Bharti Thakur

Rajiv Yadav

Arkadeep Mukherjee

Delphine Melayah

Roland Marmeisse

Laurence Fraissinet-Tachet

Sudhakara Mondem

Laurence Reddy

Mondem Sudhakara Fraissinet-Tachet

Reddy

Mondem Sudhakara

Protection from metal toxicity by Hsp40-like protein isolated from contaminated soil using functional metagenomic approach

Pollution in the environment due to accumulation of potentially toxic metals results in deterioration of soil and water quality, thus impacting health of all living organisms including microbes. In the present investigation, a functional metagenomics approach was adopted to mine functional genes involved in metal tolerance from potentially toxic metal contaminated site. Eukaryotic cDNA library (1.0-4.0 kb) was screened for the genes providing tolerance to cadmium (Cd) toxicity through a functional complementation assay using Cd-sensitive Saccharomyces cerevisiae mutant ycf1 Δ . Out of the 98 clones able to recover growth on Cd-supplemented selective medium, one clone designated as PLCc43 showed more tolerance to Cd along with some other clones. Sequence analysis revealed that cDNA PLCc43 encodes a 284 amino acid protein harbouring four characteristic zinc finger motif repeats (CXXCXGXG) and showing partial homology with heat shock protein (Hsp40) of Acanthamoeba castellanii. qPCR analysis revealed the induction of PLCc43 in the presence of Cd, which was further supported by accumulation of Cd in ycf1 Δ /PLCc43 mutant. Cu-sensitive (cup1 Δ ), Zn-sensitive (zrc1 Δ ) and Co-sensitive (cot1 Δ ) yeast mutant strains were rescued from sensitivity when transformed with cDNA PLCc43 indicating its ability to confer tolerance to various potentially toxic metals. Oxidative stress tolerance potential of PLCc43 was also confirmed in the presence of H2O2. Present study results suggest that PLCc43 originating from a functional eukaryotic gene of soil community play an important role in detoxification of potentially toxic metals and may be used as biomarker in various contaminated sites.

Introduction

Metal contamination due to increasing anthropogenic activities is a significant problem of the industrial era because of the persistent and irreversible nature of these contaminants [START_REF] Fernández | Geographically weighted principal components analysis to assess diffuse pollution sources of soil heavy metal: application to rough mountain areas in Northwest Spain[END_REF]. Soil contamination by potentially toxic metals not only modify soil physico-chemical properties but also deteriorates its microbial quality, thus leading directly or indirectly to harmful effects on the health of all members of food chain including animals and human beings [START_REF] Dong | Assessing the concentration and potential dietary risk of heavy metals in vegetables at a Pb/Zn mine site[END_REF][START_REF] Nabulo | Assessing risk to human health from tropical leafy vegetables grown on contaminated urban soils[END_REF]. Soil is a rich habitat of highly diverse microorganisms, many of which are still unknown and uncultivable as of yet, but have a high metabolic response potential to survive in polluted environments. Exploring functional microbial communities has come across various cellular mechanisms involved in maintaining homeostasis in potentially toxic metal polluted environment, as chelation, efflux, or cellular compartmentalisation [START_REF] Clemens | Toxic metal accumulation, responses to exposure and mechanisms of tolerance in plants[END_REF][START_REF] Blindauer | Metallothioneins: unparalleled diversity in structures and functions for metal ion homeostasis and more[END_REF][START_REF] Gadd | Metals, minerals and microbes: geomicrobiology and bioremediation[END_REF][START_REF] Reddy | Differential expression of metallothioneins in response to heavy metals and their involvement in metal tolerance in the symbiotic basidiomycete Laccaria bicolor[END_REF][START_REF] Reddy | Metal induction of a Pisolithus albus metallothionein and its potential involvement in heavy metal tolerance during mycorrhizal symbiosis[END_REF]. To harness the hidden functional diversity and in situ mechanisms for revitalizing the polluted soil by attenuation of the detrimental effects of accumulated toxic metals, metatranscriptomics approach has been introduced recently. Metatranscriptomics is a powerful tool in exploring complex biological system as this approach is designed without any limitation to living organisms and opened to any strata of life [START_REF] Gonzalez | Trees, fungi and bacteria: tripartite metatranscriptomics of a root microbiome responding to soil contamination[END_REF][START_REF] Thavamani | Microbes from mined sites: harnessing their potential for reclamation of derelict mine sites[END_REF]. Function-based approach of metagenomics involves conversion of mRNAs into complementary DNA libraries followed by transcription and expression in suitable hosts [START_REF] Bailly | Soil eukaryotic functional diversity, a metatranscriptomic approach[END_REF][START_REF] Grant | Identification of eukaryotic open reading frames in metagenomic cDNA libraries made from environmental samples[END_REF]. It has been reported that function-based metagenomic approach may explore the functional diversity by directly using mRNAs, thus providing actively expressed gene repertoire in environmental samples [START_REF] Bailly | Soil eukaryotic functional diversity, a metatranscriptomic approach[END_REF][START_REF] Marmeisse | Discovering protein-coding genes from the environment: time for the eukaryotes?[END_REF][START_REF] Thakur | Isolation of multi-metal tolerant ubiquitin fusion protein from metal polluted soil by metatranscriptomic approach[END_REF]. This approach is a function-driven approach and recognized as an authoritative tool in unearthing various functional genes playing fundamental role in numerous phenomena including biological response mechanisms to environmental conditions [START_REF] Damon | A novel fungal family of oligopeptide transporters identified by functional metatranscriptomics of soil eukaryotes[END_REF][START_REF] De Menezes | Comparative metatranscriptomics reveals widespread community responses during phenanthrene degradation in soil[END_REF][START_REF] Zielinski | Patterns of transcript abundance of eukaryotic biogeochemically-relevant genes in the Amazon River Plume[END_REF]. Function-based metagenomics has identified numerous ecologically significant genes as those encoding for oligopeptide transporters [START_REF] Damon | A novel fungal family of oligopeptide transporters identified by functional metatranscriptomics of soil eukaryotes[END_REF], cysteine-rich metallothioneins [START_REF] Ziller | Metagenomics analysis reveals a new metallothionein family: Sequence and metalbinding features of new environmental cysteine-rich proteins[END_REF]) and candidate proteins involved in metal tolerance [START_REF] Thakur | Multi-metal tolerance of von Willebrand factor type D domain isolated from metal contaminated site by metatranscriptomics approach[END_REF]. This approach provided an unprecedented overview of gene expression profiling in microbial communities, thus providing huge information on functional microbial diversity. The functional microbial diversity of muskoxen rumen [START_REF] Qi | Snapshot of the eukaryotic gene expression in muskoxen rumen -a metatranscriptomic approach[END_REF], forest soils [START_REF] Damon | A novel fungal family of oligopeptide transporters identified by functional metatranscriptomics of soil eukaryotes[END_REF]), phenanthrene-contaminated soil (de Menezes et al. 2012), activated sludge from wastewater [START_REF] Yu | Metagenomic and metatranscriptomic analysis of microbial community structure and gene expression of activated sludge[END_REF], acid mine drainage and root microbiome [START_REF] Chen | Comparative metagenomic and metatranscriptomic analyses of microbial communities in acid mine drainage[END_REF][START_REF] Gonzalez | Trees, fungi and bacteria: tripartite metatranscriptomics of a root microbiome responding to soil contamination[END_REF][START_REF] Teng | Microbial diversity and community assembly across environmental gradients in acid mine drainage[END_REF]) are some of the examples. Metal stress usually alters cell homeostasis by inactivating enzymes and by suppressing protein functions [START_REF] Hossain | Proteomic study of b-aminobutyric acid-mediated cadmium stress alleviation in soybean[END_REF]. Induction of heat shock proteins (HSPs) may represent a critical protective, eco-physiologically adaptive and genetically conserved response in some living organisms that undergo environmental stress [START_REF] Hasan | Responses of plant proteins to heavy metal stress-a review[END_REF]. HSPs play a key function by restoring protein conformation and consequently cell homeostasis [START_REF] Rhee | Heat shock protein (Hsp) gene responses of the intertidal copepod Tigriopusjaponicus to environmental toxicants[END_REF]). Among the major categories of HSPs, Hsp40 proteins play the role of molecular chaperones that act either in an independent way or as co-chaperones with Hsp70 [START_REF] Craig | The diverse roles of J proteins, the obligate Hsp70 cochaperone[END_REF]. Hsp40, also known as J-proteins, are categorized into three different classes, types I, II, and III, depending upon the presence or absence of one or more of four structurally defined domains. Type I Hsp40 proteins possess all the four domains similar to those found in E. coli DNAJ, i.e. an N-terminal J-domain linked by a glycine/phenylalanine (G/F)-rich region to a cysteine-rich (CXXCXGXG) zinc finger region and followed by a C-terminal substrate-binding domain. Type II Hsp40s are similar to the class I proteins, except the absence of zinc finger domain in their structure, whereas type III Hsp40 proteins only retain the J domain [START_REF] Tamadaddi | J domain independent functions of J proteins[END_REF]. In addition, Hsp40 proteins provide tolerance to multiple types of stressors and protect living organisms from oxidative stress [START_REF] Jiang | A cytosolic class I small heat shock protein, RcHSP17.8, of Rosa chinensis confers resistance to a variety of stresses to Escherichia coli, yeast and Arabidopsis thaliana[END_REF]). These proteins accumulate in a wide range of plant and animal species in response to environmental stressors to maintain cellular homeostasis [START_REF] Gillis | The DNAJB6 and DNAJB8 protein chaperones prevent intracellular aggregation of polyglutamine peptides[END_REF][START_REF] Gupta | Heat shock proteins in toxicology: how close and how far?[END_REF]. Screening of yeast cDNA library constructed from RNA isolated from a metal-contaminated site allowed us to identify several genes involved in Cd tolerance when expressed in yeast [START_REF] Thakur | Isolation of multi-metal tolerant ubiquitin fusion protein from metal polluted soil by metatranscriptomic approach[END_REF][START_REF] Thakur | Multi-metal tolerance of von Willebrand factor type D domain isolated from metal contaminated site by metatranscriptomics approach[END_REF][START_REF] Mukherjee | Heavy metal hypotolerant eukaryotic aldehyde dehydrogenase isolated from metal contaminated soil by metatranscriptomic approach[END_REF]). In the present study, one of the cDNAs, PLCc43, whose deduced protein sequence showed partial sequence similarity to eukaryotic type I Hsp40 proteins, was characterized and its role in providing tolerance to potentially toxic metals in metal-sensitive yeast mutants was assessed.

