Thomas Rousseau
email: thomas.rousseau@ls2n.fr

Nicolò Pedemonte
email: nicolo.pedemonte@irt-jules-verne.fr

Stéphane Caro
email: stephane.caro@ls2n.fr

Constant Distance and Orientation Following of an Unknown Surface with a Cable-Driven Parallel Robot

are welladapted to large workspaces since they replace rigid links by cables. However, they lack in positioning accuracy and new control methods are necessary to achieve profile-following tasks. This paper presents a control scheme designed for these tasks, relying on a combination of accurate boarded distance sensors and of a less accurate remote camera. The profile-following task is divided into two subtasks that are partially conflicting: maintaining a parallel orientation and a constant distance with the surface to follow, and following a trajectory between two points on the surface. The data fusion to solve the redundancy is based on the Gradient Projection Method. This control scheme is validated experimentally on a CDPR prototype and shown to provide the expected behaviour.

I. INTRODUCTION

Cable-Driven Parallel Robots (CDPRs) are robots able to carry loads in large workspaces, and thus hold a potential for industrial applications in large assembly operations, for instance in the aeronautics or naval sectors. However, these robots are still lacking in precision, including in tasks requiring an accurate positioning with regards to a given object. This adds to the complexity of profile-following tasks. Most of the research works focus on improving the CDPR model, especially regarding cable elasticity and sagging [START_REF] Nguyen | On the simplifications of cable model in static analysis of large-dimension cable-driven parallel robots[END_REF], [START_REF] Merlet | The kinematics of cable-driven parallel robots with sagging cables: preliminary results[END_REF], [START_REF] Fortin-Côté | Improving cable driven parallel robot accuracy through angular position sensors[END_REF], [START_REF] Xie | High-precision trajectory tracking control of cable-driven parallel robots using robust synchronization[END_REF], [START_REF] Merlet | A panorama of methods for dealing with sagging cables in cable-driven parallel robots[END_REF], [START_REF] Mishra | Forward Kinematics for Suspended Under-Actuated Cable-Driven Parallel Robots With Elastic Cables: A Neural Network Approach[END_REF], [START_REF] Métillon | Geometrical modelling of a cable-driven parallel robot winch[END_REF] but other approaches such as vision-based control have also been proposed to improve the precision of CDPRs [START_REF] Dallej | Modeling and vision-based control of large-dimension cable-driven parallel robots using a multiple-camera setup[END_REF], [START_REF] Zavatta | A vision-based referencing procedure for cable-driven parallel manipulators[END_REF]. The robustness of trajectory planning and tracking for CDPRs was evaluated in [START_REF] Zake | Robust 2 1/2D Visual Servoing of a Cable-Driven Parallel Robot Thanks to Trajectory Tracking[END_REF].

As the accuracy of vision-based pose estimation decreases with the distance to the cameras, external vision-based systems have limited performance in large workspaces. For large-workspace applications, this results in often needing multiple and high-resolution cameras, costly systems that require a high computing power to operate in real-time. In order to achieve precise relative positioning of the robot with regard to a given object, both camera/robot and camera/object poses need to be measured precisely. Sensors boarded on the end-effector (EE) allow a more accurate relative positioning with respect to such an object. For France Francois.Chaumette@inria.fr instance, visual servoing was used in [START_REF] Gangloff | Visual servoing of a 6dof manipulator for unknown 3-d profile following[END_REF] to perform an unknown profile following task with a camera mounted on the EE of a robot arm. This method however requires that three parallel curves to follow are drawn on the surface. Untextured surfaces are common in industrial applications, and other solutions such as using structured light [START_REF] Pages | Optimizing plane-to-plane positioning tasks by image-based visual servoing and structured light[END_REF] or distance sensors [START_REF] Thomas | Plane-to-plane positioning by proximity-based control[END_REF] can prove effective in this case. These boarded sensors cannot however position the movingplaform (MP) of a CDPR in a large workspace alone, because of their restrained range and field of view. Conversely, one or multiple camera(s) fixed in the base reference frame can observe a large workspace and estimate the MP pose, with a lesser accuracy. Combining boarded sensors and external cameras allows to benefit from the advantages of both local and precise measurements around the end-effector and of less precise measurements but a global view of the workspace. This combination enables profile following tasks. However, the two information sources are redundant and may conflict with each other. A solution to correctly combine their measurements is thus necessary. Multiple sensor fusion approaches to solve the redundancy exist [START_REF] Siciliano | Kinematic control of redundant robot manipulators: A tutorial[END_REF], notably the welldocumented Gradient Projection Method (GPM) designed for prioritizing tasks [START_REF] Liegeois | Automatic supervisory control of the configuration and behavior of multibody mechanisms[END_REF], [START_REF] Nakamura | Task-priority based redundancy control of robot manipulators[END_REF], [START_REF] Samson | Robot control: the task function approach[END_REF]. In particular, this method is used in [START_REF] Flandin | Eye-in-hand/eye-to-hand cooperation for visual servoing[END_REF] to perform a cooperation between eye-to-hand and eye-in-hand cameras to control the pose of a robot EE.

The purpose of this work is to design a control architecture able to closely follow at a constant distance and relative orientation the profile of a curved surface without any prior information on its shape. The available information when launching the robot is limited to the current location of the MP, a starting point and a target point, given by a remote vision system observing hand-placed AprilTags marking these points in the robot base reference frame. The trajectory following has to be played between these starting and target points. During the profile following, three boarded distance sensors and the remote camera provide the system with three distances to the surface and the MP pose in the robot base frame. In order to perform the task, an appropriate data fusion method based on the GPM is implemented to combine the measurements from both sensors. While this work was designed for and experimented with a CDPR, the results are applicable to any kind of robots.

The robot control strategy is presented in Section II: two subtasks are first presented and the fusion using GPM is then detailed. In Section III, an experiment designed to assess the performance of this control scheme is presented. The results are analyzed and discussed, and conclusions are drawn in Section IV.

II. ROBOT CONTROL

The profile following task can be divided into two subtasks: maintaining the MP parallel to the followed surface at a constant distance and moving from the starting pose to the target pose. The control of the robot stems from sensor-based control, especially visual servoing, and combines measurements provided by two types of sensors: an array of three distance sensors boarded on the MP, and an external camera placed in front of the robot. These measurements allow to complete the two subtasks that need to be conciliated.

The MP to control and the reference frames used in this work are shown in Fig. 1. The three distance sensors S 1 , S 2 , S 3 and the external camera are also represented. Several AprilTags are placed in the workspace. The pose of the reference tag in F c is measured precisely offline in order to compute the transformation matrix from F c to F w . The AprilTag placed on the MP allows its pose estimation in F c and then in F w . The trajectory start and trajectory target tags mark the points between which the profile following is played. The trajectory planner computes a straight line trajectory between the starting pose of the platform, above the start tag, towards the target tag. The robot then has to follow this trajectory while also respecting the distance and orientation constraints above the ground then above the curved surface to follow, as shown in Fig. 1. A. Sensors 1) Distance sensors: Three SICK UM18 ultrasonic distance sensors are placed below the MP, facing downwards along the z-axis of F p . These sensors have a resolution of 0.2 mm and an accuracy at 200 mm (working distance) of 0.3 mm. They provide the system with three distance measurements each 80 ms. Their current output is processed by an analogic-digital converter (ADC) connected to the robot control. The ADC introduces a quantum error corresponding to σ s,q = 0.04 mm. Their calibration also introduces an error, estimated to 2 mm. The combined distance error for a distance sensor is then estimated to σ d = 2.01 mm.

