Dr Yannick Viero

Dr Qihao He

Marc Fouet

Aurélien Bancaud
email: abancaud@laas.fr

Dr H Ranchon

Single molecule study of DNA collision

Keywords: DNA-obstacle collision, DNA separation, Ellipical nanopost, Nanopost arrays, Hydrodynamic actuation

published or not. The documents may come

Our data shows that the asymmetry of the obstacles has minor effect on unhooking dynamics, and thus confirms recent predictions obtained by Brownian dynamics simulations. In addition, we show that the disengagement dynamics are correctly predicted by models of electrophoresis, and propose that this consistency is associated to the confinement in slit-like channels. We finally conclude that elliptical posts are expected to marginally improve the performances of separation devices.

Size separation of DNA molecules with electrophoresis is a key process of molecular biology, which has been widely investigated over decades [1]. Separation cannot be performed in free solution [2], requiring the use of matrices to achieve size-dependent DNA mobilities. Slab gels are the most common separation matrices, but optimal performances have been reached with capillaries of hundreds of µm in diameter filled with concentrated polymer solutions. This technology features excellent physical properties associated to rapid heat dissipation and reduction of convection [3], and the chemical composition of polymer solutions has been tailored to enhance the resolution of separation experiments, which now reach base-pair (bp) resolution for fragments up to ~500-1000 bp [4]. Despite these successes, the nanoscale arrangement of polymer solutions is intrinsically disordered, making it difficult to precisely control the migration of DNA at the molecular level. Therefore Volkmuth and Austin proposed to use micro or nanofabricated solid state artificial matrices for DNA separation [5]. These systems consist of arrays of posts etched in glass or silicon with exquisite control over their size, spacing, and shape from the nano to the millimeter scale. This approach showed impressive results for long DNA chains of ~100.10 3 base pairs (100 kbp) [6] that were separated in tens of seconds in comparison to hours otherwise [7]. For shorter DNA fragments, artificial matrices of nanoposts of ~200 nm in radius have been devised, also showing exquisite performances [8].

Machining technologies offer many choices for the design of the matrix, yet hexagonal arrays of cylindrical obstacles have been predominantly characterized in the literature [9]. The idea of using asymmetric obstacles to separate biomolecules has nevertheless been documented in the context of e.g. Brownian ratchet devices [10], which enable to perform spatial separations of DNA based on the size dependence of its diffusion coefficient [11]. Dense and asymmetric obstacles have also been incorporated into artificial matrices to test their separation power [12], and it was concluded that DNA migration velocity was dependent on the lattice geometry. Because the contribution of obstacle geometry on hooking events was not investigated in the latter study, Brownian dynamics simulations of single DNA molecule colliding with elliptical posts were conducted [13]. This research indicated that the holdup time was relatively insensitive to the orientation of the ellipse, and it was suggested that elliptical obstacles should be less effective than cylindrical obstacles for DNA separations.

In this report, we set out to check this prediction using single molecule microscopy, focusing on DNA collision with small elliptical obstacles of different geometries. We use hydrodynamics to convey DNA molecules in order to disregard the adverse effects of uncontrolled electro-osmotic flows [12]. This actuation principle is also more favorable to monitor DNA conformation during collisions, because hooks, usually referred to as U/J collisions, are observed at an occurrence larger than 90% with hydrodynamics [14], whereas they are less frequent using electrophoresis [15,16]. Therefore we focus on U/J collisions in this report.

Nanoposts were generated using dry etching of silicon after nanopatterning a photoresist by PDMSbased phase-shift lithography (see [17] for details of the fabrication process). The size of elliptical obstacles was set to D=575+/-75 nm and d=190+/-45 nm in major and minor diameters (Fig. 1A). The height of these obstacles was 0.8+/-0.1 µm. We chose to use the smallest obstacles we could fabricate regarding the height of the obstacles. This choice was made based on our previous conclusions [14] that showed that the unhooking characteristic time was greater for nanometric obstacles, enhancing in our opinion the possibility to detect dynamics differences when changing only the obstacle orientation. The tilt angle a between the obstacle major axis and the flow has been set to three different values of 47+/-4°, 63+/-4° and 76+/-6°.

Although the hooking probability seems to be highest at 0° [13], we just considered these 3 angles due to fabrication constraints linked to our low-cost nanofabrication process [17]. As a reference, our results obtained with cylindrical nanoposts of 270 and 500 nm in diameter were also considered [14]. The minimal distance separating obstacles was ~4.5 µm, so as to minimize entanglement of the DNA with multiple posts during collisions. Solid state matrices were eventually processed as fluidic devices by drilling two inlet holes through silicon, and the resulting chips were sealed on microscope coverslips coated with 5 µm of PDMS after oxygen plasma surface activation [17].

The flow field in the network of obstacles was simulated by finite element modeling using COMSOL in 3D or in 2D with the Hele-Shaw approximation. Both simulations yield similar results, and the latter was shown in Fig 1B . The shallow thickness of our devices of ~0.8 µm, which screens out hydrodynamic interactions, and the small dimension of obstacles of ~0.5 µm in comparison to the distance between consecutive posts of ~4.5 µm, enables to consider obstacles as isolated. The variations of the flow around the obstacle occur over lateral distances of ~500 nm for every configuration (Fig. 1B). Our simulations suggest that the flow velocity V away from the posts is similar for the three tilt configurations. Therefore the Péclet number (Pe), which is equal to Vl p /D with l p DNA persistence length and D its diffusion coefficient, and which characterizes the deformation of DNA during hooking events [15], is roughly constant for the three lattices at a given migration velocity. The diffusion coefficient of 35 and 49 kbp molecules is 0.5 and 0.4 µm 2 /s, respectively [14]. Notably the lateral variations of the field around posts occur over much longer distances with hydrodynamic actuation than with electrophoresis, which is associated to sharp field gradients at the edges of the posts (Fig. 1B). Nevertheless, because we are working with molecules of 10 µm or more in this study, we expect these local effects to have minor consequences on unhooking dynamics (see more below).

We then focused on the holdup time of 35 kbp molecules, as defined by the total time of collision events. Using manual tracking of 200 to 600 molecules at a migration velocity of V~40 µm/s (Pe~4), the distribution of holdup times was extracted for the three elliptical post and compared to a cylindrical obstacle of comparable dimension of 270 nm (Fig. 2A). The tail of histograms is accurately adjusted with an exponential fit, confirming previous results of simulations with electrophoresis [13]. The decay time of the exponential is nearly constant for every type of obstacle on the order of ~200 ms (see results in the inset of Fig. 2A), thus suggesting that the geometry of obstacles has minor effects on DNA hooking dynamics. This effect is associated to the fact that much of the hooked DNA resides far from the obstacle, so the forces triggering its disengagement are marginally affected by the geometry of a small obstacle. Further we investigated long-lived collisions by measuring the unhooking time of at least 10 molecules of 35 or 49 kb at a migration velocity of V~20 and 40 µm/s. The unhooking time t unh is defined by the disengagement time after the complete unraveling of the molecule (see inset in Fig. 2B). It can be studied quantitatively using the model of Randall and Doyle [15], which derived a relationship between t unh and DNA conformation at the beginning of the disengagement described by the length of the shorter arm x 1 (0) and the end-to-end length L of the molecule (inset in Fig. 2B):

