Omar C Lezzar

A Bellel

M Boutamine

S Sahli

Y Segui

P Raynaud

Thin Film Coated QCM-Sensors and Pattern Recognition Methods for Discrimination of VOCs

Keywords: Discrimination of gas, pattern recognition, multi sensors, BFGS Quasi Newton algorithm. I

discrimination and quantification of volatile organic compounds (VOCs) using a non-selective sensor requires a combination of sensors followed by pattern recognition methods. Based on this concept, this paper deals with the discrimination of gas from the responses of several gas sensors coated with different type of polymer. Quartz crystal microbalance (QCM) electrodes were coated from hexamethyldisiloxane (HMDSO), hexamethyldisilazane (HMDSN) and tetraethoxysilane (TEOS) for the elaboration of gas sensors with different chemical affinity towards VOC molecules. The sensitivity of the elaborated QCMbased sensors was evaluated by monitoring the frequency shifts of the quartz exposed to different concentrations of volatile organic compounds, such as; ethanol, benzene and chloroform. The sensors responses data have been used for the identification and quantification of VOCs. The principal component analysis (PCA) and the neural-network (NNs) pattern recognition analysis were used for the discrimination of gas species and concentrations. Good separation among gases has been obtained using the principal component analysis. The feed-forward multilayer neural network (MLNNs) with a hidden layer and trained by Broyden Fletcher Goldfarb Shanno (BFGS) Quasi Newton algorithm has been implemented in order to identify and quantify the VOCs. By increasing the number of the neuron in the hidden layer, the precision of the estimate concentration increases. The approach is standard, however its application on the elaborated sensors have not been studied in depth so far.

INTRODUCTION

Volatile organic compounds (VOCs) can be found in a variety of settings, including industrial and residential sites. These compounds can cause disastrous effects on the environment through premature degradation of the surrounding area and health hazards to people living around the contaminated areas [START_REF] Nagle | The how and why of electronic noses[END_REF]. The identification and monitoring of VOCs have become serious tasks in many countries of the world and are important for the early control of environmental pollution [START_REF] Marıa | Discrimination of volatile compounds through an electronic nose based on ZnO SAW sensors[END_REF]. The need for an accurate, cost-effective and objective system for detection and identification of VOCs is therefore undisputed. In this study, the surface of the QCM electrode is coated with organosilicon material capable of interacting sensitively with the molecules of interest. However, the selectivity of sensors for gas is not possible in most cases. Instead, a multi-sensor system answering to these VOCs in different way is used to identify. The development of such system is based on the choice of sensors and multivariable analysis techniques [START_REF] Lay-Ekuakille | predicting VOC Concentration measurements : cognitive approach for sensor networks[END_REF]. Over the last decade, a lot of important work has been done on developing gas recognition systems from its smell (electronic noses) using pattern recognition methods such as: principal component analysis (PCA) and multi layers neural networks (MLNNs).

PCA is a very useful classification technique widely used in the gas-sensing area [START_REF] Yin | classification of chinese drinks by gas sensors array and combination of the PCA with wilks distribution[END_REF] and the neural networks are analogue computer systems, which are inspired by studies on the human brain and known to be universal approximates. Multi-layer neural networks (MLNNs) have been successfully used in replacing conventional pattern recognition methods for identification of chemical gases. Implementation of neural network to analyses the response of multi gas sensor offers several advantages over the conventional signal processing in terms of adaptability [START_REF] Gulbag | A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures[END_REF].

In this study, Plasma enhanced chemical vapor deposition (PECVD) technique has been used to produce QCM-coating with different physical and chemical structures layers. The sensing layers were elaborated from pure vapor of HMDSO, HMDSN and TEOS. The sensing properties of the elaborated QCM sensors have been evaluated towards VOCs molecules such as ethanol, chloroform and benzene. Subsequently, the data generated by these sensors are analyzed by pattern recognition methods that allow the identification and quantification of these VOCs. PCA and artificial neural networks multilayer perception (ANNMLP) are used as feature extractor using an experimentally obtained dataset.

II. EXPERIMENTAL

A. Thin Film Elaboration

Thin sensitive coatings were elaborated using PECVD technique [START_REF] Guermat | Thin plasmapolymerized layers of hexamethyldisiloxane for humidity sensor development[END_REF]. The films were deposited in low frequency plasma reactor from vapors of HMDSO, HMDSN and TEOS in order to obtain sensors with different chemical affinity. The system consisted of parallel symmetrical electrodes, vacuum system (composed of Alcatel primary pump) and a monomer inlet system (Fig 1). The pressure in the reactor was monitored by a pressure measurement system (Pirani).

QCM samples were placed in the grounded lower electrode and the reactor chamber was pumped down to 10 Pa. A constant partial pressure of monomers HMDSO, HMDSN and TEOS was adjusted to 30 Pa and injected to the reactor from the lower electrode. The monomers were varied in order to elaborate VOC sensors with different sensing properties and to create an impression of each VOC analyte.

B. Sensors and Measurement System

The principle of the QCM sensors is based on changes in the fundamental oscillation frequency Δf upon sorption of molecules from the gas phase. To a first approximation the frequency change Δf results from an increase in the oscillating mass Δm. This phenomenon can be described by the Sauerbrey equation (1) [START_REF] Mumyakmaz | predicting gas concentrations of ternary gas mixtures for predefined 3d sample space[END_REF].

Where A is the area of the sensitive layer, C f is the mass sensitivity constant (2.26×10 -10 m 2 s g -1 ) of the quartz crystal and f 0 is the fundamental resonance frequency of the quartz crystal.

"Fig 2 " shows the schematic view of the experimental setup. The used piezoelectric crystals were AT-Cut 5 MHz quartz crystal (ICM International crystal Manufacturers) with gold plated electrodes (8 mm of diameter) on both sides. The frequency changes of vibrating crystal were measured by a universal frequency counter QCM2000 (USA) connected to a personal computer via RS232 interface. The coated QCMbased sensors were tested for its sensitivity by monitoring the frequency shifts of the quartz exposed to different concentrations of VOC vapors. A liquid of known volume and density was introduced in the testing cell using a syringe and heated to evaporate freely. After evaporation and diffusion towards the electrode surface, the injected vapor was subsequently adsorbed onto the surface of the functionalized QCM electrode which induced a frequency shifts. This effect is reversible since the crystal is able to return to its initial state when the desorption occurs after purging the testing cell with dry air. The sensitivity of the elaborated QCM-based sensor was evaluated towards different concentrations of ethanol, benzene and chloroform. The concentration of injected analyte was calculated in parts per million (ppm) according to the following equation:

Where C is the concentration in ppm (1 mg/l 1 ppm), 𝛒 is the density of liquid sample in mg/l, V l is the volume of liquid sample in l, V is the volume of the testing cell in l.

The concentrations of the VOCs molecules were varied from about 40 to 200 ppm. The frequency shifts (Hz) versus concentrations (ppm) characteristics were recorded using three QCM sensors (coated with HMDSO, HMDSN and TEOS). Ethanol, Benzene and chloroform were used as analyte species. For discrimination of VOCs, the frequency shifts of balance state obtained from these measurements were used as an input data for pattern recognition methods. all measurements have been carried out at room temperature and humidity relative of about 30%. in order to take into account the effect of humidity which originates from the ambient, the resonance frequency of the QCM was measured and taken as the absolute resonance frequency before measurements.