External camera

The relative distance between the three sensors was chosen to minimize the perturbations from reflections. Since all sensor directions are parallel, for the MP to be locally parallel to the surface, the three sensors must return the same distance (see Fig. 2).

In the eye-in-hand configuration used for the boarded sensors, the time variation of the measured distance is given by the proximity sensor model presented in [START_REF] Samson | Robot control: the task function approach[END_REF]:

ṡi = - 1 n Ti n Si (n Ti ν S -([s i n Si ] × n Ti ) ω S ) (1)
with n Ti the unit vector normal to the surface at the target feature point and n Si the unit vector defining the axis of the sensor (see Fig. 2). ν s is the linear velocity of the sensor and ω s is its angular velocity, both expressed in the sensor frame.

Note that due to the chosen configuration of the sensors, n Si = (0, 0, 1) in F p , i = 1, . . . , 3. Knowing the position O p S i = (x i , y i , 0) of the sensors in F p , it is possible to express their time variation in function of the instantaneous velocity v p of the MP, expressed in F p from Eq. ( 1):

ṡi = - 1 n Ti n S (n Ti ν p -([s i n S + O p S i ] × n Ti ) ω P ) (2)
for i = 1, . . . , 3. Doing so, the feature variation, noted ṡ where s = (s 1 , s 2 , s 3 ) is related to the instantaneous velocity v p = (ν p , ω p ) of the MP expressed in F p through:

ṡ = p L 1 v p (3)
p L 1 is the interaction matrix of the distance sensors [START_REF] Samson | Robot control: the task function approach[END_REF], [START_REF] Chaumette | Visual servoing[END_REF]. Eq. ( 2) can be reshaped into the form of (3) and evaluated for the particular case where s = s * , for which n Ti = (0, 0, 1) in F p for i = 1, . . . , 3:

p L 1|s=s * =   0 0 -1 -y 1 x 1 0 0 0 -1 -y 2 x 2 0 0 0 -1 -y 3 x 3 0   (4)
Using a constant value of the interaction matrix corresponding to the desired configuration is indeed a usual choice in sensor-based control [START_REF] Chaumette | Visual servoing[END_REF].

2) External camera: The external camera is an IDS uEye 3240CP with a resolution of 640 × 480 px at 30 images per second. The images acquired are processed by an algorithm recognising the AprilTags and estimating their relative pose with regard to the camera frame F c . April Tags were chosen because they offer a simple solution for pinpointing locations on an unknwown and untextured surface. Then, this pose is expressed in F w , and, as for Pose-Based Visual Servoing (PBVS), the visual features are selected as:

s v = w t p θu (5)
where w t p is the position of the platform in the world reference frame and θu is the axis-angle representation of the rotation matrix p * R p . The target pose is defined by:

s * v = w t * p 0 3 (6)
where w t * p is the position of the target AprilTag in F w . The interaction matrix of these visual features is well known and given by [START_REF] Chaumette | Visual servoing[END_REF]:

w L 2 = I 3 -[ w t p ] × 0 3 L ω (7)
with [ w t p ] × the skew-symmetric matrix of w t p , I 3 the threedimensional identity, and L ω can be approximated by I 3 . This interaction matrix is then expressed in F p with:

p L 2 = w L 2 c V p (8)
such that:

w V p = w R p [ w t p ] × w R p 0 3 w R p (9)
The accuracy of the pose estimation by the vision system depends on several factors: the resolution of the camera, but also the size and the pose of the observed object. Here, all AprilTags have a side of 80 mm, except for the one attached to the MP, which is 64 mm, while the observed objects are placed within a range of 1-1.5 m away from the camera. Angles between the image plane and the AprilTags are comprised between -90 • and +10 • . The accuracy along the optical axis of the camera is worse because the depth estimation is based on the perceived size of the tracked object, accumulating the errors of several measures. The depth error increases with the distance. In the working area, the accuracy of the position estimation (neglecting the rotation errors) was measured using a Creaform C-Track (see Section III-A) to σ v = 70 mm.

B. Choice of a fusion method

On one hand, performing the first task with the three distance sensors locks three degrees of freedom (DoF) : two rotations and one translation. On the other hand, controlling the MP pose with the remote camera, required to complete the trajectory tracking involves six DoF. Since the MP has only n = 6 DoF, the degree of measurement redundancy r is equal to three considering all available measurements. Various sensor fusion techniques allow to deal with this redundancy, and two main approaches are available: featurelevel fusion (fusing errors) and control-level fusion (fusing velocities).

The respective precision of the two sensor sets needs to be taken into account by the fusion algorithm. For instance, inverse variance weighting gives for the z p coordinate of the platform:

z p = (σ -2 d z p,d + σ -2 v z p,v )/σ 2 (10)
with

σ 2 = (σ -2 d + σ -2 v )
. The variances can be calculated from the precision of the distance sensors and the visionbased pose estimation. σ is the standard deviation of the combined measurements:

σ d = 2.0 mm; σ v = 70 mm; σ = 4.0 mm ( 11
)
While values of z p,d and z p,v have the same order of magnitude, it follows that σ -2 d z p,d >> σ -2 v z p,v and the resulting standard deviation σ is twice superior to the theoretical precision σ d when taking into account only the distance sensors. Combining information with such a technique is thus detrimental to the precision of the system and a strict decoupling of the two sensor sets is more adapted to control this DoF with the best precision possible. In other words, the vision input can be neglected for this component z p , which is equivalent to using binary weights (1 for z p,d and 0 for z p,v ). This is the same for the other two rotational DoFs that can be controlled by the three distance sensors. The same reasoning is valid for a fusion at the control level. The best is thus to use the well-known Gradient Projection Method that is perfectly suited in this case. Indeed, it consists in projecting a secondary task on the kernel of a main task, which ensures that the main task will not suffer from any perturbation induced by the secondary task.

C. Fusion using the Gradient Projection Method

With this method, the main task T 1 is considered as a priority, while only the part of the secondary task T 2 compatible with the realization of the main one is retained.

We naturally select for the main task the measurements e 1 provided by the distance sensors, that is:

e 1 = (s 1 -s * , s 2 -s * , s 3 -s * ) T (12)
where s i is the distance measured by the sensor S i and s * is the common reference distance so that the MP is locally parallel to the followed surface (in practice, we select s * = 0.2 m). The next step is to determine the Jacobian of the main task. As already seen in Section II-A.1, the time variation of the main task is related to the MP twist v p through (3). As the inputs of the low-level controller of the CDPR are the velocities ˙l of the m cable lengths, we use:

˙l = A v p ( 13
)
where the forward Jacobian matrix A of the CDPR is given by [START_REF] Zake | Vision-based control and stability analysis of a cable-driven parallel robot[END_REF]:

A = u 1 . . . u i . . . u m b 1 × u 1 . . . b i × u i . . . b m × u m T ( 14
)
where u i are the cable direction unit vectors, pointing from the exit points (pulleys) of the CDPR to the anchor points placed on the MP, and where b i are the position vectors of the anchors points, known from the platform design. All these vectors are expressed in F p . u i vectors are calculated geometrically from the MP pose expressed in F w using the straight and inelastic cable model [START_REF] Zake | Vision-based control and stability analysis of a cable-driven parallel robot[END_REF]. We recall that the MP pose in F w is estimated from its pose in F c and the known transform w T b between the reference AprilTag (whose pose is also estimated in F c ) and F w .

