R Llido

A Sarafianos

O Gagliano

V Serradeil

V Goubier

M Lisart

G Haller

V Pouget

D Lewis

J M Dutertre

A Tria

Characterization and TCAD Simulation of 90nm Technology PMOS Transistor Under Continuous Photoelectric Laser Stimulation for Failure Analysis Improvement

This study responds to our need to optimize failure analysis methodologies based on laser/silicon interactions, using the functional response of an integrated circuit to local laser stimulation. Thus it is mandatory to understand the behavior of elementary devices under laser stimulation, in order to model and anticipate the behavior of more complex circuits. This paper characterizes and analyses effects induced by a static photoelectric laser on a 90 nm technology PMOS transistor. Comparisons between currents induced in short or long channel transistors for both ON and OFF states are made. Experimental measurements are correlated to Finite Elements Modeling Technology Computer Aided Design (TCAD) analyses. These physical simulations give a physical insight of carriers generation and charge transport phenomena in the devices.

Introduction

While performing Photoelectric Laser Stimulation (PLS) with a 1064 nm wavelength laser electron-hole pairs are generated in silicon, since the laser energy is greater than the band gap energy of silicon. In electric-field free areas or silicon substrate these carriers will diffuse and recombine rapidly without any significant effect. However, inside Space Charge Regions (SCR) of PN junctions (such as Drain-Source/Nwell or Nwell/substrate junctions of a transistor), those electronholes pairs will be separated by the internal electric field and an Optical Beam Induced Current (OBIC) will be generated [START_REF] Haraguchi | Microscopic optical beam induced current measurements and their applications[END_REF].

Our purpose is to analyze complex circuits response to PLS. For that, elementary devices are studied first and then more and more complex ones. This work follows a previous investigation on the 90nm technology NMOS transistor [2 3]. We study in this paper the effects of static PLS on a 90 nm technology PMOS transistor, in function of its channel length and its state. Electrical measurements associated with Finite Element Modeling software are useful and completing tools in order to understand and characterize PLS impact. TCAD models the complex flow of semiconductor fabrication steps leading to detailed information on geometric shape and doping profile distribution of a semiconductor device in scope (like a MOS transistor). It also uses this information to predict characteristics of semiconductor devices leading to circuit simulation models as implemented in any circuit simulator like PSPICE [START_REF] Minixhofer | TCAD as an integral part of the semiconductor manufacturing environment[END_REF]. Three-dimensional simulation is computationally intensive and requires very long simulation times. Thus, only two dimensional (2D) TCAD simulations have been considered. We assume that the third space dimension does not significantly affect the behavior of the fundamental mechanisms involved in the following results, as the third dimension of the device (width of the transistor) is taken long enough to neglect edge effects (W=10µm). Moreover, comparisons between simulations and measures will rather be qualitative than quantitative because TCAD simulations are used as an analysis tool of physical laws effects inside a structure, and should be accurately calibrated before any quantitative analysis.

In a first section, this paper presents experimental characterization of the effect of PLS on a PN junction, which is compared to TCAD simulations. Then, a detailed analysis of PLS impact on a PMOS transistor is presented in function of the substrate polarization.

P+/Nwell junction

In this section, PLS impact on a P+/Nwell junction, already characterized in [5 6] is studied and compared to TCAD simulation in order to validate the models used on a well known case.

Experimental characterization

The Device Under Test (DUT) is embedded in a 90nm technology test structure and its surface is about 200µm². The experimentation was realized on an i-Phemos Hamamatsu system. The laser spot was positioned in the center of the junction and laser power was adjustable below 100mW. Current-voltage (I-V) characteristic was measured for different laser power, and presented on Figure 1. When the PN junction is reverse-biased under 0.6V, the photocurrent is almost constant independently of applied voltage since the SCR width does not vary significantly. The induced photocurrent is higher than the junction reverse leakage and depends on laser power. Then, when applied voltage raises over 0.6V, the SCR width starts to decrease and consequently the amount of induced photocurrent is also reduced. When the applied voltage reaches diode built-in potential (black cross on Figure 1), the SCR decreases, the induced photocurrent is therefore reduced and becomes negligible with respect to the junction forward current. Moreover, the so far negligible resistances of the P and N-type regions start to play a significant role in forward mode and get reduced under the illumination, which leads to a current increase with laser intensity.

Figure 1. Measured I-V curve of P+/Nwell junction vs. laser power.

TCAD simulations

The goal of this simulation was to reproduce the photocurrent induced in the SCR of the device. In this work, Synopsys simulation tools are used and especially Sentaurus Device Editor (SDE) for grid generation and SDevice for device simulation. Sentaurus Process tool has been used to generate structures, reproducing all process steps used to build the devices. The simulated device is a Drain/Substrate (or Source/Substrate) junction obtained removing the gate and taking half of a PMOS transistor (Figure 2). Thus a single structure is generated and information about phenomena taking place into the transistor's junctions is obtained. I-V characteristics of the junction are simulated in function of the intensity (from 100 W.cm -² to 500 W.cm -²) of the 1064 nm smooth wave simulator (Figure 3). Results are correlated to measurements, currents levels are not identical to measurements because TCAD simulator is not calibrated to fit silicon results, it is only used to have trends.

Correlation of measurement and simulation

The same trends are obtained by experiments and by TCAD simulations: under photoelectric stimulation (1064 nm) a reverse-biased junction can be modeled by a current source, and the quantity of photocurrent does not significantly depend on the applied reverse voltage (valid for this technology).

Although junctions used for measurements and simulations are different in terms of geometry, the purpose of this study is to have a qualitative comparison (current trends) rather than a quantitative comparison (current amplitudes) between experimental measurements and simulations. The aim was to validate simulation models on basic devices already studied, in order to simulate after that with confidence the effect of PLS on a more complicated component: a PMOS transistor.

PMOS transistor study

The effect of pulsed PLS on transistors has already been analyzed [7 8]. However, we aspire at understanding effects of static PLS on a PMOS transistor which can help us to interpret OBIC or LIVA cartography obtained in the field of a failure analysis. DUT are embedded in a 90 nm technology test structure and the laser power is adjustable below 100mW.

For each study, the laser spot is positioned in the center of the transistor and current of each electrode is measured in function of the laser power [START_REF] Brahma | Functional Analysis of MOSFETs and HEMTs with Laser Stimulation and Photonemission[END_REF]. In case of a PMOS transistor study, two cases must be investigated: when the p-substrate is floating and grounded.

Floating p-substrate

Long channel transistor OFF state: A 10µm x 10µm PMOS transistor is biased as follows: Gate, Source and Nwell are biased at 1.2V and the Drain is grounded. Evolution of these four electrodes currents in function of the laser power is given in Figure 4. Obviously the gate current is always equal to zero. The current conservation law is respected since the sum of all currents is always equal to zero. Moreover, the same quantity of current is approximately generated in Drain/Substrate and Source/substrate junctions. ON state: A 10µm x 10µm PMOS transistor is biased as follows: Gate and Drain are grounded; Source and Nwell are biased at 1.2V. Evolution of these four electrodes currents in function of the laser power is presented in Figure 5, and the conclusion is the same than in OFF state. Moreover, |I source | decreases since the starting up in conduction of the channel leads an additional current which opposes to the circulation of Source photocurrent. On the other hand, |I drain | increases because it comes to be added to the normal circulation of electrons in the channel (due to its starting in conduction). Finally, the same quantity of photocurrent is induced in the transistor in both ON and OFF states (Figure 6): approximately 30 µA in Source-Nwell or Drain-Nwell junctions and 65 µA in Nwell-Psubstrate junction (at maximum laser power). In conclusion, the effect of PLS is the same whether the long channel PMOS transistor is in OFF or ON state: its conduction is only slightly increased, and leakage currents are induced. This result is identical to the one obtained during the study of an NMOS transistor [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continuous photoelectric laser stimulation for failure analysis improvement[END_REF]. Consequently the effect of PLS is identical for both long channel NMOS and PMOS (with floating substrate) transistors.

