B Xia

J J Ganem

E Briand

S Steydli

H Tancrez

I Vickridge

The carbon and hydrogen contents in ALD-grown ZnO films define a narrow ALD temperature window

Keywords: Atomic Layer Deposition (ALD), ZnO thin films, Ion beam analysis (IBA)

Zinc oxide thin films grown by atomic layer deposition have been subject to great attention over the past few years. In this work, we study ZnO films grown on Si substrates by atomic layer deposition with di-ethyl zinc (DEZ) as metal precursor and H2O or D2O water vapour as oxidant. Film composition as a function of growth temperature is studied by Ion Beam Analysis (IBA) using Rutherford Backscattering Spectrometry (RBS) to determine Zn areal density; Nuclear Reaction Analysis (NRA) for C and O areal density, and Elastic Recoil Detection Analysis (ERDA) for hydrogen areal density. The Zn/O ratio is close to 1 within a broad ALD window consistent with previous studies, however the carbon content varies substantially within this window and is minimum only within a much narrower temperature range.

Introduction

Zinc oxide film as a semiconductor material with low production cost has attracted great attention in recent years, in particular due to its wide and direct band gap of 3.37 eV, and a high exciton binding energy of 60 meV that allows bright room temperature emission. Applications of thin-film ZnO have been found across numerous fields, such as supercapacitors, sensors and solar cells [START_REF] Ashrafi | Role of ZnS buffer layers in growth of zincblende ZnO on GaAs substrates by metalorganic molecular-beam epitaxy[END_REF][START_REF] Son | 11% efficient perovskite solar cell based on ZnO nanorods: an effective charge collection system[END_REF][START_REF] He | Single-crystal ZnO nanorod/amorphous and nanoporous metal oxide shell composites: Controllable electrochemical synthesis and enhanced supercapacitor performances[END_REF][START_REF] Wang | Highly sensitive, temperaturedependent gas sensor based on hierarchical ZnO nanorod arrays[END_REF].

ZnO films are typically grown by Atomic Layer Deposition (ALD) [START_REF] Tammenmaa | Zinc chalcogenide thin films grown by the atomic layer epitaxy technique using zinc acetate as source material[END_REF][START_REF] Zhang | Sr/ZnO doped titania nanotube array: an effective surface system with excellent osteoinductivity and self-antibacterial activity[END_REF], Chemical Vapour Deposition (CVD) [START_REF] Park | Controlled synthesis and properties of ZnO nanostructures grown by metalorganic chemical vapor deposition: A review[END_REF][START_REF] Willander | Zinc oxide nanorod based photonic devices: recent progress in growth, light emitting diodes and lasers[END_REF][START_REF] Pan | Metal-organic chemical vapor deposition of ZnO[END_REF], Pulsed Laser Deposition (PLD) [START_REF] Ohtomo | Pulsed laser deposition of thin films and superlattices based on ZnO[END_REF][START_REF] Fan | Microstructure and photoluminescence properties of ZnO thin films grown by PLD on Si (1 1 1) substrates[END_REF][START_REF] Koleva | Fabrication of Ag/ZnO nanostructures for SERS applications[END_REF] and sol-gel methods [START_REF] Barhoum | Sol-gel hot injection synthesis of ZnO nanoparticles into a porous silica matrix and reaction mechanism[END_REF][START_REF] Mote | Structural, morphological, physical and dielectric properties of Mn doped ZnO nanocrystals synthesized by sol-gel method[END_REF].

The requirements for reducing dimensions and increasing control in many applications, especially in microelectronics, have led to a strong interest in depositing ZnO films by ALD because of its selflimiting and highly conformal growth [START_REF] Tynell | Atomic layer deposition of Aldoped ZnO thin films[END_REF]. The composition, structure and growth mechanisms of ALD ZnO films have been studied by ellipsometry, transmission electron microscopy (TEM), and X-ray reflectivity (XRR) [START_REF] Langereis | In situ spectroscopic ellipsometry study on the growth of ultrathin TiN films by plasma-assisted atomic layer deposition[END_REF][START_REF] Ferguson | Surface chemistry and infrared absorbance changes during ZnO atomic layer deposition on Zr O 2 and Ba Ti O 3 particles[END_REF][START_REF] Tynell | ZnO: Hydroquinone superlattice structures fabricated by atomic/molecular layer deposition[END_REF][START_REF] Besleaga | Double layer structure of ZnO thin films deposited by RF-magnetron sputtering on glass substrate[END_REF] amongst others. These observations, together with theoretical studies using density functional theory (DFT), molecular dynamics simulations and monte carlo methods have also yielded significant insight into the underlying reaction kinetics and growth modes of this ALD process [START_REF] Puurunen | Growth per cycle in atomic layer deposition: a theoretical model[END_REF][START_REF] Ren | Initial growth mechanism of atomic layer deposition of ZnO on the hydroxylated Si (1 0 0)-2× 1: A density functional theory study[END_REF][START_REF] Weckman | Kinetic monte carlo study of the atomic layer deposition of zinc oxide[END_REF]. The general characteristics of the surface chemistry at different temperatures, for various precursor pulse durations and pressures and as a function of the number of ALD cycles have been carefully presented in the previous works. The temperature window for nearly constant Growth Per Cycle (GPC), which is the significant parameter guiding ALD film preparation, has also been explored on different substrates. However, most of these studies defined the ALD window as that where the growth rate was independent of deposition temperature, but paid little attention to the actual chemical composition of the films. The ALD window defined in this way was found to be of wide range and extending down to quite low temperatures. For example, S.-Y. Pung et.al. used di-ethyl zinc (DEZ) and water as precursors to deposit ZnO films on Si(100) and determined that the ZnO ALD window ranged from 125 to 150℃ [START_REF] Pung | Preferential growth of ZnO thin films by the atomic layer deposition technique[END_REF]. S. Jeon et.al. presented the ALD window of ZnO films on Si(100) as ranging from 70 to 200℃ with the same precursors [START_REF] Jeon | Structural and electrical properties of ZnO thin films deposited by atomic layer deposition at low temperatures[END_REF]. Kwon et.al. defined a higher ALD window for ZnO with temperature ranging from 150 to 200℃ [START_REF] Kwon | Effect of process parameters on remote PEALD for highly transparent ZnO film growth[END_REF], whilst Tapily et.al. and Illiberi et.al. found ALD windows for ZnO films from 100-160℃ and 110-160℃, respectively [START_REF] Tapily | Mechanical and structural characterization of atomic layer deposition-based ZnO films[END_REF][START_REF] Poodt | Low temperature and roll-toroll spatial atomic layer deposition for flexible electronics[END_REF]. Moataz Bellah et. al. give a much wider ALD window with temperature from 100-250℃ [START_REF] Mousa | Effect of temperature and gas velocity on growth per cycle during Al2O3 and ZnO atomic layer deposition at atmospheric pressure[END_REF]. These works have made important contributions to the development of ZnO films grown by ALD, offering much insight into the growth mechanisms and highlighting the great potential application of ZnO films. However, the wide range and diversity of the observed ALD windows suggests that growth rate alone may not be sufficient to establish the best ALD growth conditions for ZnO.

