Sizhe Li

Sebastien Lamant

Julien Carlier

Malika Toubal

Pierre Campistron

Xiumei Xu

Guy Vereecke

Vincent Senez

Vincent Thomy

Bertrand Nongaillard

High-Frequency Acoustic for Nanostructure Wetting Characterization

Nanostructure wetting is a key problem when developing superhydrophobic surfaces. Conventional methods do not allow us to draw conclusions about the partial or complete wetting of structures on the nanoscale. Moreover, advanced techniques are not always compatible with an in situ, real time, multiscale (from macro to nanoscale) characterization. A high-frequency (1 GHz) acoustic method is used for the first time to characterize locally partial wetting and the wetting transition between nanostructures according to the surface tension of liquids (the variation is obtained by ethanol concentration modification). We can see that this method is extremely sensitive both to the level of liquid imbibition and to the impalement dynamic. We thus demonstrate the possibility to evaluate the critical surface tension of a liquid for which total wetting occurs according to the aspect ratio of the nanostructures. We also manage to identify intermediate states according to the height of the nanotexturation. Finally, ourmeasurements revealed that the drop impalement depending on the surface tension of the liquid also depends on the aspect ratio of the nanostructures. We do believe that our method may lead to new insights into nanoscale wetting characterization by accessing the dynamic mapping of the liquid imbibition under the droplet.

INTRODUCTION

The wettability of solid surfaces is governed by both the chemical composition and the geometry of a surface on the micro or nanoscale. The thermodynamic equilibrium contact angles on rough and heterogeneous surfaces are commonly described as Wenzel and Cassie-Baxter models of interaction. [START_REF] Lafuma | Superhydrophobic states[END_REF] These two models describe two possible wetting states: the Wenzel and the composite (Cassie-Baxter) regimes. In the Wenzel state, the liquid penetrates the roughness of the interface. In the Cassie-Baxter model, air is trapped in the cavities of a rough surface, resulting in a composite solidliquid-air interface. Superhydrophobic surfaces with a contact angle (CA) higher than 150 o and low contact angle hysteresis (CAH < 5°) display extremely low adhesion to water. The wellknown lotus effect 2 that denotes roughness-induced superhydrophobicity and the self-cleaning abilities of biological and artificial surfaces has been adopted in various applications in many disciplines, leading to a huge number of articles for more than a decade. [START_REF] Herminghaus | Roughness-induced non-wetting[END_REF][START_REF] Marmur | From hygrophilic to superhygrophobic: theoretical conditions for making high-contact-angle surfaces from low-contactangle materials[END_REF][START_REF] Bocquet | A smooth future ?[END_REF][START_REF] Tuteja | Designing Superoleophobic Surfaces[END_REF][START_REF] Kwon | Is the Cassie-Baxter formula relevant?[END_REF] An important application of nanoscale wetting concerns nanoscale wet cleaning, drying, and patterning in the field of nanoelectronics. All of these wet processes are based on nanoscale effective wetting. As a consequence, partial imbibition (either the homogeneous partial wetting of a liquid in a semi-Wenzel state or heterogeneous wetting of a liquid in a local Wenzel or Cassie state) and the nonwetting of patterned nanostructures in nanocomponent fabrication limit chemical reactions and the efficiency of the processes. These limitations concern mainly high-aspect-ratio structures for which wetting (from its characterization, modeling, and optimization) is a challenge on the nanoscale. [START_REF] Xu | Wetting Challenges in Cleaning of High Aspect Ratio Nano-Structures[END_REF] The existence of partial wetting states in micro or nanostructures affects etching or cleaning processes during nanoscale structure fabrication. Reliable wet cleaning of nanopatterned Si wafers is an extremely important issue in the latest nanoelectronic technologies. Compared to organic chemistry, the semiconductor manufacturing industry prefers to use aqueous chemistry with a lower cost of ownership and a greener environmental profile but a higher surface tension. The realization of the hydrophilic regime plays a crucial role here, and the potential incomplete wetting of the nanopatterned Si surfaces could impede the applicability of the current Si technology due to the absence of reliable wet cleaning on the nanoscale. [START_REF] Mrsella | Handbook of Cleaning for Semiconductor Manufacturing[END_REF] This concern has gained more interest since the discovery that even hydrophilic materials can present (super)hydrophobic properties after patterning. This effect was observed for patterned surfaces constructed from hydrogenated silicon oxycarbide (SiCOH) with feature sizes of about 150 nm [START_REF] Harder | Static and Dynamic Wetting Characteristics of Nano-patterned Surfaces[END_REF] and for nanopillars with different aspect ratios functionalized with self-assembled monolayers. [START_REF] Xu | Wetting Challenges in Cleaning of High Aspect Ratio Nano-Structures[END_REF][START_REF] Xu | Capturing Wetting States in Nanopatterned Silicon[END_REF] Among the applications concerned, one can mention the adsorption behavior of surfactant during the patterning wet etching or cleaning process in the field of nanoelectronics and the self-cleaning of functional solar cell devices. [START_REF] Weiwei | Surface Engineering Method to Fabricate a Bendable Self-Cleaning Surface with High Robustness[END_REF] Wettability research on the nanoscale has promoted the development of wet pattern fabrication, especially patterned organic molecules. [START_REF] Joachim | Electronics using hybrid-molecular and mono-molecular devices[END_REF] Droplets could be deposited on a specific area of the surface in which a chemical or topographical nanoscale pattern is imposed. [START_REF] Aronov | Electron-induced wettability modification[END_REF] As a consequence, it seems necessary to evaluate the wetting properties of structured surfaces on the nanoscale and to assess the wetting heterogeneity of the surface. On one hand, the most widely used method of characterizing wetting properties that remains a reference consists of a macroscopic measurement (goniometer method) at the liquid/gas/solid triple line of the drop apparent contact angle and the contact angle hysteresis. This latter value is equal to the difference between advancing and receding apparent contact angles obtained either through the tilting or the sessile drop method. [START_REF] Marmur | The Lotus Effect: Superhydrophobicity and Metastability[END_REF] From a fundamental point of view, contact angles depend only on the liquid-surface interactions in the vicinity of the contact line (i.e., at the droplet rim). But working with the same surface/ liquid couple, we find that the measurement of the apparent contact angle can lead to different values according to the way that the droplet is deposited (deposition speed, flow rate used to form the droplet, applied pressure, etc.). Contact angle hysteresis permits us to define the range of possible variation of the apparent angle. It is necessary to note that this information is not directly related to the local liquid-solid interaction occurring below the drop and cannot allow us to distinguish composite states. [START_REF] Gao | Wetting 101°[END_REF] By an abuse of language, the Cassie and Wenzel states are usually defined through the contact angle measurements, in the worst case by simply indicating the apparent contact angle and in the best way by stating advancing and receding contact angles. In the latter case, it is commonly admitted that hysteresis below 10°represents a nonimpaled state at the droplet rim (Cassie state by extension) and beyond 50°, an imbibed state (Wenzel sate). But it is clear that this vision corresponds to partial reality, proving the interest in detecting real wetting heterogeneity.

