Dr Daniela ; Munteanu
email: daniela.munteanu@univ-provence.fr

Mathieu Moreau

Jean-Luc L Autran

A Compact Model for the Ballistic Subthreshold Current in Ultra-Thin Independent Double-Gate MOSFETs

Keywords: ballistic transport, Independent Double-Gate MOSFET, compact modeling, quantum effects, subthreshold current model Independently Driven Double-Gate MOSFET, ballistic transport, quantum effects, subthreshold current model

come

with respect to the ITRS specifications for deca-nanometer channel lengths. The main advantage of this architecture is to offer a reinforced electrostatic coupling between the conduction channel and the gate electrode. A double-gate structure can efficiently sandwich the semiconductor element playing the role of the transistor channel, which can be a Silicon thin layer or nanowire, a Carbon nanotube, a molecule or an atomic linear chain [START_REF] Bescond | Atomic-scale modeling of Double-Gate MOSFETs using a tight-binding Green's function formalism[END_REF]. In spite of excellent electrical performances due to its multiple conduction surfaces, conventional DG MOSFET allows only three-terminal operation because the two gate electrodes, i.e. the front gate and the back gate, are generally tied together. DG structures with independent gates have been proposed [START_REF] Masahara | Demonstration, Analysis, and Device Design Considerations for Independent DG MOSFETs[END_REF]- [START_REF] Mathew | CMOS vertical Multiple Independent Gate Field Effect Transistor (MIGFET)[END_REF], having a four terminal operation. Independent Double-Gate (IDG) MOSFETs offer additional potentialities, such as a dynamic threshold voltage control by one of the two gates, transconductance modulation, signal mixer, in addition to the conventional switching operation. Thus, IDG MOSFETs are promising for future high performance and low power consumption very large scale integrated circuits. However, one of the identified challenges for IDG MOSFET optimization remains the development of compact models [START_REF] Zhang | Physical Insights Regarding Design and Performance of Independent-Gate FinFETs[END_REF]- [START_REF] Munteanu | Quantum Short-Channel Compact Modeling of Drain-Current in Double-Gate MOSFET[END_REF] taking into account the main physical phenomena (such as short-channel effects, quantum confinement, ballistic transport)

governing the devices at this scale of integration. In this work, an analytical subthreshold model of ultra-thin IDG MOSFETs working in the ballistic regime is presented. The present approach captures the essential physics of such ultimate devices: short-channel effects, quantum confinement, thermionic current and tunneling of carriers through the source-to-drain barrier. Important device parameters, such as the off-state current (I off ) or the subthreshold swing, can be easily evaluated through this full analytical approach which also provides a complete set of equations for developing equivalent-circuit model used in ICs simulation. The highest value of the source-to-drain current which can be obtained for a given MOSFET geometry corresponds to the pure ballistic current limit. As the channel length is increased, the current decreases from this maximum value due to scattering effects. The transport makes a transition from the ballistic to quasi-ballistic or drift-diffusive regime with the longer channel lengths [START_REF] Martinie | Impact of ballistic and quasi-ballistic transport on performances of Double-Gate MOSFET-based circuits[END_REF]. The carrier transport in the channel is considered to be ballistic when carriers travel from the source to the drain regions without encountering a scattering event. This may be possible if the feature size of the device becomes smaller than the carrier mean free path [START_REF] Lundstrom | Fundamentals of Carrier Transport[END_REF]. If the carrier transport is purely ballistic in the channel, modeling the device behavior reduces to the description of the carrier transmission over and through the source-to-drain potential barrier [START_REF] Munteanu | Two-dimensional Modeling of Quantum Ballistic Transport in Ultimate Double-Gate SOI Devices[END_REF]. The amplitude and the width of the channel barrier are modulated by the front gate, the back gate and drain voltages. As explained in [START_REF] Munteanu | Two-dimensional Modeling of Quantum Ballistic Transport in Ultimate Double-Gate SOI Devices[END_REF] and shown in figure 1, carriers having energy higher than the maximum of the barrier are transmitted from source to drain by thermionic emission, while carrier with lower energy can traverse the channel only by quantummechanical tunneling through the source-to-drain barrier.

Drain current modeling

Potential profile in the subthreshold regime

The model proposed here is developed for ultra-thin Silicon film IDG MOSFETs working in the subthreshold regime. Figure 1 shows the schematic n-channel IDG MOSFET considered in this work. Carrier transport in the ultra-thin silicon film (thickness t Si ) is considered 1-D in the x-direction and the resulting current is controlled by both the front and back gate-to-source (V G1 and V G2 ) and drain-tosource (V D ) voltages which impact the shape as well as the amplitude of the source-to-drain energy barrier. In the subthreshold regime, minority carriers can be neglected and Poisson's equation is analytically solved in the x-direction with explicit boundary conditions at the two oxide/silicon interfaces taking into account the electrostatic influence of V G1 and V G2 . We define the electrostatic potential ψ as the band bending with respect to the intrinsic Fermi level in the silicon film and choosing the Fermi level in the source reservoir as the potential reference. The expression of ψ(x) is obtained by applying the Gauss's law to the particular closed dashed surface shown in figure 1 [12]:

Si Si A 2 S 1 S Si Si 2 dx t qN ) x ( E ) x ( E 2 t ) dx x ( E 2 t ) x ( E ε - = + - + + - (1)
where N A is the channel doping, E S1 and E S2 are the electric fields at the front interface and at the back interfaces, respectively. E S1 and E S2 are given by:

ox 1 FB 1 G Si ox 1 S t V V E ψ - - ε ε = (2) ox b 2 FB 2 G Si ox 2 S t V V E ψ - - ε ε - = (3)
where ψ and ψ b , are the surface potentials at the front and back oxide/film interfaces, respectively. Due to the 1D character of the electrostatic potential ψ in the Silicon film the electric field E(x) can be approximated as dx

) x ( d ) x ( E ψ - ≈ [13].
Using the development presented in [START_REF] Munteanu | Compact modeling of drain current in Independently Driven Double-Gate MOSFETs[END_REF] and after some algebraic manipulations, we obtained the following differential equation for the electrostatic potential in the Silicon film:

[ ] where V FB1 and V FB2 are the flat-band voltages at the front and the back gate interfaces, respectively, φ F is the bulk potential of the Silicon film, and

