Vincent Girard

David Chiche
email: david.chiche@ifpen.fr

Arnaud Baudot

Delphine Bazer-Bachi

Isabelle Clémençon

Florent Moreau

Christophe Geantet

Innovative low temperature regenerable zinc based mixed oxide sorbents for synthesis gas desulfurization

Keywords: Desulfurization, synthesis gas, purification, sorbents regeneration

Zinc oxide-based materials are commonly used for the final desulfurization of synthesis gas in IGCC and Fischer-Tropsch based XTL processes. The formation of large amount of solid waste is the major issue of this process. In-situ oxidative regeneration is a promising way to reduce this waste formation and enhance desulfurization process efficiency and economics.

However, previous studies showed that one of the major drawbacks of oxidative regeneration of sulfided oxides relies in the high operating temperature range required to overcome the formation of inhibitory sulfate phases. A preliminary work of the authors focused on single oxides identified that regeneration temperature of zinc oxide-based sorbent could be reduced through the addition of molybdenum oxide. Two composites oxides -a single oxides mixture (ZnO and MoO 3 ) and a mixed oxide (ZnMoO 4 ) -were synthesized and characterized. Their sulfidation and oxidative regeneration properties were investigated through thermogravimetry and in-situ characterizations. Sulfidation of the single oxides mixture was shown to be similar to the combination of the sulfidation of both independent single oxides. Mixed oxide sulfidation leads to ZnMoO 4 phase demixing into ZnS, MoS 2 and ZnMoO 3 . The oxidative regenerations of the sulfided single oxide mixtures and mixed oxide are initiated, respectively, at 350°C and 300°C. These temperatures are 250°C and 300°C lower than the regeneration temperature of a pure ZnS. For the sulfided mixed oxide regeneration is even complete at 2 500°C under isothermal conditions. Regeneration of sulfided oxides mixture and mixed oxide was thus shown to exhibit synergetic effects, resulting from exothermic oxidative reactions of molybdenum phases. Heat energy released during these reactions is assumed to enhance ZnS oxidation kinetics at a temperature lower than the previously measured one.

Highlights

• ZnO and MoO 3 based materials have been studied as regenerable sulfur sorbents.

• Composite oxides show regeneration temperature up to 300°C lower than ZnO sorbents.

• Transitional phases formations were evidenced through in situ XRD.

• Regeneration of sulfided mixed oxide is shown to exhibit synergetic effects.

• Synergetic effects result from exothermic oxidative reactions of molybdenum phases.

Introduction

Integrated Gasification Combined Cycles (IGCC) power generation processes and Fischer-Tropsch based XTL processes (XTL for X=Biomass, Coal, or Gas To Liquids) constitute efficient and cleaner alternative technologies for future energy production. [1][2][3] Both technologies are based on a first step of feed gasification into a synthesis gas or syngas, composed mainly of a mixture of CO and H 2 . [START_REF] Higman | Gasification[END_REF] In IGCC processes, the syngas is burnt into a gas turbine to produce electrical energy. In the XTL processes, the syngas is converted into hydrocarbon fuel via the Fischer-Tropsch reaction. [5][6][START_REF]Studies in Surface Science and Catalysis[END_REF] Hydrogen sulfide constitutes one of the main impurities of synthesis gases. [START_REF] Chiche | [END_REF] This compound mixed to H 2 , is responsible for the corrosion of the industrial units, 9 especially the combustion turbine blades used in the IGCC.

On another hand, it is also a poison of Fischer-Tropsch catalysts (cobalt-, iron-, and ruthenium-based) because of its irreversible chemisorption on their active sites. [10][11][12] Therefore, the sulfur content specifications in syngas are very drastic for such industrial units, below 10 ppmv for IGCC 13,14 and 0.05 ppmv for XTL. 15,16 Syngas bulk desulfurization is commonly conducted by chemical or physical solvents, [START_REF] Weissermel | Chimie organique industrielle[END_REF][START_REF] Kohl | Gas Purification[END_REF] though these technologies do not allow to achieve complete H 2 S removal. Indeed, expected H 2 S syngas content at solvent-based acid gas removal systems outlet (in the range 0.1 -1 mol. ppm) remains higher than sulfur syngas content tolerated at Fischer-Tropsch process inlet. [START_REF] Chiche | [END_REF]11 Therefore deep desulfurization of syngas is generally achieved with solid sorbents based on metal oxides which can irreversibly react with H 2 S according to the following generic reaction: Me x O y (s) + y H 2 S(g) → Me x S y (s) + y H 2 O(g) Because of the important and various initial amounts of H 2 S (1 to 10000 ppm), 19 the drastic sulfur specifications required, and the large syngas flow rates to be treated (around 100 kg.s -1 for an IGCC power plant), 20 the use of metal oxides based sorbents imposes either huge amounts of solids with large reactor or the use of small reactors and frequent changes of solid sorbent. In both cases, the large amount of solid waste produced is the major disadvantage of the sorbent-based syngas desulfurization, and may also alter process operation, efficiency, and economics. The in-situ regeneration of the sulfided sorbent, back to the oxide phase, during the process is an identified solution to improve desulfurization processes.

The optimization of such regenerable H 2 S sorbent should be guided by two main targets: (1) offering irreversible sulfidation reaction when in contact with H 2 S in order to minimize to the lowest possible level the H 2 S concentration in the syngas, and (2) being able to get back to the oxide state when in contact with O 2 at the lowest possible temperature. The sulfidation and oxidative regeneration properties of single oxides were studied in the literature through thermochemical calculations. [21][22][23][24] It could be concluded from these studies that the single oxide sulfidation reaction was strongly affected by the syngas reductive atmosphere.

Under these conditions, ZnO can be reduced into Zn which volatilized above 600°C, [START_REF] Gibson | Industrial & Engineering Chemistry Process Design and Development[END_REF] reductions of CuO, [START_REF] Sick | [END_REF] Fe x O y , 27 and Mn x O y 28 are faster than the related sulfidation reactions.

Moreover, the sulfidation of Cu or Fe and the reduced manganese oxide are kinetically limited. [START_REF] Sick | [END_REF]27,29 The sulfidation equilibrium of CeO 2 in reductive atmosphere seems to be favorable and leads to the formation of Ce 2 O 2 S. 30 However, the formation and thermal stability of refractory sulfate phases in oxidative regeneration conditions were pointed out as the main issues of this regeneration process. 24,[31][32][33] Indeed sulfate formation leads to a decrease of the sorbent reactivity and durability, 29,[START_REF] Jha | Enhanced Sorbent Durability for Hot Coal Gas Desulfurization[END_REF][START_REF] Siriwardane | [END_REF] and detrimental particles sintering is favored at the high regeneration temperature required for sulfate decomposition. 36 Furthermore, the high temperatures required for the sulfate decomposition will affect the overall thermal efficiency of the process and will impose the use of refractory alloys to manufacture the industrial reactors. 20 In order to overcome the limitations of single oxide-based sorbents, "composite oxide sorbents" such as for example zinc ferrite (ZnFe 2 O 4 ), [START_REF] Grindley | Proceedings of the Second Annuals Contractors Meeting on Contaminant Control in Hot Coal Derived Gas Stream[END_REF] zinc titanate (Zn x Ti y O x+2y ), 38 or copper chromite (CuO-CrO 2 ) 39 were developed through a series of incremental improvements in order to stabilize the long-term performance of H 2 S sorbent, 40 optimize the operating conditions of the process, 41 and decrease the regeneration temperature below 500°C. [41][42][43][44] In a previous work, 45 a global approach was proposed to study the physical and chemical phenomena involved during both sulfidation and regeneration steps of single oxides and corresponding sulfides by a confrontation between thermochemical calculations and experimental approaches. Some single oxides were thus identified to design a low temperature regenerable H 2 S sorbent. In particular, zinc oxide was shown to exhibit high sulfidation ability and restricted sulfate formation upon the ZnS surface particles during the oxidative regeneration process, in comparison to other sulfides of transition metal such as copper, manganese or iron. MoO 3 shown attractive properties, as the oxidative regeneration of the sulfided MoO 3 occurs through fast, highly exothermic and complete reaction below 500°C.

Regarding the distinct properties of these two metal oxides, we focus, in this paper on the reactivity of two different zinc and molybdenum based materials: a ZnO and MoO 3 single oxides physical mixture and the ZnMoO 4 mixed oxide. The objectives of this work are to evaluate the difference between the two materials and to identify potential synergetic effects between zinc and molybdenum phases, to study the physical and chemical phenomenon involved in sulfidation and oxidative regeneration reactions of these materials, and to validate the global approach previously proposed.

Materials and Methods

Oxide materials syntheses

The single oxides mixture, ZnO-MoO 3 was obtained by physical milling of MoO 3 and ZnO materials. ZnO material was synthesized from thermal decomposition of precursor at 500°C for 2 hours. The precursor was (ZnCO 3 ) 2 .(Zn(OH) 2 ) 3 (Aldrich, purity > 58% Zn). MoO 3

precursor was obtained from a chemical supplier, and was used as received after material characterizations.

Mixed oxide ZnMoO 4 material was synthesized by successive calcinations steps at 450°C and 500°C and physical milling of the previous ZnO-MoO 3 single oxides mixture.