Investigation of the impurities near to the surface as well as their depth distribution are extremely important for film application. However, few reports give quantified analysis of the impurity distribution from a few atomic layers to nano or deeper layer in ZnO film. ToF-SIMS measurements by P. Boryło et. al. [START_REF] Boryło | The influence of atomic layer deposition process temperature on ZnO thin film structure[END_REF], in which a 30 keV Bi + beam was used to generate H + and ZnOH + secondary ions gave some indication of the presence of hydrogen in ALD grown ZnO films, however the signal to noise ratio is rather modest and no quantitative conclusions are drawn. Aleksandra Seweryn et.al reported the contents of hydrogen and carbon in the films were determined with use of secondary ion mass spectroscopy (SIMS) [START_REF] Seweryn | Structural and Electrical Parameters of ZnO Thin Films Grown by ALD with either Water or Ozone as Oxygen Precursors[END_REF]. They carefully investigate the carbon and hydrogen contents in the ZnO films even for samples deposited at different temperatures which give us some intuitive results. They are, however, short of distinguishing the source of hydrogen and giving the quantified analysis for H and D atoms, respectively. Table 1 shows a literature list of reported impurities and GPC for the ALD grown ZnO films from several precursors and different oxidants. Except to the mentioned above, there is few quantitative measurements of impurities distribution in ZnO films.

Table 1. the literature overview of the impurity level and GPC for ALD grown ZnO films from different ALD system, metal precursors and oxidants. Me = CH3, Et = C2H5, -= not measured, XPS = X-ray photoelectron spectroscopy, TOF-ERDA = Time-of-Flight Elastic Recoil Detection Analysis, SIMS= Secondary ion mass spectrometry, T-ALD = thermal ALD, P-ALD = plasma-assistant ALD, QL= qualitative. In this work, we systematically explore the impurity content and in particular the carbon and hydrogen areal density especially at low deposition temperature, since the presence of these elements could be indicators of incomplete precursor reactions. The different pulse time and purge time are selected to investigate the impurities level in the film. ZnO film densities grown at different temperature are seriously give considering the H and C impurities influence. In an attempt to identify the origin of the hydrogen that was found in the films, some films were grown using D2O.

Materials and methods

The ZnO thin films were grown on Si (100), cleaned with acetone and ethanol, by atomic layer deposition in the growth chamber described in NIIMB [START_REF] Xia | RBS and NRA analysis for films with high growth rate prepared by atomic layer deposition[END_REF] using DEZ and water. The precursors were transported to the growth chamber with N2 gas, and a long N2 purge time of 30s was used in order to prevent precursors mixing during deposition. DEZ was supplied in 15ms pulses, whilst the water pulse length ranged from 15ms to 100ms. The surface deposition temperature was controlled from 100℃ to 300℃ and the number of ALD cycles was varied from 5 to 60. The film deposition process is presented schematically in Fig. 1a. Growth of films with isotopically enriched water was achieved simply by replacing the water feed bottle by a new one with a few ml of D2 18 O. This precursor is expensive, however the amount of water used for film growth is sufficiently small that many hundreds of films can be grown from just a ml or two of water. Ion Beam Analysis (IBA) was performed on the SAFIR platform at Sorbonne Université. Rutherford Backscattering Spectrometry (RBS) employed 20-100 nA 4 He + beams of 1.5-2.1 MeV, in a 1mm diameter beamspot, with particle detection at 165° scattering angle (Fig. 1b). The detector solid angle was calibrated with 3% uncertainty using a Bi-implanted reference sample [START_REF] Davies | Calibration of the Harwell series II Bi-implanted RBS standards[END_REF][START_REF] Lemelle | Study of the (010) olivine surface by Rutherford backscattering spectrometry in channeling geometry[END_REF]. Only the Zn areal density [Zn] was deduced from the RBS spectra. Nuclear Reaction Analysis (NRA) (Fig. 1c) was used to determine the 16 O and the 12 ± 5×10 15 atoms/cm 2 as deduced from the film thickness and density determined by ellipsometry [START_REF] Volintiru | In situ spectroscopic ellipsometry growth studies on the Al-doped ZnO films deposited by remote plasma-enhanced metalorganic chemical vapor deposition[END_REF].

An 860 keV deuteron beam of 85 nA in a 2mm beamspot was used, and the protons produced by the nuclear reactions were detected at 150° in a 300mm 2 detector behind a 13m mylar foil which stopped the high flux of elastically scattered deuterons from saturating the detector. The oxygen reference also serves as a reference for the carbon areal density [ 12 C], since the cross sections and their ratio are known [START_REF] Lennard | Revisiting the 12C (d, p) 13C reaction cross section using condensed gas targets[END_REF]. We can write:

[ 𝐶 12 ] = 𝑌 𝐶 Y O Q o Q C σ O σ C [ 𝑂 16 ], (1)
where YO and YC are the yields of protons from 16 O(d,p1) 17 O obtained on the reference sample and of protons from 12 C(d,p0) 13 C obtained from the films, respectively; [C] and [O] are the 12 C areal density in the ZnO films and the 16 O areal density of the reference sample; C and O are the corresponding nuclear reaction cross sections, and Qo and Qc are the total incident beam charges for the film and reference measurements.

Hydrogen and deuterium areal densities were determined by Elastic Recoil Detection Analysis (ERDA) (Fig. 1d) with an 80 nA 1.8 or 2.0 MeV 4 He + incident beam, and recoil detection at 30°

scattering angle in a collimated detector behind a 9.15 m mylar film. Spin-coated polyethylene films with the hydrogen (C2H4)n or deuterium (C2D4)n content deduced from the C content measured by NRA, were used as H and D references [START_REF] Amsel | Microanalysis by the direct observation of nuclear reactions using a 2 MeV Van de Graaff[END_REF][START_REF] Quillet | Absolute cross section measurements for H and D elastic recoil using 1 to 2.5 MeV 4He ions, and for the 12C (d, p) 13C and 16O (d, p1) 17O nuclear reactions[END_REF].

Atomic force microscope (AFM) measurements of surface morphology were carried out with a

Bruker Extended D3100 AFM. The crystallinity and phase composition of films were determined by X-ray diffraction (XRD). A PaNalytical X'Pert MRD 4 circles diffractometer was used, with a 1.54056 Å X-ray source, a hybrid two-bounce Ge (220) monochromator, and a Pixel detector. Film density was deduced from the film physical thickness and mass thickness obtained from spectroscopic ellipsometry (SE) and RBS, respectively. The SE measurements were made with a FS-1 Multi-Wavelength Ellipsometer system in the wavelength range of 465-635 nm at an angle of incidence of 65°. and is a maximum in the ALD window, before decreasing again as the temperatures rises above 175°C. This is consistent with the proposal by M. Rueter that the DEZ may be decomposed into Zn and ethyl fragments on the growth surface at higher temperatures, inhibiting growth on the substrate [START_REF] Rueter | The surface reactions of ethyl groups on Si (100) formed via dissociation of adsorbed diethylzinc[END_REF]. Water desorption at higher temperature also inhibits the formation of -OH groups, giving an additional cause for the lower growth rate. The stable [Zn]/cycle in the temperature window between 150 and 175℃ suggests that self-limiting ALD growth of ZnO films has been obtained, as has been observed for very similar ALD windows in previous works [START_REF] Ferguson | Surface chemistry and infrared absorbance changes during ZnO atomic layer deposition on Zr O 2 and Ba Ti O 3 particles[END_REF][START_REF] Tynell | ZnO: Hydroquinone superlattice structures fabricated by atomic/molecular layer deposition[END_REF][START_REF] Besleaga | Double layer structure of ZnO thin films deposited by RF-magnetron sputtering on glass substrate[END_REF]. The excess ratio 1.06 of Zn to O at lower temperature also illustrates the dissociation of DEZ and incomplete reaction with -OH on the Si (100) surface (Fig. 8a).