ℎ = - 2 �1 - 2 1 (0) � (1)
with τ c the disengagement time, which is = / for a free draining chain [15]. Figure 2B shows the hooking time as a function of DNA initial conformation, and the dataset are adjusted with equation (1) (bold lines in Fig. 2B, and Supplementary Fig. S1). The disengagement time is given for the three different tilt angles and for a cylindrical post in Table 1. If the dynamics with 63°, 76° and cylindrical obstacles were similar (blue, green, and gray datasets, respectively), an slightly enhanced disengagement time was detected for 47° obstacles. Given the similar profiles of holdup time distributions, we nevertheless conclude that unhooking dynamics are marginally changed by the geometry of nanoposts. Next, we measured the end-toend lengths of 35 and 49 kbp for each considered event and averaged them in order to calculate the unhooking time τ c (table 1). Because the lengths were 16+/-1 or 12+/-1 µm for 49 and 35 kbp, respectively, the free draining chain model predicts that τ c is 0.8+/-0.1 and 0.6+/-0.1 s at 20 µm/s, respectively. The consistency of this model is somewhat surprising because the free draining hypothesis does not apply for the conformation of DNA under hydrodynamic actuation in low-confining geometries (that is, channel height strongly greater than the persistence length of the molecule). In fact, the disengagement time is determined by the balance between viscous vs. pulling forces. The viscous drag coefficient is proportional to DNA total length for free-draining chains, but it is dependent on the molecule conformation with hydrodynamic interactions. We recently showed that the shear stress imposed by the Poiseuille flows in confined slit-like channels slows down DNA relaxation, and enhances its deformability during hooking events [14], which adopts a linear elongation, as shown in the inset of Fig. 2B. We thus suggest that each arm can be modeled by a series of homogeneous blobs with minimal inter-blob hydrodynamic interactions, so that the drag coefficient is expected to scale linearly with the molecule length. Consequently the conformational space of hooking events differs in hydrodynamic and electrophoresis [14], but the disengagement kinetics can be described with a free-draining model regardless the actuation principle. Notably the confinement in slit-like geometries appears to be essential for this conclusion, because long-distance hydrodynamic couplings are screened out. This argument is consonant with the results obtained on DNA hooking dynamics in slit channels driven by an electrophoretic force [18], which showed that the viscous drag increased as confinement was enhanced and hence hydrodynamic interactions were screened. As was performed in [18],

it would thus be interesting to investigate the difference between hydrodynamics and electrophoresis in the case of channel of ~5 µm in height and with obstacles of different diameters to gain more insights on the difference in formalism between these two actuation principles.

Altogether this study confirms that hydrodynamic actuation provides an efficient solution to manipulate DNA in artificial separation matrices. In addition our single molecule study of DNA disengagement dynamics with elliptical obstacles confirms results of Brownian dynamics simulations, which indicated that DNA unhooking was relatively insensitive to the orientation of the ellipse. We finally ask whether elliptical obstacles are more favorable for DNA separation. For this we evaluated the disengagement time for 35 and 49 kbp molecules as a function of the tilt angle a, and observed that the disengagement dynamics was independent on the geometry of the post (Supplementary Fig. S1). Although more data on the behavior of DNA during its migration before and at the onset of the collision is still needed, our work suggests that the challenge associated to fabrication of elliptical nanoposts should not be rewarded by significant improvements in separation performances. Pe~2.4)

1.1 +/-0.1 s 1.2 +/-0.1 s 0.9 +/-0.1 s 0.9 +/-0.1 s 0.8+/-0.1 s 16+/-1 µm

Figure 1 :

 1 Figure 1: Fabrication of elliptical nanopost arrays and fluid flow modeling. (A) The electron micrographs

Figure 2 :

 2 Figure 2: Single molecule unhooking dynamics. (A) The holdup time distribution of U/J collisions is

Table 1 :

 1 Disengagement time for different geometries of obstacles and for two DNA sizes.

					Free draining
						Chain
	Cylindrical	a=47°	a=63°	a=76°	disengagement
						length
					time
	τ c (35 kb;
	0.7 +/-0.1 s	0.9 +/-0.2 s	0.7 +/-0.1	0.6 +/-0.1 s	0.6+/-0.1 s	12+/-1 µm
	Pe~2)
	τ c (49 kb;

Acknowledgements This work was also supported by the LAAS-CNRS technology platform, a member of the French Basic Technology Research Network. Y.V. and H.R. thank the French Defense Procurement Agency and the French Ministry of Research for funding. This work and the fellowship of Q.H. were supported by the ANR program JC08_341867. The authors thank Véronique Langlands for proofreading.


G Teyssedre
email: gilbert.teyssedre@laplace.univ-tlse.fr

C Villeneuve

P Pons

L Boudou

K Makasheva

B Despax

Challenges in probing space charge at sub-micrometer scale

An overview of current limitations and challenges with techniques, based either on acoustic or thermal perturbation, providing charge density profiles within insulations, is presented. Even though the resolution could be somewhat improved, technical limitations readily appear, related to the bandwidth of signals to be detected and to the sensitivity. Instead, our purpose here is to exploit near field techniques derived from AFM -Atomic Force Microscopy-. A booming of the availability and versatility of equipments is observed today. A spatial resolution of some tens of nanometers is accessible for charge detection which therefore let's the possibility to investigate selectively regions with specific properties. The measuring conditions and operating mode for both the sensitivity and spatial resolution of the techniques are addressed and examples of application of these techniques to charge detection in insulating materials are presented.

I. INTRODUCTION

One of the common properties of all solid dielectrics, being used as electrical insulating parts or as energy storage components, and whatever the size of devices (application related to electrotechnics, power electronics, microsystems, microelectronics, etc.) is their propensity to accumulate electrical charges beyond a critical electric field. Whatever the application field considered, it is actually necessary to implement diagnostic means of the charging state that are fitting to the stresses, environment, nature and geometry of materials in conditions that are representative of their real conditions in the application. Along this aim, a wealth of techniques has been developed over the last decades enabling probing charge build for dielectric thickness ranging from several cm to some tens of µm. The lower scale limit actually represents a serious limitation as many dielectrics are used in the sub-micron range of thickness. Without being exhaustive, one can quote here wiring of motors (typically 20-40µm thickness), insulation/passivation in power electronics (some µm), protective insulations in outer parts of spacecraft (some tens of µm), integrated and miniaturized devices: actuators, MEMS, lab-on-a-chip, dosimeters, passivation layers, with µm or sub-µm scale; microelectronics: SiO 2 gates, non-volatile memory devices (nm scale); photovoltaic devices: 3 rd generation solar cells (nm scale).

Besides, identification and understanding of physical phenomena at play require implementing techniques to provide information at the scale where physical processes occur, e.g. at interfaces, whether formed by the limits of the solid insulation (metal-dielectric interface, air/dielectric) or at the borders of domains of different nature, i.e. at dielectric/dielectric interfaces. Triboelectrification, flow electrification phenomena, corona charging are examples of processes analyzed so far only as surface charging phenomena due to a lack of in-depth information. In the dielectric bulk, at the nanometer scale, charge storage is controlled by atoms or atom groups liable to introduce impurity levels owing to their specific electronic properties. At the meso-scale of some tens or hundreds of nm, it is crystalline, supermolecular architecture that constitutes specific domain in respect to the amorphous phase. It is therefore at these different scales that electronic properties of insulating materials must be probed as a route to feed models describing transport properties at the macroscale with the intention to reveal the physical phenomenon at play.

In this communication, we first briefly recall the present performances obtained by techniques with in-depth resolution for charge density profile measurements. We also comment on the technical limitations in further improvement of the resolution. As improving the resolution beyond some tenths of µm does actually represent a huge challenge, strategies consisting in applying near-field techniques derived from atomic force microscopy -AFM-are heavily developing. The principles of such techniques and the options for detection of electrical charges based on electrostatic force interactions between the AFM tip and the dielectric surface are described.

Finally, examples of results obtained by probing silicon oxynitride layers are shown.