Thermogravimetry measurements

The sulfidation of the composite oxides was followed by thermogravimetric analysis (TGA) at 350°C and atmospheric pressure, using a commercial magnetic suspension balance (Rubotherm). The feed was composed of 0.9% H 2 S diluted in H 2 with a constant flow (10.8

NL.h -1 ). Oxide samples were firstly heated under a Helium flow up to 350°C, and then the inlet valve was switched to the H 2 S-H 2 mixture under isothermal conditions. Sulfidation step had been conducted for 12 hours.

Oxidative regeneration reaction of the sulfided sorbents were followed by TGA-DSC (Netzsch STA-449C Jupiter). Experiments with linear temperature variation were conducted by heating sulfided samples in an oxidative gas mixture (5% O 2 in N 2 ) at 5°C/min from 49°C to 625°C. Isothermal experiments were also conducted on sulfided samples, which were first heated at 5°C/min to 500°C under N 2 , and then treated at 500°C by an oxidative gas mixture (5% O 2 in N 2 ) for 2 hours. This gas mixture composition was chosen to limit sulfate formation. 45 In both kinds of experiments, gas flow rate was set to 9 NL.h -1 . The exhaust gases were analyzed by mass spectrometry (Netzsch QMS-430C Aeolos).

Materials characterizations

Materials crystalline phases were characterized by X-ray diffraction (XRD) using a PANalytical X'Pert PRO diffractometer operating with CuK α radiation (λ=1.54182 Å).

Materials textural properties were studied through the measurements of Kr or N 2 adsorption isotherms at 77 K, using a Micromeritics ASAP 2420 instrument. Specific surface areas were obtained using the BET model. Composition of sulfided and regenerated solids was determined by elemental analysis (Flash 2000 CHNS).

Scanning electron microscopy (SEM) observations were performed using a JEOL JSM-6340F (Field Emission Scanning Electron Microscope) apparatus operating at 1 kV.

Transmission electron microscopy (TEM) observations and energy dispersive X-ray analysis (EDX) cartographies were performed with a TEM-EDX JEOL 2100-F-FEG (Field Emission Gun) instrument operating at 200 kV. TEM observations and EFTEM cartographies were performed with TEM-EFTEM JEOL 2100F instrument, equipped with a GATAN GIF Tridiem spectrometer. Particles observed by TEM were prepared by ultramicrotomy (70 nm thickness).

In-situ XRD experiments

Sulfidation and regeneration properties of the two studied composite oxides were investigated by in-situ XRD experiments using a PANalytical X'Pert PRO diffractometer operating with CuK α radiation (λ=1.54182 Å), and equipped with a XRF cell. The oxides were sulfided at 350°C with a 0.9% H 2 S in H 2 gas mixture. This sulfided material was regenerated under air at 500°C. Between each XRD acquisition, reacting gas was introduced for a restricted time, then reaction was stopped by a N 2 flow before and during XRD measurements. This enabled to avoid any materials modification during measurements. During the sulfidation, XRD measurements were performed on materials after 10, 20, 40, 60, 240 and 420 minutes of reaction. During the regeneration, XRD measurements were performed after 2, 4, 6, 10, 15, 20 and 30 minutes of reaction. These durations were set from the analysis of the TGA data.

Results and discussion

Oxides characterizations

As shown in Fig. 1, the single oxides mixture is made of pure ZnO and MoO 3 . This solid is composed of small monocrystalline ZnO particles (~25-50 nm) deposited on the surface of MoO 3 platelets (100 nm to micrometer scale). This solid textural properties are in agreement with its measured specific surface area (10 m².g -1 ). The SEM micrograph (Fig. 2) indicated that ZnO particles are aggregated and mixed to or deposited on the MoO 3 platelets. The mixed oxide is composed of ZnMoO 4 (Fig. 3). As presented on Fig. 4, it was made of micrometric spherical particles, formed by agglomerated and faceted grains (size below 500 nm). Moreover, the textural properties of this solid are in agreement with the measured specific surface area (< 1 m².g -1 ).

Sulfidation studies

Weight variations measured by TGA under sulfiding conditions as a function of time are represented for each composite oxide sample on Fig. 5. Whatever the solid, the weight variations were characterized by an abrupt weight loss followed by a gain. The sulfidation of the ZnO-MoO 3 mixture led to a solid composed by the following crystalline phases: ZnS, MoO 2 and MoS 2 (Fig. 6A), while the sulfidation of ZnMoO 4 mixed oxide led to the formation of ZnS, MoS 2 and ZnMoO 3 (Fig. 6B). TGA measurements were completed by in-situ XRD analyses performed on the composite oxides. As it can be seen on Fig. 7 for the ZnO-MoO 3 single oxides mixture sulfidation at 350°C, MoO 3 was both reduced and sulfided into MoO 2 and MoS 2 ; 46 while ZnO was sulfided into ZnS. 47,48 Under the same conditions, ZnMoO 4 mixed oxide is simultaneously reduced into ZnMoO 3 and sulfided into ZnS and MoS 2 (Cf. Fig. 8). Consequently the initial weight losses, measured by TGA, could be attributed to the reductions of MoO 3 and ZnMoO 4 into MoO 2 and ZnMoO 3 . However, it appeared that the reduction process was incomplete for both solids, as the experimental weight losses were lower than theoretical weight losses anticipated (-7wt.% for the reduction of both materials).

The following weight gains could be due to the sulfidation of these compounds into MoS 2 and ZnS. To conclude, in agreement with the in-situ XRD experiments, the sulfidation and reduction reactions occurred simultaneously for both solids under the chosen operating conditions.

Textural properties of the sulfided composites oxides

The characteristics of the sulfided sorbents are summarized in Table 1. The specific surface area of the sulfided ZnO-MoO 3 increased during the sulfidation reaction. This result agrees with our previous study. 45 The specific surface area does not evolve during the sulfidation of the mixed oxide. As previously observed, 40,49 the addition of molybdenum and the formation of the mixed phase ZnMoO 4 might stabilize the textural properties during the sulfidation steps.

The sulfur sorption capacity (Table 1) is more important in the sulfided ZnMoO 4 mixed oxide than in the other sorbent. Assuming zinc reactivity is the same, this result might be explained by a more important formation of MoS 2 during the sulfidation of ZnMoO 4 . SEM images of the sulfided ZnO-MoO 3 single oxides mixture are shown in Fig. 9. Small particles, with sizes close to the initial ZnO particles ones can be observed, could be ZnS particles. These particles may form 10 microns aggregates or may be deposited on the surface of facetted crystals, probably MoO 2 . MoS 2 layers can also be observed. 50

Sulfidation mechanism

As schematized in Fig. 12, the reactivity of ZnO-MoO 3 single oxides mixture is equivalent to the combination of the two independent single oxides ZnO and MoO 3 . ZnO is sulfided into ZnS thanks to a growth mechanism with an outward development, due to zinc and oxygen solid-state diffusion through the ZnS layer and the formation of cavities at the ZnS/ZnO internal interface. This phenomenon results from the Kirkendall effect. 52 Bulk MoO 3 sulfidation mechanism is also well known, 46,53 Under reductive and sulfiding conditions, the mixed oxide phase ZnMoO 4 is demixed into the reduced mixed oxide phase ZnMoO 3 and the sulfided phases ZnS and MoS 2 (Fig 13). The mechanisms involved in phase demixing were not clearly identified. However, the outward development of ZnS might be the result of a kinetic competition between internal and external diffusion of species involved in the sulfidation reaction. 51 The MoS 2 core might be the result of the ZnMoO 3 sulfidation. Indeed, various studies on mixed oxide outlined the different reactivity between the single oxide and the mixed oxide, such as CuO, [START_REF] Sick | [END_REF]29

Oxidative regeneration reactions study under temperature increase

TGA-DSC and mass spectrometry analyses for the oxidative regenerations of the sulfided single oxides mixture and mixed oxide materials are presented, respectively, in Fig. 14 No sulfur was detected by the elemental analysis indicating the complete regeneration of the two sulfided materials back to oxide phases. The XRD patterns of the regenerated ZnO-MoO 3 (Fig. 16) and ZnMoO 4 (Fig. 17) evidence the presence of the mixed oxide phase ZnMoO 4 . MoO 3 is also detected in the regenerated single oxide mixture; it indicates that MoO 3 was likely to be over stoechiometric in the original oxide. For both solids, it is believed that the first exothermic events are linked to the molybdenum

Oxidative regeneration reactions study under isothermal conditions at 500°C

with 5%O 2 -N 2 TGA-DSC and mass spectrometry analyses for the oxidative regeneration under isothermal conditions at 500°C for both sulfided solids are presented in the Fig. 18 and 19. For both sulfided materials, the contact between the solid and the oxidizing gas mixture is characterized by an exothermic reaction and a release of SO 2 (g). However, the weight variation during the experiment with the sulfided ZnO-MoO 3 is positive, indicating that the sulfided phases are not fully oxidized into oxide phases. This result is coherent with elemental analysis and XRD patterns of the solid obtained after the regeneration step under isothermal conditions at 500°C (Fig. 20). Indeed, ZnS phase is still detected on this regenerated solid, and 7 weight % of sulfur are measured through elemental analysis. The study of the sulfided ZnMoO 4 mixed oxide regenerated at 500°C outlined the full regeneration of this material into the ZnMoO 4 starting oxide. Indeed, no sulfur and sulfided phases are detected by the elemental analysis and by XRD characterizations (Fig. 21).