Materials and methods

Soil sampling and analysis

Soil samples were collected from the metal contaminated site of Pierrelaye (49° 1′ 45″ N; 2° 10′ 32″ E), northwest of Paris, France. Poplar plants are cultivated in this site, which was earlier an agricultural land and became contaminated with potentially toxic metals due to extended use of untreated wastewater for irrigation. Twenty soil cores were collected, mixed in equal volumes to make a composite sample, sieved through 2.0-mm mesh, and preserved in dry ice and then stored at -70 °C until further experiments. The physicochemical properties of the soil were determined as described in [START_REF] Thakur | Multi-metal tolerance of von Willebrand factor type D domain isolated from metal contaminated site by metatranscriptomics approach[END_REF] and were as follows: pH 7.15; total phosphorus 291 mg/kg; available phosphorus 14.2 mg/kg; organic carbon 1.6%; and total nitrogen 0.12%. The metal contents (mg/kg) of the soil samples were as follows: Cd 2.5, Zn 385 and Cu 64.

Strains, culture conditions and transformation

The mutant strains of Saccharomyces cerevisiae sensitive to cadmium (ycf1 Δ ), copper (cup1 Δ ), zinc (zrc1 Δ ) and cobalt (cot1 Δ ) were used for transformation [START_REF] Gietz | High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method[END_REF]. The ycf1 Δ , zrc1 Δ and cot1 Δ strains are derived from the wild-type strain BY4741 (MATα, his3 Δ 1, met15 Δ 0, leu2Δ0, ura3Δ0). The ycf1 Δ yeast strain lacks an ABC transporter encoded by gene YCF1 that mediates the transport of Cd conjugates into vacuoles [START_REF] Li | A new pathway for vacuolar cadmium sequestration in Saccharomyces cerevisiae, YCF1Δ-catalyzed transport of bis(glutathionato) cadmium[END_REF], while zrc1 Δ lacks the transporter protein ZRC1p responsible for Zn sequestration into vacuoles [START_REF] Li | Defects in the yeast high affinity iron transport system in increased metal sensitivity because of the increased expression of transporters with a broad transition metal specificity[END_REF]. The hypersensitivity of cot1 Δ towards Co metal is due to deletion of COT1 gene that helps in efflux of cobalt ions into the vacuole [START_REF] Conklin | COT1, a gene involved in cobalt accumulation in Saccharomyces cerevisiae[END_REF]. Similarly, cup1 Δ mutant strain is derived from knocking out the copperthionein gene [START_REF] Longo | Superoxide dismutase activity is essential for stationary phase survival in Saccharomyces cerevisiae[END_REF] from its isogenic wild-type strain cup1 s (MATα, leu2-3, 112his3 Δ 1, trp1-1, ura3-50, gal1, CUP1 S ). Yeast strains BY4742 and its derivative superoxide dismutase mutant sod2 Δ (BY4742 sod2::KanMX) were used for oxidative stress experiments. For yeast complementation assays, minimal medium supplemented with 2% glucose (Hi-Media Laboratories, India) containing dropout supplement without uracil (SD-Ura) (Takara-Clontech, Japan) was used. Yeast strains were maintained on yeast extract peptone dextrose (YEPD) (Hi-Media Laboratories, India). Bacterial transformations were carried out by using One Shot® TOP10 Electrocomp™ E. coli cells (Thermofisher, USA). The yeast strains were grown at 30 °C while bacteria at 37 °C. Construction of sized cDNA library and screening for Cd tolerance Total RNA was isolated from the soil composite samples using the RNA PowerSoil® Total RNA Isolation Kit (MoBio Laboratories, Carlsbad, CA) according to the manufacturer's instructions. Extracted total RNA was checked for quality by agarose gel electrophoresis and Bioanalyzer (Agilent Technologies, USA). The extraction yields ranged from 330 to 980 ng/g of soil and at least 3 μg of total soil RNA were obtained from each sample. cDNAs were synthesized from total soil RNA with Mint-2 cDNA synthesis kit (Evrogen, Moscow, Russia). Size fractionation of cDNAs was performed as described in [START_REF] Yadav | Construction of sized eukaryotic cDNA libraries using low input of total environmental metatranscriptomic RNA[END_REF]. Size fractions of cDNAs, A (< 0.5 kb), B (0.5-1 kb) and C (> 1 kb) were ligated downstream of PGK1 promoter of S. cerevisiae shuttle plasmid vector pFL61 modified with SfiA and SfiB restriction sites [START_REF] Minet | Complementation of Saccharomyces cerevisiae auxotrophic mutants by Arabidopsis thaliana cDNAs[END_REF]). These recombinant plasmids were introduced into DH10 β electrocompetant E. coli cells (One Shot® TOP10 Electrocomp™ E. coli, Invitrogen). More than 10 6 colonies growing on ampicillin-containing Luria agar plates (100 μg ampicillin per ml of medium) were pooled to represent each of the cDNA size libraries. Plasmid DNA from the pooled cells representing the cDNA library C (> 1.0 kb) was extracted with QIAprep Spin Miniprep Kit (Qiagen, USA) and transformed into Cd-sensitive ycf1 Δ yeast mutant by lithium acetate method [START_REF] Gietz | High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method[END_REF] and were regenerated on SD-Ura medium amended with 40 μM Cd. The selected clones were treated with 5-fluoroorotic acid (5-FOA) to eliminate false-positive colonies. One of the positive clones, PLCc43, was selected for further characterization based on its ability to tolerate high concentrations of Cd.

Sequence analysis

Plasmid DNA from yeast ycf1 Δ /PLCc43 was isolated with Zymoprep yeast plasmid Miniprep II kit (Zymo Research) and sequenced the insert PLCc43 with vector-specific primers NF and NR (Table 1) by using Sanger sequencing technology. Sequence homology search was performed by using BLASTX (https://blast.ncbi.nlm.nih.gov/Blast.cgi/). Open reading frame of the sequence was determined by using the webserver https://www.ncbi.nlm.nih.gov/orffinder/ and molecular weight and theoretical isoelectric point (pI) for the putative encoded protein by http://web.expasy.org/ compute_pi/. Clustal Omega (http://www.ebi.ac.uk/Tools/msa/ clustalo/) was used to align the homologous sequences along with PLCc43. Phosphorylation sites within the deduced polypeptide PLCc43 was predicted with NetPhos 2.0 server (http://www.cbs.dtu.dk/services/ NetPhos/). Gene Name Sequence

pFL61 pFL61 NF 5'-CAGATCATCAAGGAAGTAATTATCTAC-3' pFL61 NR 5'-CAGAAAAGCAGGCTGGG AAGC-3' Actin qACTF 5'-CGAATTGAGAGTTGCCCCAG-3' qACTR 5'-CAAGGACAAAACGGCTTGGA-3' TAF10 qTAF10F 5'-GCTAACAACAGTCAGGCGAG-3' qTAF10R 5'-GAGCCCGTATTCAGCAACAG-3' TFC1 qTFC1F 5'-ACGACGCTGCTTTGGAAAAT-3' qTFC1R 5'-GCTTTTCATTGTTTCCGCGG-3' PLCc43 qHspF 5'-GACCGTATGCCCCAAATGTC 3' qHspR 5'-GGGAATTACACCAGGCTCCT-3'

Relative quantification of PLCc43

Cadmium-sensitive yeast mutant ycf1 Δ carrying PLCc43 was tested for its relative quantification in presence of different concentrations of Cd. To accomplish this, flasks containing 50 ml of SD-Ura broth amended with different concentrations of Cd (0, 40, 60 and 80 μM) was inoculated with ycf1 Δ /PLCc43 cells and incubated for 48 h at 30 °C. The cells were harvested and grounded with liquid nitrogen. Total RNA was isolated by Trizol reagent, and RNase-free DNaseI (Thermo Scientific, USA) was used to remove genomic DNA contamination. cDNA was synthesized from total RNA by using PrimeScript TM first strand cDNA synthesis kit (Takara, Japan) as per manufacturer's instructions. To assess the relative abundance of PLCc43 and their relative basal levels, 25 μl qPCR reaction mixtures were prepared. The reaction contained 12.5 μl SYBR® Premix Ex Taq™ (Tli RNase H Plus) (Takara, Japan) as recommended by the manufacturer, 0.2 μM specific primers (Table 1), 1.0 μL diluted cDNA (20 fold dilution; approximately 50 ng) and H2O to make the final volume. The qPCR analysis was performed on the Mastercycler® ep realplex system (Eppendorf AG, Hamburg, Germany). The PCR conditions were 95 °C for 2 min (1 cycle), followed by 35 cycles of 95 °C for 15 s, 53 °C for 15 s and 68 °C for 20 s. The E values for PLCc43 were within the range of 0.94 to 1.27. Three reference genes, actin (ACT; SGD: S000001855), TATA-binding protein-associated factor (TAF10; SGD:S000002574) and transcription factor class C (TFC1; SGD: S000000327) were used in this study [START_REF] Teste | Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae[END_REF]). The relative amount of PLCc43 was determined according to 2 -ΔΔCt method (Livak and Schmittgen 2001) by using TFC1 as reference gene because of its minimum stability than other two reference genes. The base level expression of PLCc43 in control cells (without Cd augmentation) was considered as one and calculated the fold increase in Cd augmented cells. To confirm that there were no traces of DNA present in RNA extracts, PCR was performed with nonreverse-transcribed RNA samples as control. The reaction mixture without cDNA served as negative control. All qPCR measurements were performed on RNA extracted from three independent biological samples (experimental replicates), and for each RNA extract, three technical replicates were performed.

Tolerance to metal toxicity and metal content Cd-sensitive (ycf1 Δ ), Cu-sensitive (cup1 Δ ), Zn-sensitive (zrc1 Δ ) and Co-sensitive (cot1 Δ ) yeast mutants expressing PLCc43 were tested for tolerance towards Cd, Cu, Zn and Co, respectively as follows: serial dilutions (0.1-0.0001) were prepared from calibrated overnight grown transformant yeast cells and empty vector pFL61 carrying control cultures (wild-type BY4741 and mutant strains). Five microliters of each dilution was dropped on SD-Ura plates supplemented with respective metal concentrations, i.e. CdSO4 40 μM, CuSO4 150 μM, ZnSO4 10 mM and CoCl2 2 mM. Liquid broth assays were performed in parallel to validate the broad spectrum of metal tolerance. To do this, 20 ml of SD-Ura were inoculated with exponentially cultured cells to achieve an initial OD600 = 0.02 and were allowed to grow at 30 °C for 3 h. Then, varying concentrations of CdSO4 (40 μM-80 μM), CuSO4 (150 μM-500 μM), ZnSO4 (10 mM-12 mM) and CoCl2 (2 mM-4 mM) were added separately to the respective yeast cell cultures. Growth was monitored via OD measurements at 600 nm at intervals of 3 h for 48 h. Empty vector pFL61 transformed yeast mutants and wild-type BY4741 served as controls.

For metal accumulation studies, all yeast cells were allowed to grow overnight in SD-Ura medium till the OD600 = 1.0 was attained. Cells were harvested by centrifugation and re-suspended in sterile water.