) V V ( C 2 ) V V ( C 2 t qN t 1 t C 2 dx d F 2 FB 2 G ox F 1 FB 1 G ox Si A Si Si Si Si ox 2 2 φ - - - φ - - - ε = Ψ ε - Ψ (4)
ox ox ox t C ε =
is the gate capacitance per unit area. The analytical solution of equation ( 4) can be then expressed under the form:

2 2 1 R ) x exp( C ) x exp( C ) x ( α - α - + α = ψ (5)
where the coefficients C 1 and C 2 are given by:

) L sinh( 2 ) L exp( 1 R V )] L exp( 1 [ C 2 D S 1 α α α - - + + α - - φ = ( 6
)
) L sinh( 2 ) L exp( 1 R V )] L exp( 1 [ C 2 D S 2 α α α - + + α - φ - = (7) with: ( ) ( )
        + γ γ - - + γ + γ - - ε = Si ox ox 2 FB 2 G ox Si ox Si ox 1 FB 1 G ox Si A Si Si t t 2 t V V C 2 t t 2 t t V V C 2 t qN t 1 R (8) Si Si ox t C 2 ε = α (9)         = φ 2 i SD A S n N N ln q kT (10)         = φ i A F n N ln q kT ( 11
)
where N SD is the doping level in the source and drain regions.

Quantum confinement effects

Quantum-mechanical (QM) confinement of inversion-layer carriers are well-known to significantly density-of-states) [START_REF] Taur | Fundamentals of Modern VLSI Devices[END_REF][START_REF] Hareland | A Physically-Based Model for Quantization Effects in Hole Inversion Layers[END_REF], such that the lowest of the allowed energy levels for electrons (resp. for holes) in the well does not coincide with the bottom of the conduction band (resp. the top of the valence band). In addition, the total density-of-states in a 2-D system is less than that in a three-dimensional (3-D) (or classical) system, especially for low energies. Carriers occupying the lowest energy levels behave like quantized carriers while those lying at higher energies, which are not as tightly confined in the potential well, can behave like classical (3-D) particles with three degrees of freedom. As the surface electric field increases, the system becomes more quantized as more and more carriers become confined in the potential well. The quantum-mechanical confinement considerably modifies the carrier distribution in the channel: the maximum of the inversion charge is shifted away from the interface into the Silicon film.

In thin-film IDG MOSFETs, the potential well is defined by both the front and back gates [START_REF] Ernst | Investigation of SOI MOSFETs with Ultimate Thickness[END_REF]. In these devices carriers are confined due to two main phenomena (figure 2): (a) strong electric field at the interface leading to electric field-induced quantum confinement (EC-QM) and (b) t Si -induced structural quantum confinement (SC-QM) due to the narrow potential well defined by thin Silicon film. Since carriers in thin-film IDG MOSFETs are subjected to structural confinement, in addition to the electric field-induced quantum confinement, quantum-mechanical effects on the threshold voltage and drain current are quite important [START_REF] Trivedi | Quantum-Mechanical Effects on the Threshold Voltage of Undoped Double-Gate MOSFETs[END_REF], [START_REF] Moreau | Simulation Analysis of Quantum Confinement and Short-Channel Effects in Independent Double-Gate Metal-Oxide-Semiconductor Field-Effect Transistors[END_REF]. If the Silicon film in the IDG MOSFETs is thinner than 15 nm [START_REF] Moreau | Simulation Analysis of Quantum Confinement and Short-Channel Effects in Independent Double-Gate Metal-Oxide-Semiconductor Field-Effect Transistors[END_REF], quantum confinement cannot be ignored, and Poisson's equation should be solved self-consistently with Schrödinger's equation. In this case, an analytical solution is not possible without making assumptions of either the shape of the potential distribution or of the electron distribution [START_REF] Ge | Analytical modeling of quantization and volume inversion in thin Si-film DG MOSFETs[END_REF]. When an IDG MOSFET is considered to work in the weak inversion regime (and the free-carrier term in the Poisson's equation can be neglected), the quantum energy levels can be calculated using the expressions developed in [START_REF] Ge | Analytical modeling of quantization and volume inversion in thin Si-film DG MOSFETs[END_REF] using a variational approach. Considering the limit case of an ultra-thin Silicon film, we can assume only one energy subband for the vertical confinement of carriers, given by [START_REF] Ge | Analytical modeling of quantization and volume inversion in thin Si-film DG MOSFETs[END_REF]:

                π + - × +         π ×         ≅ 2 Si 2 2 Si 2 q ) / t A ( 1 1 3 4 3 A t m 2 E l l l h ( 12
) 3 / 1 2 x qE m 2 4 3 A         × ≅ h l l (13)
where m ℓ is the electron longitudinal effective mass (vertical confinement) and E x is the (spatially constant) transverse electric field in the ultra-thin body silicon film, dependent on the front-and backgate voltage differences [START_REF] Ge | Analytical modeling of quantization and volume inversion in thin Si-film DG MOSFETs[END_REF]:

( ) ( ) ox ox Si Si 2 G 1 G 2 FB 1 FB x t 2 t V V V V E ε ε + - - - - = (14)
The first energy subband profile E 1 (x) can be easily derived as:

( ) q S 1 E ) x ( q ) x ( E + ψ - φ = (15)

Ballistic current modeling in the ballistic subthreshold regime

Once E 1 (x) is known as a function of V G1 , V G2 and V D , the total ballistic current (per device width unit) can be evaluated as follows:

Tun Therm DS where I Term and I Tun are the thermionic and the tunneling components of the ballistic current respectively. For a two-dimensional gas of electrons [START_REF] Ferry | Tranport in Nanostructures[END_REF], I therm and I tun are given by:

I I I + = (16)
[ ] ∫ - × ∫ π = +∞ +∞ ∞ - max , 1 E x FD FS z 2 Therm dE ) E , E ( f ) E , E ( f dk q 2 I h (17)
[ ]