II. TECHNIQUES WITH IN-DEPTH RESOLUTION OF CHARGES: SPATIAL RESOLUTION

The ultimate spatial resolution of conventional space charge measurement techniques (based on acoustic or thermal principles) is often evaluated considering the shape of the perturbation being applied [START_REF] Fleming | Space charge profile measurement techniques: recent advances and future directions[END_REF][START_REF] Holé | Resolution of direct space charge distribution measurement methods[END_REF]. With this criterion, the spatial resolution is by principle quite different weather one considers wave propagation techniques (either pressure-wave -PIPWP-or pulsed electroacoustic -PEA-methods) or thermal methods (Laser Induced Modulation Method -LIMM-or Thermal Pulse -TP-). Ideally, the definition should correspond to the shortest distance between 2 plans of charges that can be distinguished. An excitation-related criterion consists in considering the shortest distance over which the perturbation varies from 10 to 90% of its amplitude [START_REF] Dagher | A preliminary study of space charge distribution measurements at nanometer spatial resolution[END_REF]. For wave propagation method (with symmetrical pulse), the resolution is of the form:

max 2 R f   , ( 1
)
where  is the risetime of the excitation pulse and  is the sound velocity; f max is the required bandwidth of the detection system. For reaching a resolution of 100nm, considering a sound velocity of 2000m/s typical of polymers, (1) leads to a bandwidth of 10GHz.

For thermal methods the resolution depends on the diffusivity (D) of heat and on time (t):

max 2.35 2.35 2
D RD t f  . ( 2
)
For 100nm resolution (at 50% amplitude) and a diffusivity of 10 -7 m²/s (of polymers), f max is of the order of 10MHz.

As depicted in Fig. 1, the in-depth resolution is not dependent on the position in the sample for pressure wave methods. Conversely, the resolution decreases with the penetration of the heat front in the bulk with thermal methods. For this reason, although better resolution has been achieved so far for thermal methods, it must be said that it is only close to the surface on which the heat flux is provided. The actual resolution is impeded by factors other than the shape of the perturbation, some of them being inherent to the shape/nature of test materials, like attenuation and dispersion of acoustic waves by the sample, which may impose to work on thin dielectrics to maintain a targeted resolution. The geometry and electromechanical losses of the piezoelectric sensor (PEA method) and the deconvolution procedure [START_REF] Arnaout | Study of signal treatment for a pulsed electro-acoustic measurement cell: a way of improving the transfer matrix condition number[END_REF] are influential in the resolution achieved. Impedance matching between voltage pulse and sample capacitance becomes an issue when increasing bandwidth. Thermal methods need an accurate and simultaneous measurement of surface temperature profile for accurate deconvolution. Improving resolution means increasing bandwidth, which goes with a loss in amplifier gain and with impedance mismatch issues. Also the perturbation energy decreases as the frequency increases meaning that sensitivity and signal-to-noise ratio will provide new challenges. Fig. 2 represents current achievements of various techniques (LIMM and TP for thermal methods, and PEA). A resolution of 0.5µm has been reported for TP, very close to the sample surface [START_REF] Mellinger | Three-dimensional mapping of polarization profiles with thermal pulses[END_REF]. In case of PEA, a resolution of 3-4µm has been achieved [START_REF] Maeno | High-resolution PEA charge distribution measurement system[END_REF]. Incremental improvement of the PEA method with the current principle could perhaps bring the resolution to 1µm or slightly beyond, but a switch to frequency scan for the excitation would be preferable for impedance mismatch issues. The LIMM method could also be improved through appropriate amplifier is settled and surface temperature can be monitored. In both cases, with the bandwidth figures given above, it is difficult to envisage a resolution better than 100nm.

Dagher et al. [START_REF] Dagher | A preliminary study of space charge distribution measurements at nanometer spatial resolution[END_REF] reconsidered the different techniques using perturbation based on femtosecond laser pulses, providing thermal pulse (TP) excitation or thermally-induced elastic waves (PIPWP) or ps field pulses via a semiconducting crystal (PEA). The detection side would consist in electromagnetic field detection through an electro-optical crystal (TP and PIPWP) or surface displacement detection by interferometry (PEA). Though the specifications have been settled targeting a resolution of 60nm in SiO 2 , the development of such instruments remains extremely challenging.

III. INPUT OF SCANNING PROBE MICROSCOPY

Scanning probe microscopy -SPM-has emerged and spread in a family of techniques since the development of scanning tunnelling microscopy, in 1982, which was limited to topography investigation of conductive materials at the nanometer scale [START_REF] Binnig | Atomic Force Microscope[END_REF]. AFM has brought an important progress as it could be applied to either conductive or insulating materials to obtain images at atomic scale. The contrast is essentially based on the gradient of interaction forces between a tip and the surface to be probed. A wealth of techniques has appeared based on the scanning of forces of different natures (magnetic, mechanical, chemical, etc...). As the influence length of different forces depends on their nature, different images can be obtained as a function of the scanning distance from the surface. Table 1 provides with order of magnitude of forces probed by Scanning Force Microscopy -SFM-and on their influence length. The detection of electrostatic forces has naturally emerged as a way to carry out imaging based on surface potential contrast, induced e.g. by electrical charges stored on the surface. EFM (Electrostatic Force Microscopy) and KPFM (Kelvin Probe Force Microscopy) are two of the variants developed for that purpose [START_REF] Stevens | Nano-and meso-measurement methods in the study of dielectrics[END_REF][START_REF] Rezende | Detection of charge distributions in insulator surfaces[END_REF]. EFM and KPFM techniques differ by the way electrostatic forces are measured. Only a brief description is given here [START_REF] Mélin | Scanning Probe Microscopy in Nanoscience and Nanotechnology[END_REF]. In EFM, the frequency variations  m of the tip vibration due to static electrostatic forces F DC are measured:

2 2 1 S DC V dz dC F  and ) ( 2 1 0 o DC m m z dz dF k     , ( 3
)
where Vs is the tip-surface potential difference and C the tipsurface capacitance. In KPFM, an ac voltage is applied to the tip that provides a dynamic force of the form:

) sin( ) ( ) ( t V V V dz dC F AC FB S      (4)
The dynamic force is cancelled out by applying a feedback loop V FB on the potential of the tip. As F() depends on the 1 st derivative of capacitance whereas  m depends on the 2 nd derivative, KFPM is more sensitive to parasitic capacitance. However, the surface potential is obtained directly, and there is no perturbation due to influence charge on the tip.

In our context, the objective is not only to record images (and optimize spatial resolution) of the surface based on electrostatic properties contrast. A more important goal is to bring the technique to quantitative and reliable surface charge density measurement. So, the relative merit of the techniques has to be evaluated based on that objective.

An important feature of these techniques is that only lateral resolution is achieved with no in-depth resolution. This represents a serious limitations compared to techniques presented in section I. However, strategies have been implemented for obtaining field profiles by EFM technique along the inter-electrode distance by using imbedded electrodes [START_REF] Silveira | Microscopic view of charge injection in an organic semiconductor[END_REF] with resolution well beyond the µm scale. This has been applied to semiconducting materials, however there is no apparent issue preventing its extension to insulating materials.

IV. KPFM ON a-SiO x N y :H LAYERS WITH DIFFERENT CONDUCTIVITIES

Pulsed plasma-enhanced chemical vapor deposition (PPECVD) is used as deposition technique to elaborate the layers [START_REF] Makasheva | Dielectric layers for RF-MEMS switches: design and study of appropriate structures preventing electrostatic charging[END_REF]. Thin amorphous silicon oxy-nitride layers (a-SiO x N y :H, x < 2, y < 1) are deposited on Au/Ti-metallized Si wafers in the plasma of radiofrequency (f = 13.56 MHz) capacitively coupled discharge (P = 5 W, p = 65 Pa), modulated with square pulses of period T = 400 ms and pulse width of 100 ms. The gas mixture contains SiH 4, N 2 O and He (40%). The ratio  = N 2 O/SiH 4 allows adjusting the silicon content in the film. The substrate temperature is kept to 300°C during deposition. Layers with three different -values (100, 10 and 5) have been used for the current study.  = 100 implies a layer with composition close to a SiO 2 -layer. When decreasing the -value the Si-concentration in the layer increases, hence its conductivity.