On the other hand, several methods have been developed to capture, on the micro/nanoscale, the interface configuration below the drop using optical methods. Moulinet and Bartolo used reflection interference contrast microscopy (RICM) to monitor the Cassie-Wenzel transition. [START_REF] Moulinet | Life and death of a fakir droplet: Impalement transitions on superhydrophobic surfaces[END_REF] Also, Rathgen and Mugele analyzed diffraction patterns to capture the interface deformation on a superhydrophobic silicon surface made of micrometric rectangular grooves. [START_REF] Rathgen | Microscopic shape and contact angle measurement at a superhydrophobic surface[END_REF] Very recently some papers [START_REF] Papadopoulos | How superhydrophobicity breaks down[END_REF][START_REF] Rykaczewski | Direct Imaging of Complex Nano-to Microscale Interfaces Involving Solid, Liquid, and Gas Phases[END_REF][START_REF] Paxson | Self-similarity of contact line depinning from textured surfaces[END_REF] have presented methods to characterize wetting on the nanoscale level, proving the keen interest in this fundamental physics issue. Thus, while it has been shown that these latter techniques can give reliable information on the liquid-surface interaction from the macro to the nanoscale, none of them are compatible with an in situ, real time, multiscale (from macro to nanoscale) characterization.

Acoustic methods have been known for long time to allow bulk material investigation and interface characterization and make it possible to localize embedded defects in depth using the time-of-flight of the acoustic wave. The main common application concerns nondestructive evaluation such as defect characterization and adhesion evaluation. The investigation of biological tissue is commonly performed using acoustic echography. Among its advantages and using the different types of acoustic waves (bulk acoustic waves, BAW, or surface acoustic waves, SAW), authors usually highlight a nondestructive method not requiring a transparent surface and sensitivity to detect thin films at the interfaces. Depending on the type of acoustic wave, transducers can be placed either on the same side of the interface to be characterized (SAW) or on the opposite face (BAW). In the field of acoustics, only a few methods were used to characterize the wetting configuration on microstructures. To the best of our knowledge, the only case concerned roughness (phononic crystals) characterization rather than the roughness/fluid interaction. Roach et al. observed different quartz crystal microbalances (QCM) responses [START_REF] Roach | Decoupling of the Liquid Response of a Superhydrophobic Quartz Crystal Microbalance[END_REF] for the penetrating and nonpenetrating liquid states. This method has shown the possibility to characterize wetting on polymer-based microstructures. Interesting results have been obtained by Paumet et al. regarding ultrasonic transmission evaluation modeling in a lower frequency range (MHz) to characterize microstructure wetting states (using a nonintegrated method in which the transducers are not directly coupled to the chip). [START_REF] Paumel | Modeling of ultrasound transmission through a solid-liquid interface comprising a network of gas pockets[END_REF] We have already demonstrated the potential of a high-frequency acoustic method for detecting wetting states on micropatterned silicon surfaces [START_REF] Saad | Characterization of the state of a droplet on a micro-textured silicon wafer using ultrasound[END_REF] and also for tracking wetting transitions on these microstructures. [START_REF] Dufour | Acoustic Tracking of Cassie to Wenzel Wetting Transitions[END_REF] In this paper we present for the first time a high-frequency (1 GHz) acoustic wave echography principle to determine the wetting of a droplet on a silicon nanopatterned surface. Surprisingly our results indicate that the wetting state (in either the Cassie or Wenzel state) of a droplet placed on this type of superhydrophobic surface can be determined, even if its roughness dimensions are far below the acoustic wavelength. This technique is also highly sensitive for tracking wetting transition dynamics (partial impalement before reaching a fully imbibed state) in the nanoscale range. Moreover a comparison to classical goniometric methods will be presented, proving the complementarity of our method for detecting unobservable phenomena with conventional methods.

RESULTS AND DISCUSSION

Ordered arrays of nanopillars are etched into a 300 mm silicon wafer using a Lam Research 2300 Kiyo C series conductor etch system. [START_REF] Vos | Silicon Nano-Pillar Test Structures for Quantitative Evaluation of Wafer Drying Induced Pattern Collapse[END_REF] The pillar arrays are square packed with a 90 nm pitch; the pillars have a mean diameter of 43-31 nm and a height of 71-416 nm for an aspect ratio (AR, depth versus mean diameter) of 2-13 (Table 1). Measurements have been made thanks to SEM images (Figure 1). The top solid fraction f s corresponds to the surface in contact with the liquid (top of the pillars) divided by the projected area of the surface. The calculated values vary from 0.13 to 0.17. To modify the intrinsic wettability of the nanopillars, a self-assembled monolayer (SAM) of FDTS (1H,1H,2H,2H-perfluorodecyltrichlorosilane) was grafted from the vapor phase to functionalize the silicon surface.

Wetting Characterization on Nanostructures: Goniometry as a Reference Method. The goniometric method is used to determine the wettability of the nanostructured interfaces. The tested liquids were mixtures of deionized water and ethanol (Supporting Information Table s1, section 1). The results obtained with this method will be used as references for the rest of the paper.

In order to measure the contact angle, a drop shape analysis system (DSA100, Kruss GmbH, Germany) is used, placed in clean room facilities with controlled temperature and humidity. It includes an automated dosing unit and tilting table, CCD camera (50 fps), and analysis software. A static apparent contact angle of θ 0 * ± 2°was measured just after drop deposition (without any needle or capillary inside the droplet). Advancing and receding apparent contact angles, θ A * and θ R *, respectively, are measured using the tilting plate method: the tilting table is rotated from 0 to 90°(in steps of 2°per second), and drop deformation and displacement are recorded (50 fps, 10 computations per second) with a droplet volume equal to 8 μL. Advancing/receding apparent contact angles were calculated just before advancing/receding contact line depinning, respectively. Contact angle hysteresis (CAH) was calculated as the difference between θ A * and θ R *.