Short channel transistor

OFF state: A 10µm x 0.09µm PMOS transistor is used. Evolution of electrodes currents in function of the laser power is presented in Figure 7. The quantity of photocurrent induced is approximately three times smaller than in the long channel transistor case (for example I Drain-short channel = -8.1µA and I Drain- long channel = -27µA). ON state: A 10µm x 0.09µm PMOS transistor is used. The current flow through these four electrodes as a function of the laser power is depicted on Figure 8. Contrary to the long channel transistor, the effect of laser stimulation is not visible even at maximum laser power. Indeed, current amplitudes measured when this short channel transistor is turned on are very important (approximately 2.7 mA for Source and Drain) compared to the quantity of photocurrent generated (about 20 µA), so the effect of PLS is negligible. Indeed, Drain current (at fixed V Gate ) varies proportionally to 1/L, that's why effect of PLS can be observed in ON state on a long channel transistor but not on a short channel one. A more detailed study on transistor length dimension variation is proposed in the following part.

These results are identical to those obtained during the NMOS transistor study [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continuous photoelectric laser stimulation for failure analysis improvement[END_REF]. In conclusion, the effect of PLS is the same on both short channel NMOS or PMOS (with floating substrate) transistors.

Figure 8. Short channel transistor currents vs. laser power (transistor in ON state).

Effect of transistor length variation

The previous part highlights the fact that in OFF state the quantity of induced photocurrent is three times smaller in case of a short channel. This point is investigated in this section through the study of the transistor gate length variation. Current values are measured at maximum laser power for different lengths (0.09µm, 0.1µm, 0.24µm, 0.5µm, 1.2µm, 5µm and 10µm), results are illustrated in Figure 9 and show that photocurrents increase with the transistor length. Same results are obtained with TCAD simulation tool. In all cases (from L=0.09µm to 10µm) current conservation is verified, but the amount of induced photocurrent is smaller for small L values although transistor is in OFF state (V gate =0V), width are identical (W=10µm), and Source/Drain junctions volumes are identical. The reason is the same as in the NMOS transistor case [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continuous photoelectric laser stimulation for failure analysis improvement[END_REF] : a part of the total photocurrent collected on Source and Drain contact is a contribution from the channel depletion region.

In conclusion, effect of PLS on a PMOS transistor with floating p-substrate is similar than on a NMOS transistor: it depends on its gate length and on the applied voltages, conditioning the existence and the size of depletion regions in the Source and the Drain, and eventually in the channel region where photocurrents are generated. However, the most realistic situation of the CMOS technologies is when the Substrate is biased to the potential of reference (ground). In that case, the behavior under PLS of the PMOS transistor is very different.

- 11, and the conclusion is the same than for a long channel transistor although Drain and Source current amplitudes are higher than in OFF state (since the channel is in conduction), but the effect of PLS remains smaller on them (even at maximum laser power). Finally, the same quantity of photocurrent is induced whether the transistor is in ON or OFF state (Figure 12). In conclusion, when the p-Substrate of a long channel PMOS transistor is grounded, effect of PLS is dominant on Nwell/Substrate junction compared to Source/Nwell and Drain/Nwell junctions. Two reasons explain this conclusion. First, if these three junctions had the same surface the Nwell photocurrent would be larger than the Source/Drain one because Nwell region doping is weaker (Figure 13), consequently Nwell SCR width is bigger than the Source/Drain one. The fact that in a PMOS transistor the Nwell junction surface is more important stresses this point. Finally, if these three junctions had the same doping, photocurrent induced in the Nwell junction would be higher than in Source/Drain one because photocurrent is proportional to the surface : I ph = j ph *S with S the junction surface. From both points of view the photocourant induced in Nwell junction is higher than in Source or Drain one. ON state: The same experiment than in part A is realized on a 10µm x 1.2µm PMOS transistor with p-Substrate grounded.

-
Results are presented in Figure 15. As in the long channel case, Drain and Source current amplitudes are higher than in OFF state (since the channel is in conduction) but effect of PLS remains all the same negligible (even at maximum laser power).

In conclusion, the same magnitudes of photocurrent seem to be induced whether the transistor is short or long channel, in ON or OFF state.

Effect of transistor length variation

Photocurrents induced in Drain, Source, Nwell and p-Substrate in function of the transistor gate length when the transistor is in OFF state is studied in this paragraph. Currents values are measured at maximum laser power for different length dimensions (0.09µm, 1.2µm, 5µm and 10µm), results are presented in Figure 16 and show that photocurrent induced in each electrode slightly decrease between 0 and 2 µm, but remains constant over 2 µm. The same experiment is simulated with TCAD tool, results are presented in Figure 17. The trend of curves presented in Figure 16 and Figure 17 are different because in simulation the laser is simulated as a plane wave so there is the same power density over the whole structure. Whereas in measurements, the laser power density follows a Gaussian shape (see Figure 18). This means that in case of a very short channel transistor (L=0.09µm) source and drain are completely covered and centered in the laser spot, so they are irradiated with the maximum power and their photocurrent amplitudes are maximum. For short channel (between 0.1 and 2 µm), source and drain are at the edge of the laser spot, so source and drain photocurrent amplitudes are smaller than for smaller length values. When gate length is over 2µm, source and drain are outside the laser spot, so they are irradiated by the "feet" of the Gaussian and levels of photocurrents are smaller than for smaller gate length values, and keep almost constant.

In conclusion, when the p-substrate of a long channel PMOS transistor is grounded, the same quantity of photocurrent is theoretically induced whether the transistor is short or long channel. In practice, more photocurrent is induced in transistors owning gate length smaller than the laser spot diameter.

Conclusions

Effects of PLS on a 90 nm PMOS transistor have been investigated. Comparisons between photocurrents induced in case of short or long channel, or turned ON or OFF transistor are presented. Moreover, if the p-Substrate is floating, PMOS transistor behavior is equivalent to that of NMOS transistor.

This study gives the possibility to optimize failure analysis methodologies. Indeed, it reveals that static PLS induces non negligible leakages in a transistor (several micro amps in a single transistor) which explains why OBIC cartographies are difficult to interpret. Moreover, PLS does not bring enough energy to make a transistor change its state since in static the density of induced carriers is not important enough. This confirms that OBIC technique cannot be implemented such as OBIRCh [10 11] one and should rather be used in a dynamic approach [START_REF] Llido | Improving defect localization techniques with laser beam with specific analysis and set-up modules[END_REF]. This way, in presence of defects or if the product is in a state in limit of functionality, the very weak electric variations led by the laser stimulation will be able to modify the functionality of the integrated circuit, allowing the localization of sensitive zones. It will be possible to localize for example sources of Latch-Up triggering [START_REF] Llido | Photoelectric Laser Stimulation applied to Latch-Up phenomenon and localization of parasitic transistors in an industrial failure analysis laboratory[END_REF], or advance/delay of CMOS gates[ 14 15]. Finally, based on this study we developed an ELDO model (based on SPICE language) of a PMOS transistor submitted to PLS, that fit very well measurements. It will be useful in case of PLS of complex circuit to predict results and compare them with simulation [START_REF] Sarafianos | Building the electrical model of the Photoelectric Laser Stimulation of a PMOS transistor in 90nm technology[END_REF].

Future work will characterize PLS effect on simple logic gates or MOS inverter under PLS. The test with a pulsed laser will also be conducted to explore higher laser energy ranges [START_REF] Perdu | Identification of some key parameters for photoelectric laser stimulation of IC: an experimental approach[END_REF].

Figure 2 .

 2 Figure 2. Structure used for junctions TCAD simulations.

Figure 3 .

 3 Figure 3. Simulated I-V curve of P+/Nwell junction vs. laser power.

Figure 4 .

 4 Figure 4. Long channel transistor currents vs. laser power (transistor in OFF state).

Figure 5 .

 5 Figure 5. Long channel transistor currents vs. laser power (transistor in ON state).

Figure 6 .

 6 Figure 6. Long channel transistor photocurrent generation difference between OFF and ON states (in this case subtraction between the current values obtained with and without laser stimulation).