One percent inoculum was used to inoculate 50 ml of SD-Ura broth in 250 ml flasks and was incubated at 30 °C at 200 rpm. After 5 h, various concentrations of CdSO4 (40 μM-80 μM), CuSO4 (150 μM-500 μM), ZnSO4 (10 mM-12 mM) and CoCl2 (2 mM-4 mM) were amended in the media similar to growth kinetics study. After 40 h of growth, cells were harvested and washed 3 times with 10 mM EDTA solution. Final washing was done with sterile deionized water to eliminate residual EDTA and dried for 24 h at 80 °C. A combination of HNO3 and HClO4 (ratio 2:1 v/v) was used to digest dry yeast biomass (Hseu ( 2004) and the metal content was determined through ICP-MS (Element XR, Thermo Fisher Scientific, Germany). Experiments were conducted in a 3 × 3 factorial design with yeast strains and concentrations of the metals arranged in a completely randomized block design with three replicates. The experiment consisted of three yeast inoculation treatments (ycf1 Δ /pFL61, ycf1 Δ /PLCg39 and BY4741) and three concentrations of CdSO4 (40, 60 and 80 μM). Similar design of experiments were conducted for Cu, Zn and Co using the respective yeast mutants. Furthermore, the involvement of PLCc43 in providing the tolerance to metal toxicity was tested by transforming PLCc43 into the wild-type strain BY4741, which already consists of gene for yeast Hsp40 protein. The transformed cells were grown in presence of 60 μM of CdSO4. The wild-type strain BY4741 transformed with empty vector pFL61 was used as positive control. Calibrated inoculums consisting of 2 × 10 6 cells from actively growing cultures were used as starting materials and the growth of both the strains was recorded for 36 h with an interval of 3 h.

Oxidative stress

The superoxide dismutase yeast mutant sod2 Δ was used to examine the role of PLCc43 in oxidative stress. To do this, functional complementation of sod2 Δ using PLCc43 was evaluated in SD-Ura medium supplemented with H2O2 (0-0.5 mM) following drop assays and growth kinetics study. For controls, the parent-type BY4742 and sod2 Δ both transformed with empty vector pFL61 were used. Growth response was determined by recording the OD600 until 30 h at an interval of 5 h.

Statistical analysis

The statistical tests were carried out by using GraphPad Prism (version 5.04) (GraphPad Prism, San Diego California, USA) software. Two-way analysis of variance was performed to examine the effect of various concentrations of metals on the cell growth and metal uptake of different yeast strains and their interactions. The significant differences among the means were compared with Tukey's test at P < 0.05 when one way-ANOVA was used

Results and discussion

Screening of cadmium tolerant cDNAs

Yeast mutant sensitive to Cd (ycf1 Δ ) was used for screening of the environmental cDNAs (library C, > 1.0 kb insert size) synthesized from total soil RNA and the quality of total RNA was determined by agarose gel. Among various clones screened through functional complementation by drop assay, 98 clones were retrieved, which are capable of growing in Cd-amended medium (Supplementary figure S1). These results indicated that some of the transformed clones were able to rescue ycf1 Δ cells from Cd sensitivity. The clone ycf1 Δ /PLCc43 was selected for further studies based on its tolerance to high Cd concentrations. [START_REF] Thakur | Isolation of multi-metal tolerant ubiquitin fusion protein from metal polluted soil by metatranscriptomic approach[END_REF] reported a 695-bp-long eukaryotic transcript PLBe1 providing tolerance towards potentially toxic metals such as Co, Cu, Zn and Cd. Numerous ecologically important genes playing a significant role in many biological processes have been identified using function-based metagenomics approach [START_REF] Damon | Metatranscriptomics reveals the diversity of genes expressed by eukaryotes in forest soils[END_REF][START_REF] Lehembre | Soil metatranscriptomics for mining eukaryotic heavy metal resistance genes[END_REF][START_REF]  Ren | Diversity and contributions to nitrogen cycling and carbon fixation of soil salinity shaped microbial communities in Tarim Basin[END_REF][START_REF] Ziller | Metagenomics analysis reveals a new metallothionein family: Sequence and metalbinding features of new environmental cysteine-rich proteins[END_REF].

Sequence analysis of PLCc43

Sequence analysis of PLCc43 revealed that it consisted of 1269 bp with 855 bp open reading frame encoding a putative protein of 284 amino acids with theoretical molecular mass of 31.2 kDa and calculated isoelectric point of 8.99. The sequence showed 51% sequence similarity (65% positives) with Hsp40 protein of Acanthamoeba castellanii. Generally, Hsp40 proteins are categorised based on presence of different combinations of three domains, namely, J domain that helps in stimulating the ATPase activity of Hsp70, long stretch of G/F amino acid-rich region and cysteine-rich region that contains zinc-binding CXXCXGXG motif repeats and a carboxy-terminal substrate-binding domain [START_REF] Caplan | Characterization of YDJ1: a yeast homologue of the bacterial dnaJ protein[END_REF][START_REF] Qiu | The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones[END_REF]. Putative PLCc43p may display the fundamental structural similarities with type I Hsp40 proteins, i.e. a partial J domain with the conserved tri-peptide HPD (histidine, proline and aspartic acid) motif, a stretch of glycine (G)/phenylalanine (F)-rich region and four zinc finger motif repeats although the final G amino acid in the fourth stretch CXXCXGXG was missing (Fig. 1a). Sequence alignment with other homologous type I Hsp40 proteins showed highly conserved pattern of PLCc43 (Fig. 1b). The fully conserved tripeptide HPD is highlighted in yellow; the cysteine residues from cysteine-rich domains CXXCXGXG are coloured in red, respectively. Hsp40 from Acanthamoeba castellini (XP_004349556) and DnaJ homolog members from Daniorerio (NP_998658) and Xenopus tropicalis (NP_001004807) are shown Hsp40 proteins are highly conserved proteins that play an important role in protein degradation, translocation, folding and unfolding of protein by stimulating the ATPase activity of another 70-kDa chaperone, Hsp70, which is induced by stress [START_REF] Caplan | Characterization of YDJ1: a yeast homologue of the bacterial dnaJ protein[END_REF][START_REF] Cyr | DnaJ-like proteins: molecular chaperones and specific regulators of Hsp70[END_REF][START_REF] Qiu | The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones[END_REF]. Biochemical and genetic studies have reported the functional redundancy between E. coli DnaJ proteins and eukaryotic Hsp40 proteins; hence, the concept of regulation of Hsp70 by Hsp40 via stimulating ATPase activity with the help of J domain as reported in E. coli has also been conserved in higher organisms as well [START_REF] Cyr | DnaJ-like proteins: molecular chaperones and specific regulators of Hsp70[END_REF]. Similarly, the tri-peptide motif, HPD, has also been evidenced to play crucial role in functional regulation of Hsp70 as mutations in this conserved motif block the activity of the Hsp70 ATPase [START_REF] Tsai | A conserved HPD sequence of the J domain is necessary for YDJ1 stimulation of Hsp70 ATPase activity at a site distinct from substrate binding[END_REF][START_REF] Wall | The NH2-terminal 108 amino acids of the Escherichia coli DnaJ protein stimulate the ATPase activity of DnaK and are sufficient for lambda replication[END_REF]. Although J domain is considered as an essential feature for functioning of Hsp40 proteins, studies have identified Hsp40 proteins that do not need J domain to perform all their functions (Hageman et al. 2010;[START_REF] Sahi | Cwc23, an essential J protein critical for pre-mRNA splicing with a dispensable J domain[END_REF]. This further suggests that Hsp40 proteins can perform J domain-independent functions and not all the functions of Hsp40 proteins are Hsp70 dependent. For instance, DnaJA1, a class I Hsp40 protein in mammals, which acts as host factor for influenza A virus replication, could associate itself with the PA and PB2 subunits of RNA polymerase by C-terminal substrate-binding domain, which lacks J domain [START_REF] Cao | DnaJA1/Hsp40 is co-opted by influenza A virus to enhance its viral RNA polymerase activity[END_REF]. Further, cysteine-rich zinc finger domains of polypeptide encoded by cDNA PLCc43 possess same sequence pattern as in type I Hsp40 protein of S. cerevisiae Ydj1 [START_REF] Caplan | Characterization of YDJ1: a yeast homologue of the bacterial dnaJ protein[END_REF]. When analysing the structure of Hsp40 proteins, [START_REF] Banecki | Structure-function analysis of the zinc finger region of the DnaJ molecular chaperone[END_REF] reported that the four cysteinerich zinc finger domains combine by pairs, each of the pairs binding to one unit of Zn 2+ ion, hence allowing the Hsp40 proteins to complex two Zn 2+ ions. Zinc finger domains known to prevent the aggregation of denatured proteins, is also a part of peptide-binding domain, which is responsible for chaperone function of Hsp40 proteins [START_REF] Banecki | Structure-function analysis of the zinc finger region of the DnaJ molecular chaperone[END_REF][START_REF] Szabo | A zinc finger-like domain of the molecular chaperone DnaJ is involved in binding to denatured protein substrates[END_REF]. The fourth domain found in the carboxyl-terminal part of the protein was less conserved and plays a significant role in substrate binding [START_REF] Banecki | Structure-function analysis of the zinc finger region of the DnaJ molecular chaperone[END_REF]. [START_REF] Lu | The conserved carboxyl terminus and zinc finger like domain of the co-chaperone Ydj1 assist Hsp70 in protein folding[END_REF] reported that the mutant Ydj1179-384 protein with completely lacking J domain, G/F region and a large part of cysteine rich zinc finger region, was able to suppress the aggregation of rhodanese in vitro thus arguing that some functions rely on the C-terminal substrate-binding fragment only. Similarly, [START_REF] Sahi | Sequential duplications of an ancient member of the DnaJ-family expanded the functional chaperone network in the eukaryotic cytosol[END_REF] advocates that C-terminal region of Hsp40 Class I proteins is the most evolved region of Hsp40 proteins, which results into a more diverse and specific Hsp40 class I protein family with binding capability towards different polypeptides. Polypeptide analysis of PLCc43p coding for Hsp40-like protein also marked the presence of 22 metabolically important phosphorylation sites (Fig. 1). The sequence characterization of cDNA PLCc43 thus evidenced features supporting that the encoded polypeptide may belong to the class I of Hsp40 proteins. The sequence of PLCc43 obtained in this study was deposited at NCBI database under the accession number MK079355.

Relative quantification of PLCc43

Expression levels of PLCc43 increased with increase in Cd concentration and the maximum expression of 68-fold increase was observed at 80 μM of CdSO4. Eighteen-fold increase at 40 μM and 55-fold increases at 60 μM CdSO4 was recorded, respectively, in ycf1 Δ/ PLCc43 cells compared to control cells (Fig. 2). Several studies have reported the over-expression of Hsp40 proteins along with other heat shock proteins and their role in degradation and refolding of proteins in various organisms in response to various accumulated metals [START_REF] Barque | Different heat-shock proteins are constitutively overexpressed in cadmium and pentachlorophenol adapted Euglena gracilis cells[END_REF][START_REF] Cai | DNaJ-like protein cDNA cloning and its expression under heavy metal stress[END_REF][START_REF] Fusco | Identification of cadmium-regulated genes by cDNA-AFLP in the heavy metal accumulator Brassica juncea L[END_REF].