If no sliding is observed for the droplet (i.e., tilting angle of 90°with no sliding), then hysteresis cannot be determined but it indicates the impalement of the liquid, at least at the triplephase line, corresponding to a local Wenzel state.

The tested liquids are mixtures of deionized water and ethanol. Volume proportions along with corresponding surface tensions γ are reported in Table 2. The use of such mixtures enables the easy tuning of γ while avoiding large variations in viscosity η and density ρ. The drop volume was fixed at 8 μL for all experiments. This ensures easy droplet sliding upon tilting while keeping the drop radius (∼1 mm) slightly below the capillary length (depending on the ethanol concentration). For the apparent contact angle computation, part of the drop profile which lies near the contact line is adapted to fita polynomial function. Apparent contact angles are then determined from the slope at the triple point. This method avoids measurement errors due to drop shape deformation under gravity. [START_REF] Patankar | Hysteresis with regard to Cassie and Wenzel states on superhydrophobic surfaces[END_REF] For each surface-liquid set, apparent contact angles are averaged over five measurements. Then the standard deviation is also calculated.

It is to be noted that contact angle measurements were performed within approximately10 s to avoid the influence of the evaporation phenomenon. (During this short time, the surface tension of the mixture is not expected to increase more than 1 mN/m, which corresponds to a 2 to 4% error.) When the hysteresis is measured, the measurement takes less than 30 s (tilting angle inferior to 50°).

Typical evolutions of the contact angle measurements are presented in Figures 2 and3. Using the tilting plate method, we find that the advancing contact angles are equal to 150°for an ethanol concentration lower than 20% on the AR3 surface. For these same liquids, receding angles are comprised between 130 and 124 ± 2°, leading to a CAH inferior to 25 ± 2°. Once the concentration is higher than 20%, the tilting method leads to the pinning of the droplet even for a tilting angle of 90°as detailed in Figure 2. At this point, no hysteresis can be measured. On the AR13 surface, a value of 150 ± 2°is obtained for liquid presenting an ethanol concentration lower than 30% (Figure 3). At 35%, this value slightly decreases to reach 120 ± 2°at 40%. As stated before, such information is not sufficient to describe the wetting state of the droplet on a rough surface. Contact angle hysteresis measurements were then made. These results obtained on AR13 surfaces are summarized in Figure 3.

Using the tilting plate method, the advancing contact angles are equal to 148 ± 2°for an ethanol concentration lower than 30% on the AR13 surface. For these same liquids, receding angles are comprised between 130 and 123 ± 2°, leading to a CAH lower than 23 ± 2°. Once the concentration is higher than the critical concentration of 30%, the tilting method led to a pinning of the droplet even for a tilting angle of 90°: because there is no triple line movement, determinations of the advancing and receding angles are not possible.

Wetting characterization on a rough surface, by a goniometer method, of both of these surfaces led to the determination of a critical ethanol volume concentration beyond which the droplet rim is definitely impaled inside the nanostructuration. This critical value is dependent on the height of the structures, given as 35 and 25% for AR13 and AR3 respectively. According to the usual denomination adopted by the scientific community, we can assert that below this critical volume concentration a Cassie state is maintained while beyond a Wenzel state is obtained. Details of the level of impalement and of the dynamic of imbibition cannot be determined through this goniometric method.

Acoustic Measurements on Nanostructured Surfaces. This method is based on the high-frequency echography principle. It consists of the evaluation of the reflection of a highfrequency (1 GHz) longitudinal wave on a patterned interface. Depending on the wettability of these structures, the reflection/ transmission coefficients are modified.

The acoustic reflection coefficient is deduced from the measurement of the electrical impedance of the ZnO piezoelectric transducer placed on the back side of a superhydrophobic silicon surface (Figure 4). These transducers are fabricated thanks to ZnO sputtering on the back side of the wafer. A ground layer is first deposited on the substrate (Ti/Pt: 10/100 nm). After a lithography step, a layer of 2.4 μm of ZnO is sputtered and a layer of 100 nm of Pt is evaporated to achieve the top electrode. The transducers are then obtained thanks to the classical lift-off technique. The transducers have a central resonance frequency of 1 GHz with a 3 dB bandwidth of about 800 MHz.

The power applied to the transducer via the vector network analyzer is 0 dBm. Because the excitation wave is a longitudinal wave (compression mode), there is almost no flexural plate or torsion mode that is excited. We have calculated the resonance frequency for the compression mode (clamped pillar) of the highest nanopillars, which is in the range of 5 GHz (the quarter wavelength should be equal to the height of the AR13 nanopillars) that is out of the bandwidth of the measurement. As a consequence, with an incident power of 1 mW (from which electrical and electromechanical losses have to be withdrawn), there is no influence on the wetting phenomena.

The transducer is used to generate the acoustic wave incident at the interface. The S 11 scattering parameter (ratio of the complex amplitudes of the reflected and incident signals) is measured using a SussMicrotech probe coupled with a Rohde & Schwarz 300 kHz-8 GHz vector network analyzer. Then an inverse Fourier transform is applied to the S 11 (f) signal to process the reflected echoes in the time domain. The amplitude of the measured signal is normalized to air in order to remove the intrinsic insertion losses from measurements.

These reflected waves have been modeled and confirmed through experimental results on microstructures. [START_REF] Saad | Characterization of the state of a droplet on a micro-textured silicon wafer using ultrasound[END_REF] The feasibility of following the evolution of the wetting state in various experiments (electrowetting on dielectric, evaporation) has also been demonstrated in previous work. [START_REF] Dufour | Acoustic Tracking of Cassie to Wenzel Wetting Transitions[END_REF] On microstructures, two reflection coefficients can be observed at the top and bottom of the microstructures. (In the case of highfrequency acoustic characterization, the frequency bandwidth of the transducers makes it possible, in the time domain, to separate the echoes reflected on top and bottom interfaces.) It is possible to evaluate the presence of liquid above the microstructures or between the microstructures if the liquid is in direct contact with the bottom interface.

In the case of nanostructures, the acoustic wave interaction with the solid is different due to the fact that the structural dimensions are much smaller than the longitudinal bulk acoustic wavelengths (at 1 GHz about 8.5 and 1.5 μmi n silicon and water, respectively). The incident wave on the nanostructured interface generates a vibrational state of the nanopillars modified by the different wetting state. Thus, according to the nanostructures' imbibition, this nanostructured interface presents an equivalent mechanical impedance modifying the reflection coefficient.