-

Figure 7 .

 7 Figure 7. Short channel transistor currents vs. laser power (transistor in OFF state).

Figure 11 .

 11 Figure 11. Long channel transistor currents in function of laser power (transistor in ON state).

Figure 12 .

 12 Figure 12. Long channel transistor photocurrent generation difference between OFF and ON states (in this case subtraction between the current values obtained with and without laser stimulation).

Figure 13 .

 13 Figure 13. Doping concentration mapping and SCR displayed in white lines (a) and doping profile of the mapping along the white dashed line (b)

Figure 14 .

 14 Figure 14. Short channel transistor currents in function of laser power (transistor in OFF state).

Figure 15 .

 15 Figure 15. Short channel transistor currents in function of laser power (transistor in ON state).

Figure 16 .

 16 Figure 16. Measurements of photocurrent induced (at maximum laser power) in function of transistor lentgh (W=10µm).

Figure 17 .

 17 Figure 17. Simulation of photocurrent induced (at maximum laser power) in function of transistor lentgh (W=10µm).

Figure 18 .

 18 Figure 18. Laser spot size with respect to the gate length.

  The same experiment than in part A is done with the p-Substrate grounded. Results are presented in Figure10. Gate current is always equal to zero, current conservation law is respected. When the p-Substrate is grounded, photocurrents collected at Drain and Source are small compared to the photocurrent induced in Nwell-Substrate junction. In this case we can consider that the laser stimulation has a dominating effect only on the Nwell-Substrate junction.

									Inwell	Isource	Igate
									Idrain	Sum
								3.0E-03
								2.0E-03
							Current (A)	-1.0E-03 0.0E+00 1.0E-03	0%	20%	40%	60%	80%	100%
		2.0E-05						-2.0E-03
		1.5E-05	Igate Inwell Isource					-3.0E-03
		1.0E-05	Idrain Sum					-4.0E-03	Laser power (%)
	Current (A)	0.0E+00 5.0E-06 Figure 9. Photocurrent induced (at maximum laser power) vs.transistor lentgh (W=10µm). P-substrate grounded
	0% Long channel transistor 20%	40%	60%	80%	100%
	1.5E-05 -1.0E-05 -5.0E-06 OFF state: Figure 10. Long channel transistor currents vs. laser power Laser power (%)
			(transistor in OFF state).
	ON state: The same experiment than in part A is done with p-
	Substrate grounded. Results are presented in Figure


A Sarafianos

R Llido

O Gagliano

V Serradeil

M Lisart

V Goubier

J M Dutertre

A Tria

V Pouget

D Lewis

Building the electrical model of the Photoelectric Laser Stimulation of an NMOS transistor in 90nm technology

This paper presents the electrical model of an NMOS transistor in 90nm technology under 1064nm Photoelectric Laser Stimulation. The model was built and tuned from measurements made on test structures and from the results of physical simulation using Finite Element Modeling (TCAD). The latter is a useful tool in order to understand and correlate the effects seen by measurement by given a physical insight of carrier generation and transport in devices. This electrical model enables to simulate the effect of a continuous laser wave on an NMOS transistor by taking into account the laser's parameters (i.e. spot size and power), spatial parameters (i.e. the spot location and the NMOS' geometry) and the NMOS' bias. It offers a significant gain of time for experiment processes and makes it possible to build 3D photocurrent cartographies generated by the laser on the NMOS, in order to predict its response independently of the laser beam location.

Introduction

In the photoelectric effect, electrons are emitted from silicon as a consequence of their absorption of energy from light. Failure analysis (FA) methodologies make an extensive use of laser stimulation techniques. In this paper, we present an electrical model of the Photoelectric Laser Stimulation (PLS) of an NMOS transistor in 90nm technology. This electrical model makes it possible to simulate the response of an NMOS transistor to PLS in a very small amount of calculation time by comparison with real experiments on a laser equipment, or TCAD simulations. Thus, the correct behaviour of a device under test may be predicted by simulation. It may serve as a "golden" reference to detect any defect by comparison and measurements. Obviously the obtained models are technology and layout dependent. Thus, a new model has to be built for any technology improvement. Such models are intended to reduce the time passed on laser equipments. This work presents the electrical model (thanks to ELDO, the SPICE simulator from Mentor Graphics [START_REF]ELDO user's Manual -Software Version 6.6_1 Release[END_REF]), of the photoelectric effect applied to an NMOS transistor in 90nm CMOS technology under backside continuous PLS. This model is valid for small laser power (from 0 to 100mW). In order to validate and correlate the model, measurements on NMOS transistors under laser illumination at a wavelength of 1064nm were performed with an I-phemos Hamamatsu equipment [START_REF]Inverted Emission Microscope iPHEMOS Series Hamamatsu[END_REF]. Moreover TCAD analyses are needed, in order to understand better the phenomenon involved when an NMOS transistor is under PLS. TCAD takes into account the complex flow of semiconductor fabrication steps leading to detailed information on geometric shape and doping profile distribution of a semiconductor device in scope (like a MOS transistor) [START_REF] Minixhofer | TCAD as an integral part of the semiconductor manufacturing environment[END_REF] to model its behaviour. Three-dimensional simulation is computationally intensive and requires very long simulation times. Thus, only two dimensional (2D) TCAD simulations have been considered in this paper. We assume that the third space dimension does not significantly affect the behaviour of the fundamental mechanisms involved in the following results, as the third dimension of the device (width of the transistor) is taken as long enough to neglect edge effects (W=10μm). The laser waves used in all TCAD simulations are in every case, a plane wave which illuminates all the backside of devices. Moreover, comparisons between measures and simulations will rather be qualitative than quantitative because TCAD simulations are first used as an analysis tool of physical laws effects inside a structure, and should be accurately calibrated before any quantitative analysis. Electrical measurements associated with Finite Element Modeling software are the tools necessary to understand and model PLS impact on NMOS transistor. The novelty of the model presented in this paper is that it takes into accounts the laser's parameters (i.e. spot size and power), spatial parameters (i.e. the spot location and the NMOS' geometry), and the NMOS' bias. Therefore, the model is calibrated for each laser conditions (power, spot size, location, etc.) This article is organized as follows. Section 2 reports the behaviour under PLS of PN junctions encountered in NMOS transistors: N+/Psubstrate junctions. Measurements made on silicon and TCAD simulations permit to build an accurate electrical model. This model is used in turn to build a complete electrical model of an NMOS transistor under PLS, with comparison and measurements for different laser and NMOS parameters as described in section 3. This model offers the ability to draw 3D cartographies of NMOS under PLS for FA purposes as reported in section 4. Finally, our findings are summarized in the concluding section 5 with some perspectives.

Electrical modeling of the PN junction found in an NMOS transistor

In an NMOS transistor there are mainly two PN junctions which give rise to a photoelectric effect if exposed to a laser beam (Drain/Psubstrate and Source/Psubstrate). Therefore a first step toward building the electrical model of NMOS transistors under PLS is the study and modeling of PN junctions under laser illumination.

PN junctions measurements under PLS

The Device Under Test (DUT) is an N+ on Psubstrate junction embedded in a test structure of a STMicroelectronics 90nm technology. The test series was performed with a 20X objective and a laser spot located in the middle of the diode focused on the active zone. The laser power at the output of the chosen objective was adjustable between 0 and below ~100mW. The main characteristics of the experiment are presented on figure 1. The red spot represents the location of the laser beam on the diode layout. The I(V) characteristics depicted on figure 2 were obtained for various laser power according the aforementioned settings. A strong induced photocurrent was measured for the PN junction in reverse-biasing. The wide Space Charge Region (SCR) of the junction in reverse bias explains that result. At the given power settings, any variation in the bias has no effect on the photocurrent provided it is maintained in reverse-biasing.

