line roughness [1]. Fast Fourier Transforms (FFT) simulations revealed that the Line Width Roughness (LWR) defined as a Power Spectral Density (PSD) can be measured in a SAXS pattern at some specific positions in the reciprocal space [2]. In the present study, a comparison of the LWR PSD extracted by SAXS and by Scanning Electron Microscope (SEM) on one set of samples was done.

In the microelectronics industry, most of the dimensional metrology relies on Critical Dimension (CD) estimation. These measurements are mainly performed by Critical Dimension Scanning Electron Microscopy (CD-SEM), because it is a very fast, mainly non-destructive method, and enables direct measurements on wafers. To measure CDs, the distance is estimated between the edges of the observed pattern on a SEM image. As the critical dimension becomes smaller and smaller, the needs for more reliable metrology techniques emerge. In order to obtain more meaningful and reproducible CD measurements regardless of the pattern type (line, space, contact, hole . . . ), one needs to perform a CD measurement at a known and constant height thanks to a methodology that determines the topographic shape of the pattern from SEM images. A SEM capable of bending the electron beam (up to 12° in our case) allows to catch images at different angles, giving access to more information. From the analysis of such images, pattern height and sidewall angles can be consequently determined using geometric considerations.1 Understanding interactions between 3D shapes, pattern's material and the electron beam, becomes essential to correlate topography information. A preliminary work based on Monte-Carlo simulations was conducted using JMONSEL, a software developed by the NIST. Thanks to this analysis, it is possible to determine theoretical trends for different topographies and beam tilt conditions. Thanks to the effects highlighted by simulations, the processing of the tilted beam SEM images will be presented, as well as the method used to create a mathematical model allowing topographic reconstruction from these images. Finally some reconstruction using this model will be shown and compare to reference measurements. The overall flow used to process images is presented. First, images are transformed into grayscale profiles in order to process them. After a smoothing procedure, positional descriptors are computed for specific profile derivatives values. Then, from these descriptors coming from two images of the same pattern taken at different tilt angles, we use a low-complexity linear model in order to obtain the geometrical parameters of the structure. This model is created and calibrated thanks to JMONSEL simulations and then re-calibrated on real silicon patterns. We demonstrate that the use of real SEM images coming from real silicon patterns with our model leads to results that are coherent with conventional 3D measurements techniques taken as reference. Moreover, we are able to make reliable reconstructions on patterns of various heights with a single calibrated model. Our batch of experiment shows a 3-sigma standard deviation of 13% on the estimated height. We also show, thanks to simulations, that we are able to reconstruct the corners rounding (CR) from SEM images. However, because our wafer do not present a variability, the CR measurements still need to be assessed.

For the most advanced nodes, line roughness reaches the same order of magnitude as the CD. It results in a huge impact on power consumptions and leads to some device failures. Hence, the control of this morphological aspect needs an adapted metrology. CD-SEM is considered as an adapted technique for roughness extraction. It is based on the PSD extraction that allows to obtain roughness information in frequency domain. CD-SAXS has been mentioned as one of the highest potential techniques for microelectronics by ITRS with an expected resolution better than one angstrom. The study presented in this article is based on programmed roughness simulations and first experimental measurements. It demonstrates that a complete PSD can also be extracted from a CD-SAXS analysis and that extended information of roughness can be so deduced. Comparison of SEM and SAXS proves the capability of SAXS technique for the PSD extraction of line roughness. Next challenges to improve this extraction are mentioned.