III. RESULTS AND DISCUSSION

A. Sensors Responses

"Fig 3 " shows the typical real time frequency response curves of the coated QCM sensors. For all types of VOCs vapors, the kinetic response characteristics showed that the absolute value of the QCM frequency shift increases gradually with time then reaches a steady value. The maximum responses were taken until the frequency variations were less than 1 Hz. When the maximum adsorption of the QCM sensor was obtained, the test chamber was purged with dry air and the VOCs vapor desorption process took place. The application of 4b) has a great affinity to ethanol than chloroform and benzene. Slight variation of the shift frequency for chloroform and benzene compared to ethanol is observed, therefore, the sensor coated with HMDSN thin film present selectivity for ethanol.

From the sensors responses characteristics, it is clearly seen that there is not of overall selectivity. This drawback can be compensated by the use of the three sensors response data. The combination of sensors is used to obtain a specific fingerprint for each gas and allow the sensors to identify it last.

B. Discrimination of VOCs by Principal Component Analysis

PCA analyzes the data by transforming interdependent coordinates into independent orthogonal set of coordinates called Principal Components (PCs) to maximize their variance [START_REF] Barshan | Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds[END_REF]. PCA reduces a matrix of large data in a smaller losing less information size to detect the relationship between the data provided by the sensors and grouping them with similar characteristics (group of VOCs) [START_REF] Peng | evaluation of the aroma quality of chinese traditional soy paste during storage based on principal component analysis[END_REF]. Its main purpose is to summarize the whole of a complex matrix of data in a graphical representation of two or three main axes [START_REF] Doymaz | A strategy for detection and isolation of sensor failures and process upsets[END_REF]. First two or three (PCs) axis explains the maximum variance and is most suitable to analyze the data [START_REF] Srivastava | On the performance evaluation of hybrid and mono class sensor arrays in selective detection of VOCs: A comparative study[END_REF][START_REF] Alizadeh | electronic nose based on the polymer coated saw sensors array for the warfare agent simulants classification[END_REF][START_REF] Han | Nanoparticle-structured sensing array materials and pattern recognition for VOC detection[END_REF]; Since PCs are obtained as linear combinations of original variables (sensors). Significant PCs are selected on the basis of their eigenvalues. Thus, PCA not only reveals redundant information in the data set and helps selecting important features (sensors). PCA is provided by the STATISTICA software " Fig 5" shows the result obtained using PCA method, 12 samples were used each sample has three variable corresponding to each sensor. The raw data shall be entered in the STATISTICA software, a common way for determining the PCs of a data set is by calculating the eigenvectors of the data correlation matrix. It delivers to the end of the analysis a graphical presentation that showing the relationship between the samples grouping them according to their similarities in the different areas of gases. The PCs are ordered so that PC1 displayed the greatest amount of variance followed by the next greatest PC2 and so on; the first two principal components have a high cumulative variance (99.46%), indicating that the plane PC1 PC2 is very descriptive to classify gases. The results indicate that, with the PCA it was possible to distinguish three groups of gas such as ethanol, chloroform and benzene with different concentrations. The largest concentrations of ethanol are separated from the center of gravity of this latter which is due to the large sensors response to high concentrations of ethanol compared to chloroform and benzene, especially for sensor coated with HMDSN vapor. So, it was possible to identify the ethanol, chloroform and benzene from each other with the PCA method.

C. Identification and Quantification of VOCs by MLNNs

The MLNNs structure used for this purpose is shown in (Fig 6), the input layer is composed of a number of neurons corresponding to the number of used sensors, three neurons each neuron corresponds to a sensor and the output layer corresponds to the number of studied gases (identification or quantification), tree neurons each neurons correspond to a gas. The network may have more neurons in hidden layers. An activation function is applied to each neuron and it is identical to the neurons of the same layer. In this study, the activation function of hidden layer is "tansig" and the activation function of output layer is "logsig" The equations used in the neural network model are shown in Eqs (3)-( 4).

Tansig function:

Logsig function:

Where: X m the hidden layer neuron value Eqs (5) and Y n the output layer neuron value Eqs (6)

∑( ) ∑ ( )

Where: Δf is the sensors response, W ij is the weight from the input layer to hidden layer, b ij bias of hidden layer, W jk is the weight from the hidden layer to output layer, b jk bias of the output layer, l is the number of the input neurons, m is the number of the hidden layer neurons and n is the number of the output neurons.

To train the network, there are several learning algorithms, a number of researchers have conducted comparative studies of learning algorithms [START_REF] Gulbag | Quantitative discrimination of the binary gas mixtures using a combinational structure of the probabilistic and multilayer neural networks[END_REF]. STATISTICA software offers three types of learning algorithms: Quasi Newton, conjugate gradient and gradient descent.

Quasi-Newton (QN) is an advanced method for training of the MLNNs, QN algorithm used in this study is one of the fastest types of these algorithms and this method converges faster than conjugate gradient and gradient descent methods [START_REF] Gulbag | A study on radial basis function neural network size reduction for quantitative identification of individual gas concentrations in their gas mixtures[END_REF][START_REF] Sagiroglu | Control chart pattern recognition using artificial neural networks[END_REF]. The Newton algorithm is computed according to the following:

⃑⃑⃑ ⃑⃑⃑ ⃑ ⃑ Where: ⃑⃑⃑ ⃑⃑⃑ ⃑ ⃑ ⃑⃑⃑

⃑⃑⃑

Where: Hessian matrix ⃑ ⃑ is the second derivative of the mean squared error function ⃑⃑⃑ at the current values of the weights and biases.

The neural network was trained and validated by samples. The training samples are composed of pairs of data, the input data that includes the responses of the sensors and the output data that includes the identification of gas and estimation of the concentration. These pairs (input, target) are presented to the network to adjust the parameter weights and biases to build the model. The training is completed when the output of the network approaches the interest values (target) when the error Eqs (8) between the desired output (target) and the network output is smaller. As long as the error is large, the learning algorithm continues updating the bias (b) and synaptic weights (w).

∑

The training of sensors has been carried out using a large range of analyte concentration and the test of the model has been carried out using unknown concentration for the quantitative and gas identification.

For identification, four neurons in the hidden layer is sufficient to obtain a perfect identification of tested gas, each 1" summarizes the result of few training samples of concentration estimation by the network with different number of neuron in hidden layer. Using the structure with four neurons in the hidden layer the estimate concentration presents a great difference between the target and the output of the network. Increasing the number of neurons in the hidden layer can decrease the difference between output and target.

The power of neural networks has led to the identification at 100% of the target gas and estimated its corresponding concentration.

IV. CONCLUSION

In this study, the use of a combination of non-selective sensors has allowed the identification and quantification of gas with the PCA and MLNNs, The application of PCA, that is a method of visual analysis showed a good separation of areas of the studied gas and the neural networks have confirmed this separation by identifying the gas introduced into the chamber. The increase in the number of neurons in the hidden layer increases the network performance, for quantification. The use of four neurons in the hidden layer was enough to get a good result, but the quantification was difficult, which is overcome by the increase of the number of neurons in hidden layer.

Figure 1 .

 1 Figure 1. plasma reactor used for QCM Coating.

Figure 2 .

 2 Figure 2. Experimental setup used for sensor response evaluation.

Figure 3 .

 3 Figure 3. typical sensor response coated with HMDSO for tree types of VOCs.

Figure 4 .