Finally, from ( 3) and ( 13), we obtain:

ė1 = J 1 ˙l (15)
where the Jacobian J 1 of the main task is given by

J 1 = p L 1 A + (16)
A + being the pseudo-inverse of A.

Regarding the secondary task T 2 , it is given by:

e 2 = s v -s * v ( 17
)
where s v and s * v are given by ( 5) and ( 6). From ( 8) and ( 13), we have:

ė2 = J 2 ˙l ( 18
)
where the Jacobian J 2 of T 2 is given by:

J 2 = p L 2 A + (19)
In practice, the exact value of J 1 and J 2 is not perfectly known, that is why approximations J 1 and J 2 have to be used in the control scheme. Doing so and applying the GPM, the cable velocity vector ˙l to be sent to the low-level robot controller has the following form:

˙l = J + 1 ė1 + P 1 z ( 20
)
where P 1 = (I m -J + 1 J 1 ) is a projection operator on the kernel of J 1 , and P 1 z is the part of the secondary task that can be realized without disturbing the main one. For considering the potential part of T 2 achieved by T 1 , the term z can be chosen as [START_REF] Mansard | Task sequencing for high-level sensorbased control[END_REF]:

z = ( J 2 P 1 ) + ( ė2 -J 2 J + 1 ė1 ) (21)
Since P 1 is Hermitian and idempotent, we have P 1 ( J 2 P 1 )

+ = ( J 2 P 1 ) + .
Eq. ( 20) can thus be rewritten as:

˙l = J + 1 ė1 + ( J 2 P 1 ) + ( ė2 -J 2 J + 1 ė1 ) (22)
To end with the design of the control law, an exponential decoupled decrease of each error e i is specified, that is, ėi = -λ i e i , λ i > 0, i = {1,2}. The control law can then be expressed in terms of e 1 and e 2 :

˙l = -λ 1 J + 1 e 1 -λ 1 (J 2 P 1 ) + (r λ e 2 -J 2 J + 1 e 1 ) (23)
where r λ = λ 2 /λ 1 is the gain ratio. To simplify the notations, the term ẽ2 = r λ e 2 -J 2 J + 1 e 1 and matrix J2 = J 2 P 1 are defined such that the control law has finally the simple form (see Fig. 3):

˙l = -λ 1 ( J + 1 e 1 + J+ 2 ẽ2 ) (24)
Fig. 3: Control scheme of the robot with the GPM.

Note that, in practice, reaching the desired pose w t * p is done by subdividing the trajectory from the starting pose to w t * p by a series of intermediate poses.

III. EXPERIMENTS A. Experimental setup

The robot used in the experimental platform is a six degrees of freedom Cable-Driven Parallel Robot named ACROBOT, located at IRT Jules Verne, Nantes, France. It consists of a cubic cell within a frame measuring 1.2 m × 1.2 m × 1.2 m in which a platform is suspended to 8 actuated cables. The moving-platform of the robot is 0.18 m long, 0.18 m wide and 0.150 m high. It boards 3 SICK UM18 ultrasonic distance sensors, placed on the bottom and facing downwards, measuring the distance between the MP and the surface below. The external camera is placed in front of the cell so as to observe the MP inside the robot workspace and estimate its pose using a set of AprilTags. The setup is shown Fig. 4. The robot is controlled by a B&R automation interface connected to a PC where a ROS system computes and transmits the input velocities to the robot through an Ethernet link. Trajectories are generated with a trapezoidal speed profile and, as already said, are composed of intermediary waypoints placed at each timestep (0.1 s) so that the desired path is a straight line. The starting point of the trajectory is located above an AprilTag placed on the bottom plane of the CDPR workspace and the target AprilTag is placed on an unknown piece of fuselage with a small curvature. The trajectory between these points allows testing the surface following and the steep change of curvature between two surfaces. A view of the MP above the curved surface at the end of the trajectory is shown All the data exchanged between the various ROS control modules is recorded for the analysis, and the trajectories are also filmed using an additional camera. To assess the MP positioning accuracy obtained with the vision system, a Creaform C-Track device observes the scene and tracks the MP pose in the target frame F t (see Fig. 1). The measurement accuracy and repeatability of this optical metrology device are equal to 0.01 mm and 0.02 mm, respectively.

B. Experimental results

The behavior of the robot with regard to the main task (profile following) can be assessed by observing the curves of the distance sensors output along the trajectory shown in Fig. 6.

From Fig. 6, it should be noted that s 1 value decreases below the target when the MP starts crossing the threshold of the curved surface. The distances s 2 and s 3 briefly increase (peaks at t = 13 s) while the MP is pivoting around its xaxis until all the sensors correctly face the surface to follow for the remainder of the trajectory (t ≥ 15s). The settling time on the plane surface, measured between t = 2 s and t = 8 s, is τ r = 3.4 s. Figure 6 shows that the control method is able to correctly solve the task conflict and to satisfy both the orientation and the distance constraints since all sensors measurements correctly converge to zero, namely, ss * |t≥17 s = 0. Results regarding the secondary task can be analyzed from Fig. 7. The Cartesian coordinates of the MP position measured by the external camera are expressed in the target frame F t . Figure 7a shows that t x p and t y p coordinates correctly converge towards zero (±10 mm) and the shape of the t y p coordinate corresponds to the variation in position resulting of a trapezoidal velocity profile. The t z p coordinate value reaches 238 mm, higher than the reference target distance of 200 mm but within the margin of error for the vision-based estimation. This demonstrates the inacuracy of the vision sensor to estimate this component that is hopefully not used in the control scheme thanks to the GPM that prioritizes the distance sensors. Figure 7b shows that the rotation of the platform along the y and z axes remains close to zero during the whole trajectory, and the only significant rotation is about x t . Finally, the rotation components correctly converge towards zero, satisfying the orientation constraint given by the main task.

C. Accuracy

The ground truth measurements obtained with the C-track are shown Fig. 8. They show that all the coordinates from the vision-based position estimation share the same evolution with their ground truth counterpart, with an offset. The offset is approximately 35 mm on the x axis, 50 mm for the y axis and 36 mm on the z axis. The vision-based position estimation error at time t is given by: e

p (t) = ∥ t x p,v (t) -t x p,c (t)∥ 2 (25)
where t x p,v is the position of the platform estimated by the system and t p,c is the position obtained from the C-track. The final vision-based position estimation error of the MP is e p = 50 mm. The distance error when the platform is parallel to the surface is given by:

e dist = s i -z p,c (26)
The final distance error is e dist = 3 mm. This error could be reduced by a better calibration of the distance sensors. While the error due to the inaccuracy of the vision system is significant, the proposed control scheme manages to keep an error on the main task more than 15 times lower than the position estimation error.

IV. CONCLUSIONS This paper proposed a control scheme to perform a profile following with a CDPR using a combination of boarded sensors and an external camera while implementing the Gradient Projection Method to deal with the measurement redundancy. The validity and the performance of this control scheme were assessed experimentally.

In spite of the poor precision of the external camera and thanks to the distance sensors, the proposed method is able to follow the surface with a positioning accuracy 15 times better than the precision provided by the external camera. The robot is thus able to follow a trajectory on an unknown profile with the vision system, while a purely distance-based approach would not be possible, but with an increase in precision compared to a pure visual control performed with the same camera. This validates the proposed approach, opening the possibility to implement this control strategy on a larger CDPR to really benefit from the advantage of the sensor cooperation.