Spectroscopic ellipsometry has been used to record the spectra aiming at a structural characterization of the layers. Forouhi-Bloomer approximation [START_REF] Forouhi | Optical dispersion relations for amorphous semiconductors and amorphous dielectrics[END_REF] is applied for the spectra processing to find out thicknesses and optical properties of the layers. The optical band gap is deduced from Tauc's law. The results are summarized in Table 2.

One can notice that the γ = 100 a-SiO x N y :H-layer has a refractive index close to that of fused silica layer (n = 1.457). The obtained quite high optical band gap of γ = 100-layer describes it is as a good dielectric layer. Decreasing the value leads to deposition of layers with higher refractive indexes and lower optical band gaps mainly due to a higher Sicontent, hence different Si-environment in the layer. The structural characterization of γ = 5 layer shows that this layer can represent semi-conducting properties (E g is only 2.8 eV). KPFM measurements are carried after charging the material by an AFM tip with voltages up to 25V for times from 10s up to 10min. Results for 25V are shown in the following. Note that the field is relatively high (order 100-200kV/mm), compared to that obtained for conductivity values (3-60kV/mm) given in Table 2 [START_REF] Makasheva | Dielectric layers for RF-MEMS switches: design and study of appropriate structures preventing electrostatic charging[END_REF]. To improve accuracy and reproducibility, some parameters need to be controlled: (i) humidity is the most critical one [START_REF] Zaghloul | On the influence of environment gases, relative humidity and gas purification on dielectric charging/discharging processes in electrostatically driven MEMS/NEMS devices[END_REF]. Measurement are done under N 2 flow and Force Distance Curve is used to follow adhesion due to water adsorbed on sample surface; and (ii) contact force which is kept small to ensure tip integrity. Injected charge is punctual, so only a cross-section of potential map is shown. Three parameters are extracted from this profile: maximum potential V m , full width at half maximum, W h and the integrated intensity I s along the profile. KFM measurement of surface potential emphasizes different behaviour for each layer in terms of charges injection and decay. Indeed, resulting potential profiles of Fig. 3 exhibit strong differences between different layers. For γ=100 and γ=5 charges are stored close to the injection contact point (W h =0.9 and 4.6µm, respectively) whereas γ=10 layer exhibits strong lateral charges spreading (W h =37µm). For γ=100 and γ=5 layers, a saturation of injected charge is observed with increasing charging time (results not shown). In γ=10 layer, the charge spot broadens and grows without saturation effect with injection time. Fig. 4 shows the change in time of parameters of the potential profiles during release of the charges. Again, marked contrasts are observed. For γ=100 layer, peak intensity V m and area I s remain constant in time and no broadening is observed: charges remain trapped. For γ=10, a decrease of both V m and I s is observed, meaning that drift along the surface (reflected by significant peak broadening) and through the bulk of the dielectric (reflected by changes in I s ) occur concomitantly. Finally, for =5, the peak broadens at short time and then stays unchanged. The area and the intensity of the peak follow a parallel behaviour. Hence, charges would be dominantly evacuated through the dielectric bulk to the ground. Results for the different materials can be understood as follows. For γ =100, owing to the good insulation properties, it can be supposed that charges get trapped close to the injecting electrode and form a screening field that prevent further charge injection. For =5, a significant leakage of charges to the ground during charging occurs. The saturation effect in charging corresponds to the building-up of equilibrium between charging and leakage. The case of =10 is much more appealing. A significant charge cloud expansion along the surface is observed both during charging and discharging. The conductivity value obtained in the linear regime is close to that for =5, but the trend can be different for the high field range. Anyway, conductivity alone cannot explain the features (spreading of charges over several tens of µm with a layer thickness of 200nm). A possible effect is the formation of deep traps, induced by high electric field under AFM tip. Such traps would limit charge conduction in the volume and would promote occupation of lateral shallow traps. More work needs to be done for further understanding the marked contrasts observed by KPFM on the dielectric properties of these layers.

V. CONCLUSION

A technical limit in spatial resolution of charge distribution measurement methods is being reached, preventing their use in µm scale dielectric thicknesses. The increasing needs for characterizing relatively thin film dielectrics can be covered by electrostatic techniques based on scanning force microscopy which come to a mature stage even though developments are on the way. The examples shown here have not touched the spatial resolution of the techniques (≈50nm) because of the nature of materials that tend to spread charges. Promising results are obtained on these a-SiO N y :H layers.

Fig. 1 .

 1 Fig. 1. Spatial resolution in charge measurement vs. depth in the sample for pressure wave (left) and thermal methods (right). From [3].

Fig. 2 .

 2 Fig.2. Sketch of achieved resolution, and hypothetical improvements that could be brought for thermal and acoustic principles.

Fig. 3 .

 3 Fig. 3. Potential profiles induced by charge injection at 25V during 2min for different layers.

Fig. 4 .

 4 Fig. 4. Evolution of the normalized maximum Vmn (solid symbols), the normalized integrated intensity Isn (open symbols) and normalized peak width (grey) after charge injection for different layers γ = 100 (squares), γ = 10 (triangles) and γ = 5 (circles). Bias injection of 25V during 2min.

TABLE 1 .

 1 ORDER OF MAGNITUDE AND INFLUENCE LENGTH OF FORCES BETWEEN AFM TIP AND SAMPLE SURFACE MEASURED BY SFM

	Origin	Nature	Influence length	Force strength
	Electron cloud (Pauli)	Repulsive	Very short (0-1nm)	1nN
	Van Der Waals	Attractive	Short	5nN
	Capillarity (strong in air)	Attractive	(0-10nm)	a few 100nN
	Molecular Adhesion	Attractive	Middle (0-50nm)
	Electrostatic (surface charges)	Attractive/ Repulsive	Long	1nN to some 10nN
	Magnetostatic (magnetic	Attractive/	(0-100nm)
	materials)	Repulsive

TABLE 2 .

 2

		THICKNESSES, OPTICAL PROPERTIES
		AND LOW FIELD RESISTIVITY OF a-SiOxNy:H LAYERS
	a-SiOxNy:H layer γ = 100 γ = 10 γ = 5	Thickness [nm] 116.6  0.7 207.0  6.3 165.7  4.6	Refractive index-n (at =632.8nm) 1.49 1.55 1.61	Optical band gap Eg [eV] 5.03 4.2 2.8	Resistivity [.m] 2.2  10 15 4.9  10 14 3.8  10 14


Nicoletta Caramia

Fabien Lotte

Stefano Ramat

OPTIMIZING SPATIAL FILTER PAIRS FOR EEG CLASSIFICATION BASED ON PHASE-SYNCHRONIZATION

Keywords: Brain-Computer Interfaces, EEG signal processing, Phase Locking Value, Spatial Filter, Genetic Algorithm

Brain-Computer Interfaces (BCI) aim at translating brain signals, typically ElectroEncephaloGraphy (EEG),

into commands for external devices. Spatial filters are powerful tools for EEG classification, able to reduce spatial blurring effects. In particular, optimal spatial filters have been designed to classify EEG signals based on band power features. Unfortunately, there are other relevant EEG features for which no optimal spatial filter exists. This is the case for Phase Locking Value (PLV) features, which measure the synchronization between 2 EEG channels. Therefore, this paper proposes to create such a pair of optimal spatial filters for PLV-features. To do so, we optimized a functional measuring the discriminability of PLV-features based on a genetic algorithm. An evaluation of our algorithm on a motor imagery EEG data set showed that using optimized spatial filters led to higher classification performances, and that combining the resulting PLV features with traditional methods boosts the overall BCI performances.