The reflection coefficient is the one normalized with respect to air. From a theoretical point of view, considering a plane acoustic wave propagating at normal incidence onto an interface separating two media of mechanical acoustic impedances Z 1 and Z 2 , the absolute value of the reflection coefficient R 2/1 can be calculated from the following equation:

| |= - + RZ Z Z Z () / () 1/2 2 1 1 2
(1) Z = ρv, with ρ being the density of the medium and v being the acoustic velocity of the wave propagating inside. (As a first approximation, only the real part of the mechanical impedance is considered.) In our case, Z 2 is the acoustic impedance of the liquid and Z 1 is the acoustic impedance of the (100) silicon substrate.

In order to characterize the wetting properties of the nanostructured interface, the evaluation of the acoustic reflection coefficient will be achieved using liquids of known acoustic properties. Ethanol/water mixtures, which make it possible to modify the surface tension of a liquid, will first be characterized on nonpatterned substrates so as to calibrate the acoustic method. Then the wettability of the nanostructured surfaces will be studied using the same mixtures.

To understand the physical significance of reflection coefficient R, eq 2 describes the influence of the different parameters on our measurements.

=+ - R RA R A (1 )
Si/w cw Si/air cw (2) where R Si/W is the reflection coefficient at the silicon-water interface, R Si/air is the reflection coefficient at the silicon-air interface, A cw is the part of the area of the transducer covered by water, and 1 -A cw is the complementary part of the area of the transducer covered by air Thus, the reflection coefficient is a function of the part of air, liquid, and silicon in front of the transducer. The sensitivity of the method mainly depends on the high contrast between air and liquid impedances (Supporting Information)

A first set of data dealing with the acoustic characterization of the wetting on nanotextured surfaces at different ethanol concentrations is presented in Figure 5. This figure represents the evolution of the acoustic reflection coefficient on three different nanotextured surfaces presenting the same aspect ratio (AR2). The results are compared to a reference corresponding to a planar (nontextured) silicon wafer on which the surface treatment based on the use of a self-assembled monolayer (SAM) described previously was also achieved. For Figures 5 and6, error bars indicate the general repeatability of the acoustic reflection coefficient measurement (±0.003).

In dealing with the AR2 surface, the measurement matching the ethanol concentration C = 0% corresponds to a water droplet. The experiments have been achieved using a droplet of each ethanol-based solution prepared separately. A droplet with a known concentration has been deposited on the surface. Once the acoustic measurement was made, the structures were dried with nitrogen. Then another droplet was deposited, and operations were repeated as described previously. The droplets came from the prepared solution from lowest to highest ethanol concentration. We have chosen this order so as to obtain wetting states from Cassie-Baxter to Wenzel so as to limit surface pollution. This pollution comes from the deposition of particles present in the initial solution, regardless of the care taken in the purity of the liquid or from the particles present on the surface and deposited/concentrated on the structures. This can modify the original wetting properties of the structures. While the measurement is calibrated to obtain a value equal to 1 for a surface without any droplets, the value of the reflection coefficient on AR2 is equal to 0.99, which corresponds to the influence of liquid at the top of the pillars. Indeed, a part of the acoustic energy is transmitted to the liquid, proportionately to the top solid fraction f s given in Table 1. At this point it is to be noted that we can observe a slight decrease in the acoustic reflection coefficient for ethanol concentrations of up to 15% for the AR2 surface. This slope can be compared to the same evolution obtained on a planar surface: the variation of the reflection coefficient is due to the variation of the mechanical impedance of the mixture according to the ethanol concentration. The gap between the planar and the rough surface curves is explained up to this 15% concentration by the presence of air on AR2, revealing a Cassie state. For concentrations of between 15 and 20%, a transient state is observed for which the acoustic reflection coefficient decreases rapidly from 0.99 to 0.85. This huge variation is ascribed to the increase in the area of the interface between silicon and liquid embedded inside the nanopillars under the droplet (placed in front of the transducer), which is modifying the reflection coefficient.

From the 20% ethanol concentration, the reflection coefficient is almost the same for both AR2 and planar surface. It indicates that from an acoustic point of view, no difference can be made between the planar surface covered by a liquid droplet and the nanotextured surface covered by the same liquid droplet. Thus, a perfect contact appears between surface and liquid in both cases, revealing a total Wenzel state for the AR2 surface in front of the entire transducer surface. Finally it is to be noted that the nonmonotonous curve on the planar surface is directly linked to the classical variation of the mechanical impedance of the liquid according to the ethanol concentration (details I Supporting Information, Figure S1, section 1). [START_REF] V A T A N D A S | U l t r a s o n i cv e l o c i t y measurements in ethanol-water and methanol-water mixtures[END_REF] Moreover, evaporation, which may be critical to the water/ethanol droplet, is considered to be negligible (modification of 0.2% of the reflection coefficient) according to the duration of the experiment (10 s) (details in Supporting Information, Figure S2, section 2).

The same measurements have been achieved on different nanotextured surfaces presenting aspect ratios from 2 to 13. The results are presented in Figure 6. Compared to AR2 surfaces, for which wetting properties have already been presented, the general trend in these curves is identical. For ethanol concentrations below a certain critical value, the acoustic reflection coefficient starts from 0.99 and slowly decreases to 0.985, and then a sharp decrease is observed. Beyond this concentration value, the signal on the nanotextured surface coincides with that on the planar surface. The only difference is the critical ethanol concentration for which the reflection coefficient falls down: it is directly linked to the aspect ratio AR.

DISCUSSION

Measurements obtained by both goniometric and acoustic methods on nanotextured surfaces led to the same critical ethanol concentration for which a modification of the wetting state is assumed. At first sight, this modification is called a transition from a Cassie to a Wenzel state. Nevertheless the optical methods, considered to be the reference, do not authorize us to go further in the analysis of the Cassie-Baxter to Wenzel transition.

Indeed, from a physical point of view, it is important to underline that even if we confront two different wetting characterization techniques (goniometric and acoustic methods), the information obtained is not totally comparable. As detailed before, theoretically, contact angle hysteresis depends on the triple-phase line (not on the wetting state under the droplet), while the acoustic method is sensitive to partial impalement in the nanostructuration facing the acoustic transducer. We thus measured different wetting transitions (local/outskirt), but it seems that in our case there is not a huge difference in the critical ethanol concentration.