Fig. 2

Fold increase in expression levels of cDNA ycf1 Δ /PLCc43 after 48-h incubation in medium supplemented with different concentrations of Cd. Values plotted are referred to the control condition (expression level in transformant ycf1 Δ /PLCc43 without metal treatment). Individual data points are shown in each bar. Error bars are ± SD. Bars sharing a common letter are not significant at P < 0.05

Tolerance to metal toxicity and metal content

The role of PLCc43 in conferring metal tolerance when over-expressed within different hypersensitive mutants of S. cerevisiae was studied. The heterologous expression of PLCc43 allowed all hypersensitive yeast mutant strains used (ycf1 Δ , cup1 Δ , zrc1 Δ and cot1 Δ ) to tolerate towards Cd, Cu, Zn and Co toxicity compared to yeast strains carrying empty vector (Fig. 3A, B, C &D). The tolerance potential of PLCc43 towards the four metals used in this study was further authenticated by growing the transformed yeast mutants into metal-amended liquid media (Fig. 4). The transformant ycf1 Δ /PLCc43 showed high tolerance towards Cd in the medium compared to wild-type strain BY4741 and ycf1 Δ transformed with empty vector. However, with increase in external metal concentration, the growth was significantly reduced. Maximum growth was observed when ycf1 Δ /PLCc43 cells were grown in presence of 40 μM of Cd compared to other concentrations (Fig. 4a). The growth of yeast cells decreased with increase in concentrations of external CuSO4 in the medium. The cup1 Δ /PLCc43 cells showed more growth in presence of CuSO4 compared to wild-type strain BY4741, the maximum growth being at 150 μM of CuSO4 (Fig. 4B). The growth of zrc1 Δ cells carrying PLCc43 showed more tolerance to Zn compared to wild-type strain as well as zrc1 Δ cells carrying empty vector. The growth of yeast cells decreased significantly with increase in concentrations and the maximum growth was observed in zrc1 Δ /PLCc43 cells at 10 mM ZnSO4 (Fig. 4c). In the case of Co, over-expression of PLCc43 in hypersensitive yeast mutant cot1 Δ provided more tolerance against Co toxicity compared to the wild-type strain BY4741 and the cot1 Δ cells transformed with empty vector. cot1 Δ cells transformed with PLCc43 showed maximum growth at 2 mM of CoCl2 compared to other concentrations (Fig. 4d). Significant variation among the treatments of yeast strains and concentrations of metals along with their interaction effect was observed when analysed by ANOVA (Table 2).

Fig. 3

Drop assay of metal-sensitive mutant strains ycf1 Δ , cup1 Δ , zrc1 Δ and cot1 Δ expressing PLCc43 at various concentrations a Cd 40 μM, b Cu 150 μM, c Zn 10 mM and d Co 2 mM. Wild strain BY4741/EV, cup1 s /EV serves as positive controls and ycf1 Δ /EV, cup1 Δ /EV, zrc1 Δ /EV and cot1 Δ /EV as negative controls, respectively. EV stands for empty vector Accumulation of metal by respective yeast mutant strain carrying cDNA PLCc43 was further estimated by ICP-MS. The ycf1 Δ cells transformed with PLCc43 showed significantly higher accumulation of Cd at different concentrations compared to wild-type BY4741 as well as ycf1 Δ cells transformed with empty vector in all concentrations of Cd. The accumulation of Cd levels increased up to 60 μM of CdSO4 and decreased at 80 μM of CdSO4, which might be due to decrease in the growth of yeast cells (Fig. 5a). the level of Cu accumulation increased with concentrations up to 300 μM of CuSO4 after which a slight but not significant decrease was observed at 500 μM of Cu concentration (Fig. 5b). In case of Zn and Co metals, maximum metal accumulation was observed for 10 mM of ZnSO4 and 2 mM of CoCl2, respectively, after which a sharp decrease was observed for either concentration of metal used (Fig. 5c andd). Overall, metal accumulation levels were significantly higher in yeast mutants expressing PLCc43 than in mutants or wild-type strain transformed with empty vector. A significant variation was observed among the yeast strains, metal concentration, and their interaction in relation to the metal accumulation (Table 2). To investigate the combined effect of the yeast Hsp40 of BY4741 and PLCc43, the BY4741 transformed cells were grown in SD-Ura amended with 60 μM of CdSO4. When compared to BY4741 carrying empty vector, growth of BY4741 transformed with PLCc43 showed significantly higher growth. This result clearly indicated that PLCc43 is providing additional tolerance to yeast cells compared to control in presence of 60 μM of CdSO4 (Fig. 6).

Fig. 6 Growth response of BY4741 transformed with PLCc43 and empty vector pFL61in SD-Ura medium supplemented with 60 μM of Cd. Individual data points are shown in each bar. Error bars are ±SD [START_REF] Nover | Heat shock response[END_REF] reported that the divalent cations of transition metal series such as Cd, Cu, Zn and Hg act as heat stress-like chemical stressors, which induce the production of various stress combating proteins such as Hsp40s. In many plant species, high expression of HSPs especially Hsp40s was observed under a variety of metal stress. Degradation of non-native proteins or proteolytic inactivation of regulatory proteins is a major cellular response of living organisms in metal-contaminated environments. It has been reported that Hsp40 proteins are involved in maintaining the cell homeostasis through translocation, refolding or degradation of aberrant proteins during cellular stress such as metal toxicity [START_REF] Cyr | DnaJ-like proteins: molecular chaperones and specific regulators of Hsp70[END_REF][START_REF] Suzuki | Screening of cadmium responsive genes in Arabidopsis thaliana[END_REF]. It was observed that Hsp40 in association with Hsp70 prevents aggregation of proteins and assist in refolding of non-native proteins under both normal and stressed environments [START_REF] Frydman | Folding of newly translated proteins i9n vivo: the role of molecular chaperones[END_REF][START_REF] Hartl | Molecular chaperones in cellular protein folding[END_REF]. In a study, a total of twenty-one cDNAs including Hsp40 were reported in birch Betula pendula playing significant role in maintaining the homeostasis and in alleviating the toxic effects of accumulated Cu metal [START_REF] Keinänen | Isolation of genes up-regulated by copper in a copper-tolerant birch (Betula pendula) clone[END_REF]).

Similarly, higher expression of Hsp40-like protein encoding gene conferred tolerance to beans when exposed to mercuric chloride [START_REF] Chai | Cloning of cDNA and expression analysis of a DnaJ-like gene under heavy metal stress in bean[END_REF]. The mechanism behind this might be direct binding trafficking, which has also been observed experimentally with copper [START_REF] Field | Copper chaperones: personal escorts for metal ions[END_REF], or by refolding of denatured proteins due to the stress conditions [START_REF] Meacham | The Hdj-2/Hsc70 chaperone pair facilitates early steps in CFTR biogenesis[END_REF][START_REF] Shen | ERdj3, a stress-inducible endoplasmic reticulum DnaJ homologue, serves as a cofactor for BiP's interactions with unfolded substrates[END_REF]. The expression of Hsp40 and Hsp70 limits the proteotoxic symptoms of metal ions and helps the detoxification and sequestration of these metal ions by metallothioneins [START_REF] Haap | Metallothionein and Hsp70 trade-off against one another in Daphnia magna cross-tolerance to cadmium and heat stress[END_REF]. Furthermore, in a study, deletion of Hsp40 protein Mdj1 in the filamentous entomopathogenic fungus Beauveria bassiana resulted into sensitivity towards various metals such as Zn, Cu and Fe along with some growth defects such as impaired cell wall integrity, reduced osmo-tolerance and antioxidant activities [START_REF] Wang | Vital role for the J-domain protein Mdj1 in asexual development, multiple stress tolerance, and virulence of Beauveria bassiana[END_REF]. Similarly, DnJC3 protein, an Hsp40 protein in honeybee, Apis cerana cerana has regulated the ill effects of various stress factors including metal ions [START_REF] Zhang | Identification of a DnaJC3 gene in Apis cerana cerana and its involvement in various stress responses[END_REF]). Hence, overall, significant tolerance towards potentially toxic metals used in this study, i.e. Cd, Cu, Zn and Co was shown in model eukaryotic living organism that expressed soil originating cDNA, PLCc43.

Oxidative stress

The potential role of PLCc43 in response to oxidative stress was examined using the superoxide dismutase yeast mutant sod2 Δ expressing the cDNA PLCc43. The growth of wild-type BY472, sod2 Δ transformed with empty vector and sod2 Δ /PLCc43 showed similar growth pattern when grown in absence of H2O2. The sod2 Δ expressing PLCc43 were able to confer tolerance to 0.25 mM and 0.5 mM of H2O2. The growth of BY4742 and sod2 Δ /PLCc43 were comparable at these concentrations and were significantly higher than the sod2 Δ transformed with empty vector (Fig. 7). Significant variation among the yeast strains and the days along with their interaction was observed (Table 2) Oxidative stress is a result of uncontrolled oxidation process, which results in the accumulation of reactive oxygen species (ROS). The generation of ROS because of metal toxicity is very common. Mostly, metals cause inhibition of metabolic processes and are involved in direct electron transfer involving metal cations [START_REF] Grimsrud | Oxidative stress and covalent modification of protein with bioactive aldehydes[END_REF]. The abiotic stress due formation of ROS is confronted by various stress-responsive proteins, out of which heat shock proteins play an important role. In a study by [START_REF] Qiu | The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones[END_REF], over-expression of LeCDJ1 DnaJ gene in tomato not only resulted in improving heat tolerance but also in increasing superoxide dismutase activity, which reduced accumulation of O2 - Similar studies on the effects of HSPs on ROS scavenging proteins and related to heat stress have been investigated. In Arabidopsis, over-expression of RcHSP17.8 improved SOD activity [START_REF] Jiang | A cytosolic class I small heat shock protein, RcHSP17.8, of Rosa chinensis confers resistance to a variety of stresses to Escherichia coli, yeast and Arabidopsis thaliana[END_REF]. Also, in tobacco, when the HSP ZmHSP16.9 was over-expressed, it confirmed increase in the levels of peroxidase, catalase and superoxide dismutase activity [START_REF] Sun | ZmHSP16.9, a cytosolic class I small heat shock protein in maize (Zea mays), confers heat tolerance in transgenic tobacco[END_REF]. A study by [START_REF] Kim | TAT-Hsp40 inhibits oxidative stress-mediated cytotoxicity via the inhibition of Hsp70 ubiquitination[END_REF] indicated a possible mechanism of H2O2 resistance by HSPs. It was concluded that the increase in resistance against oxidative stress conferred by HSPs was due to the prevention of proteasome activity, which ultimately provided resistance against cell toxicity. Furthermore, these findings illustrate the role of proteins like type I Hsp40 in combating the metal toxicity and advocate the significance of functional approach of functional metagenomics in mining, the hidden treasures of soil for remediation of various contaminated sites.