Oxidative regeneration mechanism under isothermal conditions at 500°C

As schematized in Fig. 24 from SEM observations (Cf. Fig. 9), the sulfided ZnO-MoO

Conclusions

This study highlighted the interest of the MoO 3 addition to ZnO material. Two types of material were studied: a mixed oxide, ZnMoO 4 , and a ZnO-MoO 3 single oxides mixture.

Their sulfidation and oxidative regeneration properties were investigated through thermogravimetry and in-situ characterizations. Sulfidation of the single oxides mixture was shown to be similar to the combination of the sulfidation of both independent single oxides.

However, ZnO-MoO 3 single oxides mixture presents different reactivity under sulfiding and reductive atmospheres compared to ZnMoO 4 mixed oxide. Indeed, the mixed oxide sulfidation process leads to ZnMoO 4 phase demixing into ZnS, MoS 2 and ZnMoO 3 .

Moreover, during the oxidative regeneration, a very significant decrease of the starting regeneration temperature was observed compared to isolated single oxides. The oxidative regenerations of the sulfided single oxide mixtures and mixed oxide are initiated, respectively, at 350°C and 300°C. These temperatures are 250°C and 300°C lower than the regeneration temperature of a pure ZnS in experiments conducted in similar operating conditions. For the sulfided mixed oxide regeneration is even complete at 500°C under isothermal conditions.

Regeneration of sulfided oxides mixture and mixed oxide was thus shown to exhibit synergetic effects, resulting from exothermic oxidative reactions of molybdenum phases. Heat energy released during these reactions is assumed to enhance ZnS oxidation kinetics at a temperature lower than the previously measured one.

Finally, ZnMoO 4 mixed oxide has been shown to exhibit attractive properties for synthesis gas regenerative desulfurization processes. This work included in a multi-technique approach also brings novel and rational insights on these gas-solid reactions, helping for a better understanding of the solid-state transformation mechanisms involved in both sulfidation and oxidative regeneration processes.

In future works, ZnMoO 4 mixed oxide should be studied to overcome the issues raised by an industrial application of such regenerable sulfur sorbent material. In particular, breakthrough experiments will be performed to lead to a complete study of the performance of a shaped ZnMoO 4 based sorbent during multiple sulfidation-regeneration steps. Attention will be firstly paid on the evolution of the performance of this material during the sulfidation and regeneration cycling, that will be led with model synthesis gas compositions, and various oxidant gas compositions. Experiments with a fully representative coalderived syngas and oxidant that would be used in a practical application, such as air, are then expected. The occurrence of side reactions during the sulfidation step conducted on model synthesis gases, such as methanation or water gas shift reactions, should be also investigated. Attention will be paid to the possible catalytic activity of ZnMoO 4 and other zinc and molybdenum phases formed during reaction with H 2 S, as a function of operating conditions. The evolution of the mechanical properties both after sulfidation and highly exothermic regeneration steps with the above synthesis gas and oxidant gas compositions will be also considered.

performing XRD analyses; Jean-Pierre Reyt and Christine Bounie for technical help on thermogravimetry apparatus.

3 Fig. 1 Fig. 2

 312 Fig. 1 XRD pattern of the ZnO-MoO 3 solid.

Fig. 3

 3 Fig. 3 XRD pattern of the mixed oxide.

Fig. 4

 4 Fig. 4 SEM micrographs of ZnMoO 4 .

Fig. 5

 5 Fig. 5 Weight variations measured by TGA during the sulfidation at 350°C with the gas mixture 0.9%H 2 S-H 2 during 12 hours, of the two sorbents: ZnO-MoO 3 and ZnMoO 4 .

Fig. 6

 6 Fig. 6 XRD patterns of the sulfided ZnO-MoO 3 (A) and ZnMoO 4 (B).

Fig. 7

 7 Fig.7In-situ XRD sequence for the ZnO-MoO 3 against sulfidation time at 350°C with the gas mixture 0.9%H 2 S-99.1%H 2 . For the sake of clarity, XRD patterns are not all presented.

Fig. 8

 8 Fig.8In-situ XRD sequence for ZnMoO 4 against sulfidation time at 350°C with the gas mixture 0.9%H 2 S-99.1%H 2 . For the sake of clarity, XRD patterns are not all presented.

Fig. 9 Fig. 10 Fig. 11

 91011 Fig. 9 SEM micrographs of sulfided ZnO-MoO 3 single oxides mixture.

and 15 .Fig. 14 Fig. 15

 151415 Fig. 14 TGA-DSC and mass spectrometry analysis during the regeneration step between 40 and 625°C in 5%O 2 -N 2 of the sulfided ZnO-MoO 3 single oxides mixture.

3 Fig. 16

 316 Fig. 16 XRD pattern of the regenerated ZnO-MoO 3 at 625°C under 5%O 2 -N 2 .

phases (MoS 2 ,

 2 MoO 2 and ZnMoO 3 ) oxidations into MoO 3 and ZnMoO 4 . Then, the second exothermic event, characterized by a weight loss and a release of SO 2 (g), might be resulting from the ZnS oxidation into ZnO or ZnMoO 4 in the presence of the molybdenum phases.Consequently, in these two solids, the ZnS oxidation could start at 470°C (sulfided ZnO-MoO 3 ) and 430°C (sulfided ZnMoO 4 ). These temperatures are, respectively, 130°C and 170°C lower than the oxidation temperature of a pure ZnS material.45 Such combined thermal effect has been previously observed on a Zn 2 TiO 4 promoted by Co and/or Ni.41,42,44

Fig. 17

 17 Fig. 17 XRD pattern of the regenerated ZnMoO 4 at 625°C under 5%O 2 -N 2 .

Fig 18

 18 Fig 18 TGA-DSC and mass spectrometry analysis for sulfided ZnO-MoO 3 single oxides mixture regeneration step at 500°C under 5%O 2 -N 2 .

Fig. 19

 19 Fig. 19 TGA-DSC and mass spectrometry analysis for sulfided ZnMoO 4 mixed oxide regeneration step at 500°C under 5%O 2 -N 2 .

Fig. 20

 20 Fig. 20 XRD pattern of the ZnO-MoO 3 single oxides mixture after the regeneration step under isothermal conditions at 500°C in 5%O 2 -N 2 .

Fig. 21

 21 Fig. 21 XRD pattern of ZnMoO 4 mixed oxide after the regeneration step under isothermal conditions at 500°C in 5%O 2 -N 2 .

Fig. 22 Fig. 23

 2223 Fig. 22 In-situ XRD sequence for oxidative regeneration of the sulfided ZnO-MoO 3 single oxides mixture at 500°C with 5%O 2 -N 2 gas mixture.300

Table 1

 1 Structural and textural properties of the sulfided sorbents.

		Sulfided ZnO-MoO 3 single	Sulfided ZnMoO 4 mixed
		oxides mixture	oxide
	Crystalline phases (XRD)	ZnS MoO 2 , MoS 2	ZnMoO 3 , ZnS, MoS 2
	Specific surface area	23 m 2 .g -1 (N 2 adsorption isotherm)	2 m 2 .g -1 (Kr adsorption isotherm)
	Sulfur uptake capacity (Weight %)	19	31

  and implies first the formation of the oxisulfide MoOS 2 phase, which decomposition leads to surface MoS 2 layers formation. The bulk of MoO 3 particles is reduced into MoO 2 .

	H 2 S/H 2
	H 2 O

Fig. 12 Schematic representation of the ZnO-MoO 3 single oxides mixture sulfidation process. ZnO: light brown spheres, MoO 3 : light yellow platelets; ZnS: dark yellow spheres, MoO 2 : blue boxes, MoS 2 : black lines.

  3 single oxides mixture is composed of aggregated ZnS particles coating the surface of MoO 2 /MoS 2 facetted crystals. From in-situ XRD characterizations, the following reactions involved in the oxidative regeneration under isothermal conditions at 500°C could be suggested: contact between the solid and the oxidizing gas mixture initiates the oxidation reactions of MoS 2 into MoO 2 then MoO 3 . The heat released from these exothermic reactions might start a direct reaction between ZnS, the freshly formed MoO 3 and O 2 to obtain ZnMoO 4 phase, as the ZnO phase is not detected. This sequence can be summarized , the presence of residual ZnS at the end of the TGA experiments indicates that the oxidative regeneration at 500°C of the sulfided single oxides mixture did not lead to a complete regeneration of pure oxide species. It can be explained by the strong heterogeneity of the sulfided sorbent.As schematized in Fig.25from SEM observations (Cf. Fig.10 and 11), the sulfided ZnMoO 4 mixed oxide is composed by a MoS 2 core mixed with ZnMoO 3 phase, surrounded by a ZnS layer. The following reactions involved in the oxidative regeneration under isothermal conditions at 500°C could be proposed: the contact between the solid and the oxidizing gas mixture initiates the oxidation reactions of MoS 2 and ZnMoO 3 into, respectively, MoO 2 and ZnMoO 4 . As it was observed for the sulfided single oxides mixture, the heat released from these exothermic reactions might initiate a direct reaction between ZnS, the freshly formed MoO 2 and O 2 to obtain the ZnMoO 4 phase. This sequence can be The homogeneity between zinc and molybdenum components, due to sulfided solid core-shell configuration, could explain the complete oxidation of this material, and the formation of ZnMoO 4 . Indeed, thermal energy generated by the oxidation of MoS 2 and ZnMoO 3 phases present in bulk of aggregated particles is evacuated through the ZnS layer, and favor the ZnS oxidation and reaction with molybdenum containing phases and lead to the formation of ZnMoO 4 phase.