Results and discussion

The film growth rate

The carbon content of the ZnO films

With increasing temperature, the [C]/cycle decreases to its lowest level of about 1.5×10 15 at/cm 2 corresponding to about 1 atom% at 160°C, within the ALD window. The DEZ molecules interact with the -OH on the surface and form adsorbed ZnO islands or clusters [START_REF] Cho | Effects of growth temperature on performance and stability of zinc oxide thin film transistors fabricated by thermal atomic layer deposition[END_REF]. The ethyl groups can react with hydrogen released from -OH groups, forming ethane which is flushed away during the N2 purge.

However, the [C]/cycle in the films increases again between 175 and 225℃. We surmise here that some of the adsorbed DEZ molecules decomposed before bridging with -OH on the Si (100) surface.

This would lead to the ethylene fragments bonding with the surface -OH and occupying the reactive site for -Zn-CH2-CH3 or -Zn-OH formation. Previous study showed that DEZ molecules heated to about 177℃ decompose completely to form hydrocarbon molecules and zinc atoms [START_REF] Son | 11% efficient perovskite solar cell based on ZnO nanorods: an effective charge collection system[END_REF]. This is consistent with our results and adsorption of hydrocarbon on the growth surface is probably the source of C content at higher temperature.

Furthermore, at higher temperature, although the chamber is not deliberately heated, thermal radiation from the substrate still increases the temperature of the elements surrounding the substrate plate, including the chamber walls, where the DEZ may be decomposed into Zn and ethyl fragments before coming into contact with the substrate, reducing the availability of DEZ for reaction with the sample growth surface.

In summary, we propose that deposition temperatures below the ALD window will cause higher carbon areal density in the films due to incomplete reaction, while for deposition temperatures above the ALD window increased carbon areal density is the result of the decomposition reaction of DEZ molecules on contact with the growth surface. Above about 225°C, DEZ decomposes into atomic Zn and weakly reactive organic fragments before coming into contact with the growth surface, resulting in lower residual carbon areal density in the films.

Fig. 3. a: Carbon analysis obtained from the NRA spectra (Fig. 1); b: Carbon areal density per cycle in ZnO film.

Hydrogen content of the ZnO films

As it is well known that hydrogen is also involved in the ALD process [START_REF] Puurunen | Growth per cycle in atomic layer deposition: a theoretical model[END_REF], we employed elastic recoil detection analysis (ERDA) to determine the hydrogen depth distribution in the ZnO films.

Fig. 4 shows ERDA spectra from film growth with deposition temperature, and at the lower and higher limits of the ALD temperature window. Fig. 5 shows the hydrogen concentration depth profiles deduced by simulation with SimNRA [START_REF] Quillet | Absolute cross section measurements for H and D elastic recoil using 1 to 2.5 MeV 4He ions, and for the 12C (d, p) 13C and 16O (d, p1) 17O nuclear reactions[END_REF][START_REF] Dodder | Elastic scattering of protons by helium 4: New experiments and analysis[END_REF]. Spectra and profiles from intermediate growth temperature are shown in supplementary information (Figure S2 andS3). The recoil crosssections for the H and D are non-Rutherford. The cross-section data of SigmaCalc [START_REF] Dodder | Elastic scattering of protons by helium 4: New experiments and analysis[END_REF] and Quillet,

V [START_REF] Quillet | Absolute cross section measurements for H and D elastic recoil using 1 to 2.5 MeV 4He ions, and for the 12C (d, p) 13C and 16O (d, p1) 17O nuclear reactions[END_REF] for H(α,p) 4 He and 2 H(α,d) 4 He were used for the simulation, respectively. For all fitting, a contamination layer of 20×10 15 at/cm 2 of CHx (H=3.3×10 15 at/cm 2 ) was assumed for the surface contamination. This is consistent with what we often observe in ERDA measurements of many types of sample, and the conclusions drawn here are not very sensistive to variations of a factor of two in this assumed layer.

Fig. 4a shows the ERDA analysis for film deposited at 100℃. The channels from 300 to 275, represent the hydrogen in the ZnO films, which could be due either to remaining unreacted -OH groups or to adsorbed DEZ molecules that have not reacted with the water vapor. This observation and the high level of carbon at this temperature, are consistent with adsorption of DEZ molecules which have not reacted completely. According to the DFT calculation by Weckman, DEZ molecules form a pre-adsorbed state with the surface -OH group. The low adsorption energy of the interaction suggested that the adsorption is not chemical but rather a weak molecular interaction [START_REF] Weckman | Atomic layer deposition of zinc oxide: diethyl zinc reactions and surface saturation from first-principles[END_REF]. The low temperature couldn't supply enough energy for complete reaction between -OH and adsorbed DEZ.

The lower energy peak between channel 230 to 275 shows that hydrogen is also trapped near the interface between the Si substrate and the growing ZnO film. It is speculated that un-reacted -OH left on the Si surface during the surface preparation remains and/or grows during the initial H2O pulses of the ALD process. The low deposition temperature and the absence of post-annealing strongly limit hydrogen diffusion which is consistent with the heterogeneous distribution in the depth profile of ZnO films.

Fig. 5a shows the hydrogen concentration depth distribution. In all cases, there is a region about 10nm thick at the surface that is richer in hydrogen than deeper into the sample. This is not due to external contamination, as will be seen below, and may be related to roughness of the film surface.

Outside the ALD window, the films are thinner and so the hydrogen concentration is dominated by that of the surface region, and the depth resolution of the ERDA method is hardly sufficient to distinguish the surface and subsurface regions. Within the ALD window the films are thick enough that we can reliably distinguish between the surface and bulk regions for hydrogen content, however a detailed examination of the hydrogen depth profiles is beyond the scope of the present work. Let us consider, then, the total hydrogen contents of the films. In the films deposited at 140℃ and 150℃ the hydrogen concentration of 7.8 at% corresponds to the introduction of about 1.2×1015 hydrogen atoms/cm2 per cycle, which is nearly twice that observed at lower temperature (100℃), suggesting that more hydrogen atoms originating from the organic fragments have been left in the film with the increase of growth rate. Although the growth rate of film deposited at 160 ℃ is nearly the same as that of films deposited at 150℃, the hydrogen concentration decreases significantly, with about 34×10 15 atoms/cm 2 , corresponding to about 5.8 atom% (As shown in Fig. S2c andS3c).