 4 Figure 4. Sensitivity toward different concentrations of VOCs of a QCM sensor coated with: (a) HMDSO, (b) HMDSN and (c) TEOS film .

Figure 5 .Figure 8 .

 58 Figure 5. Plot of the second principal component against the first using the data for the three-sensor

Figure 6 .Figure 7 .

 67 Figure 6. Structure of MLPNN (3:4:3) for identification and quantification of ethanol, chloroform and benzene

TABLE I .

 I Quantification of VOCs

	100%.
	"Table
	Number of neurons in	Target concentration (ppm)	Output network concentration (ppm)
	hidden layer	Ethanol	Chloroform	Benzene	Ethanol	Chloroform	Benzene
		(N1)	(N2)	(N3)	(N1)	(N2)	(N3)
		142	0	0	143.61	6.75	3.53
	4	0	47	0	0	52.71	43.19
		0	0	95	0	1.5	74.13
		142	0	0	143.61	0	0
	6	0	47	0	0	37.75	1.61
		0	0	95	0	0.1	102.5
		142	0	0	148.48	0	0
	8	0	47	0	0	46.99	0
		0	0	95	0	0	95


O Antonin

R Schoeppner

M Gabureac

L Pethö

J Michler

P Raynaud

T Nelis

Nano crystalline diamond MicroWave Chemical Vapor Deposition growth on three dimension structured silicon substrate at low temperature

Keywords: CVD, MW-LPCVD, MEPS, Diamond, Nano Crystalline Diamond, 3D step coverage

Nano crystalline diamond (NCD) films grown at a temperature below 400 °C can open new applications on temperature sensitive substrates such as polymers or low-melting point alloys. One requirement for the applicative use of NCD is the ability of depositing on structured substrate showing high aspect ratio. This work presents a study of the 3D conformity NCD deposition at low temperature (350°C) and low pressure (30 Pa). Silicon wafers have been structured using a mask-less DRIE process and seeded with nano-diamond particles. The NCD films were grown on these 3D patterned Si substrate with various trench geometries to provide means of determining the limiting geometries of this technique. By measuring the step coverage with changing trench width, a threshold for conformal NCD growth can be determined. The NCD films at the bottom of the 100 µm deep trenches were continuous down to an aspect ratio of 1:7.

Introduction

The unparalleled hardness, low friction and heat conductivity properties of diamond make it attractive for many applications. The use of diamond as a coating has increased with the development of high growth-rate protocols using Hot Filament Chemical Vapor Deposition (HF-CVD), which usually operates at high substrate temperatures around 600 to 900 °C [START_REF] Fuentes-Fernandez | Synthesis and characterization of microcrystalline diamond to ultrananocrystalline diamond films via Hot Filament Chemical Vapor Deposition for scaling to large area applications[END_REF]. However, the high substrate temperatures (> 600 °C) of HF-CVD limits the possible substrate materials. Developments over the past few years in the area of Micro-Wave Plasma Enhanced CVD (MW-PE-CVD) have made the deposition of CVD diamond possible at far lower substrate temperatures (< 400 °C) [START_REF] Izak | Low temperature diamond growth by linear antenna plasma CVD over large area[END_REF][START_REF] Tsugawa | Large area and low temperature manodiamond coating by microwave plasma chemical vapor deposition[END_REF][START_REF] Baudrillart | Microstructure and growth kinetics of nanocrystalline diamond films deposited in large area/low temperature distributed antenna array microwave-plasma reactor[END_REF]. These developments have mainly focused on the deposition of Poly-Crystalline Diamond (PCD) and Nano-Crystalline diamond (NCD). In contrast to PCD, NCD has low surface roughness, while keeping similar mechanical properties [START_REF] Williams | High Young's modulus in ultra thin nanocrystalline diamond[END_REF]. Films with these properties have strong application interests [START_REF] Williams | Growth, electronic properties and applications of nanodiamond[END_REF] as protective layers for cutting tools, Micro-Electro-Mechanical System (MEMS) devices [START_REF] Possas | Fabrication and micromechanical characterization of polycrystalline diamond microcantilevers[END_REF], thermal conductive layers for high power devices, and coatings for Atomic Force Microscope (AFM) tips [START_REF] Ralchenko | Quality of diamond wafers grown by microwave plasma CVD effects of gas flow rate[END_REF][START_REF] Eaton | Modeling the effect of oxygen vapor phase diamond deposition inside microtrenches[END_REF][START_REF] Mammana | Diamond membranes with controlled porosity[END_REF][START_REF] Sumant | Ultrananocrystalline and nanocrystalline diamond thin films for MEMS/NEMS applications[END_REF]. Different approaches have been developed to achieve large area deposition of NCD at low temperatures. The linear array configuration has been successful in depositing NCD at low temperatures on large substrate areas [START_REF] Izak | Low temperature diamond growth by linear antenna plasma CVD over large area[END_REF][START_REF] Tsugawa | Large area and low temperature manodiamond coating by microwave plasma chemical vapor deposition[END_REF][START_REF] Kromka | Linear antenna microwave plasma CVD deposition of diamond films over large areas[END_REF][START_REF] Ficek | Linear antenna microwave chemical vapour deposition of diamond films on long-period fiber gratings for biosensing applications[END_REF]. This process makes use of the surface-wave, i.e. propagation of the microwave at the interface between the plasma and a dielectric. A different approach using an array of coaxial plasma sources has been used to achieve NCD deposition at low temperatures and on large surface areas [START_REF] Mehedi | Low temperature and large area deposition of nanocrystalline diamond films with distributed antenna array microwaveplasma reactor[END_REF]. In both approaches, the possibility of producing large area treatment is somewhat compromised by the need for complex power distributors and impedance matching systems. Application of the NCD deposition methods to industrial application requires deposition on structured substrates, with high aspect ratio features to determine the process's limitations. In addition, threedimensional patterned substrates are a useful tool for the study and modelling of CVD growth [START_REF] Gordon | A kinetic model for step coverage by atomic layer deposition in narrow holes or trenches[END_REF][START_REF] Komiyama | Chemical reaction engineering in the design of CVD reactors[END_REF][START_REF] Shima | High-aspect-ratio parallel-plate microchannels applicable to kinetic analysis of chemical vapor deposition[END_REF], which is still not fully understood. Floter et al. [START_REF] Floter | Deposition of diamond on patterned silicon substrates[END_REF] in 1995, reported a strong dependence of the nucleation density on the orientation of the surface when bias pre-treatment of structured Si substrate was employed. This lead to a very low or absent diamond growth on the side-wall of trenches. However, the bottom of trenches were coated with diamond for aspect ratios reaching 7, with a maximum film thickness related to the width of the trench, rather than its depth or the length of the treatment. In this study, the MW-CVD deposition was performed at elevated temperatures (750 °C-900 °C) and a pressure of 4000 Pa. Monteiro and Liu [START_REF] Monteiro | Nucleation and growth of CVD diamond films on patterned substrates[END_REF] reported in 2003 on the effect of a diamond like carbon pre-treatment on diamond growth on structured surfaces. They achieved homogeneous growth on [Tapez ici] structures showing an aspect ratio below one. The exact deposition conditions were not reported in this work, but from the crystalline structure it can be concluded that the substrate temperature was above 500 °C. Ralchenko et al. reported in 2004 [20], again in the context of large diamond crystal growth, that seeding with diamond particles in an ultra-sonic bath treatment yielded best results in 3D conformity of the pre-treatment. Eaton et al. reported in 2001 [9] on modelling the effect of oxygen on the capability of conformal 3D diamond deposition. Due to the higher lifetime of OH radicals, compared to atomic hydrogen, they predict better step coverage when oxygen is added to the gas mixture. However, even if interesting information can be found in these publications, none of them explicitly addresses the lowtemperature deposition of NCD on structured surfaces. In this publication, we present work using a set of Hi-Wave sources in a Matrix Elementary Plasma Source configuration (MEPS) [START_REF] Rats | Method for a diamond vapor deposition -US20160032450A[END_REF] for NCD deposition using low power density (< 7.6Wcm -2 ), low deposition temperature (350 °C) and low pressure of 30 Pa. The structure of the deposited NCD film was evaluated on a structured three dimension (3D) silicon substrate with trenches up to 100 μm deep with aspect ratios ranging from 1:1 to 1:7.