	summarized as: 56
	Firstly, MoS 2 (s) + 3 O 2 (g) → MoO 2 (s) + 2 SO 2 (g),
		∆ r H = -302 kJ/mol of O 2 (g)
	and, 2 ZnMoO 3 (s) + O 2 (g) → 2 ZnMoO 4 (g),
	no thermochemical data available on ZnMoO 3
	then, ZnS(s) + MoO 2 (s) + 2 O 2 (g) → ZnMoO 4 (s) + SO 2 (g),
		∆ r H = -355 kJ/mol of O 2 (g)
	Firstly, MoS 2 (s) + 3 O 2 (g) → MoO 2 (s) + 2 SO 2 (g),
		∆ r H = -302 kJ/mol of O 2 (g)
	then, 2 MoO 2 (s)+ O 2 (g) → 2 MoO 3 (s),
		∆ r H = -311 kJ/mol of O 2 (g)
	and, 2 ZnS(s) + 2 MoO 3 (s)+ 3 O 2 (g) → 2 ZnMoO 4 + 2 SO 2 (g),
		∆ r H = -370 kJ/mol of O 2 (g)
	O 2 (g) O 2 (g)	O 2 (g) O 2 (g)
	SO 2 (g) SO 2 (g)	SO 2 (g) SO 2 (g)
	Fig Schematic representation of the sulfided ZnO-MoO 3 single oxides regeneration process under isothermal Fig 25 Schematic representation of the sulfided ZnMoO 4 mixed oxide regeneration process under isothermal
	conditions at 500°C. MoO 2 : blue boxes, MoS 2 : black lines, ZnS: dark yellow spheres, Thermal energy: red lines, conditions at 500°C. MoS
	MoO 3 : light yellow shapes; ZnMoO 4 : white shape.

However2 : black lines on gray surface, ZnS: dark yellow shape, ZnMoO 3 green spots, MoO 2 : blue shape, Thermal energy: red lightnings, ZnMoO 4 : white shape.

Acknowledgments

For IFP Energies nouvelles, the authors gratefully acknowledge Anne-Lise Talleb and Anne-Sophie Gay for their help performing EFTEM cartographies; Estelle Roche for her help


Srikanth Sundaresan
email: srikanth@icsi.berkeley.edu

Nick Feamster
email: feamster@cs.princeton.edu

Renata Teixeira
email: renata@inria.fr

Measuring the Performance of User Traffic in Home Wireless Networks

This paper studies how home wireless performance characteristics affect the performance of user traffic in real homes. Previous studies have focused either on wireless metrics exclusively, without connection to the performance of user traffic; or on the performance of the home network at higher layers. In contrast, we deploy a passive measurement tool on commodity access points to correlate wireless performance metrics with TCP performance of user traffic. We implement our measurement tool, deploy it on commodity routers in 66 homes for one month, and study the relationship between wireless metrics and TCP performance of user traffic. We find that, most of the time, TCP flows from devices in the home achieve only a small fraction of available access link throughput; as the throughput of user traffic approaches the access link throughput, the characteristics of the home wireless network more directly affect performance. We also find that the 5 GHz band offers users better performance better than the 2.4 GHz band, and although the performance of devices varies within the same home, many homes do not have multiple devices sending high traffic volumes, implying that certain types of wireless contention may be uncommon in practice.

Introduction

Many home networks use 802.11 wireless as a predominant mode of communication; in fact, many consumer devices in home networks connect exclusively over a wireless connection. Despite increasingly widespread deployment of home wireless networks, there is little information about their performance in real homes, particularly as it relates to that of real user traffic and end-to-end performance. Understanding how wireless performance affects end-to-end user performance will become increasingly important as the throughput of residential access links continues to increase and the home wireless network becomes more likely a bottleneck.

There is an extensive set of previous work studying wireless network performance in many settings, including conferences, enterprise networks, and even apartment complexes [START_REF] Adya | Architecture and techniques for diagnosing faults in ieee 802.11 infrastructure networks[END_REF][START_REF] Cheng | Automating cross-layer diagnosis of enterprise wireless networks[END_REF][START_REF] Mahajan | Analyzing the mac-level behavior of wireless networks in the wild[END_REF][START_REF] Papagiannaki | Experimental Characterization of Home Wireless Networks and Design Implications[END_REF][START_REF] Patro | Observing home wireless experience through wifi aps[END_REF][START_REF] Rayanchu | Diagnosing wireless packet losses in 802.11: Separating collision from weak signal[END_REF]. These studies have measured either layer-2 performance metrics (such as wireless bitrates and retransmissions) or layer-3 performance such as TCP throughput over the wireless link. Yet, wireless network performance is extremely variable, even on short timescales, and mapping layer-2 performance to higher-level performance metrics such as throughput or latency is challenging. Additionally, active measurements can only capture the wireless network performance at one point in time; these measurements may not correspond to the performance that users see in practice for their actual application traffic.

In this paper, we explore the relationship between wireless performance metrics and TCP performance of user traffic. To do so, we passively measure wireless performance metrics on user traffic as it passes through the home wireless access point and correlate these metrics with the TCP performance of user traffic. We implement a measurement tool that runs on a commodity home wireless access point, which permits a widespread deployment and the ability to differentiate wireless versus wide-area performance. The routers that we use for our study have both 2.4 and 5 GHz radios, which allows us to compare the performance of these two bands. The deployment on commodity hardware poses strict design constraints: [START_REF] Adya | Architecture and techniques for diagnosing faults in ieee 802.11 infrastructure networks[END_REF] the devices are resource-constrained, which limits the amount of collection and processing we can do; (2) each home has only a single measurement vantage point; and (3) we aim to use unmodified drivers to avoid interfering with the very environment that we are trying to measure. We measured the performance of wireless networks in 66 home networks around the world; we passively measure user traffic to extract both the performance of active TCP connections and wireless statistics such as frame bitrate and retransmissions. Our study both confirms results from previous studies and reveals new phenomena. Specifically, we find:

• As access link capacity increases, wireless performance plays a greater role in the TCP throughput that users observe. • Latency inside the home is a significant contributor to end-to-end latency.

• The 5 GHz channel performs better than the 2.4 GHz (in particular, latency over the wireless is larger for 2.4 GHz); we also find that bitrates are generally low and retransmission rates high on 2.4 GHz. • Within the same home, different devices experience different wireless performance.

• It is rare for devices in a home to send significant traffic volumes simultaneously.

As content providers continue to place content closer to users and governments and companies continue to invest in increasing access link throughput, understanding how users' home wireless networks relate to the performance that their traffic experiences will become critical for improving user experience. Our findings are a first step towards understanding the relationship between wireless performance metrics and TCP performance of user traffic. The results from this paper may ultimately be useful for designing a system to detect and isolate performance problems in home wireless networks.

Method

We describe the passive measurement tool that we developed for home access points, and the TCP and wireless performance metrics we used.

Measurements

We perform passive measurements of user traffic from commodity home wireless access points and correlate those measurements with the wireless metrics for the corresponding traffic. In contrast to active measurements, passive measurements more accurately reflect the actual performance that users experience. Further, passive measurements do not introduce contention or other artifacts that could affect the conditions that we seek to characterize. A measurement tool that operates on commodity access points facilitates both a widespread deployment, which can take advantage of an installed base of access points, and the ability to differentiate wireless versus wide-area performance. Ultimately, such a tool could operate in practice for a large user population. On the other hand, both the resource limitations of commodity access points and the bandwidth constraints in real home networks introduce design constraints that preclude using existing network diagnostic tools.

We collect packet traces of connections from the WAN interface and both wireless interfaces on the access point using pcap. Packet traces from the WAN interface provide information about wide-area TCP connections and IP packets that traverse the access point. We configure the wireless interfaces in monitor mode to capture radiotap headers [START_REF]Radiotap[END_REF]. For each frame, the radiotap headers provide the source and destination MAC addresses, the received signal strength (RSSI), and the frame control bits (the bitrate used, and whether the frame was retransmitted).

Deployment: We deploy our measurements on BISmark [START_REF] Bismark | Broadband Internet Service Benchmark[END_REF][START_REF] Sundaresan | Bismark: A testbed for deploying measurements and applications in broadband access networks[END_REF], which uses Netgear's WNDR 3700/3800 access point. This device has an Atheros chipset with a 450 MHz processor, one 802.11gn radio and one 802.11an radio. The 3800 has 128 MB of RAM, and the 3700 has 64 MB of RAM. The devices run OpenWrt with the ath9k wireless driver that uses the Minstrel rate adaptation algorithm with the default setting to a maximum bitrate of 130 Mbps. To respect user privacy, we do not collect payloads, and we anonymize all IP addresses and MAC addresses using SHA-256 and a per-access point secret salt as the data is collected on the router. Table 1 summarizes our IRBapproved deployment and the characteristics of the home networks in this deployment; we collected data from 66 homes in 15 countries for one month in 2013. Limitations: Our collection methods limit the types of wireless performance problems that we can study. First, continuous data collection from multiple interfaces on a commodity access point imposes significant CPU and data requirements. Thus, the router collect data only every five minutes on average for 15 seconds per iteration. The router exports only flow summaries, to reduce upload bandwidth requirements. This level of sampling precludes analyzing fine-grained characteristics (e.g., transient faults, other conditions that frequently vary) or characteristics that derive from complete TCP flows. Second, because we anonymize device MACs and IP addresses, finer-grained analysis of the impact of certain types of application flows on certain devices (video in mobile devices, for example) is not possible. It is also not possible to account for device-specific issues, such as buffering in mobile devices. Third, we only have a single vantage point from which to collect our measurements, because typically homes only have a single access point. In contrast, other studies collect and correlate data from multiple radios [START_REF] Patro | Observing home wireless experience through wifi aps[END_REF] or multiple vantage points [START_REF] Cheng | Automating cross-layer diagnosis of enterprise wireless networks[END_REF][START_REF] Mahajan | Analyzing the mac-level behavior of wireless networks in the wild[END_REF][START_REF] Rayanchu | Diagnosing wireless packet losses in 802.11: Separating collision from weak signal[END_REF]. Finally, due to a driver limitation in the deployed devices, we could not scan for nearby access points. Scanning caused persistent disconnections for a small number of users; since this had a direct impact on users (BISmark devices are intended to be used as the primary access point), we did not scan the medium.