Nevertheless, the acoustic method presented here authorizes a deeper observation of the nanoscale wetting. It is then possible to extract some quantitative characteristics of the transition from the Cassie-Baxter to Wenzel state (C-B/W). Indeed, in Figure 6, one can observe that the evolution of the reflection coefficient shows an inflection point, at a critical ethanol concentration, which can be correlated to a transient state, from Cassie-Baxter to Wenzel in our case. By calculating the evolution of the slope of the local tangent related to each transition curve from Figure 6, we can determine the maximum of the absolute value of this slope. In Table 3 we report relevant values of the critical ethanol concentration C C-B/W , the surface tension γ C-B/W , and the maximum in the absolute value of the slope |S max |.

One can conclude that the higher the aspect ratio, the higher the concentration C C-B/W and the lower the surface tension γ C-B/W . It shows that increasing the aspect ratio will help to maintain the Cassie-Baxter state. Furthermore, |S max | decreases as the aspect ratio increases, revealing that the dynamics of drop impalement is strongly correlated to the nanopillar height. The impalement is all the more brusque because the aspect ratio is low.

The imbibition of the liquid in the nanostructures will thus affect the equivalent impedance at the interface, and the acoustic reflection coefficient will be modified. Furthermore, this measure is a mean value integrated over the whole transducer surface. No differentiation can be made between (i) a surface presenting a homogeneous air layer between silicon and water and (ii) a heterogeneous filling presenting both liquid/solid and solid/air/liquid mixed local states.

The interpretation of these results remains open. From the usual models applied to this type of surface, two types of impalement on vertically structured surfaces (with h being the height and l being the maximum spacing between two structures (side to side)) can be considered.

First, when high pressure is applied to the droplet, its shape between each pillar is deformed. For low pressure, the liquid/ gas interface exhibits a flat profile, but for higher pressure, the curvature radius of this interface may be on the order of magnitude of the distance between pillars. In this case, depending on the height of the pillars, the liquid comes into contact with the bottom of the texturation, leading to a wetting transition from Cassie to a Wenzel state. This transition requires a high pressure of tens of bars that could not be reached in our case Second, once the liquid contact angle is locally equal to the advancing contact angle on the wall of the microstructures, the liquid slides down the wall before reaching the bottom of the structure. The scaling law is P s ∝ γ(cos(θ a ))/l, with P s being the sliding pressure and θ a being the advancing contact angle. When the pressure becomes positive, the liquid wets the walls and then all of the structure. In this scenario, the re-entrant angle at the top of the structure according to the height of the nanopillars can affect the wettability properties of the textured surface (as an interesting result, the realization of a superoleophobic surface from an oleophilic material is achievable with reentrant structures [START_REF] V A T A N D A S | U l t r a s o n i cv e l o c i t y measurements in ethanol-water and methanol-water mixtures[END_REF] ), and P s becomes γ(sin(θ a ))/l. But according to SEM pictures given in Figure 1, this is not the case with the surfaces under investigation in this paper. [START_REF] Harder | Static and Dynamic Wetting Characteristics of Nano-patterned Surfaces[END_REF] The transition from Cassie to Wenzel appears when the lower of the two pressures P i and P s is reached.

Another possible explanation has recently been proposed by Xu et al. [START_REF] Harder | Static and Dynamic Wetting Characteristics of Nano-patterned Surfaces[END_REF] They evidence, thanks to an original optical reflectance method, that during the nanofabrication process (exposure of different crystallographic planes, nanoroughness from etching, and perturbation of SAMs packing on a curved surface) a lowering of the surface energy of pillar sidewalls appears, as compared to a planar (1 0 0) silicon wafer surface. They used water droplets combined with a large number of different SAMs presenting various surface energies. In the present study, a unique SAM is used, and the liquid surface tension varies by adding ethanol to water (Figure 5). We thus obtain, on identical surfaces, the same impalement behavior whatever the studied parameter (surface energy of the surface or surface tension of the liquid) characterized by three different methods: an original optical reflectance method (Xu et al) and, in this paper, a goniometric method and an original highfrequency acoustic method. A representation of our acoustic measurements against the Young's angle on a silicon planar surface (that can be easily deduced from Figure 6 where the representation is achieved against the ethanol volume concentration) would show a deviation in the transition criteria of more than 20°compared to classical models, similar to the observations of Xu et al. [START_REF] Xu | Capturing Wetting States in Nanopatterned Silicon[END_REF] on the same structures achieved by an optical reflectance method. This shows a coherent wetting transition observation for different aspect ratios using three different methods (acoustic, optical reflectance, and goniometry).

CONCLUSIONS

Goniometric optical methods make it possible to evaluate the wetting of a droplet on nanostructures, but this characterization is limited to the droplet rim, where the triple line is defined. On the micro/nanoscale, the liquid/solid/gas interface can be characterized by different methods to huge accuracy, but none of them are compatible with an in situ, real time, multiscale (from macro to nanoscale) characterization. For the first time, a high-frequency acoustic method has been proposed to evaluate the wetting phenomena locally on nanostructures. This acoustic method has shown its extreme sensitivity to the level of liquid imbibition. It was then possible to demonstrate the possibility of evaluating the ethanol concentration of a liquid for which total wetting can be obtained depending on the aspect ratio of the nanostructures. As an example, it was possible to demonstrate that the total wetting of nanostructures with an aspect ratio of 13 (AR13) was obtained for an ethanol concentration equal to 30% (surface tension of 29 mN/m) whereas for nanostructures with an aspect ratio of 2 (AR2), the total wetting was observed for a concentration of 20% (surface tension of 43 mN/m).

Beyond these results, it is important to underline the potential of our acoustic method. Among the different advantages, this method allows us to characterize the wetting under the droplet where the goniometer method cannot give any information. By lowering the transducer diameter, we believe that we will be able to evidence partial wetting imbibition and temporal evolution of the imbibition. Moreover, the integration of these smaller transducers in the network may lead to a mapping of the wetting under the droplet. Finally, independently of the triple line (rim of the droplet) our acoustic method can characterize the solid/liquid film, in particular, the local concentration of a mixture during evaporation processes or particle sedimentation during wet cleaning processes.

ASSOCIATED CONTENT Supporting Information

Validation of the acoustic characterization method. Evaporation effect quantification. This material is available free of charge via the Internet at http://pubs.acs.org.

Figure 1 .

 1 Figure 1. SEM images of the silicon nanopillars. (a) Tilted view of the square packed pillar arrays. (b-d) Cross-sectional SEM images of nanopillars with aspect ratios (ARs) of 2, 7, and 13, respectively. The scale bars are 50 nm.

Figure 2 .