In the microelectronics industry, most of the dimensional metrology relies on critical dimension (CD) estimation. These measurements are mainly performed by critical dimension scanning electron microscopy, because it is a very fast, mainly nondestructive method and enables direct measurements on wafers. To measure CDs, the distance is estimated between the edges of the observed pattern on an SEM image. As the CD becomes smaller and smaller, the needs for more reliable metrology techniques emerge. In order to obtain more meaningful and reproducible CD measurements regardless of the pattern type (line, space, contact, hole, etc.), one needs to perform a CD measurement at a known and constant height due to a methodology that determines the topographic shape of the pattern from SEM images. An SEM capable of bending the electron beam (up to 12 deg in our case) allows images to be caught at different angles, giving access to more information. From the analysis of such images, pattern height and sidewall angles can be determined using geometric considerations. Understanding interaction between three-dimensional (3-D) shapes, pattern materials, and the electron beam becomes essential to correlate topography information. A preliminary work based on Monte-Carlo simulations was conducted using JMONSEL, a software developed by the National Institute of Standards and Technology. With this analysis, it is possible to determine theoretical trends for different topographies and beam tilt conditions. Due to the effects highlighted by simulations, the processing of the tilted beam SEM images will be presented, as well as the method used to create a mathematical model allowing topographic reconstruction from these images. Finally some reconstruction using this model will be shown and compared to reference measurements. The overall flow used to process images is presented. First, images are transformed into grayscale profiles. After a smoothing procedure, positional descriptors are computed for specific profile derivatives values. Then, from these descriptors coming from two images of the same pattern taken at different tilt angles, we use a low-complexity linear model in order to obtain the geometrical parameters of the structure. This model is created and initially calibrated using JMONSEL simulations and then recalibrated on real silicon patterns. We demonstrate that the use of real SEM images coming from real silicon patterns with our model leads to results that are coherent with conventional 3-D measurements techniques taken as reference. Moreover, we are able to make reliable reconstructions on patterns of various heights with a single calibrated model. Our batch of experiment shows a three-sigma standard deviation of 10 nm on the estimated height for heights ranging from 50 nm to more than 200 nm. Based on simulations, we are able to reconstruct the corner rounding (CR) from SEM images. However, because our wafer has no CR variability, measurements still need to be assessed on real wafer. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

At modern manufacturing geometries, roughness control presents a huge challenge for the lithography step. For advanced nodes, this morphological aspect reaches the same order of magnitude as the critical dimension (CD). Hence, the control of roughness needs an adapted metrology. Specific samples with designed roughness have been manufactured using e-beam lithography. These samples have been characterized with three different methodologies: CD-scanning electron microscopy, optical critical dimension, and small angle x-ray scattering. The main goal is to compare the capability of each of these techniques in terms of reliability, type of information obtained, time to obtain the measurements, and level of maturity for the industry. The next step will be to develop a hybrid metrology approach for roughness determination with these techniques. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

Nowadays, roughness control presents a huge challenge for the lithography step. For advanced nodes, this morphological aspect reaches the same order of magnitude than the Critical Dimension. Hence, the control of roughness needs an adapted metrology. In this study, specific samples with designed roughness have been manufactured using e-beam lithography. These samples have been characterized with three different methodologies: CD-SEM, OCD and SAXS. The main goal of the project is to compare the capability of each of these techniques in terms of reliability, type of information obtained, time to obtain the measurements and level of maturity for the industry.

From the first digital cameras which appeared during the 70s to cameras of current smartphones, image sensors have undergone significant technological development in the last decades. The development of CMOS image sensor technologies in the 90s has been the main driver of the recent progresses. The main component of an image sensor is the pixel. A pixel contains a photodiode connected to transistors but only the photodiode area is light sensitive. This results in a significant loss of efficiency. To solve this issue, microlenses are used to focus the incident light on the photodiode. A microlens array is made out of a transparent material and has a spherical cap shape. To obtain this spherical shape, a lithography process is performed to generate resist blocks which are then annealed above their glass transition temperature (reflow). Even if the dimensions to consider are higher than in advanced IC nodes, microlenses are sensitive to process variability during lithography and reflow. A good control of the microlens dimensions is key to optimize the process and thus the performance of the final product. The purpose of this paper is to apply SEM contour metrology [1, 2, 3, 4] to microlenses in order to develop a relevant monitoring methodology and to propose new metrics to engineers to evaluate their process or optimize the design of the microlens arrays.

Currently, Line Edge Roughness (LER) and Line Width Roughness (LWR) control presents a huge challenge for the lithography step in microelectronic industries. For advanced nodes, this morphological aspect reaches the same order of magnitude than the Critical Dimension, which leads to an increased power consumption by transistors and devices. Hence, the control of roughness needs an adapted metrology. This study proposes to manufacture roughness standard samples and their validation. These samples can be used as standards to evaluate the capabilities of several tools. The preliminary part of this study has been carried out with periodical roughness sample to demonstrate the metrology approach. Further, programming of roughness based on Power Spectral Density (PSD) with Auto-Correlation Function (ACF) model is used to achieve roughness close to the real roughness case. A description of how design programmed roughness has been made and its exposition in the real conditions are detailed in this study. Moreover, a specific methodology of control has been developed, the results obtained have been compared with design inputs and mostly validated by experimental processes. This work represents the first step of manufacturing roughness standard samples based on PSD model design.