Experimental details

Substrate preparation

A 100mm diameter single crystal silicon wafer with 〈100〉 orientation and 525 μm thickness was micro patterned in a cleanroom environment. The surface was first coated with the AZ 9221 photoresist from Microchemicals to a thickness of 2 μm by spinning at 4200 RPM, then soft baking at 110 °C. This photoresist enables high resolution, straight sidewalls and good resistance to the subsequent deep reactive ion etch process, without the need for a post-development hard bake step. The resist layer was exposed by direct laser writing in a Heidelberg MLA150 with a beam diameter of 1 μm and an exposure intensity of 130 mJ/cm2. The exposed photoresist is removed by immersion into Microchemicals AZ400K developer, then the substrate is rinsed with DI water. To transfer the pattern into the silicon substrate, an Alcatel AMS 200E inductively coupled Radio Frequency (RF) plasma etcher was used to run a Bosch process with the inductively coupled plasma (ICP) power set to 1800 W. In each cycle, the substrate was exposed to the silicon etchant (SF6) for 6 s, then to the sidewall passivation gas (C4F8) for 2 s. This two-stage process was repeated until the target depth of 100 μm was achieved. The average etch rate was 4.88 μm/s. The remaining photoresist was removed using a barrel plasma etcher with 400 ml/min O2 flow and a 500W RF plasma. The silicon wafer was diced into 10×10mm chips along the crystalline axes with a Disco DAD321 automated dicing saw. All substrates were seeded by NeoCoat SA, CH, using a proprietary process by immersing in a bath containing diamond nanoparticles (around 10 nm in diameter) in suspension. This seeding process usually yields a high density of nuclei [START_REF] Mehedi | Low temperature and large area deposition of nanocrystalline diamond films with distributed antenna array microwaveplasma reactor[END_REF]. Prior to deposition, all samples were exposed to pure H2 plasma with increasing microwave power for approximately 1 h.

NCD Deposition

The MEPS reactor used for the diamond deposition was home-built [START_REF] Rats | Method for a diamond vapor deposition -US20160032450A[END_REF]. It employs seven microwaves antennas (Hi-Wave, SAIREM, FR) [START_REF] Grandemange | Facility for microwave treatment of a load -US 20140197761A1 -2014[END_REF], arranged in a hexagonal configuration with a distance of 55mm between sources. Each of the sources is individually connected via a coaxial cable to a 200W solid-state microwave generator. Each of the seven microwave generators can independently frequency tuned to reduce the reflected power below 5 W. The reactor concept resembles in many points the Distributed Antenna Array (DAA) [START_REF] Baudrillart | Microstructure and growth kinetics of nanocrystalline diamond films deposited in large area/low temperature distributed antenna array microwave-plasma reactor[END_REF][START_REF] Mehedi | Low temperature and large area deposition of nanocrystalline diamond films with distributed antenna array microwaveplasma reactor[END_REF][START_REF] Baudrillart | Nanocrystalline diamond films grown at very low substrate temperature using a distributed antenna array microwave process[END_REF]. The major differences are the use of solidstate generators and auto matched microwave antennas, allowing for operation without power distributors and additional impedance matching. A more detailed description of the Hi-Wave antennas used in this work is given in [START_REF] Latrasse | Self-matching plasma sources using 2.45 GHz solid state generators: microwave design and operating performance[END_REF]. The gas supply system consists of four mass flow controllers and an Edwards E2M40 rotary pump. The CVD growth on silicon was conducted at a pressure of 30 Pa and a substrate temperature of 350 °C with a power density of 6.8Wcm-2 (180W per microwave antenna). The plasma density, determined using Langmuir probe measurement, reaches 2 • 1011 cm-3 corresponding to approximately twice the critical density for this excitation frequency (2.45 GHz). The distance between the substrate holder and the applicators was 50 mm. Since the substrate was heated directly by the plasma, no substrate heating was necessary. The dihydrogen and methane gas flows were 47 sccm and 3 sccm, respectively, diluted in argon. This gas mixture corresponds to a 97%H2-3%CH4 concentration. It corresponds to the lower left corner in Bachmann diagram for CVD diamond growth and is typically used in the HF-CVD deposition technique. The average growth rate under these conditions was approximately 35 nm/h. The emission of CVD system was monitored by an Ultima II, Horiba-Scientific, 1m Czerny-Turner spectrometer. The observed spectra included the CH A-X, -X, C-X molecular bands, the 429.902 nm line of atomic carbon and various atomic hydrogen lines indicating a high level of dissociation. Raman spectroscopy characterization of the NCD films was performed using a MonoVista CRS+ with a ultra violet (UV) objective (×40) @ (325 nm; 20 mW) laser and visible-light lens (×50) @ (514 nm; 10 mW) laser. The detector is an InGaAs Andor Camera cooled to -70 °C. Scanning Electron Microscope (SEM) cross sectional images of the structured Si samples were obtained by cleaving and imaging in a high resolution field emission SEM, model S4800 N from Hitachi, with a resolution around 2 nm at 1.5 kV.

Results

A silicon chip was cleaved to obtain a cross-section of the structure and to determine the trench geometries by high resolution scanning electron microscopy. Trenches with opening widths of 100, 50, 20, and 10 μm were crosssectioned. The attained trench depth reduces with decreasing feature width, which is a well-known side-effect [START_REF] Huard | Role of neutral transport in aspect ratio dependent plasma etching of threedimensional features[END_REF] due to gas depletion during DRIE. For the Bosch process, this Aspect Ratio Dependent Etch (ARDE) effect becomes prominent as the aperture width decreases to a few microns. The progress of the plasma etch was monitored on a large open area and was stopped when the top-to-bottom offset reached 100 μm. The resulting trench depths were 100, 98, 80 and 70 μm, respectively; and the achieved geometries correspond to aspect ratios of 1:1, 1:2, 1:4 and 1:7 (Fig. 1). The trench length in each case was 1 mm. The sidewall scalloping is formed by the alternating etch and passivation steps of the Bosch process and slightly change with trench width due to the ARDE effect. The 10 μm wide trench had a scallop height of 610 nm, and an edge to edge width of 190 nm. The values for the 100 μm wide trenches were 680 nm and 230 nm, respectively (Fig. 2).