Fig. 4c and5c show the hydrogen content in ZnO films deposited at 175℃, which is smaller even than that at the lower range of the ALD window (150℃). Further increasing the deposition temperature to 300℃, the hydrogen concentration of the ZnO films decreases to its lowest value of about 6 at% (as shown in Fig. 4d and5d). This could be due to a decrease in the -OH concentration on the ZnO surface at higher temperature [START_REF] Kim | A kinetic study of ZnO atomic layer deposition: Effects of surface hydroxyl concentration and steric hindrance[END_REF] but is also consistent with the observation of reduced carbon contamination, which if due to the incorporation of decomposed DEZ organic fragments could also lead to a reduction of hydrogen concentration. The ERDA results for other films are given in Fig. S3b and Fig. S4b. In conclusion, the hydrogen concentration in the films decreases as the deposition temperature increases, however it is not possible to know whether the hydrogen originates from residual hydroxyl groups or from DEZ decomposition products, the concentration of both of which are expected to decrease with increasing film growth temperature above the ALD window. However, the dramatic decrease in the C concentration at 140℃ and 150℃ (Fig. 3) is not matched by a corresponding dramatic reduction of the hydrogen concentration. Ren reported that neighboring hydroxyl could facilitate the H2O half-reaction, with the reaction of H2O with -O-Zn-C2H5 preferred over reaction with -OH on the surface [START_REF] Ren | Initial growth mechanism of atomic layer deposition of ZnO on the hydroxylated Si (1 0 0)-2× 1: A density functional theory study[END_REF]. Moreover, the growth rate is very much higher compared to other deposition temperatures. If the hydrogen is coming initially from DEZ fragments, then the fact that a significant concentration of hydrogen remains in the film suggests to us that the hydrogen atoms from the H2O or DEZ fragments could diffuse into the films in the ALD window. In order to determine whether the hydrogen is coming from the H2O or DEZ fragments, we used D2O (99.8%)

as the water precursor. Since ERDA can separate these two isotopes of hydrogen, this allows us to distinguish hydrogen coming from the water vapour (deuterium) from that originating in the DEZ (hydrogen) in the ERDA spectra.

Fig. 6a shows ERDA spectra obtained from 100℃, 150℃ and 300 ℃ and the corresponding SimNRA simulations. At 100°C, the deuterium concentration is only about 17% of the total hydrogen concentration, indicating that only about 17% of hydrogen in the film originates from the labelled water (deuterium), the remaining 83% coming from DEZ fragments during the ALD process. At 150℃, only about 8% of the total film hydrogen comes from the water vapour, this ratio being even lower in the film bulk. Hence, we may suppose that most of the -OH groups produced during the water ALD cycle can almost completely react within the ALD window, and the higher content of hydrogen comes from the DEZ precursor. Previous study shows that DEZ might be dissociated into Zinc, ethyl and its fragments and so on, its ethyl groups could undergo further reaction to form ethene, ethane and butane in the reactor [START_REF] Rueter | The surface reactions of ethyl groups on Si (100) formed via dissociation of adsorbed diethylzinc[END_REF][START_REF] Dumont | Pyrolysis pathways and kinetics of thermal decomposition of diethylzinc and diethyltellurium studied by mass spectrometry[END_REF]. Hence, we supposed that the redundant hydrogen will diffuse into the films, causing higher hydrogen content in the films. At 300℃ the film has a lower overall hydrogen content of about 5.4 atom%. However, the percentage of deuterium in the film is greater than 22%, illustrating a prevalence of diffusion phenomena over the desorption of fragments. Fig. 7 shows ERDA spectra for films produced with different purge times between the precursor pulses. Fig. 7a shows that the film contains about 29.3×10 15 atoms/cm 2 of hydrogen, corresponding to about 5.1 at%, and about 4.0×10 15 atoms/cm 2 of D -much less than the H. From Fig. 7b we observe that the total H content of the film decreases when the purge time after the water pulse is increased. However, the total film thickness also decreases, and the H concentration in the film is in fact unchanged by the increased purge time. We suggest that film growth rate decreases due to the desorption of water molecules during the purge time, leading to a reduction of the density of reactive surface sites. We propose that as -OD displaces -C2H5, H atoms from DEZ molecules or to a lessor extent D atoms diffuse into the structure, so that the H(D) concentration does not depend on film thickness, but only on the -OD and -C2H5 reaction process. Fig. 7c shows the film deposited with a long purge time (60s) after the DEZ pulse, illustrating a similar trend with long H2O purge time (60s). After the ligand exchange between -Zn-C2H5 and -OD, the long purge time can be used effectively to remove unreacted DEZ and C2H5D. In this case, the long purge time could also increase the desorption of DEZ molecules, leading again to a slightly lower growth rate compared to that obtained with the short purge times of water (30s) and DEZ (30s). The long purge time after the DEZ pulse has less influence on the film growth rate than the long water purge time. Fig. 7d shows long purge time for both half-reactions. The total H and D areal densities decrease to their lowest values of 13.4×10 15 and 2.8×10 15 atoms/cm 2 , respectively. However, the growth rate of film is also the lowest of these four samples due to the double effect of the reduction of reactive site density and the desorption of DEZ. Fig. S4 and Fig. S5 present the films deposited with50ms and 15ms H2O pulses, 15ms DEZ pulse, but different purge time for water half-reaction and DEZ halfreaction. The results present the same trend as that in Fig. 7. Comparing Figs.7a, S4a and S5a, we can clearly see that the purge time has little effect on the H and D concentrations. Table 2 summarizes the H, D and C contents in ZnO films as a function of purge time, the C content in ZnO film is also higher than the film deposited with 100ms and 50ms water pulses. Thus, short water pulse time (15ms) means a smaller quantity of -OH groups are formed during the water half-reaction.

In other words, it's not conducive to removing the ethyl groups from Zn-C2H5. The ERDA results reveal that the D concentration in the film deposited within the ALD window is lower than that of films deposited either side of the ALD window. This indicates that the -OH groups react more readily with DEZ molecules in the ALD window. From the ERDA analysis for ZnO films, we supposed that lower deposition temperature will increase the hydrogen in the films even within the ALD window. Although the highest growth rate occurs within the ALD window, the film composition is different even for the small variations of deposition temperature.

The Zn to O atomic ratio and film density

To further investigate the growth mechanism, the atomic ratio of Zn to O and layer density are presented in Fig. 8 as a function of deposition temperature. The atomic ratio of Zn to O is close to 1 corresponding to stoichiometric ZnO (as shown in Fig. 8a). However, although the uncertainties are quite high, there is a hint that Zn/O is higher either side of the ALD window, and then decreases again at even higher temperatures. These seem to be the same temperature regions as was observed for carbon. and physical thickness h obtained respectively from RBS, NRA, and ERDA spectra and from ellipsometry, using the expression:

ρ=[𝑍𝑛] M ZnO 𝑥 C 𝑦 𝐻 𝑧 N A 1 ℎ M ZnO 𝑥 C 𝑦 𝐻 𝑧 = M Zn + M O [𝑂] [𝑍𝑛] + Mc [𝐶] [𝑍𝑛] + M 𝐻 [𝐻] [𝑍𝑛]
where NA is Avogadro's constant and M ZnOxCy𝐻𝑧 is the molar mass of ZnOxCyHz. Within the ALD window, the film density of 5.61 g/cm 3 is close to that for bulk ZnO, and increases slightly with increasing deposition temperature above the ALD window. Below the ALD window the film density decreases dramatically. This result is similar to that of [START_REF] Vähä-Nissi | Antibacterial and barrier properties of oriented polymer films with ZnO thin films applied with atomic layer deposition at low temperatures[END_REF] where a density of about 3.5 g/cm 3 was also found for ZnO films grown at 100℃. This is also consistent with the view that DEZ molecules pre-adsorbed on the Si (100) surface via a weak molecular interaction include fragments of ethyl and -Zn-C2H5 bonded with Si-OH leading to higher carbon and hydrogen contents in the film, responsible for the lower density of the films. As the growth temperature is increased, the ZnO films become denser because of decreasing impurity content. At temperatures above the ALD window, the density of ZnO film approaches that of bulk ZnO (5.61 g/cm 3 ).