 2 Figure 2. Ethanol/water mixture wetting on nanoscale-patterned structures with an aspect ratio of 3.

Figure 3 .

 3 Figure3. Ethanol/water mixture wetting on nanoscale-patterned structures with an aspect ratio of 13. Advancing and receding angles are determined by the tilting plate method. Beyond an ethanol concentration of 30%, these angles cannot be determined due to droplet pinning even for a tilting angle of 90°.

Figure 4 .

 4 Figure 4. Acoustic method principle for the detection of both Cassie-Baxter and Wenzel wetting states: the insets show a magnified illustration of the interface between droplets and nanopillars. The vector network analyzer (VNA) is connected to the ZnO piezoelectric transducer in order to measure its electrical impedance. This transducer generates the acoustic wave incident at the interface in order to sense liquid imbibition.

Figure 5 .

 5 Figure 5. Acoustic reflection coefficient for different water/ethanol mixtures on planar and AR2 surfaces. For an ethanol concentration varying from 0 to 15%, a Cassie state is observed, and between 15 and 20%, the acoustic reflection coefficient decreases sharply, indicating a wetting transient state before reaching a Wenzel state for concentrations beyond 20%.

Figure 6 .

 6 Figure 6. Acoustic reflection coefficient for different water/ethanol mixtures on different surfaces: nanostructures with aspect ratios from 2 to 13.

Table 1 .

 1 Dimensions of the Nanopillars

	aspect ratio: AR	2	3	7	13
	mean diameter: d (nm)	43	43	36	31
	depth: h (nm)	71	133	270	416
	top solid fraction: f s	0.17	0.18	0.13	0.13

Table 2 .

 2 Surface Tensions of DI Water/Ethanol Mixtures (Vol %) Measured Using the Pendant Drop Method (Averaged over Five Measurements)

	liquid	γ (mN/m)
	DI water 100 (%)	72.6
	DI water + eth 95/5 (%)	58.7
	DI water + eth 90/10 (%)	52.0
	DI water + eth 85/15 (%)	46.0
	DI water + eth 80/20 (%)	42.9
	DI water + eth 75/25 (%)	40.0
	DI water + eth 70/30 (%)	35.0
	DI water + eth 60/40 (%)	31.1
	DI water + eth 50/50 (%)	29.2
	DI water + eth 40/60 (%)	27.9

Table 3 .

 3 Quantitative Characterization of the Transition Curve Depending on the Aspect Ratio a

		AR2	AR3	AR7	AR13
	C C-B/W (%)	17.5	25	27.5	30
	γ C-B/W (mN/m)	44	39.5	38.2	36.5
	|S max |	0.0384	0.0344	0.0276	0.0232

a C C-B/W , ethanol concentration; T C-B/W , surface tension for which the slope of the transition curve is maximized; |S max |, calculated value of the maximum of the absolute value of the slope of the curve.

ACKNOWLEDGMENTS

This work is supported by the Nord-Pas-de-Calais Region through the 2008-2013 CIA and CISIT State Region Planning contracts and by the French Agence Nationale de la Recherche (ANR) under reference Reflex ANR-11-NANO-0007.

AUTHOR INFORMATION

Corresponding Authors E-mail: lisizhe1987@163.com. E-mail: julien.carlier@univ-valenciennes.fr.

Author Contributions

Sizhe Li and Sebastien Lamant contributed equally to this article and are first coauthors.

Notes

The authors declare no competing financial interest.


Manan Suri

Olivier Bichler

Damien Querlioz

Olga Cueto

Luca Perniola

Véronique Sousa

Dominique Vuillaume

Christian Gamrat

Barbara Desalvo

Veronique Sousa

Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction:

The development of biologically inspired neuromorphic circuits, in order to achieve low power, highly parallel, and fault-tolerant systems, has gained a lot of interest over the last few years [1]. Emergence of new resistive memories, suitable for synapse realization, owing to their simple integration scheme and analog memory capabilities, suggests that the fabrication of neuromorphic chips with a synapse density close to that of the human brain could be achievable. Nevertheless, up to now, substantial work has consisted in demonstrating biologically plausible learning rules, such as spike-time dependent plasticity (STDP), just on individual memristive devices [2][3][START_REF] Oblea | IJCNN[END_REF][START_REF] Yu | [END_REF][6]. Still there lies a considerable gap between the fields of semiconductor devices, computational neuroscience and system-design before a fully functional large scale hardware neuromorphic system will be realized [7].We choose PCM technology because of the advantages it offers compared to the other resistive memory technologies, such as maturity, scaling capability, high endurance, and good reliability [8]. Experiments: Fig. 1 shows the biological synapse and the basic concept of emulating it with PCM. An electronic device emulating the biological synapse should be able to gradually increase (long term potentiation, LTP) or decrease (long term depression, LTD) its conductance in response to neuron spikes [9]. To demonstrate these features, lance-type PCM test devices, with a 100nm-thick phase change layer and 300nm-diameter tungsten plug, were fabricated and characterized. Two different chalcogenide materials were integrated: nucleation-dominated GST and growthdominated GeTe. The R-I characteristics of GST PCM devices are shown in Fig. 2. Fig. 3 and Fig. 4 show LTP-like conductance variation of PCM devices with GST and GeTe phase change layers, respectively. Nucleation dominated behavior leads to more gradual conductance change in GST, compared to GeTe which shows abrupt conductance change due to growth dominated behavior. The saturation of the conductance programming window in GeTe occurs in less than 1/3 rd the total number of potentiating pulses required for GST. Fig. 5 shows that a gradual decrease in conductance (or LTD) cannot be obtained with the application of identical pulses to PCM. To this aim, the amplitude of the consecutive pulses should increase progressively (Fig. 2). Nevertheless, implementing such pulses with varying amplitudes can lead to practical problems, such as capacitive line charging and high power dissipation in large scale neuromorphic systems involving crossbar arrays. We propose an original solution to such problems in the following sections. Physical simulations: A 2D axi-symmetrical simulator developed in MATLAB and C++ was used to study the LTP, LTD behavior of the PCM devices. Phase change crystallization physics of the devices were modeled using Eqs.1-2 and a level set numerical method [10]. The first few points in the LTP curves (Fig. 3,4) are crucial in determining the number of intermediate conductance states within a given programming window. In Figs. 6,7, we simulate the sensitivity of conductance variation with respect to changes in nucleation and growth rates. The maximum value of conductance is attained in fewer pulses if the growth or nucleation rate is higher. The shape of the bulk amorphous region created after the initial reset pulse depends upon the values of growth and nucleation rates. A high growth rate (GR=10) leads to strong crystal growth from the amorphouscrystalline interface during the falling edge of the reset pulse, thus distorting the shape of the amorphous mushroom. A low growth rate (GR=0.1) leads to much symmetric mushroom shape of the amorphous region after the reset pulse. After the application of the 1 st pulse, conductance is more sensitive to changes in the nucleation rate compared to growth. The 2-PCM synapse: To overcome the problem of abrupt LTD (Fig. 5). We propose a new energy efficient synapse circuit consisting of two identical PCM devices ("The 2-PCM synapse") with a realistic and simplified programming pulse scheme (Fig. 9). One PCM device (LTP) has a positive current contribution, while the other PCM device (LTD) contributes negatively towards the output CMOS neuron current. In the "2-PCM synapse", crystallizing the LTP PCM device produces synaptic LTP-like effect, while crystallizing the LTD PCM device produces synaptic LTDlike effect. Since gradual crystallization can be obtained by applying simple identical pulses (Fig. 3,4) our approach simplifies the pulse schemes for implementing learning in large scale neuromorphic systems. Moreover, unlike previously demonstrated PCM synapse [6] our "2-PCM synapse" is highly energy efficient, as majority synaptic conductance changes (or weight update) events are based on crystallization and not on amorphization of the PCM devices. As shown in Fig. 2 and Fig. 17, the set (or potentiating) events would consume considerably less amount of energy compared to reset (or depressing) events in PCM technology.