Seeding Characterization

The surface coverage resulting from this seeding process was evaluated by treating HR-SEM top-view images (500K magnification) with the "Wand-Auto Measure" Tool of the ImageJ software package. It was found to be of approximately 66%, which then results in a distance between seed agglomerations of around 20 nm. For features with an aspect ratio of 1, the seed density on the bottom is not significantly different to the top of a feature. However, as the aspect ratio (AR) decreases, the seeding density at the bottom of the trenches also decreases, with the 1:7 (10 μm) trench showing very few diamond particles at the bottom of the trench (Fig. 3a). The three geometries with the largest opening widths (100 μm, 50 μm, and 20 μm) show a similar seeding density at the bottom of the trench, with only a slightly decreased density in the 20 μm geometry. Since these were imaged at a tilt angle of 70°, it is difficult to measure the exact inter-particle spacing for each of the trench geometries. However, as the opening width drops below 20 μm, noticeably fewer particles are observed on the bottom of the trench with an inter-particle spacing reaching as much as 200 nm. Similar high-resolution images of the seeding density on different sections of the sidewalls also showed a dependence on both the depth and AR of the trench. Fig. 4 shows the two extremes in the current study, comparing trenches with an AR of 1:1 and 1:7, all imaged at a tilt angle of 70°, at the top, middle and bottom of the trenches. Although the structuring resulting from the DRIE process partially disguises the seeding density, it is apparent that the seeding density on the sidewalls is much lower than the seeding density observed on the surface of the sample. In the middle of the trench, the agglomeration spacing is approximately 45 nm for the 100 μm sample, which is roughly twice what was measured for the top surface. However, for the middle section of the 10 μm trench the agglomeration spacing is even greater at about 55 nm. The top section of each trench shows a much higher density when compared to the middle, with the 10 μm trench showing significantly fewer numbers of particles than both the top surface as well as at the same depth as the 100 μm trench. However, the most drastic reduction of seed density is observed at the bottom of the trench, as was mentioned previously.

Film characterization

The Raman spectrum of NCD contains typically several components. The G peak (1600 cm -1 ) and the D peak (1350 cm -1 ) are caused by amorphous sp2 carbon, most likely at the grain boundaries. Several peaks typically associated with NCD are situated at 1460 cm -1 , 1240-1280 cm -1 and 1170 cm -1 , labelled with an S in Fig. 5. The origin of these peaks is still a subject of discussion, but is commonly associated to trans-poly acetylene present on the NCD surface. And finally the sharp peak at 1333 cm -1 caused by the first order Raman response of diamond [START_REF] Floter | Deposition of diamond on patterned silicon substrates[END_REF][START_REF] Yanguas-Gil | Growth and Transport in Nanostructured Materials: Reactive Transport in PVD, CVD and ALD[END_REF][START_REF] Baek | The microstructure of CVD diamond for highdensity thermal inkjet[END_REF][START_REF] Kononenko | Formation of antireflective surface structures on diamond films by laser patterning[END_REF]. Two laser lines were employed for the Raman measurements, the 514 nm (green) line for comparison with results found in the literature and a 325 nm (UV). Raman scattering of the sp2 components is reduced when excited with the shorter wavelength. At 514 nm, the Raman spectrum has a typical NCD Raman signature [START_REF] Ferrari | Raman spectroscopy of amorphous, nanostructured, diamond-like carbon, and nanodiamond[END_REF], where the sharp diamond peak is barely visible. When the shorter wavelength laser source is used the diamond peak becomes clearly distinguished from the background and other carbon contribution, making its identification straight-forward and un-ambiguous. Top-view SEM images, not shown in this work, were used to estimate the diamond grain size to be in the order of 50 nm-100 nm.

Film thickness

Cross-sectional images of the NCD coating on silicon show a compact, crystalline, smooth layer about 660 nm thick. As can be seen in Fig. 6, diamond growth was observed inside 100 μm deep trenches and all along the side-walls of the trench, validating the seeding and CVD plasma parameters used. The top surface of the trench (Fig. 7a) shows a 660 nm thick and compact NCD layer uniformly grown on the silicon surface. The topcorner (b) is imaged with a lower magnification to present the top surface and vertical wall of the trench. The first scallops which occur during the DRIE silicon etching procedure are visible. These scallops are about 500 nm in the vertical direction and 100 nm wide in the horizontal direction. They are fully covered with the NCD film and the undulation is visible on the NCD surface, i.e. a high level of conformity is reached. Fig. 7c shows the NCD growth at the bottom surface of a trench. Even at depth of 100 μm the bottom is coated with a compact and uniform NCD layer, with a structure very similar to the film grown on the top surface. However, at the bottom of the trench the film only grew to a thickness of 450 nm. Finally, Fig. 7d presents the bottom corner of a trench, where the NCD film follows the silicon surface, remaining dense and homogeneous. The film thickness at the vertical wall is slightly lower compared to the horizontal bottom, as was also observed in the top corner of the trench. The next series of SEM images (Fig. 7), displays the NCD films obtained in trenches with high aspect ratio. No indication of a significant change in film structure, i.e. crystalline structure, can be identified. The film thickness decreases with decreasing trench width, to reach 110 nm for the bottom 10 μm trenches and 150 nm for the 20 μm trenches. Scallops observed on the side wall are fully covered, but a small shadowing effect is observable. Whether this shadowing is a result of a reduction flux density of reactive species generated in the MW plasma or from the initial seeding process is difficult to verify. Table 1 summarizes the film thicknesses measured for the different trench sizes averaging over 7 measurements with a relative uncertainty of approximately 10%.

Discussion

The complex diamond CVD growth mechanisms involve both creation of sp3 carbon bonds and selective etching of the sp2 carbon by hydrogen. In addition to its role as a selective etchant, hydrogen bombardment creates reactive sites on the diamond surface on which CH3 radicals can dock [START_REF] Ashfold | Thin film diamond by chemical vapour deposition methods[END_REF][START_REF] Petherbridge | Modeling of the gasphase chemistry in C-H-O gas mixtures for diamond chemical vapor deposition[END_REF].

Then, the two neighbouring methyl groups can form a sp3 C-C bond and release two hydrogen atoms. These steps, creation of reactive sites on the diamond surface, docking of methyl groups and combination of 2 neighbouring methyl groups are repeated over and over again to form sp3 carbon bonds [START_REF] Beckmann | On the gas-phase mechanisms in MWCVD and HFCVD diamond deposition[END_REF]. However, different or additional growth mechanisms are discussed in the literature involving not only the methyl radical, CH3, but also CH2, CH and atomic carbon as source for sp3 carbon addition to the diamond structure [START_REF] May | The mechanism for ultrananocrystalline diamond growth[END_REF]. The step coverage (SC) defined as in Eq. ( 1) can be used to evaluated the 3D conformity of a deposition process.