Conclusions

The potential of cDNA PLCc43 to tolerate toxic concentrations of toxic metals was quite evident from the experimental observations. The translated protein had the characteristics of a type I Hsp40-like protein. Functional complementation of S. cerevisiae using PLCc43 confers metal tolerance to the mutant strains of yeast, which categorize PLCc43 as potential member of functional repertoire participating in metal tolerance. Hsp40-like proteins might play an important role not only in targeted specific hosts as previously evidenced in numerous studies, but also in still uncharacterized living organisms that prevail in environments such as metal contaminated soils. This study described Hsp40like protein as a protein originating from metal-polluted soil that may help living organisms to maintain homeostasis between flora and fauna of the soil environment through tolerating high concentrations of metals. Thus, function-based metagenomics approach can reveal diversity of functions related to stress tolerance, when applied to complex environments like polluted soil.

Fig. 1 a

 1 Deduced amino acid sequence of type I Hsp40-like protein encoded by cDNA PLCc43: highly conserved tri-peptide HPD (green highlighted) and G/F-rich region (boxes in bold) are shown. Four cysteine-rich domains CXXCXGXG (yellow-highlighted bold letters) are underlined in the polypeptide sequence. Putative phosphorylation sites are highlighted in red. b Alignment of sequences of type I Hsp40 proteins.

Fig. 4

 4 Fig. 4

Fig. 5

 5 of a Cd, b Cu, c Zn and d Co in mutant yeast cells ycf1 Δ , cup1 Δ , zrc1 Δ and cot1 Δ carrying cDNA PLCc43 and BY4741/EV. Wild-type BY4741 transformed with only empty vector pFL61 (EV) and mutant strains with EV were used as positive and negative controls, respectively. Individual data points are shown in each bar. Error bars are ± SD

Fig. 7

 7 Drop assay and growth response of yeast mutant sod2 Δ strain expressing cDNA PLCc43 in presence of different concentrations of H2O2. Wildtype strain BY4742 and sod2 Δ transformed with empty were used as controls. Individual data points are shown in each bar. Error bars are ±SD

Table 1

 1 Primers used to amplify different genes in this study.

Table 2

 2 The factorial ANOVA results indicating the effects of different concentrations of metals on cell growth and metal accumulation by different yeast strains. The P values < 0.001 are highly significant

	Variables	Yeast strains (YS)	Metal concentrations (MC) YS x MC
	growth (O.D)
	df 2, 18	2, 18	4, 18
	In presence of Cd < 0.001	< 0.001	< 0.001
	In presence of Cu < 0.001	< 0.001	< 0.001
	In presence of Zn < 0.001	< 0.001	< 0.001
	In presence of Co < 0.001	< 0.001	< 0.001
	Metal accumulation

Acknowledgements

The authors thank the Indo-French Centre for the Promotion of Advanced Research (IFCPAR), New Delhi for financial support.

Funding

This work was supported by IFCPAR under the project 4709-1 Contributions RM, LFT, DM and MSR conceived the idea of the experiments and provided technical guidance. RY, BT and AM had a major contribution to overall preparation and carrying out of the research. MSR, DM, BT and AM revised and edited the manuscript.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


domain: math spi

text:
Ayoub Tighazoui
email: ayoub.tighazoui@unistra.fr

Bertrand Rose
email: bertrand.rose@unistra.fr

Michael Schlecht
email: michael.schlecht@insa-strasbourg.fr

Jürgen Köbler
email: juergen.koebler@hs-offenburg.de

Roland De Guio
email: roland.deguio@insa-strasbourg.fr

Mathematical model for the maintenance activities scheduling in the case of railway remanufacturing systems

Keywords: maintenance scheduling, flexible job-shop, railway system, Makespan, optimization. I

In the railway technical centers, scheduling the maintenance activities is a very complex task, it consists in ordering, in the time, all the maintenance operations on the workstations, while respecting the number of resources, precedence constraints, and the workstations' availabilities. Currently, this process is not completely automatic. For improving this situation, this paper presents a mathematical model for the maintenance activities scheduling in the case of railway remanufacturing systems. The studied problem is modeled as a flexible job-shop, with the possibility for a job to be executed several times on a stage. MILP formulation is implemented with the Makespan as an objective, representing the time for remanufacturing the train. The aim is to create a generic model for optimizing the planning of the maintenance activities and improving the performance of the railway technical centers. At last, numerical results are presented, discussing the impact of the instances size on the computing time to solve the described problem.

INTRODUCTION AND LITERATURE REVIEW

To ensure the functioning of rolling stock, the railway companies have several maintenance technical centers. These technical centers are classified into two categories. The first one is the maintenance technical centers, in which is performed the maintenance of level 1 to 3. The second one is Industrial Technical Centers (ITC) in which is performed the heavy maintenance operations, modernization and remanufacturing the trains, these are the maintenance operations of level 4 and 5. In fact, most of the operations carried out at ITC are midlife operations on TGVs (High Speed Train). After 15 years of use, TGVs are sent to ITC to be completely dismantled to undergo a series of checks and repairs before being reassembled and modernized. These operations take about 4 months of working, they represent about 60000 hours of workload.

The maintenance activities include preventive maintenance and renewal. The former includes activities that restore the train components to a better condition. The latter is the replacement of components when maintaining them is no longer practical and economical [START_REF] Levy | Optimal Planning of Track Work[END_REF]. In the case of ITC, a train is composed by several wagons, each wagon represents a job and each job is made up of several operations. The operations represent the elementary work carried out on each workstation. For each operation, a duration representing its workload and a workstation in which it can be executed. Some workstations are occupied by several operators who can make the same job. From modeling point of view, this system can be considered as a flexible job-shop for the following reasons: A chain of operations must be executed on workstations in series. A train is composed by several wagons, each wagon represents a job. A job includes several operations, and each operation must be executed on a workstation representing the stage. Each job follows its own sequence on the stages. Some operations can simultaneously be executed on identical workstations when multiple operators are available. So, there are parallel resources in some stages. The schedule then determines the ordering of the wagon's operations on the workstations for minimizing the time for remanufacturing the train, regarded as a makespan.

In the railway system literature, several works modeled the planning of maintenance activities as a scheduling problem. For example, Peng et al., [START_REF] Peng | A Heuristic Approach to the Railroad Track Maintenance Scheduling Problem[END_REF] proposed a heuristic method to the railroad track maintenance scheduling. The authors are interested in the minimization of the total travel costs of the maintenance teams as well as the impact of maintenance projects on railroad operation. Budai et al., [START_REF] Budai | Scheduling Preventive Railway Maintenance Activities[END_REF] are interested in the long-term and medium-term planning for determining which preventive maintenance works will be performed on segments and its time periods. The aim is to minimize the track possession cost. The authors proposed three greedy heuristics for scheduling preventive railway maintenance activities. Consilvio et al., [START_REF] Consilvio | Stochastic scheduling approach for predictive risk-based railway maintenance[END_REF] presents a stochastic model for scheduling predictive and risk-based maintenance activities in rail sector, the authors considered the stochastic nature of railway defect occurrence. They developed a MILP formulation for modeling their problem, considering the completion time and the tardiness of the maintenance activities as an objective. Dao et al., [START_REF] Dao | Maintenance scheduling for railway tracks under limited possession time[END_REF] proposed a mathematical model for finding the best maintenance schedule for multiple components in a railway track for minimizing the total cost in the planning horizon. For other related papers dealing with railway maintenance activities scheduling, the reader may refer to [START_REF] Doganay | Maintenance plan optimization for a train fleet[END_REF], [START_REF] Consilvio | Riskbased optimal scheduling of maintenance activities in a railway network[END_REF], [START_REF] Lin | Optimization of high-level preventive maintenance scheduling for high-speed trains[END_REF], [START_REF] Bakhtiary | Multiobjective Optimization of Ballasted Track Maintenance Scheduling[END_REF], [START_REF] Zhang | A heuristic approach to integrate train timetabling, platforming, and railway network maintenance scheduling decisions[END_REF].

Most of studied works consider the scheduling of routine or ordinary maintenance activities, such as regular inspections and minor repairs. However, only few papers are interested in scheduling major maintenance activities like renewal, modernization, or remanufacturing. For instance, Torba et al., [START_REF] Torba | Robust scheduling within SNCF railway maintenance centers[END_REF] proposed a MILP formulation for scheduling maintenance operations of rolling stock to renovate the trains. The authors modeled their problem as a multi-skill resourceconstrained muti-project scheduling. To fill the void, in this paper, a MILP formulation for scheduling the maintenance activities of level 4 and 5 is implemented. The studied problem is very helpful for ITC decision makers. It allows to optimize the time of remanufacturing the train and reduce the related costs. In the classical job-shop, each job consists of a set of operations which need to be proceeded in a specific stage and only one operation in a job can be proceeded at a given time. Each job follows its own sequence on the stages. When there are multiple machines on the stage, we are talking about a flexible job-shop. In the studied problem, some jobs can be proceeded several times on a stage. For example, a wagon proceeds on the washing stage, then it proceeds on sanding, and it comes back to the washing again. Therefore, in our model, the jobs can proceed several times on a stage, but not successively. The novelty of this work is to model the maintenance activities scheduling of railway remanufacturing system (maintenance of level 4 and 5) as a flexible job-shop scheduling problem and to create a generic mathematical model using a MILP formulation. To the best of our knowledge, very few papers consider this kind of problem. The main contributions of the work are:

• Modeling the maintenance activities of the railway remanufacturing systems as a flexible job-shop scheduling problem.

• Implementing a MILP formulation for the described problem to optimize the time of remanufacturing the train.

• Discussing the results in terms of the CPU time for testing the limitation of the MILP resolution in terms of number of jobs.

The rest of the paper is organized as follows: In section 2, the problem is described. In section 3, the mathematical model is implemented. In section 4, the numerical results are discussed. Finally, we conclude and propose some perspectives in section 5.

II. PROBLEM DESCRIPTION

In the studied system, a train is composed by several wagons, each wagon represents a job, each job is made up of several operations. The operation represents the elementary work carried out on each workstation. For each operation, a duration representing its workload and a workstation in which it can be executed. Indeed, a workstation is a physical place where the operation can be performed. Some workstations are occupied by several operators who can make the same job. From modeling point of view, this system can be considered as a flexible job-shop. For the reasons mentioned in the introduction.