Metrics

We use the passive traffic traces to extract both TCP-level performance metrics and wireless performance metrics.

TCP performance metrics. The access point runs tcptrace, which processes the pcap traces to provide TCP statistics. We study the average download TCP throughput achieved during the captured lifetime of the flow. We use this metric to compute the aggregate throughput at every one-second interval by summing the average throughput of all active flows downloading traffic through a given access point during that interval. For reference, we compare the aggregate throughput with the access link capacity, which we measure using BISmark's active measurements. BISmark performs a multi-threaded TCP throughput test approximately every two hours [START_REF] Sundaresan | Broadband internet performance: A view from the gateway[END_REF][START_REF] Sundaresan | Bismark: A testbed for deploying measurements and applications in broadband access networks[END_REF]. We define the access link capacity as the 95th percentile of the multi-threaded throughput test measurements. We also study the round-trip time (RTT) of TCP connections, which tcptrace computes as the difference between the time of the data and SYN packets and its corresponding acknowledgments (tcptrace's analysis algorithm already handles many corner cases, such as delayed acknowledgments). Running tcptrace at the access point allows us to measure both the RTT between the access point and the home devices (the LAN RTT) and the RTT between the access point and destinations in the wide-area (the WAN RTT).

Wireless performance metrics. We use the bitrate, retransmission rate, and received signal strength indication (RSSI) as our indicators of wireless performance problems because we can obtain these metrics easily from packet headers. IEEE 802.11 bitrate adaptation techniques adjust the transmission bitrate as wireless channel conditions change. Although these techniques usually adapt rates even under benign conditions to determine the channel quality, rate adaptation is typically more frequent when the channel quality is poor, because wireless senders typically reduce the bitrate in response to bit errors [START_REF]Minstrel rate adaptation algorithm[END_REF]. Thus, we also use the normalized bitrate, which is the average wireless bitrate computed over one second intervals, normalized by the maximum bitrate supported by that channel, as an indicator of a poor wireless channel. Normalized bitrate tends to be low when the wireless channel quality is poor. When bitrate adaptation does not adjust the bitrate (e.g., due to varying channel conditions or contention), the normalized bitrate might not indicate channel quality, but in these cases retransmission rates are still high. We also compute the retransmission rate as the fraction of frames with the retransmit bit set in any given one-second interval.

Results

We present the results from our measurement study. We first measure the throughput and RTTs of user traffic in home networks and how these performance metrics relate to wireless performance metrics. We then explore the wireless performance characteristics of user traffic in more detail.

Cumulative fraction of time

Fig. 2: The fraction of time that the collection of active flows receive a particular ratio of flow throughput to access link throughput.

Correlating TCP & Wireless Performance

We explore the achieved throughput of user traffic and the contributions of the home wireless network to this performance. Then, we study RTTs of user traffic and how the poor wireless network performance can result in higher LAN RTTs. This finding is relevant in light of the many recent efforts by service providers to reduce latency to end-to-end services with myriad optimizations and careful placement of content.

User traffic rarely achieves the full access-link throughput. Figure 2 shows the fraction of time that the sum of TCP throughput for all flows in a home (the "aggregate throughput" as defined in Section 2.2) achieves a particular value relative to the access link throughput, as measured with BISmark's active throughput test (the normalized aggregate throughput). The results show that the user traffic rarely saturates the available access link throughput. Of course, the TCP traffic might not saturate the access link throughput for many reasons: for example, user traffic demand may be insufficient (in fact, previous studies have shown this phenomena is often the case [START_REF] Siekkinen | Performance limitations of ADSL users: A case study[END_REF]), or flows may be short enough that they fail to saturate the access link, which could happen if many short Web transfers are the dominant traffic type. Unfortunately, we have only the flow statistics exported by tcptrace, so we cannot run a tool like T-RAT [START_REF] Zhang | On the characteristics and origins of internet flow rates[END_REF] to identify with certainty when the application, as opposed to the network, was limiting TCP throughput. Nevertheless, it is remarkable that the access link is so underutilized so often. We suspect that one reason for lower utilization of the access link (when there is sufficient demand to saturate it) may be the presence of wireless bottlenecks in the home network. The rest of this section explores this possibility.

Achieved throughput often correlates with wireless performance metrics. To explore the relationship between the TCP throughput of user traffic in homes and accesslink throughput, we measure how the aggregate throughput correlates with the bitrate and the retransmission rate. We normalize the aggregate throughput by the access link capacity (normalized throughput) and correlate this value with each of the wireless performance metrics. When we consider all traffic flows, TCP throughput does not correlate with any of the wireless performance metrics: the correlation coefficient between retransmission rate and normalized throughput is -0.01; for bitrate, the correlation coefficient is -0.02; and for RSSI, the correlation is 0.06. However, when we explore the correlation for the subset of flows whose normalized throughput is greater than 0.1 (i.e., for which we determine there is sufficient user demand), correlation between wireless metrics and access link throughput is stronger. This correlation increases with the access link throughput. In Figure 3a we show how the correlation coefficient between aggregate throughput and retransmission rate varies as we only consider users with access link throughput above a certain value; we see as this value increases, the correlation becomes stronger. Figure 3b shows a similar trend when we correlate RSSI with normalized throughput (though, obviously, the opposite trend holds; normalized throughput is positively correlated with RSSI). This result makes sense: wireless is more likely to introduce a bottleneck as access link throughput increases. The coefficient for bitrate follows a similar trend, but correlation is weaker for access links whose speeds exceed 60 Mbps, where the wireless network may be operat-ing closer to its capacity. (The default setting of the access points supports a maximum bitrate of 130 Mbps, which translates to a TCP throughput of about 85 Mbps under excellent conditions; actual throughput will likely be less.) Patro et al. [START_REF] Patro | Observing home wireless experience through wifi aps[END_REF] also show in a more limited setting (dense deployments in two apartment complexes) that achieved throughput is highly correlated with the above wireless metrics.

The latency inside a home network is often a significant contributor to overall round-trip time. The TCP round-trip time between the wireless access point and a wireless client should be on the order of one millisecond. As this RTT increases, it not only signifies that the wireless link is bottlenecked due to buffering or medium access delays, but it can have an adverse impact on performance, especially for applications that are latency sensitive. Figure 4a plots the distribution of the median LAN RTT (as defined in Section 2.2) across all devices in our study. The median device on the local wireless network sees a median wireless latency of about 8 ms, but nearly 30% of the devices experience local TCP round-trip latencies greater than 15 ms. Buffering affects latency, particularly for mobile devices that sleep more frequently. While we cannot distinguish mobile devices in our dataset (because we anonymize MAC addresses and do not look into the payload), we try to minimize this issue by considering the mean of the RTTs, and only for flows with at least 25 packets in the downstream direction.

We also analyze the performance of the home network relative to the wide-area network performance; we compare the round-trip times between the devices and the access point to the round-trip times from the access point to the wide-area destination for each flow. We define the median latency ratio for a device as the median ratio of the LAN RTT to the WAN RTT (Section 2.2) across all flows for that device. Figure 4b shows the distribution of the median latency ratio across all devices. The result shows that 30% of devices have a median latency ratio greater than 0.2, meaning that for those devices, at least half of the flows have end-to-end latencies where the home wireless network contributes more than 20% of the overall end-to-end latency.

Wireless Performance of User Traffic

We now characterize wireless performance in our deployment. Our preliminary findings include: (1) the 5 GHz wireless band consistently achieves better performance than the 2.4 GHz band; (2) the performance of a home wireless network varies across individual wireless devices within the same home; and (3) multiple devices in the same home network rarely send high traffic volumes at the same time.

The 5 GHz band performs better than the 2.4 GHz band. We analyze the performance that devices in home wireless networks achieve and how performance varies depending on whether devices are on the 2.4 GHz band or the 5 GHz band. Our hypothesis was that devices on the 5 GHz band would perform better because there are generally fewer devices (and surrounding access points) in the 5 GHz band, and that the 5 GHz band also has less non-WiFi interference (e.g., microwaves, baby monitors). As shown in previous studies, devices on 2.4 GHz experience both WiFi and non-WiFi interference [START_REF] Patro | Observing home wireless experience through wifi aps[END_REF].