INTRODUCTION

Brain Computer Interfaces are communication systems able to process brain signals in order to detect specific mental states or events. The decoded brain activity can be used to command an external device allowing communication with the external world, notably for people with motor disabilities [START_REF] Wolpaw | Brain-computer interfaces for communication and control[END_REF]. Measuring brain activity is the first step in the design of a BCI, and is most commonly achieved using electroencephalography (EEG), which offers the best compromise between affordability, availability and low invasiveness. One of the major challenges in the design of BCI is to robustly decode the noisy EEG signals in order to recognize the occurrence of two or more different mental states. With the rapid recent development of BCI, several EEG signal processing techniques were developed to improve mental state classification. Among the different instruments proposed in the literature, spatial filtering proved to be particularly useful to maximize the signal-to-noise ratio (SNR). The first used spatial filters were Common Average Reference (CAR) and Laplacian, both constructed for reducing the effect of spatial blurring. CAR subtracts the average value of the entire electrode montage to each channel while Laplacian is an approximation of the second derivative of the instantaneous spatial voltage distribution that operate as a high pass spatial filter able to emphasize localized activity [START_REF] Nunez | A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging[END_REF]. Moreover in the last years, more adaptive spatial filters were developed to optimize the SNR of specific features, in particular to make the features extracted from the spatially filtered signals as discriminative as possible. One of the most useful such spatial filter is the Common Spatial Pattern (CSP), which is the optimal spatial filter for band power features [START_REF] Blankertz | The BCI Competition 2003 :Progress and Perspectives in Detection and Discrimination of EEG Single Trials[END_REF]. It has notably been used successfully for the classification of BCI based on motor imagery [START_REF] Blankertz | The BCI Competition 2003 :Progress and Perspectives in Detection and Discrimination of EEG Single Trials[END_REF]. In particular CSP is constructed in order to maximize the variance of the band-pass filtered EEG signal (i.e., the signal band power) recorded under one condition while minimizing the variance of the band-pass filtered signal during a second condition [START_REF] Blankertz | Optimizing Spatial Filters for Robust EEG Single-Trial Analysis[END_REF]. The success of CSP highlighted the usefulness of optimal spatial filters for maximizing the discriminative power of EEG features. Unfortunately, there are several EEG features for which no optimal spatial filter has been proposed. This is notably the case for connectivity features such as the phase locking value (PLV), which measures the synchronization between the signals from two different EEG channels and thus between different brain areas [START_REF] Lachaux | Measuring phase synchrony in brain signals[END_REF], [START_REF] Lachaux | Studying single-trials of phase synchronous activity in the brain[END_REF]. Such features have indeed been shown to be promising to classify EEG signals for BCI [START_REF] Wei | Amplitude and phase coupling measures for feature extraction in an EEGbased brain-computer interface[END_REF], [START_REF] Krusienski | Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface[END_REF]. Thus, the purpose of our work was to fill this gap by optimizing spatial filters for PLV features. The PLV was introduced in the 1999 as a statistics for detecting synchrony in a precise frequency range between two different recording sites [START_REF] Lachaux | Measuring phase synchrony in brain signals[END_REF]. For calculating the PLV the variability of phase difference between two signals is measured across trials making this statistics unusable for online EEGclassification. In the same year was introduced a variant of PLV, the single trial-PLV (S-PLV), in which synchrony is compared across samples [START_REF] Lachaux | Studying single-trials of phase synchronous activity in the brain[END_REF]. Based on this feature we constructed a new functional in order to find the best two spatial filters able to maximize the discriminative power of the S-PLV feature between two conditions of EEG signals (e.g., left hand motor imagery versus right hand motor imagery). In order to find the optimal filters we chose to combine a genetic algorithm (GA) with a local minimum search. Each chromosome of the GA represents the two spatial filters, using this evolutionary methods we were able to explore a large space of solutions, and then the best chromosome was taken as a starting point for the local search.

MATERIALS AND METHODS

A. Single Trial Phase Locking Value

The feature that we have chosen for the classification of EEG traces is the single trial phase locking value, which, has we have mentioned earlier, has been identified as a promising feature for EEG classification [START_REF] Wei | Amplitude and phase coupling measures for feature extraction in an EEGbased brain-computer interface[END_REF], [START_REF] Krusienski | Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface[END_REF]. The PLV is a statistic used to investigate synchronization of neural activity from EEG data and expresses a measure of connectivity. The equation that we used for this measure is:

! !"# = 1 ! ! ! ! ! ! !! ! ! ! !!! ( 1
)
Where N is the number of samples in the considered time window, ! ! and ! ! are the phase values of the two signals that we want to compare [START_REF] Lachaux | Studying single-trials of phase synchronous activity in the brain[END_REF]. The computed S-PLV is a number between 0 and 1 that reflects how the two channels are synchronized to each other. The phase value is computed using the Hilbert Transform [START_REF] Lachaux | Studying single-trials of phase synchronous activity in the brain[END_REF], [START_REF] Van Quyen | Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony[END_REF].

B. Spatial filters optimization

Considering that S-PLV is the result of a comparison between two channels we had to find two spatial filters that will create two new channels, each one a different combination of all the recorded ones. Indeed, as mentioned before, the EEG signals from individual channels is prone to have a low SNR, due to spatial blurring and volume conduction effects. In order to obtain better PLV features for EEG classification, it thus seems relevant to compute the PLV between two spatially filtered channels (thus with a higher SNR), rather than between two original channels. We therefore propose an algorithm to optimize these two spatial filters in order to maximize the resulting PLV feature discriminative power. We represented EEG traces with a 3D matrix ! ∈ ℝ !"×!"×!" (with !" the number of channels, !! the number of samples and !" the number of trials), the two spatial filters ! ! and ! ! were two vectors in ℝ !"×! . The two new channels ! ! ,! ! were computed as follows:

! ! = ! ! ! ! (2)
! ! = ! ! ! ! (3)
where t denotes transpose. The idea for optimizing the spatial filters was to maximize the difference of the S-PLV between the two new filtered channels during two different conditions, hence maximizing the following functional:

!"## = |!_!"#(! ! ! ! ! , ! ! ! ! ! ) -!_!"#(! ! ! ! ! , ! ! ! ! ! )| (4)
where ! ! were the EEG traces recorded under one condition and ! ! werethe EEG traces recorded during the second condition. In other words, this functional amounts to optimizing spatial filters such that the resulting S-PLV value is maximally different between the two conditions.

To avoid obtaining two identical spatial filters, which would be useless (a signal is necessarily synchronized with itself), we proposed to enforce the orthogonality of the two spatial filters by introducing a regularization term expressed as:

!"# = ! ! ! ! ! * !"## !"#(! ! ) (5)
In this way the orthogonality value was scaled according to the value computed in !"##.Combining the two members gives the final functional which was maximized by the GA:

!"#$%&& = !"## -! * !"# (6)
where α was a parameter between 0 and 1 indicating how the second member weighs, i.e., how much we want to enforce the spatial filter orthogonality The PLV value is meaningful only if it is computed in a certain frequency band, thus the first operation of our research process was a frequency filter, and specifically we chose to filter the signal between 8-24Hz, which contains both the Mu (8-12 Hz) and Beta (12-24 Hz) rhythms, which are involved in motor imagery [START_REF] Pfurtscheller | Motor imagery and direct brain-computer communication[END_REF]. For searching the solution we developed a Genetic Algorithm working on a population of random chromosomes, each containing the weights of the two spatial filters. The GA evolves using elitism, tournament-k selection, single point crossover maintaining gene integrity, and mutation [START_REF] Goldberg | Genetic Algorithms in Search, Optimization and Machine Learning[END_REF]. The GA was set to evolve the population for a predefined number of generations. The fitness function for evaluating each chromosome was computed according to Equation [START_REF] Lachaux | Studying single-trials of phase synchronous activity in the brain[END_REF]. The best solution found by the GA was then used as an initialization point for the fminsearch function of the MATLAB Optimization Toolbox, in order to obtain a better local solution. For using this functionality we had to search a minimum, thus we decided to minimize the inverse of (6):

!"# = ! !"#$%&& 7

C. Evaluation

Our approach was evaluated on data set 2a from BCI competition IV [START_REF] Tangermann | Review of the BCI Competition IV[END_REF], provided by the Graz group [START_REF] Naeem | Seperability of four-class motor imagery data using independent components analysis[END_REF].This set comprises EEG signals from 9 subjects who performed left hand, right hand, foot and tongue Motor Imagery (MI). The EEG signals were recorded using 22 EEG channels.