∫ - × ∫ π = ∞ + ∞ - max , 1 E 0 x x FD FS z 2 Tun dE ) E ( T ) E , E ( f ) E , E ( f dk q 2 I h (18)
where f(E F ,E FS ) is the Fermi-Dirac distribution function, E FS and E FD are the Fermi-level in the source and drain reservoirs respectively (E FD = E FS -qV DS ), k z is the electron wave vector component in the z direction, the factor 2 accounts for the two Silicon valleys characterized by m ℓ in the confinement direction (y-direction) and T(E x ) is the barrier transparency for electrons and E is the total energy of carriers in source and drain reservoirs given by:

t 2 z 2 x 1 m 2 k E E E h + + = (19)
where E 1 is the energy level of the first subband (given by equation ( 15)), E x is the carrier energy in the direction of the current, m t is the electron transverse effective mass. In equations ( 17) and ( 18), E 1,max is the maximum of the source-to-drain energy barrier given by:

) x ( E E max 1 max , 1 = (20)
where x max is obtained from equation ( 4) with the condition:

max x x for 0 dx ) x ( d = = ψ (21)         α = 1 2 max C C ln 2 1 x (22)
In equation [START_REF] Trivedi | Quantum-Mechanical Effects on the Threshold Voltage of Undoped Double-Gate MOSFETs[END_REF], the barrier transparency is calculated using the WKB approximation:

F o r P e e r R e v i e w O n l y 1 ) E E ( T dx ) E ) x ( E ( m 2 2 exp ) E ( T max , 1 x WKB x x 2 x 1 t x 2 1 = >         ∫ - - = h (23)
where x 1 and x 2 are the coordinates of the turning points (figure 1). x 1 and x 2 have literal expressions due to the analytical character of the barrier:

      ∆ ± α = 1 x 2 , 1 C 2 A ln 1 ) E ( x ( 24
)
where the quantities A and ∆ are defined as follows:

q E q E R A x q 2 S - + α + φ = (25) 2 1 2 C C 4 A - = ∆ (26)
The WKB approximation has the main advantage to be CPU inexpensive and reasonably accurate for channel lengths down to a few nanometers. Moreover, it has been shown in [START_REF] Städele | Influence of source-drain tunneling on the subthreshold behavior of sub-10nm double-gate MOSFETs[END_REF] that differences between results obtained considering the WKB approximation and full quantum treatment (tightbinding scheme) are surprisingly small (typically a few percents), which confers to the WKB approach a reasonable accuracy in the frame of the present analysis. The proposed model applies to (100) silicon inversion layers, but it can it be extended to different crystal orientations or channel materials.

Results and model validation

Figure 3 shows the first energy subband profile in the Si film calculated with the model in a L=10 nm intrinsic channel IDG MOSFET (t Si =2 nm, t ox =0.6 nm). In order to test the validity of the model, we compare these profiles with those obtained with a self-consistent Poisson-Schrödinger solver based on a real-space Non-Equilibrium Green's Function (NEGF) approach (DGGREEN2D [23]).

As shown in figure 3 a good agreement is obtained between the two barriers in the subthreshold regime. In particular, we note an excellent agreement for the positions of the maximum as well as the

Discussion: impact of the source-to-drain tunneling current

Quantum-mechanical tunneling was often neglected in several previous works, which mainly focused on the modeling of thermionic emission or scattering [24-25]. However, below 8 nm the width of the channel barrier decreases significantly (figure 6), increasing the impact of the quantum tunneling on the device characteristics. We have analyzed the impact of carrier tunneling on device performances through a detailed comparison between model predictions with and without quantum-mechanical tunneling. Two cases are considered: (1) thermionic emission for E x > E max and T(E x ) = 0 for E x < E max ;

(2) thermionic emission for E x > E max and quantum tunneling with T(E x ) given by the WKB approximation (equation 23). Figure 7 shows subthreshold I D (V G1 ) drain current characteristics calculated with the analytical model for channel lengths from 5 nm to 15 nm. Two series of curves have been plotted, considering or not the WKB tunneling contribution in the ballistic current. These results highlight the dramatic impact of the source-to-drain tunneling current on the subthreshold slope and also on the off-state current. In such a subthreshold regime, the carrier transmission by thermionic emission is reduced or even suppressed due to the high channel barrier. As a consequence, when the channel length decreases the tunneling becomes dominant and constitutes the main physical phenomenon limiting the devices scaling, typically below channel lengths of ~ 8 nm. Quantummechanical tunneling significantly degrades the off-state current especially in short channels, where the off-state current increase by more than two decades (L g =5 nm). However, the off-state current should be slightly reduced for the shorter geometries since it is calculated assuming perfectly ballistic transport and thus ignoring the partial reflection of electron wave functions on the source barrier. The subthreshold swing also increases (with about 30% for L g =8 nm with respect to L=15 nm) due to quantum-mechanical tunneling. As previously indicated for the off-state current, these results can be considered as an upper limit, since we assume perfectly ballistic transport without wave function reflection at the source barrier.

Conclusion

An analytical model for the subthreshold drain current in ultra-thin IDG MOSFETs working in the ballistic regime is presented. The model is particularly well-adapted for ultra-short IDG transistors in the decananometer scale since it accounts for the main physical phenomena related to these ultimate devices: 2-D short channel effects, quantum vertical confinement as well as carrier transmission by both thermionic emission and quantum tunneling through the source-to-drain barrier. The model is used to predict the variation with the channel length of essential subthreshold parameters, such as the offstate current and the subthreshold slope. Device parameters are the same as in figure 3.

F

V G2 =-0.4 V V G2 =0.4 V (0.2 V step) V G1 =0 V V D =0.8 V

A

  Compact Model for the Ballistic Subthreshold Current in Ultra-Thin Independent Double-Gate MOSFETs (DG) MOSFETs are extensively investigated because of their promising performances

2 .