However, when the diode is forward-biased (over its threshold voltage ~0.7V) the SCR is tightened and the induced photocurrent is almost negligible by comparison with the current flow in the diode [START_REF] Glowacki | Expanding the scope of laser stimulation techniques for functional analysis and reliability of semiconductor devices by in-depth investigation of the optical interaction with the devices[END_REF]. Current versus voltage characteristics of the diode are simulated in function of the laser power (from 1W.cm -2 to 30W.cm -2 ) of the 1064nm wavelength simulator smooth wave (See fig. 4). Results have the same behaviour than measurements. Therefore this result is a good validation of the TCAD simulation flow developed [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continous photoelectric laser stimulation for failure analysis improvement[END_REF]. Moreover, in reverse mode, the evolution of the photoelectric current generated in the diode versus the laser power is not linear. From measurements, it is indeed possible to approximate it by a second order polynomial function presented in (1). Where I laser is the photoelectric current generated in the PN junction by the laser in Ampere and P laser is the laser power in W/cm². The same behaviour (second order polynomial behaviour) can be observed thanks TCAD simulation on Drain/Psubstrate photocurrent. The graph on figure 5 depicts the photoelectrical currents obtained from both TCAD simulation and measurements versus the laser power. The values reported on both axis are normalized. It reveals a difference between physical simulation and measurements. This may result from the difference in the target's area exposed to the laser beam: in TCAD simulation the whole junction was exposed to the beam, whereas the beam obtained experimentally has a reduced effect area on the junction. In first approximation, and only with small laser power on this technology, it is possible to highlight the fact that in reverse mode the diode generates a constant photoelectric current independently of the voltage applied to its two electrodes.

Study of the spatial dependence of the induced photocurrent

The distance between the laser spot and the PN junction has a strong impact on the value of the generated photocurrent. In the following, this effect is studied for N+/Psubstrate junction.

When the laser beam is centered on the PN junction (Drain/Psubstrate junction of an NMOS transistor), the photocurrent is maximum, and when the laser moves outside the junction the value of the photocurrent decreases. (fig. 6). In order to model the spatial dependence effect on a Drain/Psubstrate diode (N+/Psubstrate junction), an NMOS transistor with long channel (W=L=10μm) was used. The measurements were made with the drain at 1.2V, the Psubstrate bias grounded, and the two other electrodes left floating. The laser spot was moved on a line from the center of the junction to a distance of 300μm. (See Fig. 6). For each step, the photocurrent was measured. This experiment was conducted for the three objectives of our I-phemos laser equipment (2.5X, 20X, and 50X). The obtained photocurrent versus distance curves are depicted on figure 7 after normalization according the maximum photocurrent (Iph2.5X, Iph20X, and Iph50X). Their shapes exhibit a Gaussian-like behaviour. For each I-phemos objective, the coefficients a, b, c1 and c2 were found different (see Table I). The Gaussian profile of laser interaction with silicon takes into account the effect of PLS in the area where the diffusion phenomenon occurred (on the both side of the PN junction).

The diffusion length of minority carrier characterizes the distance which could reach photo generated carriers before the recombination process.

In this way a TCAD simulation of a PN junction under a constant backside illumination was performed (See fig. 8). The laser wave was simulated as a plane wave. Due to simulation constraints, the laser area of effect extends over the whole structure. Therefore, the size of the component was changed (and so does the laser beam range) to simulate different spot sizes. The variable of the simulation is the parameter x expressed in micrometer, which define the volume of Psubstrate (in his case, the value x evolved from 0 to 10µm).

Then the photocurrent generated on the PN junction is extracted versus the Psubstrate volume. (See fig. 9). The value of the photocurrent generated on the PN junction increases as x increases from 0 to 1μm. After 1μm, which is approximatively the diffusion length of a PN junction the photocurrent is constant versus the silicon volume. The fact that the photocurrent increases only when the value of the distance x (presented on figure 8) is approximatively under the diffusion length of the PN junction, highlights the phenomena of electron-hole pairs collection in the diffusion volume. Therefore this effect participates to the Gaussian behaviour of the photocurrent versus the distance to the PN junction and is not negligible. Its effect on the terms of equation ( 2), is taken into account by our model, thanks to measurements presented figure 7 [START_REF] Sayad | Détermination de la longueur de diffusion des porteurs de charge minoritaires dans le silicium cristallin par interaction lumière matière[END_REF].

Electrical model of a PN junction under PLS

In order to simulate a PN junction under PLS, the solution proposed in this paper is to define a sub circuit to emulate the source or the drain photocurrent generation. This sub circuit contains a current source whose intensity value is defined for both by the parameter Iph_val (3):

gauss laser I S val Iph * * _ ( 3
)
Where S is the junction area in μm 2 , I laser is the value of the photocurrent generated on the PN junction (as defined in (1)) in Ampère, and α gauss the spatial dependency coefficient (cf. (Equation 2)).

The photocurrent generated on source and drain electrodes takes into account the value of the junction area S and the coefficient c which depends on the distance parameter d chosen by the user for the two junctions of the NMOS, as noticed in equation ( 2). Coefficient a, b, c 1 and c 2 are defined in Table 1.

Electrical model of an NMOS transistor under PLS Building the electrical model of the NMOS under PLS

After the calibration step of the sub circuit which model a PN junction under PLS, it is possible to model a 90nm technology NMOS transistor under laser illumination on the basis of manufacturer technological MOS models. On the four NMOS electrodes, two sub circuits modeling its two N+/Psubstrate junctions under PLS are connected respectively to the source and the drain nodes (Fig. 10).

When the user calls these sub circuits in the main netlist, for each junction (Drain/Psubstrate and Source/Psubstrate) he has to set two parameters: the value S (area of the junction), and d (shortest distance between the centre of the laser spot to the edge of the junction). Values of the two resistances of the model presented on the figure 10 are defined as following (with geometrical parameters in micrometer defined on figure 10):

W L R R psub psubc * (4) W L R R psub psub psub * ( 5
)
Where R psub is the value of the resistance of the Psubstrate for one square. Therefore the model takes into account the NMOS size, the location of the laser beam on the device, and its biasing.

Electrical model versus measurements

It is thus possible to simulate electrically the effect of the laser on an NMOS transistor in 90nm technology and to perform a lot of variation on the laser parameters, as the laser power, the beam position, the diameter spot size (via the I-phemos objective chosen). It is also possible to modify the bias condition of the transistor or to change its geometrical dimensions. The aim of this section is to compare electrical simulations with measurements.

The relationship between the photoelectric current induced in the PN junction and the laser power is not linear as equation [START_REF]ELDO user's Manual -Software Version 6.6_1 Release[END_REF] shows.

In order to show the influence of the laser power on the photocurrent, an NMOS W=10μm/L=0.1μm transistor is used in measures as in electrical simulations. This NMOS transistor is turned OFF. The drain is at the potential of +1.2V, the source, the gate and the bulk (Psubstrate) are grounded. The photocurrent on the four MOS electrodes is measured according to the laser power, by measurements and by simulations. The objective chosen is the 20X, parked in the middle of the transistor [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continous photoelectric laser stimulation for failure analysis improvement[END_REF].

The graph presented figure 11 shows the very good correlation between electrical simulations and measurements. Both photocurrents measured at the transistor's drain and source were found equal. It reveals that the laser spot was at equal distance from both drain and source. Moreover, the current conservation law (eq. 6) is fulfilled at any laser power [START_REF] Glowacki | Systematic characterization of integrated circuit standard components as stimulated by scanning laser beam[END_REF][START_REF] Sanchez | Delay variation mapping induced by dynamic laser stimulation[END_REF].

|I substrate | = I drain + I source . (6)
Measurements of the photocurrents for different NMOS transistor gate lengths with the same area of N+/Psubstrate junction, were performed with identical biasing. The NMOS was turned off (drain at +1.2V and the three other electrodes grounded). The laser was set at its maximal power, and parked in the centre of every transistor.

Because of the Space Charge Region (SCR) located under the gate, the area between source and drain has an impact on the photocurrent generated by the junctions. Indeed, the photocurrent was found stronger for transistors with bigger gate areas as shown in figure 12.