Preferential growth of ZnO films

ZnO film grown at temperatures higher than 150℃ are close to the bulk density of crystalline ZnO, and so the crystalline structure of the films was investigated by X-ray diffraction, using θ/2θ scans in a wide angular range from 20° to 60°.

Fig. 9 shows the X-ray diffraction patterns of ZnO films deposited at temperatures ranging from 100-300℃. The diffraction patterns of the films grown below 125℃ show no obvious peak, indicating practically no discernible long range order. The XRD pattern for the film grown at 150°C matches a polycrystalline hexagonal wurtzite ZnO structure, with three dominant peaks identified, (10.0), (00.2) and (10.1) planes (PDF#36-1451) and a dominant crystal orientation of (10.1). As the growth temperature is increased to 175℃ and then 200℃, the films continue to show (10.0), (00.2) and (10.1) peaks in the XRD spectrum, however, the dominant orientation shifts to (00.2). This shows that the orientation of ZnO films changes from polycrystalline to a preferential out-of-plane texture.

For deposition temperatures higher than 200℃, the orientation corresponding to (10.0) and (10. In Zone 0, we suggest that the high carbon contamination in the films, together with the low growth temperature reduces atomic mobility and impedes the relaxation necessary for crystalline growth, leading to the almost featureless XRD pattern. Near 150° where the carbon contamination is the smallest, the films grow in a polycrystalline hexagonal wurtzite structure with little evidence of texturing. ZnO crystals are known [START_REF] Gao | Nanoarchitectures of semiconducting and piezoelectric zinc oxide[END_REF][START_REF] Li | Growth mechanism and growth habit of oxide crystals[END_REF] to grow rapidly in the [0001] direction, and such growth on the (00.2) plane, which has the lowest surface energy according to the calculation of Fujimura [START_REF] Koleva | Fabrication of Ag/ZnO nanostructures for SERS applications[END_REF][23], could increase the film growth rate.

However, the observation of (00.2) plane alone is not a guarantee that the growth rate will be higher.

The decomposition of DEZ that occurs for deposition temperatures higher than 177℃ [START_REF] Son | 11% efficient perovskite solar cell based on ZnO nanorods: an effective charge collection system[END_REF], results in the increase of desorption of dissociation groups. For deposition temperatures between 200 and 250℃ (zone 2), the carbon content in the film clearly increases, and we proposed that DEZ decomposition products like CH3-CH2-or CH3-, could in fact inhibit Zn atom incorporation. Deposition temperatures higher than 250℃ cause a violent pre-decomposition of DEZ molecules that could reduce the growth rate. The higher growth rate at 150°C could be due to the low surface energy of the positively charged Zn-terminated surface at the (00.2) plane. At the higher temperatures, the ZnO could be terminated by negatively charged oxygen ions which can reduce the growth rate [START_REF] Wang | Induced growth of asymmetric nanocantilever arrays on polar surfaces[END_REF].

Thus, it is quite plausible to obtain ZnO films with a single (00.2)peak and low growth rate.

The surface structure of ZnO films

The evolution of surface structure with deposition temperature was investigated by atomic force microscopy (AFM). Previous studies only presented ZnO surface structure dependance on growth temperature [START_REF] Pung | Preferential growth of ZnO thin films by the atomic layer deposition technique[END_REF][START_REF] Boryło | The influence of atomic layer deposition process temperature on ZnO thin film structure[END_REF] and we are aware of no work in which the dependance of surface structure on film thickness was investigated. Fig. 10 and Fig. S6 show AFM images of ZnO obtained from samples deposited at temperatures ranging from 100-300℃, with the number of ALD cycles adjusted to obtain samples of very similar thicknesses, indicated on the AFM images.

Fig. 10a shows the AFM image of ZnO films deposited at 100℃. Long wedge-shaped structures of width and length about 70nm and 700nm, respectively are observed, with the a-axis directions parallel to the substrate. Increasing the temperature to 150 or 175℃, the XRD result shows clearly that (00.2) c-axis preferred orientation appears, and the length of wedge-shaped structures became shorter. Overall, the surface structure becomes finer with increasing the deposition temperature, we proposed that the atomic arrangement changes during the ALD process. For example, Zinc terminal growth could get thicker and rougher nanostructures as compared with oxygen terminal growth in the ZnO films. Commonly, the ZnO films grown with oxygen termination prefer to grow along the junction between (000ī) and (01ī0) rather than (00.2) surface, leading to a smaller and thiner structure. Thus, the surface structure becomes smaller with increasing deposition temperature. The wedge-shaped structures have become shorter even for films of practically the same thickness.

Conclusion

Our results show that substrate temperature significantly affects the growth rate of ZnO films grown by ALD. We revealed that the C content in the films deposited at different deposition temperatures, is lowest within the ALD window. The ZnO film densities increase with deposition temperature, reaching the standard ZnO bulk density at 150℃ and above. The C contamination in the films varies with the growth temperature even within the ALD window, reaching its lowest value of about 1 atom % at 160°C. ERDA shows that the hydrogen concentration reduces with increasing deposition temperature. Through isotope labelling (D2O) in the process, ERDA spectra show that the deuterium concentration is less than 2 atom %, indicating that the vast majority of -OH groups on the surface are probably consumed by DEZ in the ALD window. Hydrogen atomic concentration is 4 to 12 times higher than the deuterium concentration indicating that the majority of the hydrogen in the films originates from the DEZ precursor decomposition products rather than water. In conclusion, based on the lower C and H concentrations, the film density very close to the bulk ZnO density, the Zn/O ratio of practically 1, and the maximum growth rate per cycle (GPC), we recommend a narrow ALD window with deposition temperature between 150 ℃ and 175 ℃ in order to obtain optimal ALD ZnO film growth.

Fig. 1 .

 1 Fig.1. Methods. a: The deposition process of ZnO films. b, c, d: the detector geometry of RBS, NRA and ERDA, Incident angle α, exit angle β and scattering angle θ.

Fig.2a shows

  Fig.2ashows the growth per cycle measured by RBS as a function of the deposition temperature with a given DEZ precursor time of 15ms and H2O pulse time of 100ms for 40 cycles. [Zn]/cycle ranged from 1.5×1015 atoms/cm 2 to 6×1015 atoms/cm 2 . The lowest [Zn]/cycle occurs at 100℃. This is most likely due to insufficient thermal activation of the reaction between the DEZ precursor and -OH groups on the Si surface as shown by M. Rueter[START_REF] Rueter | The surface reactions of ethyl groups on Si (100) formed via dissociation of adsorbed diethylzinc[END_REF]. [Zn]/cycle increases with temperature

  [O]/cycle shown in Fig 2b presents practically the same growth trend as that of Zn, with a maximum of about 6x10 15 atoms/cm 2 per cycle between 150℃ and175℃ as for Zn. [Zn]/cycle and [O]/cycle are very similar, which demonstrates that a reasonable atomic ratio of Zn to O will be obtained. Typical RBS and NRA spectra of ZnO films at 300℃, 175℃, 140℃, 125℃ are presented in the Fig.2c and Fig.2d, respectively (details for other deposition temperatures are presented in supporting information

Fig. S1 )

 S1 Fig.S1). From the RBS and NRA determinations of [Zn] and [ 16 O], we can divide the reaction states of DEZ and -OH into three regimes according to the deposition temperature: (1) 100-150℃: incomplete reaction; (2) 150 -175℃: completed reaction (ALD window); (3) above 175℃: decomposition reaction.