Today's CD-SEM metrology is challenged when it comes to measuring complex features found in patterning hotspots (like tip to tip, tip to side, necking and bridging). Metrology analysis tools allow us to extract SEM contours of a feature and convert them into a GDS format from which dimensional data can be extracted. While the CD-SEM is being used to take images, the actual measurement and the choice of what needs to be measured is done offline. Most of the time this method is used for OPC model creation but barely for process variability analysis at nominal process conditions. We showed in a previous paper [1] that it is possible to study lithography to etch transfer behavior of a hotspot using SEM contours. The goal of the current paper is to go extend this methodology to quantify process variability of 2D features using a new tooling to measure contour data.

Today's technology nodes contain more and more complex designs bringing increasing challenges to chip manufacturing process steps. It is necessary to have an efficient metrology to assess process variability of these complex patterns and thus extract relevant data to generate process aware design rules and to improve OPC models. Today process variability is mostly addressed through the analysis of in-line monitoring features which are often designed to support robust measurements and as a consequence are not always very representative of critical design rules. CD-SEM is the main CD metrology technique used in chip manufacturing process but it is challenged when it comes to measure metrics like tip to tip, tip to line, areas or necking in high quantity and with robustness. CD-SEM images contain a lot of information that is not always used in metrology. Suppliers have provided tools that allow engineers to extract the SEM contours of their features and to convert them into a GDS [1]. Contours can be seen as the signature of the shape as it contains all the dimensional data. Thus the methodology is to use the CD-SEM to take high quality images then generate SEM contours and create a data base out of them. Contours are used to feed an offline metrology tool that will process them to extract different metrics. It was shown in two previous papers [2, 3] that it is possible to perform complex measurements on hotspots at different process steps (lithography, etch, copper CMP) by using SEM contours with an in-house offline metrology tool. In the current paper, the methodology presented previously will be expanded to improve its robustness and combined with the use of phylogeny [4] to classify the SEM images according to their geometrical proximities.

Critical dimension and overlay measurements have become a key challenge in microelectronics process control, and the weight of metrology in the success of a patterning technique is increasing. For the 14 nm node, the limit of scanner resolution can be overcome by double patterning, which requires a maximum overlay variability of 3 nm between the two reticles of the first metal level. In the double patterning case of metal layers, critical dimension of line spaces and overlay are no longer independent. In this paper, the possibility of a common measurement after the second lithography is studied. Scatterometry has been used to fit successfully the critical dimension of the two sublevels. As sensitivity to overlay is too low in device-like target, a strategy has been implemented from diffraction-based overlay measurement. So it becomes possible to provide information on the lithography step quality before the second etch process to enable rework if necessary. Finally a scatterometry target has been designed to fit simultaneously the two critical dimensions and overlay. This target, which is designed to maximize overlay sensitivity, has been placed in the next 14 nm CMOS product and is expected to make this scatterometry method even more attractive.

In microelectronics the two crucial parameters for the lithography step are the critical dimension, which is the width of the smallest printable pattern, and the misalignment error of the reticle, called overlay. For the 14 nm node, the limit of scanner resolution can be overcome by the double patterning technique, which requires a maximum overlay error between the two reticles of 3 nm [1]. The current approach in the measurements of critical dimension and overlay is to treat them separately, but it has become much more complex in the double patterning context, since they are no longer independent. In this paper, a strategy of a common measurement is developed. The aim of the strategy is to measure simultaneously overlay and critical dimension in the metal level double patterning grating before the second etch process. The scatterometry technique is well known for critical dimension measurement. This study demonstrates that the overlay between the two gratings can also be deduced. Thanks to this original scatterometry-based method, it becomes possible to provide information on the lithography step quality before the second etch process; therefore the lithography can be reworked if it is necessary.