 2 Physics of the ballistic transportIn nanoscale MOSFETs with channel lengths less than about 50 nm, the relaxation times of the carriers indicate that the drain current will have an intermediate character between drift-diffusion and ballistic/quasi-ballistic transport[START_REF] Iñiguez | Compact-Modeling Solutions For Nanoscale Double-Gate and Gate-All-Around MOSFETs[END_REF]. Then, ballistic transport has to be considered in the modeling of ultra-short Double-Gate devices. Since the conventional Drift-Diffusion model (usually used in device simulation) fails at describing ballistic transport, new specific models have to be developed this regime.

  affects the threshold voltage and gate capacitance of highly scaled bulk and DG MOSFETs. The aggressive scaling-down of MOSFETs in the deep submicrometer domain requires ultrathin oxides and channel doping levels for minimizing the drastic increase of short channel effects. The direct consequence is a strong increase of the electric field at the Si/SiO 2 interface, which creates a sufficiently steep potential well for inducing the quantization of carrier energy. In bulk-Si and partially depleted (PD) SOI (n)MOSFETs, the confinement is in the potential well defined by the gate-oxide barrier (which is virtually infinite) and the Silicon conduction (or valence) band. Carriers are then confined in a vertical direction in a quantum well (formed by the Silicon conduction band bending at the interface and the oxide/Silicon conduction band-offset) having feature size close to the electron wavelength. This gives rise to a splitting of the energy levels into subbands (two-dimensional(2-D)

  barrier between the analytical and numerical curves. The slight difference in the barrier width is due to the electric field penetration in the source and drain regions, only taken into account in the numerical approach. The subthreshold I D (V G1 ) characteristics calculated with the analytical model for IDG MOSFET with L=10 nm and different back gate voltages are shown in figure 4. Drain current characteristics for DG MOSFETs are also shown in this figure.

Figure 5

 5 compares analytical model and numerical data for IDG MOSFET with different channel lengths. These figures show that in the subthreshold regime the analytical model very well fits numerical data obtained with DGGREEN2D for devices in the decananometer range.

Figure 1 .

 1 FIGURE CAPTIONS

Figure 2 .

 2 Figure 2. Energy-band diagrams in a vertical cross-section in the Silicon film of IDG n-channel

Figure 3 .

 3 Figure 3. First energy subband profiles E 1 (x) in the Silicon film calculated with the analytical model

Figure 4 .

 4 Figure 4. Subthreshold I D (V G1 ) characteristics calculated with the analytical model (lines). Values

Figure 5 .Figure 6 .

 56 Figure 5. I D (V G1 ) characteristics of IDG MOSFET calculated with the analytical model. Values

Figure 7 .

 7 Figure 7. Subthreshold I D (V G1 ) characteristics calculated with the analytical model when considering or

Figure 3 .

 3 Figure 3.

  Figure 4.

Figure 5 .

 5 Figure 5.

Figure 7 .

 7 Figure 6.

http://mc.manuscriptcentral.com/tandf/jenmol

ACKNOWLEDGMENTS

This work was supported by the French Ministry of Research (ANR PNANO project "MULTIGRILLES").


Alexis Heloir

Sylvie Gibet

Franck Multon
email: franck.multon@uhb.fr

Nicolas Courty

Captured Motion Data Processing for Real Time Synthesis of Sign Language

The work described in this abstract presents a roadmap towards the creation and specification of a virtual humanoid capable of performing expressive gestures in real time. We present a gesture motion data acquisition protocol capable of handling the main articulators involved in human expressive gesture (whole body, fingers and face). We then present the postprocessing of captured data leading to a motion database complying with our motion specification language and capable of feeding data driven animation techniques.

Issues. Embodying a virtual humanoid with expressive gestures raises many problems such as computation-cost efficiency, realism and level of expressiveness, or high level specification of expressive gesture [START_REF] Gibet | HUGEX: virtual humanoids embodied with expressive communicative gestures[END_REF]. In this abstract, we focus on the acquisition of motion capture data from the main articulators involved in communicative gesture (whole body, face mimics and fingers motion). We then present how acquired data are postprocessed in order to build a database compatible with high level gesture specification and capable of feeding real time data-driven motion synthesis techniques. A recent automatic segmentation algorithm based on Principal Component Analysis (PCA) is then evaluated.

Motion acquisition protocol. The motion data acquisition protocol is designed to capture the whole range of articulators involved in order to produce human communicative gestures. This protocol relies on two complementary techniques, as shown in figure 1. The first technique aims at capturing facial and body motions and relies on a set of reflective markers placed on standardized anatomical landmarks and a network of 12 Vicon-MX1 infrared cameras located all around the subject. The second technique aim to capturing finger motion thanks to pair of Cybergloves2 measuring finger abduction and flexion. This technique is well suited to finger motion, as it is robust by fingers occlusions that may appear too often during signing performance. The two sets of data acquired are post processed, synchronized and merged offline. The resulting dataset describes the evolution along n frames of a skeleton hierarchy composed by k joints. for each joint i, a set of l i degrees of freedom is defined, 1 ≤ l i ≤ 3. The size of a posture vector v is then easily computed. 1. detail of a posture vector Processing motion data. One of the early step of data processing consists of segmenting motion into relevant chunks. Extracted chunks must be short enough to guarantee the synthesis of a wide range of new motions while keeping sufficient meaning to comply with high level task oriented specification language [START_REF] Gibet | High level Specification and Animation of Communicative Gestures[END_REF]. Even though it has been shown that low level motion segmentation can be achieved in a straightforward manner [START_REF] Fod | Automated derivation of primitives for movement classification[END_REF][4], Hodgins and al. recently showed that higher level motion segmentation could be efficiently achieved thanks to principal component analysis (PCA) approach. According to the results they presented [START_REF] Barbič | Segmenting Motion Capture Data into Distinct Behaviors[END_REF], PCA segmentation method applied on simple motions representing typical human activities, such as walking, running, sitting, standing idle, etc. achieved very good results: up to 80% precision call for simple body movements. This algorithm is based on the assumption that the intrinsic dimensionality of a motion sequence containing a single behavior should be smaller than the intrinsic dimensionality of a motion sequence containing multiple behaviors. Thus, from one motion sequence to another, the reconstruction error of the frames projected onto the optimal hyperplane of dimension r increases rapidly, for a fixed r. figure 2 illustrates a motion transition detection between two hand configurations. Evaluating the PCA approach to hand motion segmentation. We apply the PCA based segmentation algorithm described to a sequence representing a non signer subject finger spelling French dactylologic alphabet [START_REF] Moody | La langue des signes -Tome 1 : Histoire et grammaire[END_REF]. The sequence is 7200 frames long with 120 frames per second. To perform PCA, Decomposition is thus performed on a 7200×25 matrix extracted from the total motion data and representing right hand motion. According to our experiments, the ratio Er which indicates how information is retained by projecting the frames on the optimal r-dimensional hyperplane reaches acceptable range [START_REF] Barbič | Segmenting Motion Capture Data into Distinct Behaviors[END_REF] when r ≤ 3 for all the 20 segments we extracted manually from the alphabet spelling sequence. Further experiments lead us to set the window parameter k originally fixed at 2 seconds to 1.3 seconds, considering that hand motion is much faster than body motion.

size(v) = k i=0 l i

Results

. In parallel to automated PCA based hand motion segmentation, a human observer segmented manually the finger spelling sequence by identifying probables zones of motion transition. Figure 3 compares how the two methods assigned motion separation zones. The human observer identified 27 zones while PCA based motion segmentation algorithm identified 29 zones. Among those, 22 zones are overlapping.