In the flexible job-shop problem, we consider a set of N jobs, {1, 2, …, N} is available to be scheduled, at the beginning of the horizon, in a set of S stages, {1, 2, …, S}. Each job is composed by several operations, we denoted by Oij the i th operation of job j. Thus, for each operation Oij is given a processing time pij and a stage Stageij. When the operation Oij starts at time STij on a stage Stageij, it is processed to completion time CTij without preemption (i.e. CTij = STij + pij). All the job information (pij, Stagejij) is known in advance. The Makespan Cmax is the completion of all operations of the train, max (CTij).

III. MATHEMATICAL MODEL

In this section, a MILP is formulated. To build our mathematical model, we adapted the model presented in [START_REF] Tighazoui | Predictive-reactive Strategy for Flowshop Rescheduling Problem: Minimizing the Total Weighted Waiting Times and Instability[END_REF][START_REF] Tighazoui | Heuristics based on MILP for solving a flow shop rescheduling problem simultaneously considering efficiency and stability[END_REF][START_REF] Tighazoui | Ré-ordonnancement des systèmes de production flexibles avec contraintes de blocage mixtes soumis à des aléas de commandes ou de production[END_REF][START_REF] Tighazoui | Predictive-reactive strategy for identical parallel machine rescheduling[END_REF] in which they minimized the total weighted waiting time in scheduling problems, to consider now the flexible job-shop and the makespan as an objective. The assumptions, parameters, decision variables and constraints are given below

Assumptions

• The maintenance operators are always available to perform the tasks.

• The technical center is continuously open.

• The dismantling operations are done before.

• The operations scheduling concerns the remanufacturing of one train.

Parameters

Objective

The objective is to minimize the schedule completion time, the completion of all operations of the train. Hence, we reduce the costs related to the train delay. This date corresponds to the Makespan Cmax such as:

𝐶 𝑚𝑎𝑥 = 𝑚𝑎𝑥(𝐶𝑇 𝑖𝑗 ) ∀𝑖 ∈ {1 … 𝑛𝑗}, ∀𝑗 ∈ {1 … 𝑁}
So, the objective is min Cmax subject to the following constraints.

Constraints 𝐶 𝑚𝑎𝑥 ≥ 𝐶𝑇 𝑖𝑗 , ∀𝑖 ∈ {1 … 𝑛𝑗}, ∀𝑗 ∈ {1 … 𝑁} () ∑ ∑ ∑ 𝑋 𝑖𝑗𝑘𝑠𝑚 𝑀𝑠 𝑚=1 𝑆 𝑠=1 𝐾 𝑘=1 = 1, ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 } () ∑ ∑ 𝑋 𝑖𝑗𝑘𝑠𝑚 𝑀𝑠 𝑚=1 𝐾 𝑘=1 = 1, 𝑖𝑓 (𝑆𝑡𝑎𝑔𝑒 𝑖𝑗 = 𝑠) ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 }, ∀𝑠 ∈ {1 … 𝑆} () ∑ ∑ 𝑋 𝑖𝑗𝑘𝑠𝑚 𝑛 𝑗 𝑖=1 𝑁 𝑗=1 ≤ 1; ∀𝑘 ∈ {1 … 𝐾} ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () ∑ ∑ 𝑋 𝑖𝑗𝑘+1𝑠𝑚 𝑛 𝑗 𝑖=1 𝑁 𝑗=1 ≤ ∑ ∑ 𝑋 𝑖𝑗𝑘𝑠𝑚 𝑛 𝑗 𝑖=1 𝑁 𝑗=1 ∀𝑘 ∈ {1 … 𝐾 -1}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝑆 𝑘+1𝑠𝑚 ≥ 𝐶 𝑘𝑠𝑚 , ∀𝑘 ∈ {1 … 𝐾 -1}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝐶 𝑘𝑠𝑚 = 𝑆 𝑘𝑠𝑚 + ∑ ∑ 𝑋 𝑖𝑗𝑘𝑠𝑚 𝑛 𝑗 𝑖=1 𝑁 𝑗=1 * 𝑝 𝑖𝑗 ∀𝑘 ∈ {1 … 𝐾}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝐶𝑇 𝑖𝑗 = 𝑆𝑇 𝑖𝑗 + 𝑝 𝑖𝑗 , ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 } () 𝐶𝑇 𝑖𝑗 ≥ 𝐶 𝑘𝑠𝑚 -𝐵𝑖𝑔𝑀(1 -𝑋 𝑖𝑗𝑘𝑠𝑚 ), ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 }, ∀𝑘 ∈ {1 … 𝐾}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝐶𝑇 𝑖𝑗 ≤ 𝐶 𝑘𝑠𝑚 + 𝐵𝑖𝑔𝑀(1 -𝑋 𝑖𝑗𝑘𝑠𝑚 ), ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 }, ∀𝑘 ∈ {1 … 𝐾}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝐶𝑇 𝑖𝑗 ≤ 𝑆𝑇 𝑖′𝑗 𝑖𝑓 (𝑖 < 𝑖′) ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖, 𝑖′ ∈ {1 … 𝑛 𝑗 } () 𝑋 𝑖𝑗𝑘𝑠𝑚 ∈ {0,1}, ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 }, ∀𝑘 ∈ {1 … 𝐾}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } () 𝐶𝑇 𝑖𝑗 , 𝑆𝑇 𝑖𝑗 , 𝐶 𝑘𝑠𝑚 , 𝑆 𝑘𝑠𝑚 ≥ 0 ∀𝑗 ∈ {1 … 𝑁}, ∀𝑖 ∈ {1 … 𝑛 𝑗 }, ∀𝑘 ∈ {1 … 𝐾}, ∀𝑠 ∈ {1 … 𝑆}, ∀𝑚 ∈ {1 … 𝑀 𝑠 } ()

Constraints' description

Equation ( 1) means that the Makespan value must be greater than or equal to all completion time of positions. Equation ( 2) consists of having one operation per position. Equation (3) consists of assigning each operation to a stage. Equation (4) consists of having only one or no operation per position in a machine in a stage. Equation [START_REF] Dao | Maintenance scheduling for railway tracks under limited possession time[END_REF] ensures that if position k is not occupied by a job, position k+1 will not be occupied either. Equation (6) consists in making, for all machines, the starting time of the (k+1) th position greater than or equal to the previous position completion time. Equation [START_REF] Consilvio | Riskbased optimal scheduling of maintenance activities in a railway network[END_REF] specifies that, for all machines, the completion time of the k th position is equal to the starting time of the k th position plus the assigned job processing time. Equation (8) specifies that, the completion time of Oij is equal to the starting time of Oij plus its processing time. Equation ( 9) and [START_REF] Zhang | A heuristic approach to integrate train timetabling, platforming, and railway network maintenance scheduling decisions[END_REF] defines, for all machines and all stages, the completion time of operation Oij, which is greater than or equal to the completion time of the assigned position, where bigM must be sufficiently large. Equation [START_REF] Torba | Robust scheduling within SNCF railway maintenance centers[END_REF] making the starting time of operation Oi'j greater than the completion time of operation Oij if i' is after i. Equation ( 12) defines the domain of the variable Xijksm. Equation ( 13) is no negativity constraint.

In this formulation, the blocking constraints between successive stages are not considered. The blocking constraints can eventually be added in the equation ( 6), modeling the case where the buffer space capacity between the machines is limited.

IV. EXPERIMENTAL RESULTS

The proposed MILP model has been written on FICO Xpress IVE and the simulations have been performed on a Core i7 2.70GHz laptop. In this section, we present the numerical results obtained through the model application on a flexible job-shop. The considered processing times vary following a normal distribution with an average of 30 hours and a standard deviation of 50 hours, pij ~N (30, (50)²). For testing the limitations of the model, we varied the number of jobs N, with different values (3, 5; 10), Sg = (5;10;15). At each stage is executed one operation by one machine. On the presented results, we tested on 10 different instances the computing time (CPU). Then, we calculated the minimum, maximum, average, and standard deviation of CPU in second. The obtained results are presented in Table 1. The Average CPU increases to much with N and Sg. Thus, the average CPU depends on both, the number of jobs (wagons) and the number of stages (workstations). If these ones become large, the average and the standard deviation of CPU increase. This matter makes it difficult to estimate the necessary computing time that the MILP needs for solving the problem. Based on Table 1, the proposed MILP model is effective in solving the problems made up of 5 jobs in 10 stages, or 3 jobs in 15 stages. It is averagely equivalent to 50 operations in total. We have also performed the problems for 5 jobs in 15 stages (75 operations). The program ran for 12 hours. Then, we have interrupted the simulation.

V. CONCLUSIONS AND PERSPECTIVES

This paper proposed a MILP formulation for scheduling the maintenance activities of level 4 and 5. The problem is inspired from the railway remanufacturing systems, in which the system is modeled as a flexible job-shop scheduling, with the possibility for a job to be executed several times on a stage. The Makespan is considered as an objective, representing the time for remanufacturing the train. The aim of the study is to create a generic model for optimizing the planning of the maintenance activities and improving the performance of the technical centers. Numerical results show that the MILP model is effective in solving the problems made up of 50 operations. To solve large real industrial instances, one of the main short-term prospects is to design efficient heuristics and metaheuristics to browse even more jobs in a reasonable time, as well as a comparison of the proposed MILP and the future heuristics with other existing resolution methods. We will also consider some future aspects that are beyond the scheduling of operations, such as:

• The precedence constraints and the starting conditions between the phases. • The dismantling assembly operations.

• The workshop opening hours.

  N: number of jobs (wagons) S: number of stages (workstations) j: index of job, j=1, 2, ..., N k: index of position, k=1, 2, ..., K s: index of stage, s=1, 2, ..., S Ms: number of identical machines in the stage s. m: index of machine at stage s, m=1, 2, ..., Ms. nj: number of operations of job j. i: index of operation, i=1, 2, ..., nj Oij: i th operation of job j. pij: duration of the operation Oij. STij= starting time of operation i of job j. CTij= completion time of operation i of job j. Sksm= starting time of position k in stage s at machine m. Cksm= completion time of position k in stage s at machine m.

	Stageij: stage the operation Oij.
	BigM: A big value. In our case, it is assumed that BigM= ∑ ∑ 𝑝 𝑖𝑗 𝑛 𝑗 𝑖=1 𝑁 𝑗=1
	Ajs: number of times when job j must be executed in the
	stage s.
	K: max 𝑠 𝑖𝑛 1..𝑆	∑ 𝐴 𝑗𝑠 𝑁 𝑗=1
	Decision variables
	𝑋 𝑖𝑗𝑘𝑠𝑚 {	1 𝑖𝑓 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑂 𝑖𝑗 𝑎𝑠𝑠𝑖𝑔𝑛𝑒𝑑 𝑖𝑛 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑘 𝑖𝑛 𝑠𝑡𝑎𝑔𝑒 𝑠 𝑜𝑛 𝑚𝑎𝑐ℎ𝑖𝑛𝑒 𝑚 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

TABLE I .