The objective of this paper is to extend the ability of a more stable overall process control for the 28 nm Metal layer. A method to better control complex 2D-layout structures for this node is described. Challenges are coming from the fact that the structures, which limit the process window are mainly of 2D routing nature and are difficult to monitor. Within the framework of this study the emphasis is on how to predict these process-window-limiting structures upfront, to identify root causes and to assist in easier monitoring solutions enhancing the process control.To address those challenges, the first step is the construction of a reliable Mask-3D and Resist-3D model. Advanced 3D-modeling allows better prediction of process variation upfront. Furthermore it allows highlighting critical structures impacted by either best-focus shifts or by low-contrast resist-imaging effects, which then will be transferred non-linearly after etch.This paper has a tight attention on measuring the 3D nature of the resist profiles by multiple experimental techniques such as Cross-section scanning electron microscopy methods (X-SEM) and atomic force microscopy (AFM). Based on these measurements the most reliable data are selected to calibrate full-chip Resist-3D model with. Current results show efficient profile matching among the calibrated R3D model, wafer AFM and X-SEM measurements. In parallel this study enables the application of a new metric as result of the resist profiles behavior in function of exposure dose. In addition it renders the importance on the resist shape. Together these items are reflected to be efficient support on process optimization and improvement on the process control.

The aim of this paper is to present a new method of in-line determination of etching tool parameters deviation during the transistor fabrication. For that, we study the possibility to use an optical metrology technique, the scatterometry, and its capability to determine quickly and accurately the temporal evolution of geometric dimensions of a periodic pattern. In this case, this optical tool can be considered as an external monitoring probe. The experiments developed in this article are based on a DOE where 20 different experiments are made, followed both by scatterometric measurements and internal etching tool probes. Comparing the two outputs, we determine the correlations between the evolution of the geometrical parameters of the pattern and the fluctuation of the internal tool parameters. We conclude that the use of a scatterometer following the evolution of the geometrical parameters of a pattern during an etching process is also a good tool to in-line anticipate the drift of the etching parameters.

The low-k1 domain of immersion lithography tends to result in much smaller depths of focus (DoF) compared to prior technology nodes. For 28 nm technology and beyond it is a challenge since (metal) layers have to deal with a wide range of structures. Beside the high variety of features, the reticle induced (mask 3D) effects became non-negligible. These mask 3D effects lead to best focus shift. In order to enhance the overlapping DoF, so called usable DoF (uDoF), alignment of each individual features best focus is required. So means the mitigation of the best focus shift. This study investigates the impact of mask 3D effects and the ability to correct the wavefront in order to extend the uDoF. The generation of the wavefront correction map is possible by using computational lithographic such Tachyon simulations software (from Brion). And inside the scanner the wavefront optimization is feasible by applying a projection lens modulator, FlexWaveTM (by ASML). This study explores both the computational lithography and scanner wavefront correction capabilities. In the first part of this work, simulations are conducted based on the determination and mitigation of best focus shift (coming from mask 3D effects) so as to improve the uDoF. In order to validate the feasibility of best focus shift decrease by wavefront tuning and mitigation results, the wavefront optimization provided correction maps are introduced into a rigorous simulator. Finally these results on best focus shift and uDoF are compared to wafers exposed using FlexWave then measured by scanning electron microscopy (SEM).

S-Genius is a new universal scatterometry platform, which gathers all the LTM-CNRS know-how regarding the rigorous electromagnetic computation and several inverse problem solver solutions. This software platform is built to be a userfriendly, light, swift, accurate, user-oriented scatterometry tool, compatible with any ellipsometric measurements to fit and any types of pattern. It aims to combine a set of inverse problem solver capabilities — via adapted Levenberg- Marquard optimization, Kriging, Neural Network solutions — that greatly improve the reliability and the velocity of the solution determination. Furthermore, as the model solution is mainly vulnerable to materials optical properties, S-Genius may be coupled with an innovative material refractive indices determination. This paper will a little bit more focuses on the modified Levenberg-Marquardt optimization, one of the indirect method solver built up in parallel with the total SGenius software coding by yours truly. This modified Levenberg-Marquardt optimization corresponds to a Newton algorithm with an adapted damping parameter regarding the definition domains of the optimized parameters. Currently, S-Genius is technically ready for scientific collaboration, python-powered, multi-platform (windows/linux/macOS), multi-core, ready for 2D- (infinite features along the direction perpendicular to the incident plane), conical, and 3D-features computation, compatible with all kinds of input data from any possible ellipsometers (angle or wavelength resolved) or reflectometers, and widely used in our laboratory for resist trimming studies, etching features characterization (such as complex stack) or nano-imprint lithography measurements for instance. The work about kriging solver, neural network solver and material refractive indices determination is done (or about to) by other LTM members and about to be integrated on S-Genius platform.