An hypothesis is that a part of the total amount photocurrent collected from source and drain electrode is coming from the channel depletion region [START_REF] Llido | Characterization and TCAD simulation of 90 nm technology transistors under continous photoelectric laser stimulation for failure analysis improvement[END_REF]. It is possible to extract a linear estimation of this curve which is given in [START_REF] Glowacki | Systematic characterization of integrated circuit standard components as stimulated by scanning laser beam[END_REF]. So an additive term expressed in ( 7) is added to (3) in order to obtain equation ( 8):

gauss laser gate Iph I S val Iph * ) _ * ( _ (8)
Iph_gate is a linear function proportional to the area under the gate. [START_REF] Sanchez | Delay variation mapping induced by dynamic laser stimulation[END_REF], obtained from [START_REF] Glowacki | Systematic characterization of integrated circuit standard components as stimulated by scanning laser beam[END_REF].

Current cartographies

The aim of drawing 3D electrical current cartographies is to predict the correct behaviour of a device under test simulation independently of its location. It may serve as a "golden" reference to detect any defect by comparison and measurements.

Principe of electrical 3D current cartographies

The principe of the electrical model of cartography lies in the creation of a mesh on the layout of the transistor. The step of the meshing is a parameter called stepXY defines in the ELDO netlist. It is necessary to calculate in every point of the mesh two values of distance. The shortest distance between the centre of the laser spot and the edge of the drain junction, and the source junction.

To illustrate the principe of cartography, it is possible to take an example on an NMOS W=10µm/L=10µm transistor. Three cases are envisaged. The first one, is when the laser spot is parked on the centre of the drain junction. The second, when the laser is positioned on the source, and the last one when the beam is centred in the middle of the NMOS transistor. For each case, drain, source and Psubstrate currents were extracted from the ELDO simulation (See Fig. 13) [START_REF] Llido | Improving defect localization techniques with laser beam with specific analysis and set-up modules[END_REF].

In this case stepXY is equal to 5µm, with m being a parameter evolving between 0 and 2 by step of 1. L is the gate length of the NMOS transistor expressed in micrometer.

When the laser beam is centered in the middle of the drain, the photocurrent generated by the drain is more important than that the source, and the preservation of currents is respected [START_REF] Sayad | Détermination de la longueur de diffusion des porteurs de charge minoritaires dans le silicium cristallin par interaction lumière matière[END_REF]. If the beam is centered on the source junction, it is the photocurrent generated by the source that is more important than that generated by the drain, always by respecting the preservation of the currents (5). In the last case, when the laser beam is centered in the middle of the NMOS, the photocurrents generated by the source and the drain are identical (12). On the same principe, it is possible to create more complex cartographies, by creating a denser mesh. The same method is always used. In every point of the mesh, two values of distances are associated. The first value is the distance between the center of the laser spot to the closest edge of the drain. And the second value is the distance between the center of the laser spot to the closest edge of the source.

3D current cartographies on an NMOS transistor turned OFF

In this section, it is proposed to draw 3D current cartographies (extracted from the ELDO simulator) of photocurrents generated on an NMOS transistor W=L=10μm [START_REF] Sanchez | Delay variation mapping induced by dynamic laser stimulation[END_REF]. It is also possible to extract 3D cartographies of photocurrents generated by the two PN junctions of an NMOS [START_REF] Llido | Improving defect localization techniques with laser beam with specific analysis and set-up modules[END_REF].

Conclusion and perspective

The process of building an accurate electrical model of an NMOS transistor in 90nm technology under photoelectrical laser stimulation was reported in this paper. The validity of the approach was assessed by the very good correlation obtained between electrical simulations (based on SPICE language) and measurements. TCAD physical simulations were also ran to explore the underlying physical phenomena. In failure analysis it could have a great utility in order to compare the simulated response of a golden circuit under PLS with actual measurements. This work has been extended to PMOS transistors [START_REF] Llido | Characterization and TCAD Simulation of 90nm Technology PMOS Transistor Under Continuous Photoelectric Laser Stimulation for Failure Analysis Improvement[END_REF][START_REF] Sarafianos | Building the electrical model of the Photoelectric Laser Stimulation of a PMOS transistor in 90nm technology[END_REF]. Therefore, as a perspective, it will permit us to simulate the behavior under PLS of complex logic gates made of PMOS and NMOS transistors.

Figure 1 :

 1 Figure 1: N+ on Psubstrate junction.

Figure 2 :

 2 Figure 2: I(V) characteristics of the PN junction under PLS for different laser power.

Figure 3 :

 3 Figure 3: Structure used for diode TCAD simulation.

Figure 4 :

 4 Figure 4: TCAD I(V) characteristics of the diode under PLS for different laser power .

Figure 5 :

 5 Figure 5: Photocurent generated on a N+/Psubstrate junction extracted from TCAD simulation by comparison with measurements for different laser power.

Figure 6 :

 6 Figure 6: Schematic presentation of the experiment in order to know the spatial effect of the laser on a PN junction.

Figure 7 :

 7 Figure 7: Gaussian effect of laser on silicon according to the I-phemos objective used.

Figure 8 :

 8 Figure 8: Structure for the TCAD simulation of the photocurrent generated on the PN junction under PLS versus the volume of Psubstrate.

Figure 9 :

 9 Figure 9: TCAD simulation of the photocurrent generated on the PN junction versus the silicon volume.

Figure 10 :

 10 Figure 10: ELDO model of the NMOS transistor under PLS.

Figure 11 :

 11 Figure 11: Evolution of the photocurrent for measures in comparison with electrical simulation (ELDO) of an NMOS W=10μm/L=0.1μm transistor polarized OFF versus the laser power.

Figure 12 :

 12 Figure 12: Comparison of the photocurrent evolution according to transistor lentgh between electrical simulations (ELDO) and measurments.

Figure 12

 12 Figure12also shows that the effect of the area under the gate is well modelled by the electrical model.

Figure 13 :

 13 Figure 13: Electrical simulation thanks to ELDO for different localisation of the laser spot.

Figure 14 :

 14 Figure 14: 3D Cartography of the photoelectrical contribution of the source under PLS extracted from electrical simulation (ELDO).

Figure 15 :

 15 Figure 15: 3D Cartography of the photoelectrical contribution of the drain under PLS extracted from ELDO.

Figure 16 :

 16 Figure 16: 3D Cartography of the photoelectrical contribution of the Psubstrate under PLS extracted from ELDO.

Table I .

 I Coefficients of the Gaussian function (2) for the different I-phemos objectives for the Drain/Psubstrate study.

		2.5X	20X	50X
	a	0.4	0.6	0.7
	b	0.6	0.4	0.3
	c 1	2.5	23.8	1000
	c 2	55	654	15000


Amine Abdaoui

Jérôme Azé

Sandra Bringay

Natalia Grabar

Pascal Poncelet

Analysis of Forum Posts Written by Patients and Health Professionals

Context: Online health fora are increasingly visited by both patients and health professionals. For online fora visitors, posts written by health professionals may be more interesting since the professionals are able to well explain the problems, the symptoms, correct false affirmations and give useful advices, etc.

  Analysis of Forum Posts Written by Patients and Health Professionals a LIRMM UM2 CNRS, UMR 5506, 161 Rue Ada, 34095 Montpellier, France b STL UMR 8163 CNRS, Université Lille 3 et Lille 1, France

  Amine Abdaoui a , Jérôme Azé a , Sandra Bringay a , Natalia Grabar b and Pascal Poncelet a

	-Vocabulary
	-Emotions
	-Uncertainty
	-Question marks
	-Misspellings
					7000
					6000
					5000
					4000
					3000			Health experts
					2000			Patients
					1000
					0
					Emotions Uncertainty	Question	Misspellings
						markers	marks
	4000 posts divided equally	Cleaning			Annotation	preprocessing	Classification
	between patients and health professionals	(quotes, short posts, etc.)	questions and (emotions, uncertainty,	replacement, spelling (feature selection, slang	(Weka)
					misspellings)	correction, etc.)
	Results: 10-folds cross validation (f-measures)
	Features U	Number of features SVM SMO Naive Bayes Random Forest 1,120 0.938 0.869 0.901	JRip 0.892	Acronyms:
	U+B EM UM MI QM EM+UM+MI+QM U+EM+UM+MI+QM	2,160 1 1 1 1 4 1,124	0.921 0.565 0.682 0.636 0.560 0.751 0.940	0.865 0.529 0.660 0.601 0.516 0.66 0.872	0.902 0.564 0.657 0.641 0.613 0.725 0.901	0.889 0.609 0.689 0.653 0.653 0.751 0.900	-U: Unigrams -B: Bigrams -EM: Emotion Markers -UM: Uncertainty Markers -MI: Misspellings -QM: Question Marks
	U+B+EM+UM+ MI+QM	2,164	0.927	0.866	0.906	0.897

Objective: To automatically distinguish posts written by health professionals from those written by patients.