For the purpose of this study, only EEG signals corresponding to left and right hand MI were used. EEG signals were band-pass filtered in the 8-24 Hz frequency band using a 250th order Finite Impulse Response (FIR) filter used with Matlab filtfilt function. Indeed, this frequency band contains the mu (8-12 Hz) and beta (12-24 Hz) rhythms, i.e., the main frequencies involved in MI [START_REF] Pfurtscheller | Motor imagery and direct brain-computer communication[END_REF].

A training and a testing set were available for each subject. Both sets contained 72 trials for each class, the duration of a trial being 7 sec. Subjects performed MI within the time interval of t=3 to 7 sec of each trial. In this work, we only considered the discrete classification of the trials, i.e., we assigned a class to each trial and not to each trial sample. In order to do so, we extracted the features from the time segment t=3 to t=6 sec of each trial. We used the available training set for the GA evolution and fminsearch computation. The initial population of the GA was set to 300 chromosomes and evolves for 300 generations. Then the two spatial filters were applied to the data and a feature value for each trial was computed as the S-PLV between the two new channels (i.e., between the two spatially filtered signals).Training trials were provided as input to an Support Vector Machine (SVM) [START_REF] Lotte | A review of classification algorithms for EEGbased brain-computer interfaces[END_REF] in order to train the classifier to recognize the two classes (left hand MI vs. right hand MI). Trials belonging to the test set were then classify with the trained SVM. For all evaluations and all subjects, we used α=0.8, which we found was a good default value. Moreover in order to compare our approach with those available in the literature, we also computed the performance obtained with CSP spatial filtering in the 8-24 Hz band, evaluating the performances with both one pair of CSP filters (i.e., as many filters as with our approach) and with three pairs of CSP filters (i.e., the standard way [START_REF] Blankertz | Optimizing Spatial Filters for Robust EEG Single-Trial Analysis[END_REF]).

We also explored whether combining spatially filtered S-PLV features with CSP features was relevant. To do so, we concatenated the features extracted with CSP with the new ones derived from the proposed spatially filtered S-PLV and used them as input to the SVM classifier.

RESULTS

Table I presents the results in term of classification accuracy obtained on the test set by using the different signal processing approaches mentioned. The first row presented the best result obtained using the S-PLV computed between each pair of the available channels (i.e., without spatial filtering). Using a 5-fold-cross-validation we selected the best pair of channels for each subject in terms of classification accuracy on the training set.

In the second row is reported the classification accuracy obtained using the S-PLV between the two channels computed with the proposed spatial filters. The spatial filter algorithm outperformed the original S-PLV by about 9% in both mean classification accuracy and median classification accuracy. This confirms that our spatial filters improve the discriminative power of the S-PLV features.

The third and fourth row of Table I reports the performances obtained with the standard design, i.e., CSP features, with 1 pair and 3 pair of spatial filters respectively. In the last two rows of the table are presented the results obtained on the test set combining the CSP approach and the spatially filtered PLV approach proposed in this work. Using one pair of CSP filters with the new PLV feature we improved the mean classification accuracy by 3%, as compared to CSP alone. Otherwise using three pairs of CSP filters with PLV led to an increase in median accuracy, but no increase in mean accuracy probably due to overfitting.

Overall the best performances are still achieved combining CSP and PLV. Figure 1 the other. Figure 2 reports the evolution of the fitness value during the generation of the genetic algorithm. In particular, it shows the evolution of both terms composing the fitness function. It is remarkable to see how in the last generation the term related to the difference (!"##, see Equation ( 4)) still increases while the term related to the orthogonality (!"#, see Equation ( 5)) decreases.

DISCUSSION

The results obtained give several interesting insights. Concerning performances, they first show that computing a S-PLV-feature between two spatially filtered signals does lead to increased classification performances as compared to computing the S-PLV between two raw EEG channels. This further confirms the usefulness of designing optimal spatial filters for the different EEG features that are relevant for BCI design. Second, our results show that, at least for motor imagery-based BCI, spatially filtered PLV features do extract a different information than spatially filtered band power features (i.e., CSP features) since combining them increases the overall classification performances. This makes optimally spatially filtered S-PLV features a valuable addition to the repertoire of features that can be used by EEG-based BCI designers.

In this work, we optimized a single pair of spatial filters which resulted in a single PLV feature. As such, our method might not have extracted all the relevant information, and maybe some other spatial filter pairs could have been used to increase the performances further. In the future, it may be useful to use, e.g., deflation approaches to extract several pairs of spatial filters from which computing PLV features. Furthermore GA generation takes time (several hours) then it could be useful to find an analytical solution to optimize the proposed functional.

From a neurophysiological point of view, it is worth noting that the obtained spatial filters can be interpreted to check the plausibility of the extracted features or to gain some insights about the brain dynamics. Here, as can be seen in Figure 1, one of the two spatial filters has strong weights on the brain motor areas (electrodes C3 and C4, images B and D), while the other one has strong weights on the brain frontal areas (electrodes FCz, Fz). This suggests that phase synchronization between the frontal and motor areas, in the Mu and Beta bands, might change during motor imagery and could be used to identify the type of motor imagery (left hand vs. right hand). However, since such spatial filters are "backward" models, this interpretation may be erroneous and should be considered with caution [START_REF] Haufe | On the interpretation of weight vectors of linear models in multivariate neuroimaging[END_REF]. Nonetheless, this is in line with previous research on PLV-feature based BCI design [START_REF] Wei | Amplitude and phase coupling measures for feature extraction in an EEGbased brain-computer interface[END_REF], which confirms the validity and relevance of the proposed optimization algorithm.

CONCLUSION

In this paper, we proposed a method, based on a genetic algorithm, to create an optimal spatial filter pair for EEG signal classification with S-PLV-features. We demonstrated the validity of our method for classifying signal coming from BCI experiments based on motor imagery. We notably showed that our spatial filters led to higher classification performances, and that combining the resulting S-PLV features with CSP boosts the overall BCI performances. Future work could aim at applying the proposed algorithm to different data sets and BCI tasks, to explore its scope of applicability. Moreover, we could optimize more pairs of orthogonal spatial filters in order to further increase classification performance and also it could be interesting to consider time as well. Overall, we hope our method could be a new and valuable addition to the repertoire of EEG signal processing tools.