Figure 5a shows the impact of spectrum on flow throughput for flows that have throughput greater than 1 Mbps. We present the normalized flow throughput to elimi- nate any bias related to the access link capacity. Flows to devices on the 5 GHz spectrum have higher normalized throughput than those on 2.4 GHz. Similarly, we see in Figure 5b that the LAN RTT for flows in 2.4 GHz are much higher than for flows in 5 GHz. The distribution of normalized flow throughput in each spectrum is similar between the 2.4 GHz and 5 GHz when we consider flows whose normalized throughput is less than 0.1. We are investigating this phenomenon, but these could include cases where we suspect that there is not enough application demand. Even in those cases, however, the LAN RTTs are smaller for devices connected over 5 GHz.

Figure 6 plots the CDF of the median bitrate for all devices in all homes, for both the 2.4 GHz band and the 5 GHz bands. Only 30% of 2.4 GHz devices see median bitrates above 65 Mbps; in contrast, more than 50% of devices in the 5 GHz spectrum see bitrates greater than 100 Mbps. It is worth noting that the wireless bitrates do not correspond to the actual throughput. Even under perfect conditions, a wireless bitrate The retransmission rates between the access point and clients in a single home network. In this home retransmission rates are high. Interestingly, one device has a significantly higher retransmission rate.

of 130 Mbps corresponds to an actual TCP throughput of about 80 Mbps. The bitrate values thus reflect a loose upper bound on the achievable end-to-end throughput.

Figure 7 shows the median bitrate per device for each home network, normalized by the maximum supported bitrate of the corresponding wireless protocol (between 65 Mbps and 300 Mbps for 802.11n, and 54 Mbps for 802.11a/g). Many devices, especially those in the 2.4 GHz range, often operate close to the maximum bitrate supported by the protocol, more so than 5 GHz devices. However we also see that the maximum bitrates of 5 GHz devices are higher. This discrepancy can be explained by the fact that many devices in the 2.4 GHz channel could be small mobile devices with single antennas that restrict their maximum bitrates to 65 Mbps. Also, attenuation is higher on 5 GHz, which could lead to more active bitrate adaptation.

Figure 8 shows the retransmission rates for all devices across all homes; the result shows similar trends with respect to the 2.4 GHz and 5 GHz ranges: retransmissions are more common in the 2.4 GHz band, with about 20% of devices having retransmission rates above 10%.

Within a single home network, individual devices can experience very different wireless performance. We also studied the performance of individual devices in a home network and the extent to which wireless performance varies across devices in the same home network. We found many cases where the median wireless retransmission rates for a device was high. For the devices in the home shown in Figure 9, nearly all of the devices have median retransmission rates greater than 10%. Interestingly, one device experiences a high retransmission rates nearly all of the time, suggesting a persistent problem that may result from device placement, interactions between the access point and that device's driver, or some other cause.

To study how wireless performance varies across devices in a single home, we measure the K-S distance of the distributions of raw wireless bitrates between each pair of devices in each home. Figure 10a plots the median and the maximum pairwise K-S distance in each home. We find that more than 80% of homes have at least one pair of devices with a K-S distance of more than 0.6, indicating that most homes have at least one poorly performing device (due to either poor placement, poor hardware, or poor drivers). We investigate the variance of RSSI across different devices and we see similar differences (Figure 10b). Future work could involve investigating the disparate performance across devices further and determining whether the variability in device performance is caused by any single factor.

Simultaneous communication is infrequent. Most of the homes in our deployment had more than one active device during our study. Interestingly, however, these devices often were not highly active at the same time. We measured one-second intervals and observed the number of times that multiple devices were sending at least 25 packets within the one-second interval. To our surprise, simultaneous communication was rare: for 85% of the one-second intervals on the 2.4 GHz band and 93% of the intervals on the 5 GHz band, we observed at most one device sending at least 25 packets in the interval. This observation relates to wireless contention and may imply that certain types of wireless contention are infrequent. This finding contrasts with previous work that shows that contention is a factor in wireless performance [START_REF] Patro | Observing home wireless experience through wifi aps[END_REF]. This discrepancy may result from differences in either deployment locations or in measurement method and warrants further exploration.

Related Work

We focus our survey of related work on studies of wireless performance in real deployments and on attempts to detect and characterize common wireless performance problems. The WiSe project is most similar to our efforts [START_REF] Patro | Observing home wireless experience through wifi aps[END_REF]; WiSe is a deployment of multiple wireless monitors in a diverse set of home environments that are configured with custom measurement and monitoring software. In this deployment, each access point is a custom OpenWrt-based embedded device with two network interface cards: one that forwards traffic and another that monitors the quality of the wireless channel. Because each WiSe access point has two interfaces, the deployment sees a much more complete picture of the wireless spectrum. The deployment is also concentrated in two dense residential complexes; our deployment is larger and more diverse, but it does not use custom hardware with multiple network interfaces, which limits our ability to collect certain types of measurements. The work also designs an estimator for TCP performance based on wireless performance metrics; validating this model in our own testbed is part of our ongoing work. Other efforts have deployed dense monitors to study wireless in conferences, and university buildings [START_REF] Cheng | Automating cross-layer diagnosis of enterprise wireless networks[END_REF][START_REF] Kotz | Analysis of a campus-wide wireless network[END_REF][START_REF] Mahajan | Analyzing the mac-level behavior of wireless networks in the wild[END_REF]; these studies explore similar characteristics but do not focus on home network settings, which are generally lightly managed (or unmanaged) and hence potentially more chaotic. Papagiannaki et al. [START_REF] Papagiannaki | Experimental Characterization of Home Wireless Networks and Design Implications[END_REF] deploy and measure wireless performance characteristics in three homes.

Other work has characterized wireless performance problems using custom hardware or active measurements. Rayanchu et al. [START_REF] Rayanchu | Airshark: detecting non-wifi rf devices using commodity wifi hardware[END_REF] detected non-wireless interference using custom hardware. Kanuparthy et al. [START_REF] Kanuparthy | Can user-level probing detect and diagnose common home-wlan pathologies[END_REF] developed a tool to detect wireless performance problems (e.g., low signal-to-noise ratio, congestion, hidden terminals) using both active probes and an additional passive monitor deployed within the network. Other techniques have also studied different types of wireless performance problems. Manweiler et al. build a tool to detect hidden interference in homes and adjust channel selection to minimize interference [START_REF] Manweiler | Rxip: Monitoring the health of home wireless networks[END_REF]; the tool requires a custom kernel and does not run on a commodity access point. Srivastava et al. develop a tool for estimating interference in enterprise wireless LANs [START_REF] Shrivastava | Pie in the sky: Online passive interference estimation for enterprise wlans[END_REF]; the tool requires collection and analysis of packet traces at multiple access points within an enterprise to perform diagnosis.

Conclusion

In this paper, we characterized the performance of user traffic in home wireless networks from 66 homes in 15 countries using passive network measurements. We analyzed passively collected traces in both the 2.4 GHz and the 5 GHz spectrum. One of the more significant challenges in executing this study involved designing a measurement tool that could operate within the tight constraints of a commodity home router and draw reasonable inferences from a single vantage point without a second monitor radio. Our study of this real user traffic in home networks revealed that most TCP flows in home networks achieve only a small fraction of the available access link throughput, that wireless characteristics have a greater effect on the performance of user traffic as access link throughput increases, that the 5 GHz channel exhibits better performance than the 2.4 GHz band, and that distinct devices within the same home can see very different wireless performance. We plan to build on these insights to create a diagnostic tool that can identify both the location of the network bottleneck link and the underlying causes of the bottleneck, should it lie in the wireless network. Finally, although wireless performance often suffers as a result of transient factors, such as microwaves, passing humans, or even minute changes in orientation of devices, many wireless performance problems are persistent. Our ability to monitor wireless performance over longer time periods in many homes may shed light on the persistent performance problems, which could offer a more holistic picture of wireless network performance problems.

Fig. 3 :

 3 Fig.3: Correlation of wireless metrics to normalized throughput at different access link throughput levels.

Fig. 4 :

 4 Fig. 4: Round-trip latency of flows.

  Flows in the 2.4 GHz band experience higher LAN RTT.

Fig. 5 :Fig. 6 :Fig. 7 :

 567 Fig. 5: Characteristics of flows in the 5 GHz vs. the 2.4 GHz spectrum.

Fig. 8 :

 8 Fig.8: Median retransmission rates, for devices in both the 2.4 GHz and 5 GHz spectrums. Retransmissions are higher in the 2.4 GHz spectrum, where nearly 30% of devices see a median retransmission rate greater than 10%.

Fig. 10 :

 10 Fig. 10: The pairwise K-S distance between devices within a home network, across all homes.


Rebecca Warzer

Elizabeth B Torres

Asaf Bachrach

Micro-Movement

Myriam Gourfink

Micro-movement as physical signature of movement intention in work of

Introduction

Torres et al. use the term micro-movement to describe the statistical microstructure of body movement inherent in the variability of velocity-or acceleration-dependent parameters in the continuous flow of motion trajectories. These can be measured with wearable sensing technology such as accelerometers, and analyzed with a newly developed technique [START_REF] Torres | Autism: the micro-movement perspective[END_REF] that enables real time tracking of the signatures of motor-output variability. In previous studies, Torres et al. used a basic pointing task to examine the statistical signatures of goal-directed and goalless motor segments [START_REF] Torres | Autism: the micro-movement perspective[END_REF]. In comparing the micro-movements of the pointing task across many different repetitions of the task, Torres et al. found that the trajectory of the goal-directed motor segment (arm extension) exhibited much more stability across trials than the goal-less retraction. Additionally, the trajectory of the goal-directed motor segment was less sensitive to experimentally induced speed variations [START_REF] Torres | Signatures of movement variability anticipate hand speed according to levels of intent[END_REF][START_REF] Torres | Two classes of movements in motor control[END_REF][START_REF] Torres | Autism: the micro-movement perspective[END_REF]. Adapting this paradigm, the present study examines the extent to which a given movement resembles goal-directed, deliberate, and intentional movement by comparing the stability of its trajectory across trials differing in speed. In this way, collecting trajectory data from dancers before and after training sessions in Gourfink's technique enables us to study any effect that the training may have had on the unintended components of the dancers' movements.