Conclusion.

We have presented a motion acquisition framework designed to manage several articulators involved in communicative gesture and in sign language performance. We then rely on the data provided by this framework to evaluate a recent automatic motion segmentation technique based on principal component analysis of hand motion. This method proves to be capable of solving high level segmentation required by our needs. In the near future, we wish to extend this technique to the whole upper body motion. In parallel, we would like to provide a better evaluation framework based on data acquired and annotated by French sign language specialists. Such a framework will provide us with the ground required to perform reliable motion analysis and performance comparisons.

Fig. 1 .

 1 Fig. 1. motion acquisition protocol

Fig. 2 .

 2 Fig.2. automatic segmentation using PCA. Cut is performed when derivative of error reaches three standard deviation from the average.

Fig. 3 .

 3 Fig. 3. segmentation results

Table 1

 1 sums up the composition of a posture vector according to our representation model.

	segment coord type joints number DOF per joint size of segment subvector
	body	angular	18	1 ≤ l ≤ 3	54
	hand	angular	18	1 ≤ l ≤ 3	25
	total	angular	36	-	79
			Table

http://www.vicon.com/products/viconmx.html

http://www.immersion.com/3d/products/cyberglove.php

Acknowledgements. This work is part of the RobEA HuGEx project and the Signe projet, respectively funded by the department STIC of CNRS and the regional council of Brittany (Ref. B/1042/2004/SIGNE). Motion capture presented in this document has been carried out in collaboration with the following research institutes: the LPBEM based at the University of Rennes 2, the LESP based at the University of Toulon and Var and the LINC based at the University of Paris 8.


Elisabeth Jamet

Rafael Pont-Lezica

The puzzle of protein location in plant proteomics

Organelle proteomics allows the characterization of complex proteomes to understand the protein networks which regulate growth and development, as well as adaptation and evolution. Purification of organelles is of paramount importance and diverse protocols are published. Some organelles such as chloroplasts, mitochondria, and the nucleus are surrounded by membranes which facilitate their purification. Others have membranes easily disrupted (vacuoles and peroxisomes), or are complex systems for protein trafficking (endoplasmic reticulum, Golgi, and secretory vesicles). The cell walls present different difficulties since they have no physical limits allowing purification. The purity of the targeted cell compartment is usually evaluated by biochemical and/or immunological methods. Nevertheless, in any sub-cellular proteomic analysis, proteins from a different compartment can be detected and the difficulty is to decide whether it is a contamination, or the unexpected location is real and has a functional significance. Software to predict sub-cellular location of proteins is available. However, since not all the targeting signals are known at present, carefulness in the use of such tools is recommended. Different tactics to solve this puzzle are discussed in this commentary.

Plants like other organisms depend on proteins to maintain their functions and to adjust to environmental changes. Proteins arrange themselves into metabolic and regulatory pathways and their accurate localization is essential for the organism. To understand the diverse protein functions, information on the identification of all the proteins, as well as the knowledge of their location, post-translational modifications, and quantitative changes in the cell are important. Proteomics can provide the basic information, but the main challenge is the biochemical complexity, and the dynamic range of proteins. Since the cell is structured in organelles with different but interconnected functions, proteomic analysis of purified cell organelles reduces sample complexity to a subset of functionally related proteins or pathway modules [START_REF] Ploscher | Organelle proteomics[END_REF][START_REF] Reisinger | Subcellular proteomics organelle proteomics: Reduction of sample complexity by enzymatic in-gel selection of native proteins[END_REF]. Reliable protein localization by proteomics requires that either organelle preparations are free of contaminants or that techniques are used to discriminate between genuine organelle residents and contaminating proteins [START_REF] Huber | Organelle proteomics: implications for subcellular fractionation in proteomics[END_REF][START_REF] Dunkley | Localization of organelle proteins by isotope tagging (LOPIT)[END_REF][START_REF] Kitsios | The Arabidopsis localizome: Subcellular protein localization and interactions in Arabidopsis[END_REF]. Nonetheless, plant organelle proteomics has been restricted by the difficulties in isolating pure sub-cellular compartments in sufficient amount.

Organelle isolation

Organelle isolation implies the choice of an appropriate method for the purification of the targeted organelle.

Reasonably pure preparations of some organelles surrounded by a double membrane, such as chloroplasts and mitochondria, can be achieved [START_REF] Heazlewood | Arabidopsis mitochondrial proteomics[END_REF][START_REF] Jarvis | The proteome of chloroplasts and other plastids[END_REF][START_REF] Kieselbach | Chloroplast, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF]. Cells may be released from tissues by enzymatic treatment or mechanical disruption; the last one being the most frequently used in plant proteomics. The exposure time as well as the strength of the forces applied has to be optimized to avoid a progressive destruction of the biological supramolecular architecture; otherwise large-scale destruction of organelles will occur and organelle yield is compromised. Nevertheless, the purity of organelle preparations can be established using microscopic observations, marker enzymes or antibodies against known proteins. The difficulty is that the analysis tool (mass spectrometry) is 100 to 1000 times more sensitive than classical biochemical and immunological tests [START_REF] Jamet | Cell wall proteins: a new insight through proteomics[END_REF]. In certain cases, microscopic observations proved to be helpful to check the quality of a sample [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF][START_REF] Huber | Organelle proteomics: implications for subcellular fractionation in proteomics[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF], but they can be subjective. It is then hazardous to use results of sub-cellular proteomics directly as a proof for the location of the identified proteins.