However, several limitations exist and could be worth exploring for improvements. An assessment of the robustness of the system, as well as the maximum admissible curvature of the followed surface are still required. This curvature is limited by the size of the CDPR moving-platform and by potential interference between the ultrasound sensors. A possible solution to handle this problem would be to replace the latter by more costly, but more precise and more directive LASER rangers to reduce interference. These sensors could then be placed closer to each other and thus follow higher and/or more localized variations in curvature. The precision of the cameras does not hamper the surface following on simple profiles, but a better resolution is likely needed for any potential industrial applications, especially in a larger workspace. Another possibility would be to use a pan-tiltzoom camera to follow the MP in the workspace.

Fig. 1 :

 1 Fig. 1: Parametrization and main components of the robotic platform. The AprilTags are represented by black squares.

Fig. 2 :

 2 Fig. 2: Sensor model: only the bottom plane of the MP is shown.

Fig. 4 :

 4 Fig. 4: The ACROBOT prototype, an eight-cable suspended CDPR located in IRT Jules Verne, Nantes, France.

  Fig 5.

Fig. 5 :

 5 Fig. 5: View of the moving-platform reaching the targeted pose, marked with the AprilTag placed on the curved surface.

Fig. 6 :

 6 Fig. 6: Distance error e 1 measured during a trajectory.

Fig. 7 :

 7 Fig. 7: Translational and rotational displacements of the MP along a trajectory.

Fig. 8 :

 8 Fig. 8: Position of the MP in F t , ground truth obtained with the C-Track measurement system.

*This work is supported by IRT Jules Verne in the framework of the PERFORM project.


A Tchoualack

L Ottaviani

W Rahajandraibe

W Vervisch

V Vervisch

J P Walder

Front End Electronic for Radiation Detectors Based on SiC: Application to High Dose per Pulse Charged Particle Beam Current Measurement

Keywords: Charged particle, Dose per Pulse, SiC, TIA, CSA, Stability, Linac

In high dose per pulse charged particle beams, all online detectors saturate due to ion recombination. It is therefore impossible to count detector pulses separately. Silicon carbide due to its high band gap, high thermal conductivity and high displacement energy, is seen as an alternative. Analyzing waveforms in real time is challenging in terms of bandwidth, measurable energy range, sensor size, data rate. In this contexte, an Analog Front-End (AFE) was designed for radiation signal processing. It is based on a TransImpedance Amplifier (TIA) and Charge Sentitive Amplifier (CSA) to analyze the shape of generated signal. The methodology used to characterize AFE for high detector capacitance is described. The results extracted from simulations, experimental and measurements in radiative environment are also presented.

I. INTRODUCTION

N Owadays, SIlicon Carbide (SiC) is among the semi- conductors used as sensors in radiation detection [START_REF] Lebedev | Radiation Hardness of Silicon Carbide[END_REF], [START_REF] Hrubčín | A Study of the Radiation Hardness of Si and SiC Detectors Using a Xe Ion Beam[END_REF]. The various researches on the silicon carbide pixilation thanks to the reduction and the integration capacity of the reading electronics has allowed to improve its competitive position compared to photomultipliers, ionization chambers and film dosimetry. On the other hand, its carbonaceous nature (Z = 6) makes it a good "tissue equivalent" and it has a very short response time thank to its high carrier saturation velocity [START_REF] Bertuccio | Silicon Carbide Detectors for in vivo Dosimetry[END_REF]. Today, in harsh environment applications, the sensors suffer from fragility at high dose of irradiation owing to their low thermal conductivity, breakdown electrical fields and threshold displacement energy [START_REF] Gregory | Radiation effects on semiconductor devices[END_REF], [START_REF] Iniewski | Radiation Effects in Semiconductors[END_REF]. In semiconductor, charged particle lose its energy by creating a pairs of electrons holes [START_REF] Spieler | Semiconductor Detector Systems[END_REF], [START_REF] Kuckuck | semiconductor detector for use in the current mode[END_REF], [START_REF] Chantepie | Étude et réalisation d'une électronique rapide à bas bruit pour un détecteur de rayons X à pixels hybrides destiné à l'imagerie du petit animal[END_REF]. After being generated by the sensor, the signal must be conditioned. Most of the existing conditioning [START_REF] Chevallier | Detection et Mesure des Rayonnements Nucleaires[END_REF] chains are based on CSA (charge sensitive amplifier) or PAI (current preamplifier) in order to detect current created by the crossing of an ionizing particle through the sensor (Fig. 1). In this context current preamplifier and charge sensitive amplifier have been studied [START_REF]Characteristics and use of charge amplifier[END_REF], [START_REF] Muller | Cmos Multichannel Single-Chp Receives for MultiGigabit Optical Data Communication[END_REF]. First of all when the particle flow becomes sufficiently high in terms of flux and energy (up to 10 7 n.cm -2 .s -1 ), the current generated by the sensor is composed in two parts: one continuous part and another part with variations around this continuous value. With such Fig. 1. Schematic diagram showing a particle extracted from an charged particle source then accelerated through a Linac (Linear Accelerator) and which subsequently interacts with a Silicon Carbide Detector. The generated signal is then conditioned using a preamplifier (PAI/CSA).

pulse, a reading amplifier with a fast response and fast fall time (tr<150ns and tf<150ns) time is required. Then PAI should be used. On the other hand, when the particle flow is not high in terms of flux and energy (φ < 10 6 n.cm -2 .s -1 ), the current generated by the sensor has low value in terms of amplitude and time (≈ nA and ≈ ns respectively) [START_REF] Nakhostin | Signal Processing for Radiation Detectors[END_REF]. Then CSA should be used.

The aim of this work is to study SiC based radiation detector response thanks to AFE. For that, the paper is organized as follows. Section II presents the used methodology, and section III, the system study. In section IV we will present material, test method and measurement results. Section V is devoted to discussion and conclusion.

II. METHODOLOGY

The readout electronic diagram used is presented in Fig. 2. In this work we focused on SiC sensor and analog Amplifier. This bloc consist on two operating modes. In TIA mode, the output presents derivatives of the output signal due to the presence of the detector capacitance [START_REF] Säckinger | The Transimpedance Limit[END_REF]. Its derivatives can be attenuated by feedback capacitance thus allowing to move the poles of the transfer function. Then process of recovering the waveform of the signal coming from the detector will be in a frequency range (zero phase) for input impedance. In the case of CSA (Charge Sensitive Amplifier) structure, the charge collection process can be incomplete due to the reduced value of OP-Amp DC gain and also the bandwidth of this one. In other word, these limitations will have the effect of attenuating the electronic output signal. The PAI/CSA structures are built around a low noise amplifier, accepting the input signal in form of a current/charge from a high impedance signal source, as a photodiode or a semiconductor based detector for radiation particles [START_REF] Silva | Regulated Common-Gate Transimpedance Amplifier Designed to Operate With a Silicon Photo-Multiplier at the Input[END_REF]. This amplifier converts signal into an output voltage. Generally, this structure is not advisable if the sensor device has a large capacitance (>200pF), and if fast rise time is required [START_REF]Low Noise Amplifiers for Small and Large Area Photodiodes[END_REF].