Fig. 3

 3 Fig.3 shows [C]/cycle obtained from equation 1. [C]/cycle is much higher at temperatures either side of the ALD window, and falls again as temperatures rise above about 240°C. The highest [C]/cycle is found to be 0.68×10 15 atoms/cm 2 , for a deposition temperature of 100°C where [Zn]/cycle and [O]/cycle were about 1.5×10 15 at/cm 2 . This corresponds to about 15 atom% of carbon in the film, and suggests that the DEZ molecule couldn't completely react with -OH on the surface even if the surface has enough -OH. Hence, a fraction of the DEZ molecules just adsorb on the surface, leading to the high content of carbon in the films. Previous study also showed that adsorbed DEZ molecules on the Si (100) surface could dissociate below 127℃, producing Zinc metal and

Fig. 4 .Fig. 5 .

 45 Fig.4. The ERDA spectra obtained from ZnO films as a function of the deposition temperature for 100°C (a), 150°C (b), 175°C (c) and 300°C (d), for 40 ALD cycles.

Fig. 6 .

 6 Fig.6. a: Typical ERDA spectra of ZnO films deposited at 100°C, 150°C and 300°C, (40 cycles). The hydrogen concentration depth distributions deduced from ERDA in ZnO film as a function of the deposition temperature for 100°C (b), 150°C (c) and 300°C (d).

Fig. 7 .

 7 Fig.7. ERDA spectra of ZnO films deposited with different purge time; a: H2O 30s +DEZ 30s, b: H2O 60s +DEZ 30s, c: H2O 30s +DEZ 60s, d: H2O 60s +DEZ 60s. The deposition temperature was kept at 160℃, pulse time of H2O and DEZ were 100ms and 15ms, respectively.

Fig.8b summarizes

  Fig.8b summarizes the film density  deduced from atomic areal densities [Zn], [O], [C] and [H]

Fig. 8 .

 8 Fig.8. a: the atomic ratio of Zn to O as a function of the substrate surface temperature. b: The variation in ZnO film density as a function of the substrate surface temperature. The dashed line represents the standard bulk density.

1 )

 1 has disappeared, indicating that the orientation of (00.2) has completely dominated. According to the XRD patterns at different deposition temperatures, we can distinguish three preferential growth directions in three different temperature zones. Zone 0: 100-150℃, Zone 1: 150-200℃, Zone 2: 200-300℃.

Fig. 9 .

 9 Fig.9. The XRD spectra of ZnO films deposited at different temperature.

Fig. 11 and

 11 Fig.11and Fig.S7show line height profiles derived from AFM pictures. The surface roughness (Rq) of the films decreases with increasing deposition temperature. The surface roughness within the ALD window is about 5nm, and higher growth temperature reduces the surface roughness, probably due to oxygen terminal growth in the ZnO films.

Fig. 10 .

 10 Fig.10. The AFM pictures of ZnO films deposited at different temperature, a: 100℃; b: 150℃; c: 175℃; d: 225℃; e: 275℃; f: 300℃.

Fig. 11 .

 11 Fig.11. Cross-sectional profiles along a reference line derived from AFM pictures, a: 100℃; b: 150℃; c: 175℃; d: 225℃; e: 275℃; f: 300℃.

Table 2 .

 2 The H, D and C content in the films deposited as function of purge time, the deposition temperature was kept as 160℃, the ALD cycles were 40 cycles.

	Pulse time (ms)	Purge time(s) H2O DEZ	Zn	O ×10 15 atoms/cm 2 H	D	C	Zn/O	H (%)	D (%)	C (%)
		30	30	239.9 240.3 29.3 4.0 4.5	1.00	5.1	0.8	0.9
	H2O: 100	60	30	169.1 177.6 18.7 3.7 7.1	0.95	5.0	1.0	1.9
	DEZ: 15	30	60	201.8 203.1 21.0 3.6 7.5	0.99	4.8	0.8	1.7
		60	60	126.8 131.4 13.4 2.8 2.8	0.96	4.8	1.0	1.0
		30	30	177.6 183.6 19.2 2.9 3.7	0.97	5.0	0.8	1.0
	H2O: 50	60	30	134.0 140.0 10.2 3.0 6.4	0.96	3.5	1.0	2.2
	DEZ: 15	30	60	165.9 171.2 12.6 3.2 6.0	0.97	3.5	0.9	1.7
		60	60	88.6	93.8	8.1	2.2 2.6	0.95	4.1	1.1	1.3
		30	30	66.2	66.2 12.1 1.6 3.1	1.00	8.1	1.1	2.1
	H2O: 15	60	30	49.0	60.9 10.5 1.3 3.6	0.81	8.4	1.0	2.9
	DEZ: 15	30	60	55.1	71.5 10.8 1.3 5.1	0.76	7.5	0.9	3.6
		60	60	36.7	44.2	6.7	1.2 2.5	0.83	7.3	1.3	2.7

Acknowledgement

The ALD system is funded in part by the CEMIP (Centre de Microélectronique de Paris Ile-de-France). The work has also benefitted from the support of the SAFIR platform of Sorbonne Université, and dedicated internal funding from the Institut des NanoSciences de Paris. We also acknowledge useful discussions with members of the French ALD network RAFALD. B. Xia is funded by the China Scholarship Council (CSC) for his PhD studies (Grant ID: 201806420022).


Kodzo Parkoo

Cheikh Sarr

Bamba Gueye

Leveraging Classification Algorithms and IoT Sensors Network to Predict Anopheles Larvae Presence

Keywords: Classification algorithms, IoT Sensors Network, Anti-larvae fight, Data analysis, Data prediction, Python

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I INTRODUCTION

According to the World Health Organization (WHO), malaria alone kills an average of 660,000 people per year. [START_REF] Botha De Meillon | Observations on a species of the anopheles funestus subgroup, a suspected exophilic vector of malaria parasites in northeastern transvaal, south africa[END_REF] places malaria ahead of yellow fever, which claims 52,000 victims, and dengue fever, which claims 22,000. Malaria is transmitted by the infected bite of a female anopheles (an). The Anopheles (an) Gambiae is the main vector in Africa.

In Senegal, we can note the presence of Anopheles Gambiae in all regions, explaining a high prevalence rate.

According to the An. gambiae s.s represented by the molecular forms S and M (currently An. gambiae and An. coluzzii respectively) is present throughout the country as shown in Figure 1, but predominates in the southern humid areas while An. arabiensis also represented in all regions is predominant in the drier areas of the center and north. The species An. melas, is localized on the coast in the mangrove and along some rivers to the limits reached by the rise of marine salt water. An. pharoensis is especially abundant in the lower valley of the Senegal River in the north, An. nili is only present in the meridional regions, close to the rivers. The data we collect for prediction in the pest control account are of a disparate nature. We will therefore find data of the numerical, count, binary types... This situation makes the choice of algorithms complex in the sense that each algorithm has particularities regarding the types of data received as input and the type of data produced as output after processing. Examples of predictions by algorithms, while plentiful, do not always expose the types of data set considered.