Materials & Methods

In the pointing experiment, dancers were instructed to point at a target on the wall (a black "X" 4 inches across) in response to a tone. Each tone that was played was one of four different pitches, and dancers were instructed to point faster in response to a higher pitch, and slower in response to a lower pitch. Varying pointing speed allows the pointing trajectory to be studied across different contexts, and using different pitches to accomplish this was the method that was least invasive to our study. Each pitch was played 25 times in a randomized order, yielding 100 trials per experiment. Each tone was 750ms long, and tones were separated by a randomly chosen interval of time between 4 and 8 seconds to hamper dancers' ability to predict the regular occurrence of a tone. Movement trajectory was measured with an inertial measurement unit (IMU) consisting of an accelerometer and a gyroscope at a sampling rate of 200 Hz. Data from the IMUs were recorded using a Max/MSP patch developed in-house. We used 10 IMUs that were paired in units of two for each subject. One IMU was worn on the pointing hand of the subject and the other worn on the subject's head.

Results & Analysis

In Figure 1A, the top graph plots data from the accelerometer mounted on one sample subject's head, and the bottom graph plots the same for the accelerometer on the subject's pointing hand. Data is automatically parsed between forward pointing segment (blue), retraction (red), and rest (green). All three segments of the gesture were performed faster in the afternoon session compared to the morning (Figure 1D). Further analysis and control experiments will be required to determine whether this acceleration was due to the dance training. We used a Gamma function family to estimate the distribution of the values of the acceleration peaks by segment and session (Figure 1C). Peak acceleration fluctuations across trials for each individual have distinct stochastic signatures. The empirical frequency distributions of such parameters can be used to estimate the two Gamma parameters that uniquely label that individual's somato-sensation [START_REF] Torres | Novel Diagnostic Tool to Quantify Signatures of Movement in Subjects with Neurobiological Disorders, Autism and Autism Spectrum Disorders[END_REF]. A log map of the two parameter values (Figure 2A) provides a visualization of the relative predictive-ness (growing from left to right) and noise (decreasing from top to bottom) of each segment regarding the next trial (by individual and by session). The three segments are clearly distinguishable by their Gamma parameters, the pointing motion being the most variable (least predictive), and the resting segment the most predictive. The low predictability of the pointing segment is most probably due to the randomly instructed speed, which, surprisingly, had a smaller effect on the retraction segment. The high predictive-ness of the resting segment is most surprising and could be due to dance training. In the afternoon session, the retraction and rest segments appear to be more predictive than the corresponding segments in the morning session. We hypothesize that this increase is the result of Myriam Gourfink's specific training, enhancing continuous conscious awareness of body and movement state. We are currently conducting additional analyses to verify this conclusion.

Figure . Figure 2 .

 .2 Figure . Methods Summary: (A) Obtain the hand tri-axial linear acceleration (x,y,z) vector time series. (B) Obtain the time series of the norm (scalar quantity) of the acceleration vector and locate the acceleration peaks. (C) Automatically parse out the forward peaks for the ballistic phase of the pointing motions (red), the uninstructed retractions (blue) and the resting phase between trials (green). (D) Gather the data in a frequency histogram for density estimation and probability distribution fitting (red morning and blue afternoon for the forward motion in this example).


J M Dutertre
email: dutertre@emse.fr

R Possamai Bastos

O Potin

M L Flottes

B Rouzeyre

G Di

Sensitivity Tuning of a Bulk Built-In Current Sensor for Optimal Transient-Fault Detection

Bulk Built-In Current Sensors (BBICSs) are able to detect anomalous transient currents induced in the bulk of integrated circuits when hit by ionizing particles. This paper presents a new strategy to design BBICSs with optimal transient-fault detection sensitivity while keeping low both area and power overheads. The approach allows increasing the detection sensitivity by setting an asymmetry in the flipping ability of the sensor's latch. In addition, we introduce a mechanism to tune the delay of the bulk access transistors that improves even more the BBICS detection sensitivity. The proposed design strategy offers a good compromise between fault detection sensitivity and power consumption; moreover it makes feasible the use of several CMOS processes.

Introduction

Bulk Built-In Current Sensors (BBICS) were introduced to monitor the currents induced in the bulk of integrated circuits by ionizing particle hits. Such currents may create transient errors into the circuit's calculations. BBICSs are used to detect unusual bulk currents and to possibly trig recovery mechanisms to maintain the circuit's functionality.

This paper is organized as follows: Section 2 describes the principles underlying BBICSs. Moreover it reviews the architectures of the Tbulk BICS [START_REF] Neto | Using bulk built-in current sensors to detect soft errors[END_REF][START_REF] Neto | Tbulk-bics: A built-in current sensor robust to process and temperature variations for soft error detection[END_REF] and also the one of a recently improved low power design [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF]. The weaknesses of these designs are discussed to highlight the difficulties in adapting these designs to a CMOS 65-nm process. Section 3 describes the strategies proposed to obtain optimal fault detection sensitivity while keeping low both area and power consumption overheads. Section 4 draws a conclusion.

State-of-the-art

Integrated circuits (ICs) are known to suffer from Single Event Effects (SEEs) in harsh and radioactive environments. In this paper we consider two kinds of SEEs: Single Event Transient (SET) and Single Event Upset (SEU). In the following sub-sections we review the elecrical phenomenon underlying SEEs, the BBICS principles, as well as the properties of two recent BBICS structures dedicated to the monitoring of the bulk currents induced by particle hits. Their main drawbacks are also reported.

Single Event Transients and related detection through bulk-current monitoring

When an ionic particle passes through silicon it generates electron-hole pairs along its path. These electrical charges generally recombine without any significant effect. However, the electron-hole pairs may be separated by the electric field found in a PN junction thus creating a transient current. Fig. 1 illustrates the inverter's case when its input is high (i.e. in = 1 and out = 0). In this configuration, the SEE sensitive part is the drain of the PMOS transistor, which is in OFF state. Fig. 1 displays the cross section view of the inverter. The path taken by the generated transient current flows from the power supply (Vdd) through the biasing contact of the Nwell (the NTAP) and then through the PMOS drain. Next, it separates in two branches before reaching the ground. One part charges the inverter's output capacitance, while the other passes through the NMOS transistor, which is in ON state. When the whole electronhole pairs have been drain away, the current ceases. The main effect is a transient alteration at the inverter's output from 0 to 1. This voltage transient, also known as SET, may thus propagate through the circuit logic creating soft errors in case of no logical, electrical or latching-window masking. Furthermore, whether a SET is induced directly in a memory element, the stored data may be flipped characterizing the so-called SEU (i.e. a soft error). Figure 2 gives a schematic view of the effect of a transient current induced by the ionic particle. A current source is used to represent the transient current generation. It illustrates a characteristic phenomenon of this transient current: it flows through the bulk of the struck sensitive transistor. BBICSs are designed to take advantage of this phenomenon. These sensors monitor the transistors' bulk currents, detect unusual currents and, consequently, the advent of SET or SEU. Note that NMOS transistors can be affected as well, however, for the sake of brevity we have only described the PMOS case and the BBICS' architecture dedicated to monitor these transistors.

BBICS principles

Bulk currents induced during normal operation of an IC are in the µA range; whereas particle-induced bulk currents have to be above some hundreds of µA to generate an SET on the related gate output.

Fig. 3 depicts the insertion of a BBICS between the bulk (the Nwell) of a PMOS transistor and the power supply. Note that the biasing at Vdd of the Nwell is provided by the BBICS. Hence, as illustrated, any transient current necessarily flows through the BBICS. The BBICS purpose is then to raise a warning flag indicating that the circuit's function may be affected. In Fig. 3, the BBICS used to monitor the Nwell of PMOS transistors is named a pBBICS. It also exists nBBICS dedicated to the monitoring of NMOS transistors. Even if pBBICS and nBBICS have different architectures they rely on the same principle, i.e. the monitoring of bulk currents. [START_REF] Neto | Using bulk built-in current sensors to detect soft errors[END_REF]. We review in the next subsection the main char-acteristics of their most recent design, the Tbulk BICS [START_REF] Neto | Tbulk-bics: A built-in current sensor robust to process and temperature variations for soft error detection[END_REF]. We also review a recent low-power improved BBICS proposed in 2012 [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF].

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The Tbulk BICS and its most recent improvement E.H. Neto et al. have introduced the first BBICS

The Tbulk BICS

Fig. 4 depicts the architecture of a Tbulk BICS used to monitor PMOS transistors. The Tbulk BICS makes use of trimming transistors in order to cope with ICs' variability. Nevertheless, we do not analyse their effect in details for the sake of simplicity. The core part of this Tbulk BICS is a memory latch made of two cross-coupled inverters (transistors M5-M6 and M7-M8). In normal operating conditions, node FLAG is at low level while node FLAG b is high. FLAG b goes low to indicate the detection of an unusual bulk current. The BBICS connection to the Nwell of the monitored transistors (node PMOS bulk in Fig. 4) is used as a bias contact to Vdd through the ON transistors M2 and M8.