Neural Network & PCM Behavioral Model:

To demonstrate complex pattern extraction from real world data a two layer feed-forward spiking neural network (Fig. 9) was simulated using a special purpose C++ event-based simulator. The neuron model used is the standard leaky integrate and fire (LIF) [11]. The PCM synapses in the network were modeled using Eq.3 with the parameters extracted from the fitting of LTP curves in Fig. 3,4. Address-Event Representation (AER) data (Fig. 10) recorded with 128x128 pixel silicon retina [START_REF] Lichtsteiner | [END_REF] was used as the input for our PCM neural network. Each of the 128 x 128 pixels in Fig. 9 is connected through two synapses to every neuron in layer 1. Likewise each neuron in layer 1 is connected to every neuron in layer 2 with a single synapse, leading to a total of 1,966,680 synapses and thus 3,933,360 PCM devices (2 PCM/synapse). The goal of the neural network is to detect cars passing in different lanes on a freeway in an unsupervised way. Learning Rule & Programming Scheme: W e u s e d a modified form of the biological STDP learning rule (Fig. 11). The modified STDP enhances simplicity in programming the synapses. The learning is competitive and lateral inhibition is implemented [START_REF] Bichler | IEEE IJCNN[END_REF]. When read with a voltage pulse, the two PCM devices have an opposite contribution to the neuron integration (Fig. 12). When the integration of an output neuron reaches the firing threshold, the neuron transmits an event to the next layer and a feedback pulse on each of its two input lines corresponding to LTP and LTD devices (Fig. 13). A write mode signal is propagated to all the input neurons such that if the input neuron was activated during the LTP time window (Fig. 11), a write pulse is emitted and its interaction with the feedback pulse increases the conductivity of the LTP device, resulting in the potentiation of the equivalent synapse. The feedback pulse alone only increases the conductivity of the LTD device, thus depressing the equivalent synapse. A systematic reset scheme (Fig. 14), allows reducing the conductivity of the PCM devices by retaining the weight of the synapse (Fig. 15). Results & Discussion: Fig. 17 shows the learning results for the AER dataset. To demonstrate the variability robustness of our neuromorphic system we implemented 20% dispersion on standard deviation of all the parameters in Eq.3. Output neurons in the 2 nd layer are able to detect cars in 4 traffic lanes out of 6 for systems based on GST-PCM synapses, and 5 out of 6 lanes for systems based on GeTe-PCM synapses, respectively. The frequency of potentiating pulses per device was about 25 times higher than the frequency of reset pulses for GST-PCM based system, and about 10 times higher for GeTe-PCM based system. Indeed, this result suggests that the efficiency of the system can be further increased by choosing the right phase-change material with the optimum conductance window. Finally, note that the proposed approach of storing maximum synaptic information by crystallization of PCM, with a write frequency of about 2Hz per PCM device, reduces the impact of the PCM resistance-drift [8], widely ascribed to be a limitation for multilevel PCM programming. Conclusions: We have developed a unique methodology to use PCM devices as energy-efficient synapses in large scale neuromorphic systems. Advanced electrical characterization, behavioral modeling and circuit level simulations allow us to show a spiking neural network with about 4 million synapses, capable of complex visual pattern extraction with an average detection rate of 92%, and a system power consumption of 112μW for learning. Fig. 12 Read circuit scheme. Current from both LTP and LTD PCM devices is integrated in the output neuron, with a positive and negative contribution, respectively.

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ - - - = ⎟ ⎠ ⎞ G G G G dt dG min max min exp β α ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ Δ - = kT G Z O I n a N * exp γ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ Δ - - = ρ γ M RT G d V v .

Fig. 1 Fig. 2 RFig. 3

 123 Fig.1Illustration of biological synapse and concept of using PCM as synapse in neural circuits.

Fig. 4

 4 Fig.[START_REF] Oblea | IJCNN[END_REF] Experimental LTP characteristics of GeTe PCM. For each curve, first a reset pulse (7V, 100ns) is applied followed by 10 consecutive identical potentiating pulses.

Fig. 5 Fig. 6

 56 Fig.5 Experimental LTD characteristics of GST and GeTe PCM. Simulations of the GST PC layer morphology with consecutive identical reset pulses shown.

Fig. 7

 7 Fig.7 Simulated LTP curves while fixing the growth rate (GR=1) and varying the nucleation rate (NR) compared to GST (reference: NR=1, GR=1).

Fig. 10 Fig. 8 Fig. 9 Fig. 11

 108911 Fig.10 AER dataset for pattern recognition. Represents 6 car lanes [14].

Fig. 13

 13 Fig.[START_REF] Lichtsteiner | [END_REF] Write circuit scheme based on the simple STDP rule. For a specific PCM, G ↗ denotes an increase while G → denotes no change in conductance.

Fig. 14 Fig. 15

 1415 Fig.14 Reset circuit scheme. For a specific PCM resistor, G ↓ denotes a decrease in conductance.

Fig. 16

 16 Fig.16 Left: Cars detection for the 6 different lanes by the 2 nd layer 10 output neurons. Each block shows the sensitive map of one output neuron. Up to down the detection rate increases. Lane 2 and 5 are learnt by 3 neurons each, while lane 6 is not learnt by any neuron. Right: Detection % of 5 best neurons for each lane.