III. SYSTEM STUDY A. Silicon Carbide Material (SiC)

Harsh environment applications require thermal and physical stability of sensor parameters. Thus, both intrinsic properties of the semiconductor and the device architecture play a critical role for stability. In semiconductor materials, harsh environment leads to a decrease in the energy band gap and an increase in carrier concentration, which in turn adversely affects the sensor performance [START_REF] Nava1 | Silicon carbide and its use as a radiation detector material[END_REF]. Materials with high bandgap and low intrinsic carrier concentration are needed for harsh environment applications see Tab.I. SiC possesses a higher energy bandgap compared to the more widely used Si and GaAs materials.

The comparative evolution of intrinsic carrier concentration of Si and SiC with temperature is presented in Fig. 3. For operation in semiconductor device, the intrinsic carrier density should be well below the intentional dopant induced carrier density [START_REF] Syed Naeem | Physics and Engineering of Radiation Detection[END_REF]. For instance, the intrinsic carrier density of Si is 10 10 cm -3 and well below the typical dopant induced carrier densities. For 4H-SiC, the intrinsic carrier density is 10 -10 cm -3 . The correlation of intrinsic concentration (ni) and temperature is given by:

ni = [N c .N v ] 1/2 .exp[ -E g (T ) 2KT ] (1)
Fig. 3. Intrinsic carrier concentration of Si is 10 10 cm -3 well below the typical dopant-induced carrier densities. For 4H-SiC, the intrinsic carrier density is 10 -10 cm -3 at 300K. As shown in Fig. 3, the intrinsic carrier concentration of SiC stays well below allowable limits even at 700K, allowing operation of SiC sensor at extremely harsh environment without suffering from intrinsic conduction effects and presenting low dark current. Drift region widths and breakdown voltages of Si, GaAs, 4H-SiC, and 6H-SiC are shown in TABLE II.

B. SiC Neutron detector

Neutrons are classified into three catégories: Thermal, Epithermal and Fast neutrons as indicated Fig.4 [START_REF] Haight | Fast-neutron detectors for nuclear physics experiments[END_REF].

Neutron is neutral particle. Thermal neutron (E=0.025eV) detection is based on indirect methods (see Fig. for sensor parameters) [START_REF] Issa | 4H-SiC Neutron Sensors Based on Ion Implanted 10B Neutron Converter Layer[END_REF]. Thermal neutrons interact with Neutron Converter Layer (NCL) containg (boron isotope 10) resulting in the production of alpha particle and lithium according to the following reactions [START_REF] Franc | Detection des neutrons par semi-conducteur dans un coeur de reacteur atomique[END_REF].

1 0 n + 10 5 B-> 3 5 Li + α-> Q = 2.792M eV 3 5 Li + α + γ-> Q = 2.31M eV (2)
Interaction of this charged particles with the detector's sensitive volume produce free charges carriers. The primary Function of this detector is to detect neutrons which can be two types: thermal or fast (E n =0.025eV or E n >10M eV respectively). This detector as part of our tests was used for neutron detection [START_REF]Comparaison du Diamant et du Carbure de Silicium (SiC) pour la détection de neutrons en milieux nucléaires[END_REF]. The PAI/CSA used in large numbers of front-end for radiation detector is a key block [START_REF] Luis | Noise Performance of a Regulated Cascode Transimpedance Amplifier for Radiation Detectors[END_REF], since it sets the limits to the performance of the whole system and it must satisfy conflicting requirements such as low noise, low power, and wideband operation. The following sections are devoted to TIA and CSA analysis. All the electrical results presented in this work were done without detector polarization.

C. Transimpedance function

The detector current pulse is transformed into voltage help to TransImpedance Amplifier (TIA) depicted in Fig. 7 [START_REF]Transimpedance Amplifier Stability[END_REF]. It presents derivatives of the output signal due to the presence of the detector capacitance. The amplifier's must be stabilized by a suitably choosen feedback capacitance C f . Owing to the presence of these capacitance (C f ), and because of the amplifier's own limitations, the system response at high frequencies will be reduced and then decrease the rising time [START_REF] De | Regulated Common-Gate Transimpedance Amplifier Design to Operate with a Silicon Photo-Multiplier at the Input[END_REF].

1) Transimpedance analysis: Amplifier's (OA)voltage gain is modeled in Laplace space as:

A(s) = A 0 1 + sτ a (3)
Where A 0 is the low-frequency gain of the OA. The gainband product of the OA is defined by:

B = A 0 τ a (4)
It is assumed that B -1 is much lower than all other time constants in the circuit and the cutoff pulsation is

ωo = 1 τ a (5)
The transimpedance function obtained by straightforward analysis, can be written as

V s R f .i d = -A0 (1+A0) ω0(1+A0) (C d +C f )R f s 2 + s 1+ω0[C d +(1+A0)C f ]R f (C d +C f )R f + ω0(1+A0) (C d +C f )R f (6)
by simplifying the wrinting of this result, we obtain:

V s i d = -R f ω 2 n s 2 + s ωn Q + ω 2 n ( 7
)
with

τ i = R f A 0 (C d + C f ) (8)
ω 2 n = 1 τ i τ a (9)
ω n = 2πf n (10) tr = 0.35 f n (11)
Q = 1 1 + η τ a τ i (12)
Fig. 8. Gain stability analysis shows that when capacitor Cf has a small value (0pF), the feedback gain transfert slope is 20dB/decade. System is therefore unstable. Increase Cf, feedback gain transfert slope change driving transimpedance towards stability.

η = C f .A 0 C d (13)
When detector is represented by its current source and detector capacitance, amplifier will never be used without an appropriate feedback loop. System stability analysis (see Fig. 8 and Fig. 9) [START_REF]Transimpedance Amplifier Stability[END_REF] shows that when capacitor Cf has a small value (Cf=0pF), the slope of the feedback gain transfer function (20dB / decade) has an opposite value to the OP-Amp gain transfer function (-20dB / decade). Then the system is unstable. An analysis on the phase margin (Fig. 9) shows that it is equal to (ψ A = 3 • < 45 • ) (see point A on the curve ). To eliminate this instability, Cf value can be increased. Fig. 8 & Fig. 9 repectively show that with Cf=250pF, the slope of the feedback gain transfer function is different to (20dB / decade) and phase margin ψ B = 74 • (see point B). For C f = 500pF phase margin ψ C = 69 • (see point C) then the system is stable in these case [START_REF] Caldwell | Op Amp Bandwidth for Transimpedance Amplifier-1[END_REF]. Increase Cf will increase envelope delay which is equivalent

to individual phase shift φ k (ω) of each pole S k = σ k ± ω k φ(ω) = arctan (f (s)) (f (s)) (14)
and the sum of this phase is:

n k=1 φ k (ω) = n k=1 arctan ω ± ω k σ k (15)
τ e = dφ(ω) dω who is near to the system feedback time constant

τ e ≈ R f × C f .
2) Input impedance frequency performance: The system input impedance was investigated.