Intuition: Use a supervised approach and test the following features with different classification models:

Your doctor, you, cordially, can you, etc.

I am, I have, me, thanks, my, have, etc.

The vocabulary used by each category

Methods:


Amine Abdaoui

Jerome Aze

Sandra Bringay

Natalia Grabar

Pascal Poncelet

Predicting Medical Roles in Online Health Fora

Keywords: Text categorization, text mining, online health fora. 1

Online health fora are increasingly visited by patients to get help and information related to their health. However, these fora are not limited to patients: a significant number of health professionals actively participate in many discussions. As experts their posted information are very important since, they are able to well explain the problems, the symptoms, correct false affirmations and give useful advices, etc. For someone interested in trusty medical information, obtaining only these kinds of posts can be very useful and informative. Unfortunately, extracting such knowledge needs to navigate over the fora in order to evaluate the information. Navigation and selection are time consuming, tedious, difficult and error-prone activities when done manually. It is thus important to propose a new method for automatically categorize information proposed both by non-experts as well as by professionals in online health fora. In this paper, we propose to use a supervised approach to evaluate what are the most representative components of a post considering vocabularies, uncertainty markers, emotions, misspellings and interrogative forms to perform efficiently this categorization. Experiments have been conducted on two real fora and shown that our approach is efficient for extracting posts done by professionals.

Introduction

The Text Mining and Natural Language Processing communities have extensively investigated the huge amount of data on online health fora for different purposes, such as: classifying lay requests to an internal medical expert [START_REF] Himmel | Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums[END_REF], assisting moderators on online health fora [START_REF] Huh | Text classification for assisting moderators in online health communities[END_REF], identifying sentiments and emotions [START_REF] Melzi | Patient's Rationale: Patient Knowledge Retrieval From Health Forums[END_REF], identifying the targets of the emotions [START_REF] Bringay | Identifying the Targets of the Emotions Expressed in Health Forums[END_REF], etc. Indeed, online health fora are increasingly visited by both sick and healthy users to get help and information related to their health [START_REF] Huh | Text classification for assisting moderators in online health communities[END_REF]. However, these fora are not limited to non-health professional users. More and more frequently, significant number of medical experts is involved in online discussions. For example, many websites, also called "Ask the doctor" services, allow non-health expert users to interact with medical experts [START_REF] Himmel | Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums[END_REF].

For users searching medical information in online health fora, it may be interesting to automatically distinguish between posts made by health professionals and those made by lay men. For instance, users may be more interested by posts made by health professionals, who should give more precise and trustier answers. Some medical web-sites hire health experts and indicate explicitly their health role. The main purpose of this study is to use such websites to build classification models that can be used to predict roles of users (medical experts vs patients) on websites while this information is not explicitly indicated. Indeed, according to personal indications we obtained, medical experts confirmed that they usually post messages to help non health professional users online, although their medical expert role may not be indicated.

Health professional and non-health professional posts present some differences that are related, for instance, to the used vocabulary, to the practice of subjectivity markers (emotion and uncertainty) and to the nature and the quality of the produced text (question forms and misspellings). We assume that health professionals may use a different vocabulary by comparison with non-health professionals. Then, lay men may show their emotions more easily than health experts, for example to express their sadness due to their illness: the pain was so bad, etc., while health professionals may use more uncertainty words, for example to make an uncertain diagnosis: you may have an arteritis, etc. Finally, non-health professionals may ask more questions and make more misspellings. In our work, we propose to consider these differences to evaluate what are the most representative components of a forum post to perform efficiently medical role categorization in online health fora.

Several studies have been proposed for user profiling [START_REF] Rangel | Overview of the author profiling task at PAN 2013[END_REF] as well as the studies proposed for the identification of user roles on social media [START_REF] Bouguessa | Identifying Authoritative Actors in Question-answering Forums: The Case of Yahoo! Answers[END_REF], [START_REF] Fisher | You Are Who You Talk To: Detecting Roles in Usenet Newsgroups[END_REF], etc. but less works are concerned with the identification of medical roles. Among the works done to automatically categorize the discourse of doctors and the discourse of patients, Chauveau-Thoumelin and Grabar [START_REF] Thoumelin | La subjectivité dans le discours médical : sur les traces de l' incertitude et des émotions[END_REF] have proposed to use subjectivity markers (emotions and uncertainty markers) in a supervised approach. The discourse of doctors was obtained from scientific papers and clinical reports, while the discourse of patients was obtained from fora posts. The results obtained by the Random Forests algorithm [START_REF] Breiman | Random Forests[END_REF] showed high F-scores (from 0.91 to 0.95 for bi-class classification and from 0.88 to 0.90 for tri-class classification). A medical consultations transcriptions corpus has been used by Tanguy et al. [START_REF] Tanguy | Caractérisation des échanges entre patients et médecins : approche outillée d'un corpus de consultations médicales[END_REF]. Using linguistic and statistical techniques, the authors have highlighted some characteristics (for example the length of the discourse, the used vocabulary, the gender, the proportion of questions, etc.) that can be interesting for improving our categorization task.

The rest of this paper is organized as follows: Section 2 presents two fora that have been used for evaluating the most significant components of a post. Section 3 introduces our categorization method. Section 4 presents the results obtained and a discussion is proposed in Section 5. Finally, Section 6 concludes and proposes future work.

Studied corpora

Two French corpora from two different fora have been collected and cleaned as described below.

2.1

Data collection

Posts from two French websites have been collected.

AlloDocteurs.

AlloDocteur is a French health forum with more than 16,000 posts1 covering a large number of topics related to health issues like potentially dangerous medicines, alcoholism, diseases, pregnancy, and sexuality. The forum contains two categories of users: health professional users and non-health professional users. The health professional category may include professional physicians or medical students.

Even if their number is limited (16 health professional users are indicated to participate in the forum discussions), their participation in the forum exchanges is important. Indeed, they posted more than 3,000 posts among the 16,000 collected.

MaSanteNet.

MaSanteNet is an online 'ask the doctor service' subject to charges, that allows users to ask one or more questions to two doctors. The range of topics covered is also large. Users can ask questions on more than 20 different topics such as nutrition, dermatology, and pregnancy. All the questions published on the website have answers. More than 12,000 posts2 have been collected from this website equitably divided between patient questions and doctor answers.

Data cleaning

Once the two corpora collected, a cleaning step has been applied in order to improve their quality. First, all posts containing quotes have been filtered out. Indeed, some health professionals repeat the questions before answering them, which may introduce patient statements into health professional posts. Furthermore, all pieces of texts such as author signatures and date of the last modification have been deleted. Finally, posts with less than 10 words have been considered as irrelevant and therefore removed.

Data preparation

After this cleaning step, we obtained two datasets with more or less balanced data from health professional posts and non-health professional posts. 1 shows that the first corpus has fewer posts than the second (about 4,400 posts from AlloDocteurs and about 12,000 posts from MaSanteNet) but more words per post (on average 85 words per post of AlloDocteurs and 57 words per post of MaSan-teNet). It also shows that in both datasets non-health professional posts are longer than health professional posts.

Methods

The proposed and implemented method consists in three main steps: annotation, preprocessing and classification.