𝑆𝐶 = 𝑑 𝑏 𝑑 𝑡 (1)
where db and dt are the layer thickness at the bottom of the trench and the top of the substrate, respectively. Many different approaches are possible to describe and model the film growth in 3D structures with high aspect ratios. One such approach, adapted to the low pressure regime used in this study, considers the view factor of surfaces in structures, with high aspect ratios. At a pressure of 30 Pa the mean free path in the rarefied gas atmosphere is in the millimetre range, significantly above the dimensions of the trenches. Assuming a sticking probability of one, growth along a wall perpendicular to the trench mouth would lead to an estimation of the SC [START_REF] Yanguas-Gil | Growth and Transport in Nanostructured Materials Reactive Transport in PVD, CVD, and ALD[END_REF]:

𝑆𝐶(𝑧, 𝑤) = [1 - 𝑧 (𝑤 2 + 𝑧 2 ) 1 2 ] ( 2
)
where z is the depth inside the trench, and w is the width of the trench opening. [Tapez ici] describe the growth phenomenon, and another mechanism must exist which includes additional factors like sticking probability. Comparing the trench coverage enhancement, Te, and surface area enhancement, Se, should show the degree of conformity achieved from this process. Te is the ratio of the amount of material deposited in the trench to the amount of material deposited on the cross-sectional surface on the top, while Se is the ratio of the total trench surface area over the surface area of the trench opening. For a perfectly conformal deposition process Te should be equal to Se [START_REF] Yanguas-Gil | Growth and Transport in Nanostructured Materials Reactive Transport in PVD, CVD, and ALD[END_REF]. Whereas, if each particle hitting the surface directly contributes to the film growth, i.e. sticking probability equal one, the trench coverage enhancement would be equal to one. In this work, the trench coverage enhancement, Te, and surface area enhancement, Se, were approximated by:

𝑇 𝑒 = 2 • 𝑑 𝑠 • 𝐿 + 𝑑 𝑏 • 𝑤 𝑑 𝑡 • 𝑤 (3)
𝑆 𝑒 = 2 • 𝐿 + 𝑤 𝑤 (4)
Where L and w, are the depth and width, the film thickness, dt at the top, db the bottom and ds at the sidewalls of the trench. The results are summarized in Table 3.

Table 3: Trench coverage enhancement

Trench width / µm 100 50 20 10 Trench coverage enhancement (Te) 1.8 2.8 3.2 2.5 Surface area enhancement (Se)

3 5 9 15
The trench coverage enhancement is clearly smaller than the surface area enhancement, indicating the process in not fully conformal. However, a trench coverage enhancement of three indicates that the limiting species entering the trench have a three times higher likelihood to eventually contribute to NCD film growth, compared to those hitting the top surface. The reason for this enhancement is most likely multiple collisions with the trench wall, which relates to a sticking probability for these growth limiting species below one.

Another simple approach to model film growth on structures with high aspect ratios is the microchannel model. It has been described by Yanguas-Gil [START_REF] Yanguas-Gil | Growth and Transport in Nanostructured Materials Reactive Transport in PVD, CVD, and ALD[END_REF] and references to original work cited. It relies on diffusion of the gaseous compounds rather than a ballistic approach. Although relying on diffusion, it is still applicable for the molecular flow regime in small feature, due to the molecule-wall interaction leading to Knudsen diffusion. This model specifically allows for nonunity sticking probability. If the microchannel model applies, it allows an estimation of the sticking probability. A sticking probability of β=0.1 seems to best fit the experimental data. However, the agreement between the experimentally observed step coverage and model prediction is still too poor, as shown in Table 4, to trust this value. This values would also be 1000 times higher than the sticking probability of CH3 on a diamond surface as estimated by Schwaederlé et al. [START_REF] Schwaederlé | Molecular dynamics calculations of CH 3 sticking coefficient onto diamond surfaces[END_REF] using molecular dynamics calculations. The evolution of the step coverage of the NCD layer on deep trenches in Si does not follow either of the two simple models analysed here. The underlying growth mechanism involving at least two different radicals (i.e. atomic hydrogen and CHx radical) is likely too complex for a simple model to accurately describe the growth mechanism. In addition, migration of reactive sites on the diamond surface is expected to play a role in NCD growth [START_REF] Schwaederlé | Molecular dynamics calculations of CH 3 sticking coefficient onto diamond surfaces[END_REF], although this additional mobility is most likely to be limited to displacement on one single crystal, in the 10 nm range. Furthermore, both of these models assumed no change in seeding density with increasing AR and depth, which was observed to be a false assumption and further complicates the growth model. More experimental data, i.e. a larger variety of 3D structures, and deposition at different pressures and substrate temperatures, are required to build a base for further detailed analyses of the growth and transport phenomena involved in NCD deposition of high aspect ratio surfaces.

Conclusions

A novel concept of MEPS microwave plasma reactor has been used for NCD deposition at low temperature. The system uses auto-matched elementary sources and solid-state microwave generators. Operating without power-dividers and impedance matching, this process offers the possibility of up-scaling in view of industrial application. This new system has been successfully used to produce compact NCD coating on 3D structured silicon substrate, i.e. trenches with aspect ratios up-to seven. The whole process involved the production of the structured silicon wafer using a mask-less etching process, followed by seeding the structured surface with diamond nanoparticles, and finally low temperature deposition of the NCD film. Although the seeding density is clearly dependent on the aspect ratio of the structures, and was very low at the bottom of trench that had an aspect ratio of 1:7, it was sufficient to allow for the growth of the compact NCD layer in these features. The variation of step coverage with the aspect ratio does not follow any of the simple approaches analysed. The NCD growth process, involving several steps and interaction of different radical produced in the MW plasma is obviously too complex to be modelled by the simple approaches used in this study. In addition, the dependence of the growth rate of the seeding density is not yet well understood. The 3D conformity of the entire process falls behind full conformity, as achieved by Atomic Layer Deposition (ALD), but is better than the conformity of many Physical Vapor Deposition (PVD) processes. For this NCD MEPS CVD process the amount of diamond grown inside the trenches is approximately 3 times higher than the amount grown on an equivalent crosssectional surface area. Achieving reasonably good 3D conformity for low temperature deposition process presents an important step forward in direction of an industrial application of this technology, for example in the cutting tool, watch, and medical -technology industries. However, there is room for improvement and further research and development is required prior to a successful industrial application. Directly related to this work, the influence of seeding density on growth rate should be studied in more detail to possibly further reduce the variation of film thickness with changing aspect ratio. Further work on the adhesion of NCD films in particular on substrate materials other than silicon is also Required .

Figure 2 :

 2 Figure 2: Cross sectional SEM images of scallops: 10 µm wide trench on left and 100 µm wide trench on right.

Figure 3 .

 3 Figure 3. SEM images of bottom seeded surfaces for different trench widths a) 10 µm b) 20 µm c) 50 µm and d) 100 µm (imaged at a tilt angle of 70°). There is a clear reduction in the seeding density when trenches are thinner than 20µm.

Figure 1 :

 1 Figure 1: Cross sectional SEM images of trenches with opening widths of a) 100 μm, b) 50 μm, imaged at a tilt angle of 70°.

Figure 1 :Figure 5 :

 15 Figure 1: SEM Images of NCD film for 100 µm wide trench, a) top, b) top corner, c) bottom, and d) bottom corner

Figure 4 .

 4 Figure 4. Changing seeding density at the top, middle, and bottom of a 10µm (left) and 100µm (right) trench.

Figure 7 :

 7 Figure 7: SEM images of NCD growth on high AR trenches. Images were taken on the sidewall of the a) 20 µm trench at 28.5 µm and b) 10 µm trench at 30 µm, as well as at the bottom of the c) 20 µm d) 10 µm trenches.

Table 1 : Summary of trench geometries and NCD growth inside the trenches.

 1

	Trench geometry
	Aspect ratio	1:1	1:2	1:4	1:7
	Trench width (w) / µm	100	50	20	10
	Depth (L) / µm	100	100	80	70
	Film thickness / nm
	Top	660	660	670	660
	At 30µm	350	381	249	108
	Bottom	450	360	250	110

Table 2

 2 compares the step coverage on side walls based on the view factor model approximation with the experimental values observed. In particular, for narrow trenches, the calculated film thickness is much lower than what is observed experimentally.