The evolution of parasite prevalence from 2009 to 2015 still ranks Senegal among the countries of Sub-Saharan Africa (SSA) where malaria is endemic and constitutes a public health problem.

The burden of the disease is still high in some areas of the country where deaths attributable According to programme national de lutte contre le paludisme du Sénégal (2020), although there has been a decrease in the mortality rate, as shown in Figure 2, the situation can still be improved. It should be noted that all malaria control proposals to date have focused on the adult Anopheles. Therefore, a focused approach of zone-specific interventions before and during larval development becomes an imperative.

In the report of the Organisation météorologique mondiale (2016), Weather and Locusts, we learn that an algorithm named CMORPH (for Climate Prediction Center MORPHing) developed by the American administration for the oceans and the atmosphere (NOAA), from passive satellite data, made it possible to estimate precipitation on the whole planet. This rainfall, over 24-hour or 10-day periods, was used to assess the movement of locusts. At no point is there a simple or clear explanation of the type of data that goes into and out of the algorithm.

Through a study on the prediction of diabetes carried out in China by Yukai [START_REF] Li | Analysis and study of diabetes follow-up datausing a data-mining-based, approach in new urban area of urumqi, xinjiang,china[END_REF], we understand that algorithms such as the decision tree, Adaboost integrated learning model and Bagging were used. We also note that data such as fasting blood glucose, age and BMI... which are continuous numerical data were used. However, we do not know exactly which algorithm was used to process which type of data. So, for the analysis of our data going into prediction, we will look at different algorithms, which can help in the (ALP), depending on our data types.

The rest of this paper is structured as follows. The section 2 deals with the study of different algorithms that can be used for our prediction and larval identification techniques. Section 3 presents the implementation of our algorithms through the presentation of our data, experimental settings, experimental setups and results. Finally, Section 4 concludes this study and depicts our on-going work.

II BRIEF OVERVIEW OF PREDICTION ALGORITHMS

According to our analysed data, we can already distinguish several metrics (temperature, pH, width and depth of the site...), counts (number of larvae identified in the water...), binaries (yes, no...). We will define as a predictive variable the value that we know, the one that represents our input and as a response variable that will be used for the interpretation after the treatment.

Logistic regression model

Logistic regression is an analysis model that allows us to associate an event based on a qualitative variable with factors that may influence this event (explanatory variables). For this particular case, the explanatory variables will be of the continuous numerical type (temperature, pH, width and depth of a deposit) and the qualitative variable will be the presence (yes/no) of larvae. Therefore, note that the logistic regression does not directly provide the yes or no answer to the presence of larvae, as it is not these answers that are modeled. Instead, we model the probability of one of these options occurring Sandro [START_REF] Sperandei | Understanding logistic regression analysis[END_REF]. This probability is modeled by a sigmoid curve following the function :

f (x) = exp(x)/1 + exp(x) = p (1)
As the curve from our function is bounded by 0 and 1, based on our continuous numerical explanatory variables, we can determine the probability of the presence or absence of larvae.

The probability of the presence or absence of larvae. Thus, we consider the following model:

pi = P rob(yi = 1jxi) = F (xib) (2)
Based on each variable, we can determine whether or not it allows the development of Anopheles larvae.

Decision tree algorithm

A tree is an undirected, acyclic and connected graph composed of nodes. There are 3 types of nodes: the root node, the internal node and the terminal node. Trees use a hierarchical representation of the data structure in the form of decision sequences to predict an outcome.

Each case, or probable situation, is described by a set of variables that are tested in the nodes of the tree, as explained by Lior [START_REF] Maimon | Data mining with decision trees : theory and applications[END_REF].

In our situation, our cases can be the probabilities from our logistic regression algorithm. Our results will be binary. Let us note that we have two (02) types of possible variables for a tree:

the categorical and numerical variables. Let us also note that each time a variable passes a node it changes class as shown in Figure 3. Thus, our base or primary class will designate our results from our prediction.

Figure 3: Overview of the decision tree.

Random forest algorithm

The random forest is an ensemblistic technique composed of several decision trees. Each of the decision trees in the forest uses the same nodes but different data. It allows to merge the decisions of several decision trees in order to find an answer, which represents the average of all these decision trees. Our interest in this algorithm lies in the limitations of the decision tree.

Indeed, it happens that the generated trees are not balanced. By this we mean that, when reading, one branch may be longer than the others because of the results obtained. It is therefore recommended to balance the database before construction, to avoid one class dominating. In addition, sometimes the generated trees are too complex and do not adequately generalise the different cases, prone to processing. In addition, there is instability with some trees. This means that slight changes in the data produce very different trees and changes in nodes close to the root affect the resulting tree a lot as mentioned by Breiman (1996a). In order to deal with the limitations of the decision tree, the random forest introduces the Bagging (Bootstrap Aggregating)

and Boosting method, which allows to combine the output of several simple classifiers to obtain a better result according to: Breiman (1996b).

Neural networks algorithm

A neural network in its classical definition is a computer system inspired by the functioning of the human brain to learn. If we go further, we will understand that it is a set of nodes linked by various connections. In its publication, [START_REF] Abdi | Les réseaux de neurones[END_REF], Artificial Neural Networks (ANNs) are organised in layers with an input layer, an invisible layer and an output layer. Triggered by an input, the input nodes will trigger the other nodes through connections. In detail, we can say that we define the direct links (or connections) between the nodes to know where the information is directed as shown in Figure 4. In addition, we assign numbers (weights) to the connections so that some connections are stronger than others, like real neurons.

For the account of our research of prediction algorithms, neural networks can allow us to refine the results of our predictions. If we choose to use ANNs, we will opt for the supervised learning type, knowing that there are three types. Its operating principle is that it will use a set of known data and results to predict the unknown result of another set of data. We will thus follow the approach of [START_REF] Baxt | Use of an artificial neural network for data analysis in clinical decision-making: The diagnosis of acute coronary occlusion[END_REF], having passed by the stages of normalizations of data, of installation of our neural network, of training and finally of the prediction.

Naive Bayes algorithms

Naïve Bayes is a collection of classification algorithms based on Bayes' theorem. A basic result theorem in probability theory, whose statement is:

P (A|B) = P (B|A) * P (A)/P (B) (3)
It is not a single algorithm, but a family of algorithms. All these algorithms share a common principle, namely that each classified feature is independent of the value of any other feature as stated by [START_REF] Mitchell | Generative and discriminative classifiers: naive bayes and logistic regression draft chapter for volume 2 of Machine Learning textbook[END_REF]. The application of Bayes' theorem on several variables makes the calculation complex. Thus, to circumvent this, one approach is to consider these variables independently of each other. This is a strong assumption. Generally, the predictor variables are related to each other.

Linear discriminant analysis

Linear discriminant analysis (LDA) is a statistical technique that aims to describe, explain and predict the membership of predefined groups (classes, modalities of the variable to be predicted, etc.) of a set of observations (individuals, examples, etc.) from a series of predictor variables (descriptors, exogenous variables, etc.). The behaviour of the LDA is not the same when it comes to describing or predicting data. Let us consider qualitative variables with K categories and P quantitative variables. In description, we will look for the linear combinations of variables that best separate the k categories. On the other hand, in prediction we will see when a new individual for which we know the predictor variables, appears in which category it should be assigned according to [START_REF] Nakache | étude de problème posés par l'analyse linéaire discriminante en pas à pas[END_REF].