Fig. 17

 17 Fig.17 Scaling trend of Reset and Set current for different PCM technologies. Set current values are extracted from literature. Interpolations are also shown Energy Calculation Equations Eset ~ Vset * Iset * tpulse (tpulse=30ns); Ereset ~ Vreset * Ireset * tpulse (tpulse =50ns). Etotal =(Eset * total set pulses) + (E reset * total reset pulses) System power = Etotal / total duration of learning

LearningLearning

  Statistics for the PCM-based Neuromorphic Circuit Total Synapses: 1,966,680 [= 1st Layer (2*128*128*60) 2 nd (60*10)] Total PCM cells: 3,933,360 [= 2* Total Synapses (LTP and LTD)] Total learning duration = 680 s (same for both GST/GeTe PCM) Statistics for the PCM-based Neuromorphic Circuit Total Synapses: 1,966,680 [= 1st Layer (2*128*128*60) 2 nd (60*10)] Total PCM cells: 3,933,360 [= 2* Total Synapses (LTP and LTD)]Total learning duration = 680 s (same for both GST/GeTe PCM)

Table 1

 1 Pulse statistics for the learning test case shown in Fig 16.

Table 2

 2 Energy statistics for the test case described in Table1, by using voltage (V)and current (I) values extracted from literature and the energy equations.


N Verplanck

M Wisztorski

J Stauber

J C Camart

M Salzet

I Fournier

V Thomy

ENHANCED SPATIAL RESOLUTION OF MALDI IMAGES USING SILICON MASKS

Keywords: MALDI imaging, mass spectrometry, silicon mask, tissue analysis

This paper reports on simple, efficient and economical methods to increase MALDI image resolution. It is not currently possible to focus the diameter of a MALDI laser down to 50x50µm², i.e. 2500µ m², without decreasing signal intensity. But using Silicon masks openings features, it might even be decreased to about 15x50µ m², i.e. 750µ m². Furthermore, the presence of a mask on the tissue section sample may result in a significant increase in the signal intensity for high m/z ratios. Finally, a huge advantage of the masks is their ease of adaptability on any MALDI mass spectrometer.

INTRODUCTION

MALDI is well adapted to the introduction of raw samples such as tissue cuts. For tissue imaging, the minimum distance between two points must be superior to the laser beam diameter, which corresponds to the irradiation of several cells in the tissue using focalisation systems available on classical commercial instruments. Ideally, the image definition should be at the scale of the cell (10-20 µm for small cells). In the art, various methods have been tried to decrease the area irradiated by the laser beam for MALDI analysis (optical techniques [START_REF]Desorption lasers in mass spectrometry: choices and selections[END_REF], aperture [START_REF] Caprioli | Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS[END_REF], etc). In view of the various drawbacks of known methods to increase MALDI imaging resolution, there is clearly a need for new alternative, simple, efficient and economical methods to increase MALDI image resolution.

EXPERIMENTAL

The masks were engineered in silicon substrates (<100> orientation). Several masks of various openings, forms and sizes were fabricated using standard microfabrication techniques. According to the technique used (wet and dry etching), two geometries of masks have been obtained. Dry etching (50 µ m) has been performed using Bosch process (ICP) through a photoresist mask: Mask A (Figure 1). For wet etching, we used KOH through a Si x N y mask which leads to V-grooves formation (following <111> orientation): Mask B (Figure 2). Both substrates were reduced in thickness to obtain a perforated membrane around 50-100 µ m.

RESULTS AND DISCUSSION

Masks were evaluated for MALDI analysis of standard peptide solutions and tissue sections. Mass spectra were either acquired on a Voyager DE-STR (Applied Biosystems) equipped with a nitrogen laser or on an Ultraflex II TOF/TOF (Bruker Daltonics) equipped with a Smartbeam laser.

First tests on standard peptides solutions and tissue sections show that the apposition of masks does not disturb the desorption/ionization process and significantly decrease the dimensions of the irradiated area by the MALDI laser without decreasing the signal intensity of the observed ions. These results demonstrate that it is possible to decrease the analyzed areas down to about 15x75 µ m², i.e. 1125 µm². Using other masks openings features, it might even be decreased to about 15x50 µm², i.e. 750 µm². Without masks, it is not currently possible to focus the diameter of a MALDI laser down to 50x50µ m², i.e. 2500µm², without decreasing signal intensity. Figure 3 In addition, it has even been observed that the presence of a mask on the tissue section sample may result in a significant increase in the signal intensity, in particular for high m/z ratios (results obtained both on standard peptide mixtures and on rat brain tissue sections).

CONCLUSIONS

Although the precise mechanism leading to this surprising increase in signal intensity is not completely elucidated, this result is particularly interesting for tissue section MALDI imaging, since most of the prior art technologies used to improve the resolution tend to result in a signal decrease. Finally, a huge advantage of the masks is certainly their ease of adaptability on any MALDI mass spectrometer, since their use does not involve any material modification or new equipment [START_REF] Wisztorski | Masks useful for MALDI imaging of tissue sections, processes of manufacture ans uses thereof[END_REF].

The next step will be the realization of a matrix of openings allowing MALDI imaging by point-to-point mass spectrometry.

Figure 1 a

 1 Figure 1 a) Schematic view of the irradiated area through a DRIE mask, b) View of the mask.

Figure 2 a

 2 Figure 2 a) Schematic view of the irradiated area through a V-groove mask, b) View of the mask.

  describes such results through mask A with different dimensions of openings (500 µ m, 240µ m, 100 µm and 50µ m).

  Figure 4 shows results without mask (Figure 4.a) and through mask B with internal openings respectively of 100 µm (Figure 4.b) and 185 µm (Figure 4.c).

Figure 3

 3 Figure 3 MALDI analysis of peptide standards through mask A with opening of a) 500µm, b) 240µm, c) 100µm and d) 50µm.

Figure 4

 4 Figure 4 MALDI Direct analysis of rat brain a) without mask, b) and c) through mask B with internal opening of respectively 100µ m and 185µ m

ACKNOWLEDGEMENTS

We thank for its technical support Mr. Christophe Boyaval (SEM imaging). The European Union, the Centre National de la Recherche Scientifique (CNRS) and the Nord-Pas de Calais region are gratefully acknowledged for financial support.