Table 2 : Comparison of calculated and experimental step coverage for side walls at approx. 30% of total trench depth, based on view factor model.

 2

	Trench width / µm	100	50	20	10
	Depth / µm	28	28	28	30
	Step Coverage (SC)
	Calculated 0.73 0.51 0.19	0.05
	Experimental 0.53 0.58 0.37	0.16
	Therefore, this specific simple model does not accurately

Table 4 : Comparison of step coverage based on micro-channel approach to experimental results for the bottom of trenches.

 4

	Trench width / µm	100	50	20	10
	Step Coverage
	Micro channel model	0.47	0.54	0.35	0.13
	Experimental	0.68	0.55	0.22	0.16

Acknowledgements

This work has been financially supported by the Swiss Federal Commission for Technology and Innovation (CTI) project 16867.2PFNM-NM. The authors would like D. Rats, NeoCoat SA, CH, for seeding the silicon substrate, financial support, and fruitful discussion. We acknowledge access to and support with UV Raman in the PVLab group at EPFL Neuchatel, CH.


Genoveva Vargas-Solar
email: genoveva.vargas@imag.fr

Are Data Science Pipelines Fuzzy Queries?

Keywords: Are Data Science Pipelines Fuzzy Queries? data science pipeline, query workflow, machine learning environment

published or not. The documents may come

INTRODUCTION

Datasets representing partial and incomplete observations of phenomena have become the backbone of scientific, analytic, and forecasting processes. It is possible to compute mathematical models to understand and predict phenomena, combining simulation techniques, artificial vision, and artificial learning with data science techniques. Therefore, datasets must go through complex and repetitive processing and analysis pipelines, namely data science pipelines. A data science pipeline combines data visualisation, cleaning, preparation, modelling or prediction, and assessment tasks.

This short paper states a scientific position that proposes a new vision of data science pipelines defined as queries, namely data science queries (DSQ's). Different from classic queries, the results of DSQ's are not only data but also estimated models with associated error and performance scores. Besides, queries can have different possible results according to the algorithms that implement them behind the scenes. A data scientist must choose the best or most adapted result in given expectations related to a target domain. In Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Conference'17, July 2017, Washington, DC, USA © 2020 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn this sense, it is possible to consider DSQ's as fuzzy queries that estimate results and choose those close to a combination of expected criteria. The poster discusses the aspects to consider for modelling a data science pipelines as fuzzy queries and possible research directions.

DSQ's can be expressed, managed and efficiently enacted. The emergence of new architectures, for instance, the cloud, have opened new challenges for executing the tasks that compose data science pipelines. It is no longer pertinent to reason with respect to a set of computing, storage and memory resources, instead, it is necessary to design algorithms and processes considering an unlimited set of resources usable via a "pay as U go model". Instead of designing processes and algorithms considering as threshold the resources' availability, the cloud imposes to consider the economic cost of the processes vs. resources use, and the exploitation of available resources.

This short paper proposes a new vision of data science pipelines defined as fuzzy queries that can be expressed, managed and efficiently enacted. The paper defines the notion of data science query discussing the main guidelines to model such a query as a workflow. It then discusses the aspects to consider enacting a data science query.

The remainder of the paper is organised as follows. Section 2 introduces data science queries that can be used to model pipelines and how they can be associated to a degree of fuzziness. It discusses the design and enacting issues related to data science queries and approaches that can be revisited for enacting, reusing, and eventually optimising them. Section 3 gives an overview of existing querying techniques and their position with respect to data science queries. Finally, Section 4 concludes the paper and discusses future work.

DATA SCIENCE PIPELINES AS QUERIES

From our point of view, data science pipelines are a new type of queries that we name data science queries. Different from classic queries devoted to retrieving data, data science queries apply operations that give structure to datasets, compute statistics, and models. Computed partial and final results have a given accuracy and error score that must be estimated and determines the extent of the veracity of these results. Data collections have different qualities regarding their completeness (percentage of missing values) and consistency (percentage of erroneous values).

Similar to classic queries, data science queries can be modelled as dataflows. Such dataflow consists of tasks representing operations applied to datasets. These operations can be of many different types: harvesting, cleaning, features' engineering, descriptive statistics, machine learning, deep learning, results' assessment [START_REF] Scott | A unified approach to interpreting model predictions[END_REF], visualisation and decision making. Tasks exchange partial results through a flow that transmits them through shared memory or data transmission protocols with different data structures (vectors, matrices, values, etc.). A data science query consists of several dataflows integrated by a control flow. The control flow defines the execution order of dataflows. It also determines whether they should all be executed or not.

For example, consider a scenario consisting of different datasets for dealing with the process of the treatment of diabetes patients. Datasets contain clinical analysis' variables that can potentially have a role for a patient to contract diabetes: physiological (pregnancy, blood pressure) and chemical variables (insulin, BMI) and the diagnosis outcome. Doctors want to query is it possible to predict whether a patient can contract diabetes, given previous diagnosis experiences? based on collected datasets that associate clinical analysis variable with a diagnosis for a set of patients.

The data science query first consists of tasks for exploring the content of the dataset (pipeline A). For example, the attributes it contains (task A-1), the values' distribution (task A-2), grouping tests into diabetic and non-diabetic and counting them (task A-3). To see which variables values lead to positive/negative diabetics diagnosis (first partial result). Deciding whether to continue or not depends on the balance between the number of observations concerning diabetic and healthy patients. Assume that the number of these two groups is balanced. For predicting diabetics other tasks can define the query depending on the prediction model used. Considering that the mathematical properties of the variables are adapted and that the query expected result is a yes/no prediction, a data scientist can decide to apply a logistic regression model 1 . The tasks of the pipeline B include the fragmentation of the dataset into training and test sets (task B-1). Then, a training task applying logistic regression to the training dataset (task B-2). This task leads to a prediction model as a result. A validation task uses the model to lead to a prediction intent (task B-3). Finally, the remaining tasks of the data science query are devoted to assessing the model using the test dataset to determine to what extent it can be used for predicting new coming cases. The assessment tasks (pipeline C) include computing the prediction score (task C-1) and computing the confusion matrix (task C-2), given that logistic regression has been applied, and then compute precision and recall (task C-3). With the results of tasks C-1, C-2 and C-3, a data scientist can interpret the results of the query.

Consumers rely on executing data science queries under different conditions and receiving several versions of the results with assessment measures that can let them compare the "performance" of the implementations of the same query. Thus, data science queries become interactive processes where the user can intervene to adapt and modify some steps and where all design and execution details must be logged to enable reproducibility. The final result of a data science query is the one that reduces errors and with the best performance scores.

Consumers of data science query results are data scientists and decision-makers that have different insight and quality expectations regarding this "new" type of queries. Data science queries execution must be guided by "quality constraints" associated with different elements composing data science queries. Quality depends on data freshness, meaningfulness, sample classes balance, provenance. Second, other quality properties are associated with the whole data science query. For example, they depend on privacy and analysis security, and pertinence of data processing tasks. Does its enactment include explicable meta-data? Under which conditions are its results assessed and shared? A data science query can have additional associated technical quality attributes. Particularly, its requirements in terms of resources. For example, its execution time concerning how critical results are, data volume to be processed, algorithms complexity, data processes and results' security and persistence, computing cycles. This means that data loading, in memory/cache/disk indexing, data persistence, query optimisation, concurrent access, consistency and access control, and other management functions must be revisited under weaker hypothesis. For example, regarding data consistency, completeness and cleanness, to support the enactment of data science queries.