Z i = V N i d = -V 0 A(s).i d ( 16
)

i d = i c + i f ( 17
)
i d = V N 1 C d + V N -V 0 1 1 R f +sC f (18) R f × i d = - V 0 [sC d R f + (1 + A(s))(1 + sC f R f )] A(s) (19)
Z i = R f sC d R f + (1 + A(s))(1 + sC f R f ) (20)
Z i = R f 1+A0 (s+ω0)(1+A0) (C d +C f )R f s 2 + s 1+ω0[C d +(1+A0)C f ]R f (C d +C f )R f + ω0(1+A0) (C d +C f )R f (21)
As the equation ( 21) shows, input impedance has three terms (

Z i =Z i1 (Z i2 +Z i3 ) ) :
-Frequency independant component:

Z i1 = R f (1 + A 0 ) (22)
-Band pass frequency component:

Z i2 = s(1+A0) (C d +C f )R f s 2 + s 1+ω0[C d +(1+A0)C f ]R f (C d +C f )R f + ω0(1+A0) (C d +C f )R f (23)
-Low pass frequency component:

Z i3 = ω0(1+A0) (C d +C f )R f s 2 + s 1+ω0[C d +(1+A0)C f ]R f (C d +C f )R f + ω0(1+A0) (C d +C f )R f (24)
Equation ( 22) is frequency independant. In first approximation, this equation can be used to evaluate input impedance. Then, in our case R f =100; A 0 =63dB; Z i1 = 0.07Ω equal to -23.36dBΩ which is a relatively low value (short-circuit) to allow detector charge evacuation towards the preamplifier and not towards detector capacitance Cd [START_REF] Margan | Transimpedance Amplifier Analysis[END_REF]. Fig. 10 show the curve of the input impedance.

3) Noise Analysis: Fig. 11 present the model used to characterize the noise [START_REF] Baker | CMOS Circuit Design, Layout, and Simulation[END_REF]. Power density square root of the flicker noise is obtained by locking at f=1Hz on ADA4817 curve input noise voltage [29]. It is define by:

V inoise,1/f (f ) = F N N f = 100nV √ Hz (25)
-FNN=(100nV

) 2 =10 × 10 -15 V 2
-Power spectral density of thermal noise (PSD,Vth) is defined by:

PSD,Vth =(4 × 10 -9 ) 2 = 16 × 10 -18 V 2 /Hz V 2 onoise (f ) = ( 10 × 10 -15 f ) + (16 × 10 -18 ) +( 4KT R f .| R f × 1/jC f w R f + 1/jC f w | 2 ) ( 26
)
The last term can be simplified as

| R f × 1/jC f w R f + 1/jC f w | 2 = 4KT R f (1 + 2πf R f C f ) 2 = KT C f (27) V 2 onoise , RM S = ((7) 2 .F N N ) + (P SD, V th × f 3dB × π 2 ) +( KT C f ) (28)
with

f 3dB = 1 2πR f C f = 3.4M Hz V 2 onoise , RM S = (49 × 10 × 10 -15 ) + (16 × 10 -18 . π 2 × 3.4M Hz) +(7.85 × 10 -12 ) (29)
The op-amp's thermal noise dominates the output RMS noise. Then :

V onoise , RM S = (0.7) 2 + (9.24) 2 + (2.8) 2 V onoise , RM S = 9.68µV
Output signal is defined by V 0 = R f × i f pp , then SNR correspond to 0dB is defined by:

i f pp = V 0noise , RM S 100 = 9.68 × 10 -6 100 = 96.8nA (30)
To have an output signal, input signal should be larger than i f > 6 × i f pp which correspond to 0.580µA.

The following section presents the method used for CSA sizing.

D. Charge Sensitive Amplifier function

The detector charge pulse is transformed into voltage thanks to charge sensitive amplifier (CSA) depicted in Fig. 12. If C f capacitance is very low (<1pF), this circuit will present derivatives or incomplete charge collection. It is for this reason that amplifier's feedback loop must be stabilized by a suitably chosen phase margin, and then capacitance Cf which is linked to system sensitivity [START_REF]Les préamplificateurs dans la chaine d'instrumentation nucléaire[END_REF].

A(s) = A 0 1 + sτ a ( 31
)
Where A 0 is the low-frequency gain of the OA. The gain bandwidth product of the OA is :

B = 2.π. A 0 τ a (32)
It is assumed that B -1 is much lower than all other time constants in the circuit and the cutoff pulsation is :

ω o = 1 τ a ( 33
)
The amplifier is designed to measure a charge with a voltage output signal, where the peak amplitude is proportional to the charge at the input. The relation of the output voltage V 0 to the input charge Q is the charge-to-voltage.

F Q = V 0 Q (34)
i = i d + i f (35) With R f ≈ M Ω , i R ≈ 0
Expression of the feedback current and detector current is:

i F = V 0 -V N Z f ( 36
) i D = V N Z d (37)
The voltage

V N = -Vo A current-to-voltage transfer function -V 0 i = A.Z d .Z f Z d (A + 1) + Z f (38)
Current is charge derivative i = dQ dt

The corresponding Laplace transformation is

L(Q (t)) = sQ(s) = i(s) (39)
Q(s) = i(s) s (40)
F Q = -A C d + C f (1 + A) (41)
The open-loop voltage gain of an operational amplifier is frequency dependent, a charge-to-voltage transfer function is obtained by

F Q (s) = -A 0 C d + C f (A 0 + 1) + sτ a (C d + C f ) (42)
F Q (s) = A0 C d +C f (1+A0) 1 + s τa(C d +C f ) C d +C f (A0+1) (43) F Q (s) = -1 C f × 1 1 + C d A0.C f × 1 1 + s C d 2π.GBP.C f (44)
The zero-frequency gain is:

F DC = A 0 C d + C f (A 0 + 1) (45)
This result shows that a finite open-loop gain A o attenuates the measured peak voltage. If A o has a high value, output signal will be independant of the value of detector capacitance (Fig. 13).

Another parameter of the CSA is output voltage rise time measured by applying charge step at its input.

| F Q (j2π.f c F Q (0) | = 1 √ 2 (46)
f c = B C d C f + (A 0 + 1) A 0 ( C d C f + 1) (47)
lim A0→∞ f c = B C d C f + 1 (48)
This result shows that the cutoff frequency and therefore the rise time of the CSA, are inversely proportional to the gain-bandwidth product of the amplifierc [START_REF] Fodisch | ChargeSensitive FrontEnd Electronics with Operational Amplifiers for CdZnTe Detectors[END_REF].

τ r = 2.2. C d 2.π.B.C f (49)
The unit step response showing rise time influence is presented in Fig. 14.

To reduce the rise time we have choisen a high speed low noise OP-amp ADA4817 (1GHz). Because charged particle beam energy is very high we have choisen Cf=5.3pF to make sure that the electronics will not saturate. Then, stability has been studied [START_REF] Giachero | Current,feedback operational amplifiers as fast charge sensitive preamplifier for photomultiplier readout[END_REF]. The transfert function Bode plot (open loop gain (Aol) ,Feedback gain β and the loop gain Aol). β plot shows that at F=8.3MHz the phase margin is roughly 89.76 • > 45 • (180 • -90.24 • =89.76 • ). This is sufficient for a stable operation (see Fig. 15).

IV. MATERIAL,TEST METHOD AND MESUREMENT RESULTS

A. Material

The machine used is a high dose-per-pulse prototype linear accelerator (linac) delivering high energy pulsed charged particle beam. This machine is able to reach a higher beam current. This machine offers the possibility to vary the dose-per-pulse and dose-rate over a large range (Fig. 16). This machine is not equipped with semi-transparent monitor chamber to control the beam as usually found because of the saturation [START_REF] Jaccard | High dose-per-pulse electron beam dosimetry: Commissioning of the Oriatron eRT6 prototype linear accelerator for preclinical use[END_REF] due to the high dose of charged particle. However, beam signals are recorded from a current transformer measuring the beam current at exit window of the accelerating section and from the primary collimator signal (Fig. 17).