Organelles surrounded by double membranes

From the protein composition point of view, mitochondria and chloroplasts are quite complex and include both very soluble proteins (present in the matrix and the intermembrane space) and very hydrophobic membrane proteins. In addition, there are membrane proteins of intermediate solubility, such as some subunits of the oxidative phosphorylation complexes or the outer membrane porins. This chemical heterogeneity is a real challenge for the proteomic analysis. Most of the chloroplast proteomic studies started with the Arabidopsis genome sequencing. Predictions indicate that 2100 to 2700 proteins are located in the Arabidopsis chloroplast [START_REF] Richly | An improved prediction of chloroplast proteins reveals diversities and commonalities in the chloroplast proteomes of Arabidopsis and rice[END_REF][START_REF] Reisinger | Subcellular proteomics organelle proteomics: Reduction of sample complexity by enzymatic in-gel selection of native proteins[END_REF]. However, only about half of these proteins have been experimentally identified [START_REF] Heazlewood | SUBA: the Arabidopsis Subcellular Database[END_REF]. The purification of intact chloroplasts by percoll gradient centrifugation produces reasonably pure preparations. However, chloroplasts are complex organelles composed of six distinct suborganellar compartments: three different membranes (the two envelope membranes and the internal thylakoid membrane) and three discrete aqueous compartments (the intermembrane space of the envelope, the stroma and the thylakoid lumen). As a result of this structural intricacy, the external and internal routing of chloroplast proteins is necessarily a complex process [START_REF] Jarvis | The proteome of chloroplasts and other plastids[END_REF][START_REF] Jarvis | Targeting of nucleus-encoded proteins to chloroplasts in plants[END_REF]. Proteomics of membrane proteins is challenging since they are poorly resolved using classical 2D-electrophoresis techniques [START_REF] Rabilloud | Membrane proteins and proteomics: love is possible, but so difficult[END_REF][START_REF] Zychlinski | Preparation and analysis of plant and plastid proteomes by 2DE[END_REF]. Proteins predicted to be targeted to the endoplasmic reticulum (ER) and mitochondria have been identified in chloroplast proteomic studies [START_REF] Kleffmann | plprot: a comprehensive proteome database for different plastid types[END_REF][START_REF] Van Der Laan | Mitochondrial preprotein translocases as dynamic molecular machines[END_REF], pointing to the need for experimental determination of protein location and the relative value of proteomic analysis.

Mitochondria can be isolated from many tissues but Arabidopsis cell suspension cultures have been largely used. On average, samples of 90-95 % purity were obtained using a Percoll gradient method [START_REF] Tan | The plant mitochondrial proteome and the challenge of hydrophobic protein analysis[END_REF], and yeast mitochondrial preparations reached 98% purity using free-flow electrophoresis [START_REF] Zischka | Improved proteome analysis of Saccharomyces cerevisiae mitochondria by free-flow electrophoresis[END_REF]. About one fourth of the expected 2000-2500 Arabidopsis mitochondrial proteins were identified by proteomics [START_REF] Millar | The plant mitochondrial proteome[END_REF]. The sub fractionation of mitochondria into the four basic compartments (outer membrane, inter-membrane space, inner membrane, and matrix) involves a complex protocol. As for chloroplasts, the proteomic characterization of mitochondrial hydrophobic proteins is a difficult task because of their low abundance and insolubility [START_REF] Millar | Genomic and proteomic analysis of mitochondrial carrier proteins in Arabidopsis[END_REF][START_REF] Brugière | The hydrophobic proteome of mitochondrial membranes from Arabidopsis cell suspensions[END_REF][START_REF] Rabilloud | Membrane proteins and proteomics: love is possible, but so difficult[END_REF].

Several pathways are presently explored to solve the problem of contamination by non-mitochondrial proteins [START_REF] Millar | The plant mitochondrial proteome[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF].

As other organelles delimited by two membranes, nuclei isolation and purification was performed on different plants by density gradient centrifugation [START_REF] Bae | Analysis of the Arabidopsis nuclear proteome and its response to cold stress[END_REF][START_REF] Chakraborty | Nucleus, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF][START_REF] Khan | Rice proteomics: recent developments and analysis of nuclear proteins[END_REF]. Most of the proteins identified were verified nuclear proteins, but also non-classical nuclear proteins were identified in different plants. A comparative study of the nuclear proteomes of Arabidopsis, rice and chickpea was done; from the 382 proteins identified in the three proteomes, only a small proportion were orthologous proteins, the others being specific for each plant [START_REF] Chakraborty | Nucleus, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF]. Most of the nuclear proteins show a high level of divergence in the protein classes between the three plants, which is surprising for an organelle having similar functions in different organisms.

Other organelles

The isolation of other organelles such as peroxisomes [START_REF] Arai | Peroxisome, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF], vacuoles [START_REF] Jaquinod | A proteomics dissection of Arabidopsis thaliana vacuoles from cell culture[END_REF][START_REF] Pan | Plant Proteomics: Technologies, Strategies, and Applications[END_REF] and oil bodies [START_REF] Jolivet | Plant Proteomics: Technologies, Strategies, and Applications[END_REF] produced samples enriched in the selected organelle. The purity of the selected organelle can be evaluated by western blotting using antibodies specific against proteins from undesirable compartments [START_REF] Jaquinod | A proteomics dissection of Arabidopsis thaliana vacuoles from cell culture[END_REF]. In these preparations, the proportion of proteins known to be present in other compartments depends on the purification method. Some authors pretend that those are new proteins for the studied compartment, others just call those proteins contaminants. However it is clear that the location of such proteins should be verified by other methods than proteomics.