ACKNOWLEDGMENTS

This was partially funded by the CARIPLO project n. 2009-2936 "Internationalization of the ICT and Bioengineering PhD Schools"

Figure 1

 1 Figure 1 Spatial filters: image A and image B represent the two spatial filters found with the GA, C and D are the spatial filters optimized with fminsearch, following the GA use.

  presents the spatial filters obtained for subject 3. The first two figures represent the solution found with the GA, while the last two the solution obtained by the local search (fminsearch function), after the GA. Comparing the 4 figures it is clear that fminsearch performed a local search around the solution of the GA. Fminsearch shapes the two spatial filters making them more specific by better identifying the cortical areas whose signals should be enhanced or suppressed to build two new channels whose signals are synchronized in one class and de-synchronized in

		Subj1	Subj 2	Subj3	Subj4	Subj5	Subj6	Subj7	Subj8	Subj9	Mean ±!"#	Median
	PLV	73.61	54.89	78.47	60.42	54.17	49.31	50.69	77.08	78.47	64.12 ±12.59	60.42
	PLV SP. FILT.	84.72	54.17	93.75	68.06	47.22	69.44	68.61	93.75	85.42	73.90±16.66	69.44
	CSP	87.50	50	94.44	72.22	51.39	74.30	61.80	99.30	90.97	75.77±18.50	74.30
	CSP (3 filter pairs)	84.72	58.33	91.67	68.75	53.47	63.88	75.69	97.91	89.58	76±15.83	75.69
	PLV+CSP	89.58	57.64	97.22	74.31	51.39	73.61	66.67	99.30	92.36	78.00±17.49	74.31
	PLV+CSP (3 filter pairs)	86.11	56.94	95.83	70.11	53.47	64.54	78.47	99.31	86.11	76.76±16.50	78.47

TABLE 1 CLASSIFICATION

 1 ACCURACIES (MEAN, MEDIAN AND STANDARD DEVIATION (STD) IN %) OBTAINED FOR EACH SUBJECT FOR DIFFERENT APPROACHES. BEST PERFORMANCES ARE DISPLAYED IN BOLD.


Patrick Pons

Hervé Aubert

Manos Tentzeris

Wireless chipless passive electromagnetic transducers for SHM applications

come

INTRODUCTION

Since the late 1990s, wireless sensor networks emerge as a key technology for numerous applications (domestic, health, environmental, industrial, military, ...) [START_REF] Atzori | The Internet of Things: A survey[END_REF][START_REF] Ruiz-Garcia | A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry[END_REF][START_REF] Sardini | Passive and Self-Powered Autonomous Sensors for Remote Measurements[END_REF][START_REF] Alemdar | Wireless sensor networks for healthcare, A survey[END_REF] and especially for Structural Health Monitoring (SHM) [START_REF] Brandon | Structural health management technologies for inflatable / deployable structures: Integrating sensing and self-healing[END_REF][START_REF] Kiremidjian | Structural Health Monitoring for Civil Infrastructure : From Instrumentation to Decision Support[END_REF][START_REF] Chang | Implementing RFIC and sensor technology to measure temperature and humidity inside concrete structures[END_REF][START_REF] Chae | Development of a wireless sensor network system for suspension bridge health monitoring[END_REF]. One can divided wireless sensors technologies into two main classes: [START_REF] Atzori | The Internet of Things: A survey[END_REF] active sensors which require RF transmitter and (2) passive sensors without RF transmitter.

Active sensors can be described by three different functional parts: a transducer which converts the input to be analyzed into electrical signal, electronic circuits for the signal conditioning and a communication unit for data transmission (Figure 1). This type of sensors works generally with a battery related to the high consumption of the transmitter. The main advantages of this kind of sensors are : a high reading distance (up to several km), a high fickleness with the possibility to use any kind of transducers, an easy identification compatible with large sensors network and a low sensitivity to electromagnetic interferences. The key limiting factor is the sensor autonomy and the sensor life time is driven by embedded quantity of energy (mainly the battery size), the data transmission rate and the reading distance. Another drawback is the low resistance to harsh environments (high temperature, high radiation level) related to the presence of electronic circuits. Then, despite the disadvantages of this type of active sensors, a lot of companies propose now wireless active sensor products that are suitable for different applications. That was made possible by the availability on the market of low cost and efficient (low consumption, low size) transducers, electronic circuits (from DC to RF) and batteries but also the availability of standardized transmission protocol (e.g., Wifi, Zigbee and Bluetooth). The main research studies in this field for increasing the sensor autonomy are concentrated on the development of new energy micro-source with high power (electrochemical, nuclear, thermo-electric, thermo-ionic, …), high efficiency of energy harvesting (solar, vibration, thermoelectric, Radio-Frequency, …) and low consumption electronic circuits. Passive sensors is typically composed of two parts: (1) a passive transducer with only passive elements and (2) a remote reader able to analyze the transducer response (Figure 2). The wireless communication between these two parts can exploit electrical, magnetic, electromagnetic, acoustic or optical link. The most popular are magnetic and electromagnetic links, related to the availability of several low costs solutions for electronic readers. The first one called "near field communication" is quite old and is limited by the very short reading distance (few tens of cm). Electromagnetic interrogation, based on the analysis of backscatter wave by the transducer, allows overcoming this limitation. Two classes of passive sensors with electromagnetic interrogation already exist on the market : RFID and SAW. Reader Input to be analyzed Transducer RFID passive sensors are based on the modulation of the backscatter wave level between two states (RCS1 and RCS2) reproducing digital coding of the signal (Figure 3). This modulation is obtained through the switching between two different loads connected to the antenna. The switch is driven by the digital output of a microcontroller that converts the analog response of the transducer. The main advantage, compared with active sensors, is the very high autonomy since the RF transmitter is removed and all the electronic units are powered through RF link. In return, the reading distance is generally lower than 5m. SAW sensors are based on piezoelectric substrate on which inter-digital electrodes (connected to an antenna) allow converting the electromagnetic waves into acoustic waves (for the input signal) and acoustic waves into electromagnetic waves (for the output signal). The backscattered signal is then analyzed in time or frequency domain to extract transducer information as the propagation speed of the acoustic wave in the piezoelectric material is dependent on several factors (stress, temperature, surface coating, ....). The two main advantages compared to RFID passive sensors are the fully autonomous nature of these sensors (no need of energy for electronic or transducer part) and the high resistance to harsh environments. In return, the sensor response is fully analog making the identification more difficult and reducing the accuracy. Moreover the sensing principles are restricted by the use of piezoelectric substrates. Even if the reading distance is generally greater than that obtained for RFID sensors, this one is usually lower than 10m, related to the low efficiency of the coupling coefficient between electromagnetic and acoustic waves (between 0.1% and 5%).

Two main antagonistic requirements for SHM applications, i.e., a very high energy autonomy (for buried or low accessibility sensors) and a high reading distances (>10m), are then often difficult to obtain with the above-described sensors. Recently a new class of passive wireless sensors emerged based on the electromagnetic transduction principle. These sensors are mainly composed of a variable RF impedance or RF resonator connected to an antenna that can be interrogated through backscattered wave analysis [START_REF] Mcgrath | Development of nano-based resonator gas sensors for wireless sensing systems[END_REF][START_REF] Chuang | Embeddable wireless strain sensor based on resonant RF cavities[END_REF][START_REF] Jatlaoui | Radio Frequency Pressure Transducer[END_REF][START_REF] Mahmood | An Evanescent-mode Cavity Resonator Based Thermal Sensor[END_REF][START_REF] Zhao | A Micromachined Airflow Sensor Based on RF Evanescent-Mode Cavity Resonator[END_REF][START_REF] Matsuzaki | Antenna/sensor multifunctional composites for the wireless detection of damage[END_REF]. These sensors combine the advantages of SAW sensors (fully autonomous, high resistance to harsh environment) with an interrogation distance higher than 10m thanks to the elimination of the electromagnetic/acoustic conversions. Another advantages of electromagnetic transducers compared to SAW consists of using millimeter-wave frequency that allows the realization of efficient antennas (lower size and higher directivity), the wide range of sensing properties through the large possibilities of sensing principles (variation of physical or electrical length of the RF transducer, modification of the material properties, modulation of RF coupling between the RF transducer and movable mechanical or fluidic parts) and the possibility to use of great choice of materials. Wireless passive and chipless electromagnetic transducers are then an interesting solution for several SHM applications. We started to work in this new research field in 2005 and several sensor principles have been validated in our lab (pressure, temperature, stress) with FMCW (Frequency Modulated Continuous Wave) Radar interrogation.