1) Dose variation approaches : RHF (Reflected High Frequency) power change with power supplied (U grid): The simplest means of varying the linac's output dose is to change the RHF power supplied of accelerator and hence change the field strength in the accelerating cavities [START_REF] Marleau | Introduction à la physique des particules[END_REF].

Width (W): The linac's output dose can change by varying the pulsed beam width [START_REF] Jorge | Dosimetric and preparation procedures for irradiating biological models with pulsed electron beam at ultra-high dose-rate[END_REF].

Frequency (F): The linac's output dose can change by varying the frequency of the signal [START_REF] Hanna | Review of energy variation approaches in medical accelerators[END_REF].

B. Test Methode: Experimental Result 1) PAI Experimental result: Front-End Amplifier has been implemented as shown in Fig. 18 using ADA4817 OP-amp with Rf=100Ω and Cf=470pF in order to obtain a peaking time below 200ns and maximize the signal to noise ratio. Transimpedance amplifier was performed for the complete circuit including two amplifier circuit to amplifier the detector signal to 92.84dbΩ. The supply OP-amp voltage is +/-5V.

The complete circuit is simulated considering that the input signal has Ipulse =20µA, and that Cd=270pF the peaking time and amplitude of the output are 167ns and 178.44mV respectively. The shape of the input current and the output voltage pulse obtained by simulation are shown in Fig. 19.

Emulator (Fig. 20) was tested with an input pulse voltage source in series with his 50Ω input resistance, the signal is carried with 50Ω cable [START_REF] Spieler | Semiconductor Detector Systems[END_REF]. To adapt the impedance, we should place another 50Ω resistance and then convert voltage pulse to current pulse with 10kΩ resistance. Capacitor of 270 pF is placed in parallel with the TIA input to emulate the SiC detector capacitance. The experimental result is presented in Fig. 21.

2) CSA Experimental result: Complete circuit amplifier has been implemented as shown in Fig. 22 using ADA4817 OP- amp with Rf=1MΩ and Cf=5.3pF in order to reduce rise time and maximize the signal to noise ratio. Charge sensitive amplifier was performed for the complete circuit including one stage of pole zero cancellation circuit to improve peak shapes and energy resolution [START_REF] Grybos | Pole-Zero Cancellation Circuit With Pulse Pile-Up Tracking System for Low Noise Charge-Sensitive Amplifiers[END_REF]. The system transfert function is defined by:

U 2 Q o = 1 C f . R 2 R 3 .( 1 s + ( R+R1 R1.R ) 1 C1 ) (50)
In simulation domain

Q o = V pulse × Cc (51)
In radiation test

Q o = E.e ( 52
)
Where E is the absorbed energy within the detector active volume, is the energy required to create an electron-hole pair which is 7.78 eV in SiC and e the electron charge.

This circuit has been designed to measure the detector charges level when traditional detector used saturates .

The complete circuit is simulated (with 1GΩ resistance placed in parallel with Cd to allow OP-AMP polarization) considering that the current time duration is 200ns with the following relation:

i = du(t) dt (53)
Cd=270pF, input charge Q=400fC .The shape of the input voltage and the output volage(U1 and U2) pulse obtained by simulation are shown in Fig. 23.

The circuit was tested with an input pulse voltage source [START_REF] Sukhrani | Simulation of Charge Sensitive Preamplifier using Multisim Software[END_REF] in series with his 50Ω input resistance, The signal is carried with 50Ω cable. To adapt the impedance we should place another 50Ω resistance and then convert voltage pulse to charge with 2pF capacitor. 270 pF detector capacitor is then placed in parallel with the CSA input to emulate the SiC detector capacitance (Fig. 24). The experimental result is presented in Fig. 25.

C. Mesurement results

1) PAI System of test: The system (detector and electronic) has been tested with high dose per pulse Linac. Their machine produce high charged particle beam Irradiation. Tests parameters are presented in Tab.III. We studied the front end electronic performance with defined configuration Fig. 26, first of all by analysing the output signal shape when we are in T1(Tab.III) configuration, electronic do not saturate. We can observe it thanks to the temporal and amplitude Fig. 27 show Reflected High Frequency (RHF) displayed as voltage vs. time plots. Values for the beam and the gun current are obtained with the conversion 1 V = -1 A, while a correction factor of -50 is applied for the collimator and the reflected high frequency since their output are connected III).

to a 50Ω resistance. Fig. 28 is electronic front end output displayed as voltage vs time plots and Fig. 29 zoom on a peak hold on between RHF signal and electronic front end output. Signal is applied during 200ms with 100HZ frequency for a total of 20 pulse, and 4µS width. The shape of the signal generated by electronic front end has the same form as that of the signal generated by the intensity transformer (Fig. 17).

2) CSA System of test: Tests parameters is presented in Tab.IV. We studied the front end electronic performance with defined configuration Fig. 26. First of all by analysing the ouput signal shape when we are in T1(Tab.IV) configuration, electronic do not saturate.

We can observe it thanks to the temporal and amplitude occupation Fig. 30 shows reflected high frequency (RHF) displayed as voltage vs time. Values for the beam and the gun current are obtained with the conversion 1 V = -1 A, while a correction factor of -50 is applied for the collimator and the Mathematical model which allows us to eliminate the noise surrounding the signal is low pass filter (Fig. 33). We use butterworth filter with with 0.02 as normalized frequency . This technique smoot out high frequency fluctuations. By applying this filter to the signal, we have a response in Fig. 34. This result allows us to appreciate the charge collection time which is directly related to the time domain signal delivered by the RHF.

D. CSA Linearity curve

In order to link the output variations to the input charge, a simple relationship is necessary. Then the linearity of the system response has been checked Fig. 35.

E. PAI Linearity curve

In order to link the output variations to the input currents, a simple relationship is necessary. Then the linearity of the system response has been checked Fig. 36.

V. DISCUSSION AND CONCLUSION

In beams of higher intensity, it is impossible to count the pulses separately. The reasons are multiple but in this work we focused on two main causes. first of all in beams of higher intensity, all online detector (ion-chambers, silicon diodes, GaAs) saturates (ions collection efficiency decrease). Secondly, the response time of the front-end can also be the cause.

So, Silicon carbide detector with high capacitance (270pf) has been developped by the laboratory for harsh environment applications. With this electronic we have recovered signal detector emitted with low rise time (< 200ns)in PAI mode for high detector capacitance and also in CSA mode. As we have shown in the previous sections, we have succeeded to count separately the pulses. The rest of the work will consist to connect this Front-End to a processing system. In the near future, we will develop a high speed front end to reduce rise time below 30ns and next design an integrated electronic circuit. This reconfigurable front-end electronic will be able to extract all useful signals(current or charge answer) from the detector having Different characteristics (high and low capacity) and resolved data (nature of the particle, spectroscopy, etc.) wil be done with on-board processor. Our system will take into account the environment of use. Vanessa. Vervisch is a PhD from Aix Marseille University. During several years she worked on semiconductor material for solar cells fabrication but since 2012 she is focused on particles detection (fast and thermal neutrons) based on wide band gap semiconductor. (vanessa.vervisch@gmail.com).