Although reasonably pure preparations of some organelles (chloroplast and mitochondria) can be achieved by centrifugation and density gradients, the isolation of other compartments can not be accomplished by centrifugation because they do not have specific buoyant densities. Such is the case of the endomembrane system which has proven recalcitrant to purification [START_REF] Gabaldon | Shaping the mitochondrial proteome[END_REF][START_REF] Peck | Update on proteomics in Arabidopsis. Where do we go from here?[END_REF]. An additional problem with the endomembrane system is that proteins move along the different organelles in a controlled way. Some are residents of a particular compartment such as the ER or the Golgi, and others are sent to specific compartments (plasma membrane, tonoplast, and vacuole) or are secreted outside the cell. A well-designed technique was developed for such organelles employing differential isotope labelling: LOPIT (Localization of Organelle Proteins by Isotope Tagging) [START_REF] Dunkley | Mapping the Arabidopsis organelle proteome[END_REF][START_REF] Dunkley | The use of isotope-coded affinity tags (ICAT) to study organelle proteomes in Arabidopsis thaliana[END_REF]. LOPIT is based on the partial separation of organelles by density gradient centrifugation followed by the analysis of protein distributions within the gradient by Isotope-Coded Affinity Tag (ICAT) labeling and mass spectrometry (MS). This technique does not depend on the production of pure organelles, and enables proteins from different sub-cellular compartments to be distinguished even if their distributions overlap.

The case of the cell wall

The cell wall cannot be defined as an organelle, but it is a very important cell compartment which requires designing specific strategies for its purification [START_REF] Jamet | Cell Wall, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF]. The lack of a delimiting membrane may result in the lost of proteins during its purification and in contamination by other cell compartments. The polysaccharide networks constitute potential traps for intracellular proteins. Two types of strategies were tried: non-destructive and destructive ones [START_REF] Jamet | Cell Wall, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF][START_REF] Lee | Digging deeper into the plant cell wall proteome[END_REF]. In both cases, proteins not expected to be present in cell walls were found, leading to the hypothesis of the existence of an alternative secretory pathway [START_REF] Slabas | Proteomic analysis of the Arabidopsis cell wall reveals unexpected proteins with new cellular locations[END_REF]. However, such a pathway, if it exists, probably targets only a few proteins to the cell wall. Indeed, intracellular proteins identified in cell wall proteomics vary from one experiment to another, and it is possible to reduce their number by maintaining the integrity of plasma membranes or improving the cell wall purification procedure [START_REF] Borderies | Proteomics of loosely bound cell wall proteins of Arabidopsis thaliana cell suspension cultures: A critical analysis[END_REF][START_REF] Boudart | Cell wall proteins in apoplastic fluids of Arabidopsis thaliana rosettes: Identification by mass spectrometry and bioinformatics[END_REF][START_REF] Feiz | Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls[END_REF].

Altogether, the results of sub-cellular proteomics contain a certain number of proteins for which a clear localization cannot be predicted. The relatively high number of proteins without a known function identified by proteomics also raises the question of their function and location. Proteomic results start to be incorporated into databases such as those listed in Table I. This will make easier comparisons between results obtained in different conditions, and both allow confirmation of sub-cellular localization and point at possible contaminants.

Protein location

Most proteins are synthesized on cytosolic ribosomes, except for chloroplast and mitochondria having their own synthesis capacities in addition to the nuclear-encoded proteins. After synthesis, proteins can be posttranslationally modified, leading to differential location. The accurate protein location is essential for all living organisms and organelle proteomic analysis can generate very large candidate list of putative constituent proteins. Therefore independent approaches are required to verify if the candidate proteins can be included in the targeted organelle. To establish the location profiles of large set of proteins, new tools were developed that generated high-throughput localization data of a large number of proteins.

Bioinformatic predictions

Many proteins may contain within their amino acid sequence information that could be used for predicting their sub-cellular location, e.g. signal peptides, transit peptides, nuclear import and export signals [START_REF] Von Heijne | Protein targeting signals[END_REF]. Numerous computational programs have been developed with the aim of predicting the location of the proteins, most of which rely on the presence of these signals [START_REF] Emanuelsson | Predicting subcellular localization of proteins based on their N-terminal amino acid sequence[END_REF][START_REF] La Cour | NESbase version 1.0: a database of nuclear export signals[END_REF][START_REF] Nakai | PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization[END_REF][START_REF] Nielsen | Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites[END_REF]. It is important to understand the uses and limits of the bioinformatic methods developed lately, and recent reviews give a comprehensive approach on the available tools [START_REF] Emanuelsson | Locating proteins in the cell using TargetP, SignalP and related tools[END_REF][START_REF] Kitsios | The Arabidopsis localizome: Subcellular protein localization and interactions in Arabidopsis[END_REF]. Since computational methods are based on different algorithms, it is recommended to use several prediction programs to make a reasonable choice. Two tools available on line are listed in Table II.

Both Aramemnon and ProtAnnDB use a series of available software predicting sub-cellular localization of proteins and compare their results [START_REF] San Clemente | Bioinformatics as a tool for assessing the quality of sub-cellular proteomics strategies and inferring functions of proteins: Plant cell wall proteomics as a test case[END_REF][START_REF] Schwacke | ARAMEMNON, a novel database for Arabidopsis integral membrane proteins[END_REF]. In addition, recent reports suggest the existence of alternative sorting routes for proteins. For instance, the chloroplastic ceQORH protein was shown to be synthesized without a canonical cleavable chloroplastic transit sequence [START_REF] Miras | TOC159-and TOC75-independent import of a transit sequence less precursor into the inner envelope of chloroplasts[END_REF][START_REF] Miras | Noncanonical transit peptide for import into the chloroplast[END_REF]. Other proteins have been shown to be targeted to the chloroplast via the secretory pathway and undergo glycosylation [START_REF] Nanjo | Rice plastidial N-glycosylated nucleotide pyrophosphatase/phosphodiesterase is transported from the ER-golgi to the chloroplast through the secretory pathway[END_REF][START_REF] Radhamony | Evidence for an ER to Golgi to chloroplast protein transport pathway[END_REF][START_REF] Villarejo | Evidence for a protein transported through the secretory pathway en route to the higher plant chloroplast[END_REF]. Moonlighting proteins make the interpretation of proteomic results more difficult since those proteins are located in several cell compartments where they have different functions [START_REF] Jeffery | Mass spectrometry and the search for moonlighting proteins[END_REF].