Annotation

The Ogmios platform [START_REF] Hamon | Le développement d'une plate-forme pour l'annotation spécialisée de documents Web : retour d'expérience[END_REF] was used to perform the following annotations:

Medical concepts. Terms belonging to three semantic types (diseases, treatments and procedures) have been detected as medical concepts using the following medical terminologies and classifications:



The Systematized Nomenclature of Human and Veterinary Medicine3 

The Thériaque database 4



The Unified Medical Language System5 

The list of authorized medication that can be marketed in France. Two lists of all medical terms detected in each corpus have been extracted for a later use.

Emotions.

A French emotion lexicon [START_REF] Augustyn | Lexique des affects : constitution de ressources pédagogiques numériques[END_REF], containing about 1,200 words, was used to annotate adjectives, verbs and nouns conveying emotions (joy, sadness, anger, fear, surprise, etc.). In addition to this lexicon, some non-lexical expressions of emotions, such as repeated letters, repeated punctuation signs, smileys, slang and capital letters, have been detected and annotated with specifically designed regular expressions.

Uncertainty. A set of 101 uncertainty words, built in previous study [START_REF] Fisher | You Are Who You Talk To: Detecting Roles in Usenet Newsgroups[END_REF], has been used to annotate verbs, nouns, adjectives and even adverbs conveying uncertainty meaning in our corpus.

Pre-processing

As observed by Balahur [START_REF] Balahur | Sentiment Analysis in Social Media Texts[END_REF], fora posts have several linguistic peculiarities that may influence the classification performance. For this reason the following pre-processing steps have been applied:

Slang replacement. Some expressions are frequently used in Social Media ("lol").

They have been replaced by the corresponding standard text ("lot of laugh").

Replacement of user tags. All user tags have been identified in our corpora and replaced by the word "Tag" (for example "@Laurie…" becomes "Tag Laurie …").

Hyperlinks and email addresses. All the hypertext links have been replaced by the word "link" and all the email addresses have been replaced by the word "mail".

Health pseudonyms. The health professional pseudonyms, previously extracted from each website, are used to replace these pseudonyms in posts by the word "fdoctor".

Similarly, pseudonyms of non-health professionals have been extracted and used for their replacement by the word "fpatient".

Lowercasing and spelling correction. All words have been lowercased and processed with the spell checker Aspell 6 . The default Aspell French dictionary was expanded with medical words extracted from our corpora during the annotation step. The number of misspellings has been computed for each post and used as attribute for the classification.

Classification

Supervised classifications to categorize health professional and non-health professional posts have been done as follows.

Descriptors used. In order to detect the most discriminative features for our classification task, the number of occurrences of medical concepts, emotions, uncertainty markers, misspellings and question marks have been calculated in both health and non-health professional posts for the two websites processed From Table 2, we can notice that medical words are used massively by both health and non-health professionals and that there is no significant difference between the two categories of users. Nevertheless, the other descriptors indicate that there is difference between these two kinds of users. Non-health professionals express their emotions more frequently than health professionals. Uncertainty markers are slightly more frequent in health professional posts. And as expected, there are also more misspellings and question marks in non-health professional posts.

According to these observations, emotions, uncertainty markers, misspellings and question marks have been chosen as descriptors in our classification task. For each feature, we compute the number of occurrences normalized by the corresponding post length. The length of each post corresponds to the number of words it contains.

In addition to the four features presented before, word ngrams have been considered. The following process has been applied to each corpus: First, all unigrams (words) and bigrams (two words sequences) that appear at least two times are extracted. Then, the number of occurrences of each considered ngram is computed for every post. This number is also normalized by the corresponding post length (number of words) and weighted by its tf-idf score (term frequency * inverse document frequency) [START_REF] Salton | Developments in Automatic Text Retrieval[END_REF]. Finally, ngrams obtained from the first corpus have been also used on the second corpus and those obtained from the second have been used on the first, which allowed us to test models learned on posts provided by one corpus with the posts from the other corpus. All these treatments were performed with the "StringToWordVector" filter from the Weka platform [START_REF] Hall | The WEKA Data Mining Software: An Update[END_REF]. Feature selection. A feature selection step has been applied to select the most discriminant features: those that frequently appear in one category of posts but not in the other one. Therefore, the selected features should characterize one category of users as compared to the other category. Another filter algorithm from the Weka platform, named "InfoGainAttributeEval", has been used to perform the selection. The gain of each attribute on the classification task has been computed and features that have negative gain (i.e. those that don't improve the classification) have been removed. Table 3 indicates most discriminant ngrams (those that had the best gain scores) for each category in the two datasets 7 . For readability reasons, ngrams have been translated from French to English.

Table 3 shows that each group uses a specific vocabulary, which is almost the same in both websites. Non-health professionals use the first person singular pronouns (I, my) while health professionals use the second person pronouns (you, your), which makes sense because the subject of the talk is often the patient and his illness. Besides, non-health professionals show their acknowledgment (thanks) while health professionals prefer using a more formal discourse (cordially).

Evaluation. Four classification algorithms implemented in Weka have been used to test our approach: SVM SMO [START_REF] Platt | Advances in Kernel Methods[END_REF], Naive Bayes [START_REF] John | Estimating Continuous Distributions in Bayesian Classifiers[END_REF], Random Forest [START_REF] Breiman | Random Forests[END_REF], JRip [START_REF] Cohen | Fast Effective Rule Induction[END_REF]. For each algorithm, Weighted F-scores are computed with different combinations of features. F-score measures the accuracy of a class; it combines both precision and recall. Usually, it is computed as the harmonic mean of the precision and the recall of the class. Weighted F-score is the mean of all class F-scores weighted by the proportion of elements in each class.

Results

Four experiments have been tested: (1) 10-fold cross validation [START_REF]Cross-validation and selection of priors[END_REF] on AlloDocteurs, (2) 10-fold cross validation on MaSanteNet, (3) AlloDocteurs as train set and MaSan-teNet as test set and finally (4) MaSanteNet as train set and AlloDocteurs as test set.

10-fold cross validation on AlloDocteurs

Discussion

Globally, the cross validations on both websites processed shows good results. First, the use of ngrams shows high F-scores (between 0.865 and 0.938 obtained on Allo-Docteurs and between 0.935 and 1 obtained on MaSanteNet) comparing to the use of emotions, uncertainty markers, misspellings and question marks which shows low and medium F-scores (between 0.516 and 0.751 obtained on AlloDocteurs and between 0.438 and 0.858 obtained on MaSanteNet). The combination of ngrams with the rest of the features increases slightly the classification performances (between 0.866 and 0.94 obtained on AlloDocteurs and between 0.942 and 1 obtained on MaSanteNet). This increase is so small (between 0.001 and 0.008) that it tends to be statistically insignificant.

The models learned on AlloDocteurs and tested on MaSanteNet shows similar results. Ngrams show high F-scores (between 0.862 and 0.97) while emotions, uncertainty markers, misspellings and question marks show low F-scores (between 0.371 and 0.773). The combination of all features doesn't improve the classification performances or improves them very little (F-scores obtained by considering all the features are between 0.866 and 0.97). These results tend to confirm the hypothesis according to which the models learned on one website can be efficiently used on other websites.

The models learned on MaSanteNet and tested on AlloDocteurs gives the worst results. Ngrams show low and medium F-scores if we do not consider Naive Bayes (between 0.334 and 0.615), but high F-scores using Naïve Bayes (between 0.816 and 0.841). Similarly, the results obtained with emotions, uncertainty markers, misspellings and question marks show low F-scores if we do not consider Naive Bayes (between 0.371 and 0.685), low and medium using it (between 0.438 and 0.821). The combination of all features doesn't improve the classification performances neither: the F-scores obtained by considering all the features are between 0.334 and 0.841.

The difference between the two last experiments can be explained by the fact that the first website is a forum, where 16 health professionals post messages in many threads. This makes the discourse of medical users more extensive and diversified, so that models learned on this website may cover the topics and medical discourse observed on the other website: these models have more chances to identify medical professional posts on other websites. On the other hand, the second website is an "Ask the doctor" service where only two medical experts answers the questions. Moreover, their answers are constrained and normalized, as they always answer in the same way. This makes the discourse of medical experts extremely specific to this website: for this reason it appears to be less adapted to learn language models that can be used on other data.