Jean-Pierre Walder CNRS research engineer, is currently responsible for the IOLab platform of the IM2NP UMR CNRS 7334 laboratory. This platform brings together the resources for characterizing connected objects and designing mixed integrated circuits. He has nearly thirty years of expertise in the design of complex mixed digital / analog circuits in CMOS and Bipolar technology. He notably joined the Lawrence Berkeley National Laboratory (LBNL) in Berkeley in California for 12 consecutive years by developing numerous circuits for various applications such as medical, space and particle physics. He then joined IM2NP in 2015 for the design of a mixed CMOS integrated circuit, as part of the APPRISE project funded by the AMIDEX foundation for the reading of electrographic signals (EKG). (jean pierre.walder@im2np.fr).

Fig. 2 .

 2 Fig. 2. Readout electronics for particle detection. TIA or CSA are used to read charged particle beam. Pulse counting is carried out using MCU. When setpoint have reached, the computer send a signal to the optocoupler circuit to stop Linac.

  [START_REF] Iniewski | Radiation Effects in Semiconductors[END_REF] and Fig.6

Fig. 4 .

 4 Fig. 4. Spectrum of neutron energy from ultracold neutrons to fast neutrons. The wavelength of the neutrons; the de Broglie wavelength; is also shown and compared with dimensions of visible light waves and atomic, nuclear, and nucleon dimensions.

Fig. 5 .

 5 Fig.5. Silicon Carbide detector configuration with 10 B as neutron converter layer for neutron conversion into charged particles (see[START_REF] Chantepie | Étude et réalisation d'une électronique rapide à bas bruit pour un détecteur de rayons X à pixels hybrides destiné à l'imagerie du petit animal[END_REF]).

Fig. 6 .

 6 Fig. 6. SiC sensor parameter and his houser.

Fig. 7 .

 7 Fig. 7. Transimpedance built around an high speed and low noise amplifier.

Fig. 9 .

 9 Fig. 9. Phase stability analysis shows that when capacitor Cf has a small value (0pF), open loop phase magin is (ψ A = 3 • < 45 • )., System is therefore unstable. Increase Cf, open loop phase margin increase driving transimpedance towards stability.

Fig. 10 .

 10 Fig. 10. Input impedance: For low frequency, transimpedance input impedance has a constant value Z i1 . This one is proportional to feedback resistance Rf. when frequency increase we observe the effect of Z i2 & Z i3 which have a pass band & low pass behavior respectively.

Fig. 11 .

 11 Fig. 11. Simplified Noise model Analysis

Fig. 12 .

 12 Fig. 12. Charge sensitive Amplifier built around an inverting amplifier.

Fig. 13 .

 13 Fig. 13. Influence of finite Gain when 50fC is applied in the input, Cd=270pF, Cf=2pF for 100ns rise time.

Fig. 14 .Fig. 15 .

 1415 Fig. 14. CSA rise time (tr) influence on ballistic deficit. Fast CSA lead to reduce ballistic deficit. This plot is done with τ f =R f *C f =1µS; and Cd=270pF

Fig. 16 .

 16 Fig. 16. Temporal structure of an ideal rectangular pulsed beam: dose-rate as a function of time. f is the pulse repetition frequency, w the temporal pulse width and Dp the dose-per-pulse. Ḋm and Ḋp are the mean dose-rate of the beam and the mean dose-rate in the pulse, respectively.

Fig. 17 .

 17 Fig. 17. Current transformer measuring the beam current. Red color is RHF and Blue color is current transformer measuring during canon opening.

Fig. 18 .

 18 Fig. 18. Electronic Front-End built around transimpedance amplifier with two amplifiers stages.

Fig. 19 .

 19 Fig. 19. Simulation result with input current form which represents detector output current.

Fig. 20 .

 20 Fig. 20. Emulator schematic used for current experimental test.

Fig. 21 .

 21 Fig. 21. System response to emulator. input 20µA is applied, transimpedance output has 2.5mV voltage peak, amplified signal has 250mV voltage peak.

Fig. 22 .

 22 Fig. 22. Charge sensitive amplifier built around inverter amplifier and pole zero cancellation.

Fig. 23 .

 23 Fig. 23. Simulation result with input voltage who has 200ns rise time and fall time respectively, 270pF detector capacitance in parallel.

Fig. 24 .

 24 Fig. 24. Emulator schematic used for charge experimental test.

Fig. 25 .

 25 Fig. 25. Experimental result with 80fC input charge applied which correspond to U1=200mV. AFE response voltage is U2=20mV.

Fig. 26 .

 26 Fig. 26. Setup for SiC detector used in high dose per pulse charged particle beam.

Fig. 27 .

 27 Fig. 27. Linac RHf Voltage with the conversion 1V = -1A.

Fig. 28 .

 28 Fig. 28. Electronic Front-End result to measure the average detector output current.

Fig. 30 .

 30 Fig. 30. Linac Rf Voltage with the conversion 1V = -1A.

Fig. 32 .

 32 Fig. 32. Electronic response and RHF(Reflected high frequency) displayed as voltage vs time for T1 (Tab.IV) configuration.

Fig. 33 .

 33 Fig. 33. Output fast fourier transform (black color) and butterworth filter transfert function used (red color) to remove noise.

  .

Fig. 34 .

 34 Fig. 34. Front-End output signal after applying low pass filter. Charged particle current beams is applying during 200ns see T1 in Tab.IV

Fig. 36 .

 36 Fig. 36. Linearity curve of current amplifier Front-End see Fig.18.

  Prof. Dr. Wenceslas RAHAJANDRAIBE is currently full professor at the University of Aix-Marseille. He received the B. Sc. degree in electrical engineering from Nice Sophia-Antipolis University, France, in 1996 and the M. Sc. (with distinction) in electrical engineering from the University of Montpellier, Science department, France, in 1998. Since 1998, he joined the microelectronics department of Informatics, Robotics and Microelectronics Laboratory of Montpellier (LIRMM) and received the Ph. D. on Microelectronics form the University of Montpellier. Since 2003, he joined the microelectronic department of Materials, Microelectronics and Nanoscience Laboratory of Provence (IM2NP) in Marseille, France where he was an Associate Professor. Since 2014, he is a Professor at Aix Marseille University where he heads the Integrated Circuit Design group of the IM2NP laboratory. He heads the Integrated Circuit Design group since 2013. He directed and co-supervised 18 PhD and 15 Master students. His research interests involve AMS and RF circuit design from transistor to architectural level. His present research activity is focused on ultralow power circuit design for smart sensor interface and embedded electronic in bioelectronic and e-health applications, wireless systems, design technique and architecture for multi-standard transceiver. He is author or co-author of 8 patents and more than 120 papers published in refereed journals and conferences. He is an expert for the ANR, the French Agency for Research. He has served on program committees of IEEE NEWCAS and ICECS. He has been and is a reviewer of contributions submitted to several IEEE conferences and journals such as ISACS, NEWCAS, MWSCAS, ESSCIRC, ESSDERC, RFIC, IEEE Transactions on Circuits and Systems I and II, IET Electronics Letters. (wenceslas.rahajandraibe@im2np.fr). Wilfried. Vervisch has been working on semiconductors for almost 20 years. He worked on power devices, energy recovery, solar cells and detection devices. He is a specialist in modelisation and simulation, particularly in Monte Carlo, finite element calculations... Besides, he developed several detection devices until measurements under radiations environment. (wilfried.vervish@im2np.fr).

TABLE I SEMICONDUCTOR

 I MATERIAL USED IN RADIATION DOMAIN.

TABLE II SEMICONDUCTOR

 II

PARAMETER.

TABLE III BEAM

 III PARAMETER USED IN PAI MODE.

TABLE IV BEAM

 IV PARAMETER IN CSA MODE.