PRESSURE SENSOR

We started this study in 2005 and the details on this sensor can be found in [START_REF] Jatlaoui | Working Principle Description of the Wireless Passive EM Transduction Pressure Sensor[END_REF]. This device is composed of a planar half-wavelength resonator (designed around 30GHz) deposited inside a circular Pyrex cavity and a high resistivity silicon membrane anodic-bonded to Pyrex (Figure 4). The initial distance between the resonator and the membrane is around few microns allowing the electromagnetic coupling between the resonant mode in the planar resonator and the transverse stationary waves in the dielectric membrane through the evanescent transverse EM field in the air slab. A weak membrane deflection under pressure leads to the strong variation of the EM coupling and then gives a high resonant frequency shift (~1GHz/µm between 0.25μm and 6μm). The sensor has been fabricated using classical micro-technology process and characterized with a specific pressure probe module allowing on wafer S 21 parameters. The sensor exhibits a very high sensitivity close to 0.37GHz/bar (up to 3 bars) for a 45µm thick silicon membrane.

TEMPERATURE MEMS SENSOR

We started this study in 2009 in collaboration with Georgia Institute of Technology and more details are given in [START_REF] Thai | Design and Development of a Novel Passive Wireless Ultrasensitive RF Temperature Transducer for Remote Sensing[END_REF]. The temperature transducer consists of split ring resonators (around 4.7 GHz) fabricated on top side of substrate and excited by a coplanar transmission line located on wafer back side (Figure 5). The slits of the rings are covered with bimorph micro-cantilevers whose layers are made from two different materials. A temperature shift induces the cantilever deflection and then a modification of the coupling capacitance between the two arms of the ring, which leads to the change of the resonant frequency. The rings and the coplanar line are deposited on a 787µm-thick Neltec substrate. The bilayers cantilever is obtained by the lamination of a 100µm thick aluminum sheet and a 50µm thick polyethylene (PET) sheet. Measurements of S 11 parameter at two different temperatures (32°C and 60°C) show a relative resonant frequency shift around 530ppm/°C and an estimated full scale variation close to 10%.

TEMPERATURE FLUIDIC SENSOR

We started this study in 2010 in collaboration with Georgia Institute of Technology and more details are given in [START_REF] Traille | A Novel Wireless Passive Temperature Sensor Utilizing Microfluidic Principles in Millimeter-Wave Frequencies[END_REF][START_REF] Bouaziz | Novel Micro-fluidic Structures for Wireless Passive Temperature Telemetry Medical Systems Using Radar Interrogation Techniques in Ka-band[END_REF]. The sensors are based on the thermal expansion of a fluid inside a polymer (SU8) micro-channel fabricated above a RF structure. The principle has been validated first with a metallic liquid by using an array of dipole antennas (Figure 6). A drastically step-by-step change of the Radar Cross Section (RCS) of the sensor is obtained by short-circuiting progressively the two arms of the different dipoles array. Devices operating around 30GHz were fabricated on Kapton with an array of 5 dipoles for which short circuits were achieved by depositing Galinstan drop between the dipole strands. RCS characterizations, performed using a millimeter-wave FMCW RADAR, have shown that one can obtain a RCS shift close to 12dBsm for the full scale. Temperature sensor principle has been also validated using dielectric fluid (water) that changes the permittivity between the two electrodes of a planar capacitor (Figure 7). The capacitor length is equal to 400μm and provides a variation of the reflection coefficient (S 11 ) around 9 dB at 30 GHz for full scale. Capacitance then varies continuously from 20fF to 140fF corresponding to temperature change of 9°C.

STRESS SENSOR

We started this study in 2010 in collaboration with Georgia Institute of Technology and more details are given in [START_REF] Thai | Novel Design of a Highly Sensitive RF Strain Transducer for Passive and Remote Sensing in Two Dimensions[END_REF]. The sensor is based on two coupled planar resonators (a patch antenna and an open loop) whose resonant frequencies are close to 2.4 GHz. The loop is closed by a variable capacitor which changes with the deformation (Figure 8). This variable capacitor is formed by a metal cantilever and the free metal of the loop, separated by an insulator of 15μm thick. Deformation of the support (100 microns thick Kapton) then causes a displacement of the anchor point of the cantilever. The surface of the capacitor is then modified with sensitivity dependent on the length of the cantilever. The total size of the sensor is 40mm x 25mm. Simulation results show a maximum sensitivity of 4.5ppm/μ. Demonstrators were manufactured with conventional technologies to fabricate the resonators and the cantilever. Then the assembly was performed more or less manually which failed to achieve accurate positioning of the cantilever. However a measured sensitivity of 1.3ppm/μ was obtained, that is as large as what has been published to date.

RADAR INTERROGATION

The interrogation system is based on the FMCW (Frequency Modulated Continuous Wave) Radar. From the measured Radar Cross Section (electromagnetic echo) of the sensors, the physical quantity can be wirelessly derived and the identification of the sensor can also be performed [START_REF] Chebila | Reconfigurable Multi-band Scatterers for Micro-Sensors Identification[END_REF][START_REF] Jatlaoui | Original Identification technique of passive EM Sensors using Loaded Transmission Delay Lines[END_REF]. A Radar operating at around 29.45 GHz has been designed and fabricated. The interrogation principle up to 30m has been first validated using 1 cm 2 passive target and few mW for Radar emission. In most cases of sensors described previously, one transducer port is connected to an antenna through a coaxial cable (1m long) and the second port of the transducer is connected to a 50  load. The RF cable which operates as a delay line allows separating the transducer echo from the antenna echo. The beat frequency level, which corresponds to the Radar output, is then modulated by the input to be analyzed (pressure, stress, temperature). A full scale variation of the beat frequency level around 5 dB is generally obtained with the different transducers and the sensor accuracy is estimated to  5% (Figure 9).

CONCLUSIONS

The principle of new passive sensors, based on electromagnetic transduction, has been validated for several physical parameters (pressure, temperature, stress) which are interesting for SHM applications. The main advantages of this new kind of sensors are: a very simple transducer part without battery and without embedded electronic circuits and a wireless reading distance greater than 10m using a FMCW Radar. Nevertheless our main objective now is focused on the realization of sensors with given specifications (sensitivity, accuracy, …) for specific applications.

Figure 2 .

 2 Figure 2. Synoptic of passive wireless sensor.

Figure 3 .

 3 Figure 3. Synoptic of typical RFID wireless sensor

Figure 4 .

 4 Figure 4. Topology of pressure sensor[START_REF] Jatlaoui | Working Principle Description of the Wireless Passive EM Transduction Pressure Sensor[END_REF]

Figure 5 .

 5 Figure5. Topology of temperature MEMS sensor[START_REF] Thai | Design and Development of a Novel Passive Wireless Ultrasensitive RF Temperature Transducer for Remote Sensing[END_REF]

Figure 6 .Figure 7 .

 67 Figure 6. Topology of temperature fluidic sensor with metallic fluid[START_REF] Traille | A Novel Wireless Passive Temperature Sensor Utilizing Microfluidic Principles in Millimeter-Wave Frequencies[END_REF][START_REF] Bouaziz | Novel Micro-fluidic Structures for Wireless Passive Temperature Telemetry Medical Systems Using Radar Interrogation Techniques in Ka-band[END_REF]

Figure 8 .

 8 Figure 8. Topology of stress sensor[START_REF] Thai | Novel Design of a Highly Sensitive RF Strain Transducer for Passive and Remote Sensing in Two Dimensions[END_REF]

Figure 9 .

 9 Figure 9. Example of Radar response with the pressure transducer

ACKNOWLEDGEMENTS

Part of this work is supported by the French Project of 'Pôle de compétivité' entitled 'Système Autonome Communicant En Réseau' (SACER). All these studies have been performed with PhD students (Mehdi JATLAOUI, Franck CHEBILA, Thai TRANG, Sofiène BOUAZIZ, Anya TRAILLE).