Larval identification

Mosquitoes are small insects (5 to 20 mm in size) with long, thin wings. They differ from other Diptera such as flies by, the presence of small scales on most veins and wings as shown in Figure 5. Their body is slender and slender, their legs have a coloration varying from light brown to black, sometimes marked with spots and bands. In the adult stage, it has the body divided into three distinct parts: the head, the thorax and the abdomen with for each part, its constituent elements. From eggs to adult, mosquitoes undergo several metamorphoses. In most species, the female needs a blood meal for the maturation of her eggs. The eggs give larvae, then nymphs. Emergence marks the transition from aquatic to aerial life as shown in Figure 6. After the emergence takes place the mating, then the females carry out the trophogonic cycle: search for a blood meal, rest for the ovarian maturation of the eggs then laying according to [START_REF] Clements | Development, Nutrition and Reproduction[END_REF] .

Our study being made on the larvas, we will be interested in the aquatic life. By observation, we can distinguish the larvae from the nymphs. Anopheles larvae can be recognized by the absence of legs and a relatively large thorax. Morphologically, the larva consists of three parts:

the head, the thorax and the abdomen. The head carries the 2 antennas, 2 large compound eyes, a pair of mouth brushes which serve to create a water current bringing the food particles to the mouth which is in ventral position. The thorax composed of 3 coalescent segments ensures the connection between the head and the abdomen. This last one includes 9 well visible segments, each one carrying various ornaments, in particular the tergal plate and the accessory plates, simple or branched or webbed bristles used to recognize the various species.

III LOGISTIC REGRESSION ALGORITHM IMPLEMENTATION

Raw collected datasets

Our dataset is collected by our sensors network as shown in Figure 7 The aim of the exploitation of these data is to allow us to find algorithms with a low error rate in the prediction and to find the most determining parameters for the presence of larvae. Given that we have about ten parameters to analyse, we try to retain only those parameters that really influence the presence of larvae. In order to achieve our objectives, we randomly assigned each record line to a new property: presence of larvae of Boolean type (0 or 1) as shown in Table 1.

Id

Experimental setting parameters

The implementation of all our algorithms was done on Python. To do this we deployed a virtual machine, Windows7, with 4 gigas of RAM, 60 gigas of storage and 4 Intel processors at 2.10

GHz. On this machine we installed Anaconda 3 with the different applications: Jupyter Lab, Jupyter Notebook and RStudio. The main steps of the data processing were: library import, data normalisation and data processing.

Experimental setup

Algorithm reliability test

Our aim was to determine which of our algorithms would be reliable, so we randomly allocated the presence of larvae and the absence of larvae to our parameters in the proportions of about 50 per cent each. Based on this, we split our data set into two parts, 60 percent and 40 percent.

The first part of the 60 percent allowed us to do the learning by our algorithms and the second part of the 40 percent, for the prediction. Knowing that we started with a rate of 50 per cent for each case we can evaluate the reliability of each algorithm on the basis of its prediction rate.

The more reliable the algorithm, the higher (above 50 percent) its prediction rate. We have logistical regression and discriminant linear analysis with 56.84 percent, random forest with 52.02 percent, naive bayes with 52.01 percent and neural networks with 50.04 percent as shown in Table 2.

Algorithm name

Search key parameters for the presence of larvae

Among our three leading algorithms, namely logistic regression, linear discriminatory analysis and random forest, only logistic regression allows us to establish the correlation between a parameter and the presence of larvae. This is due to the mode of representation of the variable "Presence of larvae", which is binary. Let us remember that logistic regression is a classification algorithm that allows us to determine two classes (true/false or yes/no). The objective of our analysis at this stage is to determine whether a parameter is a determining factor for the presence of larvae or not. Thus, we will establish two classes, namely the determining and non-determining parameters.

In a first step we determined the percentage of presence of larvae on all our available statistical data as shown in Figure 9. This value will be used as a control in the evaluation of the parameters.

Results

The experimental results allow us to identify an algorithm with a high reliability rate and to distinguish the parameters that are determinant for the presence of larvae. Thus the values of these parameters, in their evolution, influence the presence or not of larvae. The graphical interpretation of our graphs, allow us to distinguish: the electrical conductivity of the water, the turbidity of the water, the temperature of the water, the ambient temperature and the level of rain as determining factors of the presence of larvae.

The identification of these determining parameters was possible thanks to the algorithm: logistic regression. he choice of this algorithm, having itself, been the result of a test of experimental reliability, we can for the continuation of our study, retain only these determining parameters as being those coming from our network of sensors and being able to influence our prediction.

IV CONCLUSION

Following the implementation of our prediction algorithms on our statistical data, we were able to determine a list of three (03) reliable algorithms (logistic regression, ALD, random forest) to make our system optimal. Beyond deciding only on the algorithms that can be used to predict our data, we managed to determine a reliable algorithm for the types of data we process. Thus, thanks to logistic regression with a reliability rate of 56.84 percent, we went even further in determining the parameters that influence the presence of larvae.

We therefore found that only parameters such as: electrical conductivity of the water, turbidity of the water, water temperature, ambient temperature and rainfall level are determinant to predict the presence or not of larvae.

In the next part of our research, based on the results obtained, we will move on to the prediction of the presence of larvae, based on our determining parameters. This will allow us to set up a real time prediction system.

Figure 1 :

 1 Figure 1: Species of Anopheles identified in Senegal by region

  to malaria persist. Challenges still remain, including those related to universal access to key interventions such as the use of Long Lasting Insecticidal Nets (LLINs) and treatment with Artemisinin-based Combination Therapies (ACTs) down to the community level after diagnosis with Rapid Diagnostic Tests (RDTs). Despite these efforts, the morbidity and mortality rate of malaria in Senegal between 2018 and 2019 is still worrying.

Figure 2 :

 2 Figure 2: Level of morbidity and mortality indicators in 2018 and 2019.

Figure 4 :

 4 Figure 4: Simplified representation of a neural network

Figure 5 :

 5 Figure 5: Morphology of the adult mosquito.

Figure 6 :

 6 Figure 6: Representation of the life cycle of mosquitoes.

  and 8, deployed at the University Cheikh Anta Diop (UCAD) botanical garden. The water reservoir on which the parameters were taken is not a controlled environment. All our data were collected from 09/11/2018 to 15/12/2019 and are grouped after a first processing, in a database of 1863 records.

Figure 7 :

 7 Figure 7: Architecture of the measuring station.

Figure 8 :

 8 Figure 8: System for standby.

Figure 9 :

 9 Figure 9: Percentage of presence of larvae on all our data

Figure 10 :

 10 Figure 10: Graphs of non-determining parameters

Table 1 :

 1 Data presentation.

		Ph ORP C.E Turb Temp Vit Dir Hum Temp Rain P.Larv
	1	7	12	45 180.3	1.8	1.7 22.5 1.8	1.6	1.5	0
	2	8	10	60 178.8	1.9	1.7 22.5 1.9	1.7	1.5	1
	3	7	10	19 186.4	1.9	1.7 22.5 1.8	1.6	1.5	1

Table 2 :

 2 Ranking of the algorithms by reliability rate on our data.

ISSN: 2430-3038, c JIMIS, Creative Commons Volume: N -Year: YYYY, DOI: 10.18713/JIMIS-ddmmyy-v-a

J. of Interd. Method. and Issues in Science Open-access journal: http://jimis.episciences.org