Estimating results quality of Data Science queries

A data science query has no predefined set of operators and composition rules. It combines algorithms respecting I/O implicit preconditions. For a data science query, it is necessary to model both its control and dataflow to express the data dependencies of its tasks and tasks execution order. Therefore, I propose to specialise the notion of query workflow [START_REF] Cuevas-Vicenttın | The hypatia system for processing hybrid queries[END_REF] to model a data science query.

A query workflow (see Figure 1) consists of activities ordered by a control flow. Data dependencies among activities (i.e., dependent, independent, concurrent) determine the control flow. These dependencies model the dataflow of the query workflow. Activities can model operation types like statistical and aggregation, projection, filtering, etc. An activity represents a task of a data science query.

It represents an external module call that can execute a data processing operation. It specifies the input data to operations, how to transmit, distribute and store data in memory or cache so that they can be processed. Every activity has associated statistics like its execution time, computing cycles, and I/O properties (data structure, size, persistence, distribution).

The results of a data science query workflow can be associated with quality scores given by performance and precision measurements and error. Scores can be adjusted to enhance performance. So given a query, there are several query workflows that can implement it. The challenge is to choose the one that best fulfil ls given requirements like performance, average precision or error, etc. The good combination or balance among all these scores can be seen as a fuzziness objective that helps to choose the most "accurate" answer to a given data science query. Playing with different fuzziness index values can let a data scientist explore different results, decide and explain why she chooses a result and not others.

Enacting a Data Science Query Workflow

When it comes to enacting a data science query, operations combined in data science queries are not systematically intended to be used together in an integrated process. Each one has different computational complexities and requires pertinent computing, storage, and memory resources and data management strategies. The enactment of data science queries becomes a multi-aspect problem. It must deal with efficient storage systems that must provide efficient read and write operations. It must use parallel programming models to deal with the execution of greedy machine learning tasks that might require important computing and memory resources. Data science queries enactment can be done « homemade » and then in large-scale conditions for deploying final industrial solutions. Small scale and large scale queries enactment can rely on service-oriented platforms (e.g., cloud providers). These platforms can provide elastic and "transparent" environments to execute greedy processes running on top of data hosted in heterogeneous hardware architectures. Some of these challenges have been addressed by Data Management Systems, Big Data Stacks [START_REF] Sattam Alsubaiee | ASTERIX: an open source system for" Big Data" management and analysis[END_REF][START_REF] Franklin | The berkeley data analytics stack: Present and future[END_REF], Data Science and Machine Learning Environments.

Given that a data science query specifies I/O and operations features, it is necessary to provide and choose the type of underlying data management mechanisms that can support every task and the whole query. Thus, the enactment environment should use the right data structures and should prefetch, and transmit data using the right strategies to let the pipeline enactment run smoothly. This is an open issue to be included in existing ML environments [START_REF] Abadi | The Seattle Report on Database Research[END_REF]. For example, fragmenting, indexing or even compressing data according to main memory limits, data distribution/sharding when operations are executed in share-nothing architectures. There cannot be offthe-shelf data management solutions because every data science query is different and, because the operations it uses have their requirements.

Data Science and Machine Learning Environments provide all the necessary methods, and they are supported by enactment stacks that deal with the storage, fragmentation, indexing and distribution of the data required and produced by the tasks composing a pipeline. Yet, data scientists and machine learning engineers have to make decisions to combine these high-and low-level tools to "compose" their query and ensure that it will run at scale when used for processing datasets of different sizes.

In this sense, data science queries are programmed often as ad hoc solutions. This situation hinders the possibility of re-using some tasks or at least the strategies implemented for addressing target problems using specific methods. Besides, decision-making tools aiding to decide which are the most adapted data management strategies for every step of a data science query are still to come.

Coupling together data analytics methods and models with data management strategies and execution environments services for addressing data science query processing is a relevant challenge in the database community. Studying the problem in a general perspective will lead to its understanding and the identification of theoretical and technical issues. We propose to address novel challenges for efficiently designing and executing data science queries exploiting datasets.

RELATED WORK

We group querying techniques together and organise them across two families. The first one concerns classic querying in databases and information retrieval. Here the principle is that data correspond to a model of a mini-world (e.g., employees and departments of a firm, the representative terms of the content of a textual document), they are a set of facts structured according to some data model (e.g., relational). In this context, queries are stated using terms, operators (and/or/negation, relational, aggregation), and constraints. The results are collections of items that fully or partially answer queries. For example, relational query results contain all the items stored in the database that correspond with the specification of a query. In contrast, information retrieval results, provide the closest documents to the query but not necessarily all the documents that answer the query.

In this case, the results have a notion of completeness fussiness and probabilistic approximation. In the early 90's works like [START_REF] Takahashi | A fuzzy query language for relational databases[END_REF] addressed the notion of fuzzy queries and their declarative expression. The idea of introducing fuzziness into queries is to let the statement of imprecise queries and to process them and retrieve an approximate result set. Current work has started to address again imprecise (i.e., fuzzy queries) and propose different strategies for evaluating them. A survey on this issue is proposed by [START_REF] Mama | A Study of Fuzzy Query Systems for Relational Databases[END_REF].

The second family is more exploratory where pipelines explore and analyse the data to profile it quantitatively and with the objective of either modelling, prediction or recommendation [START_REF] Anderson | Sample, estimate, tune: Scaling bayesian auto-tuning of data science pipelines[END_REF][START_REF] Shang | Democratizing data science through interactive curation of ml pipelines[END_REF]. In these queries' family, the results have an associated degree of error, and they may not only be data but also queries or data samples. Exploratory queries tackle data collections that are expanding or where the structure provides little knowledge about the data. These queries run step-by-step like pipelines and the tasks often apply statistical, probabilistic, or data mining and artificial intelligence processing functions. Methodologies are still to come to integrate data management with the execution of algorithms that are often greedy.

CONCLUSION AND FUTURE WORK

Data science queries design and enactment remain artisanal today. Their design does not explain and models how statistical, machine learning and artificial intelligence models and computer technologies are used and weaved to lead to efficient enactment and representative results. Data science queries design and enactment call for well-adapted platforms that can efficiently organise data, evaluate and optimise queries, and execute algorithms that require important computing and memory resources. The design of these platforms is associated with communities like high-performance computing and distributed systems. The focus on data management functions (I/O management, indexing, storage and persistence) revisited for enabling data science queries enactment, can bring complementary and collaborative research. Having intelligent and well-adapted data management solutions along the enactment of data science queries is key to ensure their efficient execution. In this sense, our work associating data management strategies for each activity of a data science query workflow and the whole workflow can be original and ambitious. It can lead to results with a considerable expected impact on "modern data science experiments" deployed on heterogeneous high-performance target architectures.

Figure 1 :

 1 Figure 1: Data Science Query Workflow

Conference'17, July 2017, Washington, DC, USA Genoveva Vargas-Solar

Recall that for using logistic regression, the dependent variable is binary (diabetics yes/no); observations are independent of each other; there is little or no multicollinearity among the independent variables; linearity of independent variables and log odds. These properties must be verified through exploration tasks.