 I CPU VALUES

	Sg N	Min CPU	Max CPU	Avg CPU	Std dev CPU
		3	0.125	0.157	0.141	0.02
	5	5	7.815	8.96	8.38	0.8
		10	950	3200	1123.2	1203
		3	0.602	0.741	0.6715	0.09
	10	5	29.281	33.11	31.19	2.7
		10	> 12h	> 12h	> 12h	> 12h
		3	2.09	2.29	2.19	0.14
	15	5	> 12h	> 12h	> 12h	> 12h
		10	> 12h	> 12h	> 12h	> 12h

ACKNOWLEDGMENT

This research was partially carried out within the framework of the research projet VIRTFac, which is supported by European Union through the ERDF and cofinanced both by the program INTERREG V Upper Rhine and by the Offensive Science program of the RMT-TMO.


Marion Croisy
email: amarion.croisy@st.com

Erwine Pargon

Cécile Jenny

Claire Richard

Denis Guiheux

Sylvain Joblot

Alain Campo

Nicolas Posseme

Nicolas Possémé

Blister formation mechanism during high dose implanted photoresist stripping

Keywords: blister, hydrogen plasma, stripping, defectivity, implanted substrate, High Dose Implant Stripping

Introduction

Integrated circuit manufacturing requires many dry strip operations to remove photoresist (PR) either after implantation steps or for lithographic rework. Indeed, junction fabrication needs several implantations using a PR mask to define the area to be implanted. Then, the PR has to be efficiently removed usually by a dry strip process. For advanced technologies, the increase of implantation dose makes the stripping of implanted PR more and more challenging due to the formation of a hard modified layer ("crust layer") during the implantation step [START_REF] Kikuchi | A Dry Process for Stripping As+ Ion-Implanted Photoresist[END_REF]. One of the main requirements of the stripping process is to remove the implanted photoresist efficiently without leaving residues on the surface. The consumption and oxidation of underlying materials should also be minimized as well as defect formation on the implanted substrate. To fulfill all these criteria, several plasma chemistries have been evaluated in our previous study [START_REF] Croisy | Characterization and Development of High Dose Implanted Resist Stripping Processes[END_REF]. The results showed that N 2 H 2 plasma is the most suitable chemistry to remove the resist without leaving residues and with minimal substrate consumption. However, an issue has been identified with the use of hydrogen-based plasma while not with O 2 -based-plasma processes. After several dry strip processes, bubbles also called "blisters" are formed on the implanted areas as shown in Figure 1. These blisters are not observed after the implantation step. The time at which blisters appear depends on both the implantation parameters and the dry strip conditions. Jenny et al. suggested that these bubbles come from nucleation initiated on defects formed in the substrate during the implantation step [START_REF] Jenny | Blisters formation mechanisms during high dose implant stripping in 28nm FDSOI technology node[END_REF].

In this paper, the blister formation has been studied thanks to in-depth characterization techniques. The impact of implantation parameters on these defects has been studied and compared with ion implantation simulations to understand the results obtained and propose a mechanism for blistering phenomena. Finally, some solutions to delay the blisters appearance have been proposed.

Figure 1 Top-view observations of patterns after a) P implantation at an energy of 4keV and after b) 4 c) 5 and d) 6 N 2 H 2 dry strip processes. The blister phenomenon appears in the present case after 5 strip processes on the implanted area and get worse afterwards

Experimental

In this work, 300mm silicon wafers are patterned with a 215nm-thick 248nm-Deep-Ultra-Violet (DUV) photoresist. They are then exposed to As or P implantation with various ion implantation conditions in an Applied Materials VIISta ion implanter. N 2 H 2 dry strip processes are performed on a LamResearch GAMMA stripper at high temperature (285°C) with a downstream plasma containing 3% hydrogen.

After the stripping process, the defects were observed on a top-view Critical Dimension-Scanning Electron Microscope (CD-SEM) (CG5000 tool from Hitachi). Some additional off-line techniques have also been performed to characterize the defects. Cross-section view of the defects were performed using an Osiris Transmission Electron Microscope (TEM) instrument in scanning mode. Time of Flight-Second Ion Mass Spectroscopy (ToF-SIMS) analyses were carried out with an Ion-TOF TOF-SIMS5 instrument using a dual beam acquisition and detection of the negative ions only. A Bi + beam at 25 kV is used for the analysis with a 100 x 100 µm² raster, a 10.2ns cycle time and a 128 pixel resolution. The sputtering is performed using a Cs +

ACCEPTED MANUSCRIPT

(500 eV) beam, with a 400 x 400 µm² raster and an electron flood gun with -20 V bias was also used to overcome charging problems. SRIM (Stopping and Range of Ions in Matter) simulations [START_REF] Ziegler | Stopping Range of Ions in Matter[END_REF] of ion implantation in silicon substrate have been performed using quick damage calculations (Kinchin-Pease model) to determine Si recoil profiles created by the implantation. Si recoil profiles give information on the damage created by the implantation on the substrate and are an indicator of amorphization rate.

Amorphized layer thickness has also been determined on Si recoil profiles by fixing a limit above which the silicon is considered amorphous. In this study, the value is fixed at 3.6 10 21 recoils/cm 3 .

Results and discussion

Blister characterization

In this section, the impact of the implantation conditions on blister appearance is investigated, and a correlation is made between experimental results and simulated data of the damage generated by the implantation step in the silicon substrate.

Impact of implanted species

The blister appearance after N 2 H 2 dry strip processes is compared in the case of P and As implanted substrates (Table 1). For the same implantation energy and dose (4keV 2E15 atoms/cm²), blisters appeared after two strip processes for P-implanted samples and after five for As. This clearly indicates that the dopant species has an impact on blister formation. Moreover, blister top-view observations (Figure 2) show that the defects are larger with phosphorus (70-100nm) compared to arsenic (50-60nm). Thus, it is observed that for similar implantation conditions the amorphization rate is 30% higher Pt Encapsulation a b

for As than for P dopant while the amorphized silicon thickness is slightly thinner (14nm vs 18nm). These thicknesses values are also confirmed by TEM observations in Figure 3.

These results suggest that the delayed formation of blisters for As implantation could be due to either a higher amorphization rate or a thinner amorphized layer. Thus to determine which parameter has the more important impact on blisters formation, the influence of ion energy and dose during the implantation is investigated in the following sections.

Figure 4 SRIM simulation of silicon recoils profiles after As and P implantation at an energy of 4keV and a dose of 2E15 atoms/cm²

Impact of implantation dose

The impact of As implantation dose on blister formation is shown in Table 2. This implantation parameter seems to play a major role in blisters appearance. Indeed, with a dose of 1 10 15 As atoms/cm², the blisters are observed after the first stripping process whereas when the dose is doubled, they appear after 6 N 2 H 2 processes (Table 2). Si recoil profiles have also been simulated for these As-implantation conditions (Figure 5) and the extracted amorphized silicon thicknesses are reported in Table 3. These simulated results show that the amorphized layer thicknesses for both dose values are very close. However, the amorphization rate is twice higher for higher implantation dose.

These results suggest that the formation of blisters is mainly driven by the amorphization rate rather than the amorphized layer thickness. The more the silicon is amorphized; the later the blisters appear. This will be discussed in section 3.2. The Si recoil profiles simulated for 3, 4 and 5keV implantation energies are shown in Figure 6 and the extracted amorphized silicon thicknesses are reported in Table 5. These simulated results show that the amorphized layer increases with the implantation energy and that the effect on amorphization rate is not significant. In this set of experiments, no correlation between the amorphized layer thickness and blister appearance is observed meaning that a-Si

Discussion on blisters formation mechanism

Experimental and simulated data in the last section show that blisters appearance is delayed when the implantation conditions lead to higher silicon amorphization. This observation seems at first sight paradoxical because the blisters are not observed on a bulk crystalline silicon substrate exposed to N 2 H 2 dry strip processes. Thus, the blisters only appear if the silicon substrate is amorphized, but their appearance is delayed for high amorphization levels.

To understand the mechanisms involved, ToF-SIMS analyses have been performed on three samples: a P-implanted substrate with no subsequent N 2 H 2 dry strip process (no blisters), a non-implanted substrate exposed to two dry strip processes (no blisters) and a P-implanted substrate subsequently exposed to two N 2 H 2 dry strip processes. The aim is to evaluate and compare the diffusion of the species present in the N 2 H 2 plasma through implanted and nonimplanted Si substrates.

The ToF-SIMS signals of SiN -, SiH -and H -are monitored during the Si sputtering by Cs + ions to reconstruct the depth concentration profiles (Figure 7). The concentration profiles of the P-implanted substrate are used as a reference since this sample has not been exposed to N 2 H 2 plasma processes.

The diffusion mechanism of nitrogen in silicon has already been studied in literature. [START_REF] Itoh | Diffusion coefficient of a pair of nitrogen atoms in float-zone silicon[END_REF] [9] [START_REF] Sawada | Atomistic model of nitrogen-pair diffusion in silicon[END_REF] Nitrogen diffusion occurs by breaking and reforming the bonds between the nitrogen atoms and the surrounding silicon atoms. Thus, the monitoring of SiN -profiles can be used as an indicator of nitrogen diffusion in the samples. Unfortunately, the use of a Bi + beam for ToF-SIMS analyses does not allow the observation of a N -profile which would have been an indicator of the presence of N 2 molecules in the sample. Figure 7.a shows that the SiN -signal is higher for substrates exposed to N 2 H 2 plasma process indicating that the diffusion of nitrogen through silicon during the stripping process is enhanced. Moreover it is noticed that the concentration profiles in the non-implanted and implanted substrates are very similar suggesting that nitrogen diffusion is not influenced by the amorphization of silicon generated by the implantation step. So far, there is no data on the accumulation of N 2 and its impact on blister formation even if it is known that N 2 tends to aggregate in strained area like amorphized silicon and out-diffuse if few defects are present in the Si lattice. [START_REF] Itoh | Diffusion coefficient of a pair of nitrogen atoms in float-zone silicon[END_REF] A similar mechanism has been proposed in the literature for H 2 diffusion with the creation of intermediate Si-H bonds with the Si atoms. [START_REF] Biswas | Mechanism for hydrogen diffusion in amorphous silicon[END_REF] SiH -and H -profiles are then monitored (Figure 7.b) and allow to follow the diffusion of hydrogen and the accumulation of H 2 molecules.

In the reference sample, some SiH -are detected probably due to the incorporation of H during the implantation process, but no H -is detected meaning that H 2 molecules are not present and that they have probably out-diffused. [7] [12] For the non-implanted sample exposed to N 2 H 2 stripping processes, neither SiH -nor H -are detected by ToF-SIMS. Since Figure 7.a) suggests that nitrogen species diffuse in crystalline Si, it is likely that hydrogen species also penetrate into crystalline silicon lattice. Moreover, several studies have already reported on the ability of hydrogen to diffuse in both c-Si and a-Si. [START_REF] Usenko | Blistering on Silicon Surface Caused by Gettering of Hydrogen on Post-Implantation Defects[END_REF] [14] The main explanation for not detecting hydrogen species is that H atoms that diffuse though the Si substrate recombine into H 2 molecules and out-diffuse from the substrate.