Conclusion and perspectives

In this paper, we presented a supervised method that allows categorizing posts made by health professionals and those made by non-health professionals. Several features have been tested to perform the categorization: ngrams, emotions, uncertainty markers, misspellings and question marks. The experiments indicate that ngrams are the most efficient. The results indicate that models leaned on appropriate websites may be used efficiently on other websites. Moreover, models learned on more general and varied websites (like fora) where many health professionals are involved provide better data for the learning step.

The results obtained are very encouraging but they can be improved. First, the filter used in the feature selection step computes the gain of each feature independently from the other features and doesn't treat the case of redundancy between the features, which may influence the results of some classification algorithms (such as: Naive Bayes) which assume that the features are independent. Furthermore, we used a small French emotion lexicon (containing about 1,200 words). A more comprehensive emotion lexicon [START_REF]Lexique des sentiments et des émotions français[END_REF] is now under construction; we are translating and expanding to synonyms the English emotion lexicon NRC [START_REF] Mohammad | Emotions Evoked by Common Words and Phrases : Using Mechanical Turk to Create an Emotion Lexicon[END_REF] with the help of a professional translator. Up to now, the new emotion lexicon contains more than 20,000 emotion words and we expect it will become even more extensive. The spell checking can also be improved either by considering grammar rules or by a more stringent human supervision of the correction process which also implies that we may obtain a more correct number of misspellings.

The question of detecting trustier and more precise posts in online health fora may be addressed with different methods. Indeed, trust models tested on other social media may be applied either by looking at the structure of the threads (computing scores based on the number of quotes, the number of likes, the number of posts between each post and its replies, etc.) [START_REF] Skopik | Trust and Reputation Mining in Professional Virtual Communities[END_REF], [START_REF] Wanas | Automatic Scoring of Online Discussion Posts[END_REF] or by inferring these information from the text [START_REF] Feng | Learning to Detect Conversation Focus of Threaded Discussions[END_REF]. In addition to these models, we plan to include the emotional reaction of users to a specific post while computing the trust scores (for example posts arousing the anger of the users).

Finally, we are interested in other applications of Natural Language Processing and Text Mining on online health fora. Currently, we are working on a recommendation system that suggests appropriate topics where the user should post his message. We exploit the content of the posts (title and body), the gender and the age of users, etc. An additional descriptor may be related to the topics where the user has already posted the messages: we assume it may improve the automatic system because the previous preferences of the users may be indicative of his current interests.

Table 1 .

 1 The number of words and the number of posts in the two datasets

		AlloDocteurs	MaSanteNet
		Health pro-	Non health	Health pro-	Non health
		fessionals	professionals	fessionals	professionals
	Number of words	147,419	222,463	233,565	452,453
	Number of posts	2,193	2,179	5,876	6,136
	Mean words/posts	67	102	40	74

Table 2 .

 2 The number of occurrences of each feature group in both health and non-health professional posts for the two websites

		AlloDocteurs	MaSanteNet
		Health	Non-health	Health	Non-health
		professionals	professionals	professionals	professionals
	Medical concepts	8,924	8,888	21,690	22,921
	Emotions (EM)	554	2,137	865	2,962
	Uncertainty markers	5,561	3,871	8,449	7,356
	(UM)
	Misspellings (MI)	3,828	12,921	11,529	22,137
	Question marks	560	2,594	509	16,991
	(QM)

Table 3 .

 3 Most discriminant unigrams and bigrams for the two categories of users in AlloDocteurs and in MaSanteNet

		AlloDocteurs	MaSanteNet
		Non-health	Health	Non-health	Health
		professionals	professionals	professionals	professionals
	Unigrams (U)	I, am, thanks, me, have, my	Cordially, hello, you, can	Am, I, thanks, hello, my	Fpatient, must, good, cordially, have
	Bigrams (B)	I am, I have, thanks, my, that I	Your doctor, cordially, fdoctor, can you	I am, I have, thanks, that I, is it	Cordially, you must, you have, fpatient you
	7

Table 4 .

 4 Weighted F-scores obtained with 10-fold cross validation on Allo-Docteurs.

	Feature group	Number of	SVM	Naive	Random	JRip
		features	SMO	Bayes	Forest
	U	1,120	0.938	0.869	0.901	0.892
	U+B	2,160	0.921	0.865	0.902	0.889
	EM	1	0.565	0.529	0.564	0.609
	UM	1	0.682	0.660	0.657	0.689
	MI	1	0.636	0.601	0.641	0.653
	QM	1	0.560	0.516	0.613	0.653
	EM+UM+MI+QM	4	0.751	0.66	0.725	0.751
	U+EM+UM+MI+	1,124	0.940	0.872	0.901	0.900
	QM
	U+B+EM+UM+	2,164	0.927	0.866	0.906	0.897
	MI+QM

Table 5 .

 5 Weighted F-scores obtained with 10-fold cross validation on MaSan-teNet.

	Feature group	Number of	SVM	Naive	Random	JRip
		features	SMO	Bayes	Forest
	U	3,096	1.000	0.935	0.999	1.000
	U+B	4,567	1.000	0.949	1.000	1.000
	EM	1	0.503	0.495	0.542	0.558
	UM	1	0.678	0.653	0.690	0.680
	MI	1	0.438	0.648	0.739	0.686
	QM	1	0.748	0.715	0.773	0.773
	EM+UM+MI+QM	4	0.761	0.741	0.858	0.851
	U+EM+UM+MI+	3,100	1.000	0.942	0.999	1.000
	QM
	U+B+EM+UM+	4,571	1.000	0.953	1.000	0.999
	MI+QM
	4.3

AlloDocteurs as train set and MaSanteNet as test setTable 6 .

 6 Weighted F-scores obtained by considering AlloDocteurs as train set and MaSanteNet as test set

	4.4
	Feature group	Number of	SVM	Naive	Random	JRip
		features	SMO	Bayes	Forest
	U	1,120	0.948	0.862	0.938	0.960
	U+B	2,160	0.940	0.914	0.938	0.970
	EM	1	0.558	0.460	0.504	0.558
	UM	1	0.679	0.665	0.654	0.681
	MI	1	0.436	0.453	0.44	0.371
	QM	1	0.773	0.608	0.720	0.773
	EM+UM+MI+QM	4	0.677	0.605	0.679	0.705
	U+EM+UM+MI+	1,124	0.930	0.866	0.946	0.975
	QM
	U+B+EM+UM+	2,164	0.954	0.915	0.970	0.961
	MI+QM

MaSanteNet as train set and AlloDocteurs as test setTable 7 .

 7 Weighted F-scores obtained by considering MaSanteNet as train set and AlloDocteurs as test set

	Feature group	Number of	SVM	Naive	Random	JRip
		features	SMO	Bayes	Forest
	U	3,096	0.559	0.816	0.615	0.334
	U+B	4,567	0.421	0.841	0.599	0.335
	EM	1	0.610	0.555	0.579	0.610
	UM	1	0.681	0.656	0.669	0.681
	MI	1	0.313	0.438	0.490	0.440
	QM	1	0.653	0.584	0.650	0.653
	EM+UM+MI+QM	4	0.685	0.645	0.641	0.595
	U+EM+UM+MI+	3,100	0.582	0.821	0.555	0.334
	QM
	U+B+EM+UM+	4,571	0.434	0.841	0.560	0.335
	MI+QM

www.allodocteurs.fr/forum-rubrique.asp [collected on: 19-11-2013]

www.masantenet.com/questions.php [collected on: 18-02-2014]

www.ihtsdo.org/snomed-ct [last access: 06-05-2014]

www.theriaque.org [last access: 06-05-2014]

www.nlm.nih.gov/research/umls [last access: 06-05-2014]

www.aspell.net [last access: 06-05-2014]

Acknowledgement

This paper is based on studies supported by the "Maison des Sciences de l'Homme de Montpellier" (MSH-M) within the framework of the French project "Patient's mind" 8 .