David Fuard

The motility of normal and cancer cells in response to the combined influence of the substrate rigidity and anisotropic microstructure

Cell adhesion and migration are strongly influenced by extracellular matrix (ECM) architecture and rigidity, but little is known about the concomitant influence of such environmental signals to cell responses, especially when considering cells of similar origin and morphology, but exhibiting a normal or cancerous phenotype. Using micropatterned polydimethylsiloxane substrates (PDMS) with tunable stiffness (500 kPa, 750 kPa, 2000 kPa) and topography (lines, pillars or unpatterned), we systematically analyse the differential response of normal (3T3) and cancer (SaI/N) fibroblastic cells. Our results demonstrate that both cells exhibit differential morphology and motility responses to changes in substrate rigidity and microtopography. 3T3 polarisation and spreading are influenced by substrate microtopography and rigidity. The cells exhibit a persistent type of migration, which depends on the substrate anisotropy. In contrast, the dynamic of SaI/N spreading is strongly modified by the substrate topography but not by substrate rigidity. SaI/N morphology and migration seem to escape from extracellular cues: the cells exhibit uncorrelated migration trajectories and a large dispersion of their migration speed, which increases with substrate rigidity.

Measurement of residual thickness using scatterometry

Nanoimprint lithography (NIL) processes have the characteristic that a residual resist layer is always present between the nanoimprinted features. This residual resist layer must be removed to obtain usable resist masks for pattern transfer. As this resist layer is removed using oxygen-based plasma processes, the residual thickness nonuniformity translates into feature width dispersion. Thus, the uniformity of this residual thickness after imprint remains an important issue for nanoimprint lithography and a reliable metrology procedure is required for. At present, the standard measurement method is based on scanning electron microscopy (SEM) cross section, which is destructive, time consuming, and may sometimes provide only moderate accuracy. The work presented here will assess and show the interest of scatterometry, which is a nondestructive optical method of metrology that can be easily applied to NIL. This measurement procedure exhibits very good accuracy on the two-dimensional-feature geometry determi...

An inverse ellipsometric problem for thin film characterization: comparison of different optimization methods

In this paper, an ill-posed inverse ellipsometric problem for thin film characterization is studied. The aim is to determine the thickness, the refractive index and the coefficient of extinction of homogeneous films deposited on a substrate without assuming any a priori knowledge of the dispersion law. Different methods are implemented for the benchmark. The first method considers the spectroscopic ellipsometer as an addition of single wavelength ellipsometers coupled only via the film thickness. The second is an improvement of the first one and uses Tikhonov regularization in order to smooth out the parameter curve. Cross-validation technique is used to determine the best regularization coefficient. The third method consists in a library searching. The aim is to choose the best combination of parameters inside a pre-computed library. In order to be more accurate, we also used multi-angle and multi-thickness measurements combined with the Tikhonov regularization method. This complementary approach is also part of the benchmark. The same polymer resist material is used as the thin film under test, with two different thicknesses and three angles of measurement. The paper discloses the results obtained with these different methods and provides elements for the choice of the most efficient strategy.

OPC model error study through mask and SEM measurement error

Mask and metrology errors such as SEM (Scanning Electron Microscopy) measurement errors are currently not accounted for when calibrating OPC models. Nevertheless, they can lead to erroneous model parameters therefore causing inaccuracies in the model prediction if these errors are of the same order of magnitude than targeted modeling accuracy. In this study, we used a dedicated design of hundres of features exposed through a Focus Exposure Matrix for the metrology error, we compared the SEM measurements to AFM measurements for as much as 105 features exposed in various process conditions of does and defocus. These data have then been used in a OPC model calibration procedure. We show that the impact of the metrology error is not negligible and demonstrate the importance of taking into account these errors in order to improve the reliability of the OPC models.

S-Genius, a universal software platformwith versatile inverse problem resolution for scatterometry

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.

Outliers detection by fuzzy classification method for model building

Optical Proximity Correction (OPC) is used in lithography to increase the achievable resolution and pattern transfer fidelity for IC manufacturing. Nowadays, immersion lithography scanners are reaching the limits of optical resolution leading to more and more constraints on OPC models in terms of simulation reliability. The detection of outliers coming from SEM measurements is key in OPC [1]. Indeed, the model reliability is based in a large part on those measurements accuracy and reliability as they belong to the set of data used to calibrate the model. Many approaches were developed for outlier detection by studying the data and their residual errors, using linear or nonlinear regression and standard deviation as a metric [8]. In this paper, we will present a statistical approach for detection of outlier measurements. This approach consists of scanning Critical Dimension (CD) measurements by process conditions using a statistical method based on fuzzy CMean clustering and the used of a covariant distance for checking aberrant values cluster by cluster. We propose to use the Mahalanobis distance [2] in order to improve the discrimination of the outliers when quantifying the similarity within each cluster of the data set. This fuzzy classification method was applied on the SEM CD data collected for the Active layer of a 65 nm half pitch technology. The measurements were acquired through a process window of 25 (dose, defocus) conditions. We were able to detect automatically 15 potential outliers in a data distribution as large as 1500 different CD measurement. We will discuss about these results as well as the advantages and drawbacks of this technique as automatic outliers detection for large data distribution cleaning.