On the other hand, the implanted sample exposed to N 2 H 2 plasma clearly shows a significant accumulation of H 2 and Si-H bonds in the first 20nm corresponding to the amorphized area formed during the implantation step. T. Höchbauer and al observed that defects in silicon substrate act like traps for hydrogen and prevent long-range diffusion. [START_REF] Höchbauer | The influence of boron ion implantation on hydrogen blister formation in n-type silicon[END_REF] Moreover, it is known that hydrogen is easily trapped by defects like dandling bonds, vacancies, dislocation, impurities or precipitates. [START_REF] Lee | Role of strain in the blistering of hydrogen-implanted silicon[END_REF] [6] [13] [15] Based on the reported literature, a mechanism for the blistering phenomenon can be proposed: H atoms diffuse into the amorphized silicon and react with dangling bonds. As the diffusion proceeds and all the defect sites have reacted with H atoms, H recombine to form H 2 molecules which are then trapped in the voids formed in the amorphized Si layer by the agglomeration of vacancy defects. This accumulation results in an increase of internal pressure and plastic deformation. [START_REF] Weldon | On the mechanism of the hydrogen-induced exfoliation of silicon[END_REF] Consequently, the blisters are formed, and the top silicon layer swells. This is consistent with the study of Reboh and al who demonstrated that blisters are composed of H 2 and the inner surface of the cavity is made of Si-H bonds. [START_REF] Reboh | Defect engineering in H and He implanted Si[END_REF] ACCEPTED MANUSCRIPT Figure 7 Comparison of ToF-SIMS profiles of a) SiN -b)SiH -and c) H -for three investigated samples: a P-implanted substrate with no subsequent N 2 H 2 strip processes (no blisters), a nonimplanted substrate exposed to two N 2 H 2 processes (no blisters) and a P-implanted substrate also exposed to two N 2 H 2 processes (blisters) ToF-SIMS results presented in this section highlight the diffusion of hydrogen during the dry strip processes and thus help to explain the formation of blisters in amorphized silicon, but the link between blisters appearance and the amorphization rate remains unexplained. In fact, blister appearance depends on the H 2 molecules accumulation in the substrate, the hydrogen diffusion rate which can be influenced by the N 2 H 2 stripping conditions (chamber pressure and wafer temperature), and also by the amorphization rate. Indeed, if the silicon substrate is highly amorphized, hydrogen diffusion is slowed and consequently, blister formation is delayed explaining the results observed in Table 1 andTable 2.

Solution to delay blisters formation

Based on the blister formation mechanism suggested above, two strategies are proposed to limit the blistering effect: an optimization of the stripping process; and an integration modification. For a typical CMOS device, at least 3 N 2 H 2 dry strip processes are required.

Dry stripping recipe optimization

The first solution to decrease hydrogen diffusion in the substrate is to optimize the stripping process. Consistent with Morral's work [START_REF] Cabarrocas | Etching and hydrogen diffusion mechanisms during a hydrogen plasma treatment of silicon thin films[END_REF] which showed that lowering the process pressure decreases the number of hydrogen radicals in the plasma, the process temperature and pressure were lowered to reduce the amount of hydrogen reaching the silicon surface. For stripping efficiency concerns, the process time has been increased to balance the decrease in process temperature. The results presented in Table 6 show that this first optimized recipe gains one stripping process compared to the reference recipe. However, some residues remained on the non-implanted area after the low pressure and low temperature dry strip process and the following SPM-SC1 wet cleaning (Figure 8). To overcome this efficiency issue, another recipe has been tested with only lowering the pressure. The blisters were formed after the same time of exposure to N 2 H 2 chemistry, but no remaining residues were observed after the stripping.

Integration modification

Another solution is to limit hydrogen diffusion through the substrate by introducing a "screen oxide" on the top of the silicon. Because of integration considerations, the growth of this oxide layer has to be made before the lithographic and implantation steps. Consequently, the implantation step is made through the oxide layer and has to be adapted to have the same dopant profile on the Si substrate. SRIM simulations have been used to find adapted implantation conditions which could correspond to phosphorus implanted at 4keV. Considering dopant profiles and Si recoil profiles (Figure 9), phosphorus implanted at 6keV through the oxide has been found to match with an implantation made at 4keV directly on the Si substrate. The effect of the screen oxide on blister formation is shown in Table 7. The results clearly indicate that the presence of an oxide layer during the stripping process delays the appearance of defects (line 1 and 2 of Table 7). The oxide is assumed to play the role of a barrier to hydrogen diffusion. To verify this assumption, another test has been made on which the oxide is removed before the N 2 H 2 process with a HF-based clean. Table 7 (line 3) shows that the formation of blisters appears nearly at the same time as without the use of the oxide layer. This confirms that the presence of the oxide layer limits hydrogen diffusion and delays blister formation. This is consistent with hydrogen diffusity values given in literature: 10 -10 cm²/s in c-Si and 1.2 10 -13 cm²/s in SiO 2 . [START_REF] Fink | Hydrogen implantation and diffusion in silicon and silicon dioxide[END_REF] Table 7 Blister observations depending on the number of N 2 H 2 processes and the presence of thermal oxide before implantation step (P 4keV) (x indicates the observation of blisters). P implantation conditions are equivalent to 4keV implantation on Si substrate.

Substrate

Number of N 2 H 2 processes

1 2 3 4 5 Si x x x x
Thermal oxide (55A)/Si x Si (Oxide removed before stripping process)

x x x

Conclusions and perspectives

After the implantation step, the implanted PR mask can be efficiently removed by N 2 H 2 chemistry, but blisters can appear on the implanted substrate. Characterization of the defects have shown that the blisters are formed near the Si surface in the amorphized layer and that they are due to hydrogen diffusion in the implanted substrate. A mechanism is proposed: H atoms diffuse through the silicon substrate and are trapped by the defects found in the amorphized silicon layer then H atoms recombine to form H 2 molecules which accumulate in the substrate. Nitrogen diffusion has also be highlighted, but its involvement in the blistering phenomenon is not demonstrated. To minimize the blister formation phenomenon, solutions have been investigated

to limit the diffusion of hydrogen into the silicon substrate. Dry strip parameters have been optimized and especially the chamber pressure which allows to delay the blister formation without impacting the stripping efficiency. Furthermore, the growth of an oxide layer before implantation is a suitable approach to delay blister formation by limiting H diffusion into the Si substrate. The study of implantation parameters influence on the appearance of blisters has also

shown that the silicon amorphization rate is the key factor. Indeed, blister formation is delayed with higher amorphization level meaning that increasing silicon amorphization could be also a promising way to avoid the blistering issue.

Figure 2 Figure 3

 23 Figure 2 CD-SEM top view observations of blisters obtained after a) P implant and 3 N 2 H 2 processes b) As implantation and 8 N 2 H 2 processes STEM cross-section observations are carried out on silicon areas presenting blisters after implantation and dry strip processes (Figure 3). Two areas are observed for both P and As-implanted samples: a 15nm-thick amorphized silicon layer (a-Si) caused by the implantation step; and the crystalline silicon substrate (c-Si). The blisters are clearly observed in the a-Si layer.This result is consistent with other studies which clearly observed blisters in the higher defect density area.[START_REF] Lee | Role of strain in the blistering of hydrogen-implanted silicon[END_REF] [6][START_REF] Höchbauer | The influence of boron ion implantation on hydrogen blister formation in n-type silicon[END_REF]

  thickness is not the main parameter which influences blister formation in the range of energies investigated in this study.

Figure 8

 8 Figure 8 Top-view SEM-CD observations after a SPM-SC1 wet cleaning following the optimized dry strip process (low P and low T)

Figure 9

 9 Figure 9 SRIM simulation of a) dopants profile and b) silicon recoils profile after P implantation (P 6keV in SiO 2 /Si and P 4keV in Si)

Table 1

 1 Blister observations depending on the number of N 2 H 2 processes and implanted species for the same implantation dose and energy (x indicates the observation of blisters)

	Implantation	Number of N 2 H 2 processes
	conditions	1 2 3 4 5 6 7 8
	P	x x x x x x x
	As	x x x x

Table 2

 2 Blister observations depending on the number of N 2 H 2 processes and As implantation dose (x indicates the observation of blisters) Figure 5 SRIM simulations of Si recoil profile in Si substrates depending on implantation dose

	Implantation	Number of N 2 H 2 processes
	conditions	1 2 3 4 5 6 7 8
	As 3keV 2E15 at/cm²	x x x
	As 3keV 1E15 at/cm² x x x x x x x x

Table 3

 3

							2E15 atoms/cm²
	3 ) Si recoil (Atoms/cm	10 22 10 23	a-Si c-Si				1E15 atoms/cm²
		10 21	0	2	4	6	8	10	12	14
					Depth (nm)
	Amorphized layer thicknesses determined from SRIM simulations depending on
	implantation dose
							Amorphized layer
		Implantation conditions	thickness (nm)
								Simulated
		As 3keV 2E15 at/cm²			11.4
		As 3keV 1E15 at/cm²			10.4
	3.1.3. Impact of implantation energy
	Finally, the impact of implantation energy (3keV, 4keV and 5keV) on blister appearance
	after N 2 H 2 dry strip processes is shown in Table 4 in the case of As implantation. Blister defects
	appeared a little earlier with low implantation energies, but the trend is not very clear and the
	effect is limited in the range of energies studied.

Table 4

 4 Blister observations depending on the number of N 2 H 2 processes and the implantation energy used (x indicates the observation of blisters) for As implantation

	Implantation	Number of N 2 H 2 processes
	conditions	1 2 3 4 5 6 7 8
	As 5keV 2E15 at/cm²	x x
	As 4keV 2E15 at/cm²	x x x x
	As 3keV 2E15 at/cm²	x x x

Figure 6 SRIM simulations of Si recoil profile in Si substrate depending on implantation energy for As implantation.

Table 5

 5 Amorphized layer thicknesses determined from SRIM simulations depending on implantation energy

		Amorphized layer
	Implantation conditions	thickness (nm)
		Simulated
	As 5keV 2E15 at/cm²	16.4
	As 4keV 2E15 at/cm²	14
	As 3keV 2E15 at/cm²	11.4

Table 6

 6

	Blister observations depending on number of N 2 H 2 processes and stripping recipe (x
	indicates the observation of blisters)
		Number of N 2 H 2
	Dry strip recipe	processes
		1 2 3 4 5
	POR recipe	x x x x
	Low T low P	x x x
	Low P	x x x