Proteins routed through the secretory pathway can be predicted using different tools [START_REF] Emanuelsson | Locating proteins in the cell using TargetP, SignalP and related tools[END_REF]. No specific prediction program exists for vacuole, and therefore, localization can only be inferred via experimental data or homology searching referring to well-annotated proteins. Despite the knowledge of alternative secretion pathways, no current machine learning approach directly addresses the problem of predicting proteins entering the non-classical secretory pathway. However, prediction methods based on amino acid composition are in principle capable of foretelling proteins entering the non-classical secretory pathway [START_REF] Reinhardt | Using neural networks for prediction of the subcellular location of proteins[END_REF]. A sequence-based method of prediction of non-classical-triggered secretion for mammalian and bacterial proteins has also been developed [START_REF] Bendtsen | Feature-based prediction of non-classical and leaderless protein secretion[END_REF]. The assumption is that extracellular proteins share certain properties and features which can be related to protein function outside the cell, independently of the secretory process itself. Fifty proteins found in several proteomic studies of Arabidopsis cell walls and devoid of predicted signal peptide were analyzed. Only 14 were predicted as putative secreted proteins [START_REF] Jamet | Cell Wall, in Plant Proteomics: Technologies, Strategies, and Applications[END_REF], suggesting that this alternative pathway is highly exceptional.

Experimental location

The experimental testing of protein targeting is the best way to verify the computational predictions. Several methods have been used and their accuracy is uneven. The first protein localizations were made by biochemical methods, and some abundant proteins were characterized and turned into marker enzymes for several cell compartments. The biochemical tools consist mainly in destructive approaches allowing cell fractionation.

Fractionation has several restrictions since, as mentioned above, some compartments cannot be easily separated;

still those organelles that can be fractionated are not free of contaminants [START_REF] Huber | Organelle proteomics: implications for subcellular fractionation in proteomics[END_REF]. Immunoprecipitation of tagged proteins may be useful to identify other proteins within a complex.

Cytological localization is a non-destructive technique allowing a good localization of proteins.

Immunolabeling of cells supply a good resolution and high specificity, depending on the antibody. Proteins encoded by multigenic families may be difficult to localize precisely because highly specific antibodies are necessary to discriminate them. This is critical when different members of such protein families are targeted to different compartments. The development of a small peptide tag for covalently label proteins such as c-myc or His is a good approach because a larger protein tags may affect the function of the protein of interest. This allows the use of commercial antibodies.

One of the most popular methods to localize proteins in-vivo is the Green Fluorescent Protein (GFP)-fusion technology. GFP is a small protein from jellyfish that can be visualized by fluorescence in a non-invasive way [START_REF] Chalfie | GFP: Lighting up life[END_REF]. In-vivo uptake assays are widespread since they maintain the cellular environment, and targeting into all organelles can be tested at the same time. The principal drawback is that the tag may modify the location of the native protein and may even alter the condition of the wider system through dominant effects. It is important to test such constructs in a null mutant for the studied protein and with the native expression signals; if not there is inevitably a degree of overexpression. Another important point is to verify that the distribution of the GFP signal is consistent with gene expression visualized via in situ RNA hybridization. Localization of proteins in the endomembrane system is more complex than in chloroplasts or mitochondria since there is an exchange of membrane constituents. Furthermore the proteins of interest may be distributed between more than one compartment, and still cycle between them [START_REF] Millar | Exploring the function-location nexus: Using multiple lines of evidence in defining the subcellular location of plant proteins[END_REF][START_REF] Moore | Validating the location of fluorescent protein fusions in the endomembrane system[END_REF]. The critical point is the subjective interpretation one puts on each observation in light of the experimental conditions and other pertinent data.

Conclusion

The results of sub-cellular proteomics cannot be considered sufficient to ensure the correct determination of protein location in cells. The use of several localization techniques is recommended, even if this seems to be redundant. Bioinformatic software based on experimental data provides valuable tools to predict sub-cellular localization of proteins. Even if a protein can accumulate at several places in the cell and be active at a place where it does not accumulate, combining approaches that take into account targeting and accumulation of proteins may give more confident localization [START_REF] Millar | Exploring the function-location nexus: Using multiple lines of evidence in defining the subcellular location of plant proteins[END_REF]. Finally, the integration of localization data with expected biological function and within metabolic networks is important to confirm the location of a particular protein.

Table I . Plant proteomic databases Database Name Cell compartment Web site

 I

	Reference	[18]			[22]		[31]				[3]		[50]		[52]			[16]		[35]				[21]		[36]		[7]
	Species	Arabidopsis	thaliana,	Zea mays	Arabidopsis	thaliana	all organisms		Arabidopsis	thaliana	Arabidopsis	thaliana	all organisms		Arabidopsis	thaliana,	Oryza sativa	Arabidopsis	thaliana	Arabidopsis	thaliana,	Nicotiana	tabacum	Arabidopsis	thaliana	Arabidopsis	thaliana	Arabidopsis	thaliana
		http://ppdb.tc.cornell.edu/			http://www.plantenergy.uwa.edu.au/suba2/		http://www.ebi.ac.uk/pride/		http://proteomics.arabidopsis.info/		http://fgcz-atproteome.unizh.ch/		http://gpcr2.biocomp.unibo.it/esldb/index.htm		http://www.polebio.scsv.ups-tlse.fr/WallProtDB/			http://www.grenoble.prabi.fr/data/PlantProteomics07/		http://www.plprot.ethz.ch/				http://www.plantenergy.uwa.edu.au/applications/ampdb/		http://www.gartenbau.uni-hannover.de/arabidopsis.html		http://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home
		PPDB any	compartment		SUBA any	compartment	PRIDE any	compartment	NASC proteomics any	database compartment	AtProteome any	compartment	eSLDB any	compartment	WallProtDB cell wall			PlantProteomics07 plasma	membrane	plProt plastid				AMPDB mitochondrion		AMPP mitochondrion		AtNoPDB nucleolus

Table II . Bioinformatic tools for plant sub-cellular proteomics Database name Cell compartment Web site Species Reference

 II

	[58]			[59]
	Arabidopsis	thaliana,	Oryza sativa	Arabidopsis	thaliana,	Oryza sativa,	Vitis vinifera,	Zea mays,	Other seed	plants
	http://www.polebio.scsv.ups-tlse.fr/ProtAnnDB/			http://aramemnon.botanik.uni-koeln.de/
	any	compartment		membranes
	ProtAnnDB			Aramemnon

Acknowledgements

The authors are thankful to CNRS and Université Paul Sabatier (Toulouse III, France) for supporting their research.