When a particle hits the device, the induced current flows to the Nwell (i.e. the bulk) from the Tbulk BICS through PMOS bulk and M2. It discharges the capacitance of node FLAG b (a counterbalancing charging current also flows from Vdd through M8 to FLAG b). As a result, the voltage of node FLAG b decreases to the point where the BBICS' core latch flips. FLAG b going low indicates an SEE detection. In the meantime, M2 switches to OFF state and M1 to ON state respectively. The latter ensures a proper biasing of the Nwell to Vdd.

A key point in designing a BBICS is to obtain a sensitivity level lower than the level of appearance of SEEs. The purpose of transistors M9, M10 and M11 is to weaken the core latch stability to that end.

BBICS with low-power sleep-mode

The work in [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF] proposes a BBICS architecture (depicted in Fig. 5) that overcomes with the main drawback of the Tbulk BICS architecture: the huge static power consumption due to transistors M9, M10, and M11 illustrated in Fig. 4. In fact, these three transistors act to improve the detection sensitivity of the sensor by setting a small permanent voltage offset at FLAG node. It makes easier the flipping ability of the Tbulk BICS latch; however it also considerably increases the sub-threshold leakage current of transistors M7 and M10. Moreover, simulation results presented in [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF] show that the Tbulk BICS without trimming transistors suffers from overheads in area (above 55%) and in power consumption (higher than 100%) when compared to an unprotected reference circuit. Furthermore, the BBICS version in [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF] introduces transistor M9 (detailed in Fig. 5) as a lowpower sleep-mode feature that can be enable in case of the system is left on standby. Results from [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF] hilights overheads of 25% and 40% respectively in area and power consumption. If the sleep-mode is active, the power overhead is reduced to 25%.

Adequacy with different CMOS technology nodes

The structures of the Tbulk BICS (Fig. 4) and its relative low-power version (Fig. 5) were designed by using 32-nm CMOS predictive technology models [START_REF]Predictive Technology Model[END_REF]. In order to produce a test chip, we have analysed the adequacy of the architecture in Fig. 5 for use with a commercial 65-nm CMOS technology. The monitoring capability of this pBBICS was found below the minimum SEE detection sensitivity of the process. An analysis of the design simulation results showed that any decrease in the voltage of node FLAG b, which is mandatory to flip the BBICS' core latch, also made transistor M1 switching progressively to ON state. Hence, it gives rise to a competition effect between two component of the bulk current, which will pass through transistors M1 and M2. Consequently, the voltage of node FLAG b is not able to go down the threshold voltage of transistor M6. This study shows that an additional modification of the low-power BBICS is mandatory for achieving proper SEE detection in the CMOS 65-nm technology.

Improved BBICS design for optimal transientfault detection in 65-nm technology

Proposed architecture

The original Tbulk BICS' sensitivity was enhanced by introducing an asymmetry in its core latch (transistors M9 to M11 in Fig. 4). The downside was an increase of its static power consumption.

We used an alternative unconventional approach that has a significant smaller effect on the power consumption (similar to that of the BBICS of [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF]): the use of transistors with low and high thresholds voltage (hereafter denoted LVT and HVT respectively) in place of standard ones. These three types of transistors are normally provided by most of commercial technologies. An LVT (HVT resp.) transistor has a highest (lowest resp.) current driving capability in ON state and passes from OFF to ON state more (less resp.) rapidly. Consequently, an inverter made of one LVT NMOS and one HVT PMOS has an asymmetrical behaviour: its output changes more easily from 1 to 0, than from 0 to 1. Respectively, the output of an inverter made of one HVT NMOS and one LVT PMOS change more easily from 0 to 1, than from 1 to 0. We used these two inverters features to increase the ability of the core latch to flip when a bulk current appears. We also used an LVT transistor for M2 and an HVT transistor for M1. Hence, the competition effect between the currents of M2 and M1 is modified in favour of M2's current, which increases the detection sensitivity by helping FLAG b voltage to diminish. A reinforcing effect due to the timing of the commutations of M1 and M2 was also obtained thanks to an adequate control of the switching time from ON to OFF and OFF to ON states of these access transistors. In fact, the M2 switch is tuned to open (from ON to OFF) only after FLAG b voltage has decreased enough to flip the BBICS latch. Further, the transition of M1 from OFF to ON in order to replace the PMOS bulk to Vdd is done just after the switching of M2. Our architectural solution uses a two inverters chain as depicted in Fig. 7. The chain input is connected to FLAG b, its intermediate node (FLAG out) is connected to M2's gate, and its output to M1's gate. This new architecture ensures that the command of M1 and M2 is postponed after the detection of any bulk current.

Simulation results

For the purpose of both setting a sensitivity target and tuning the sizes of the BBICS' transistors, a first set of simulations was run on a chain of ten inverters. We used the smallest sizing for the NMOS and PMOS of the inverters: L N M OS = L P M OS = 60nm, W N M OS = 200nm, W P M OS = 280nm. The PMOS' bulk was connected to a pBBICS and the NMOS' bulk to an nBBICS. At this stage, the circuit's parasitic resistances and capacitances were not considered. On simulation basis, the minimal bulk current pulse on PMOS that gives rise to an SET or an SEU was determined to 130µA amplitude at 50ps duration (respectively 99µA amplitude at 50ps duration on NMOS bulk). Fig. 8 displays the successful detection of an SEE transient current (147µA@50ps) on a PMOS of the inverters chain (identical to the pulse used for the simulation results reported in Fig. 6). FLAG b's voltage is drawn below the switching threshold of the pBBICS by the SEE transient current. Consequently, the pBBICS flips and the alert signal FLAG out goes high. Similar simulations were also run to ascertain that the sensitivity target was reach in case of SEE transient current on an NMOS: the monitoring nBBICS raised successfully its alarm flag. As we highlighted in section 2.4, the architecture of the low-power BBICS was not able to reproduce the minimum SEE detection sensitivity of the CMOS 65-nm process by using its SVT transistors. Table 1 draws a comparison be-tween these two BBICS architectures in terms of SEE detection capability and area.

These simulations were run for three corners of the technology: Typical-Typical, Fast-Fast, and Slow-Slow (TT, FF, and SS hereafter). The improved BBICSs passed successfully these tests contrary to the low-power sleep-mode BBICSs. We have also assessed the efficiency of the improved pBBICS and nBBICS for different lengths of the monitored inverters chains. The corresponding simulations were run at typical conditions (TT corner) for a pulse duration of 50ps. Simulation results in terms of sensitivity threshold (the minimal transient current pulse sufficient for raising the BBICS' flag) and SET threshold (the minimal transient current pulse that results in an SET propagation from the input of the inverters chain to its output) are reported in table 2 for chains of 10, 30 and 50 inverters. The sensitivity threshold increases with the number of monitored inverters. However, it remains below or close to the SET threshold until 50 inverters. Given the size of an inverter cell, 1.56µm 2 , the area of the 50 inverters chain is 78µm 2 . Thus the area overhead due to both pBBICS and nBBICS is 30% which is similar to the overhead reported in [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF].

Implementation

Design of a test vehicle

A test vehicle was designed in 65-nm CMOS technology to ascertain on experimental grounds the validity of the improved BBICS architecture. The test chip embeds various patterns of inverters chains monitored by pBBICS and nBBICS. This subsection reports electrical simulations that takes into account the parasitics elements of the standard cells in the target 65-nm CMOS technology. Fault-injection simulations were performed by using the technology's smallest inverter in order to analyse the minimum transient-fault profiles. Thereby, we inject a double exponential current source on the technology's lowest capacitance (i.e. on the node between two inverters with minor dimensions). The injected current must create a minimal voltage shape able to propagate through the technology's smallest inverter and to switch the output level of the smallest flip-flop. In accord with the description of the injection current source presented in [START_REF] Simionovski | A bulk built-in current sensor for set detection with dynamic memory cell[END_REF], process and temperature corners were verified. Results for a target circuit compounded of 2 chains of 10 inverters protected by the proposed BBICS are summarized in table 3. For an injected current profile with a time rise T r = 8.2ps, a time width T w = 124ps (measured at the half amplitude of the current), and a time fall T f = 330ps, the minimum transient-fault profiles that are detectable by the BBICS are presented in terms of the minimum charges Q min and the minimum current peaks I peak that provoke a successful indication of fault.

Impact on standard cell design

The use of BBICS usually goes along with a huge re-design work: the adjustment of the standard cells in order to disconnect their bulk biasing contacts from the power supply and ground and to connect them to the BBICS's inputs. However, the CMOS 65-nm technology we used to design our test vehicle has an interesting feature: its standard cells do not embed any biasing contact. The biasing of the circuit's bulks is provided by dedicated fillers which are interspersed among the standard cells. Figure 9 -(a) depicts the layout of those biasing fillers: the Nwell (respectively the P substrate) is biased to Vdd (resp. to the ground) through the NTAP (resp. the PTAP) and a contact (CON) to the metal 1 (MET1) supply rail. Thus, the only cell to be re-designed is the biasing filler. Figure 9 -(b) shows the layout of the new biasing filler used to connect the bulks of the monitored cells to the BICCSs. The contacts (CON) between the power rails and the NTAP and PTAP have been removed (their shapes were also slightly modified). New contacts have been added with corresponding metal 1 accesses to pBBICS's PMOS bulk and nBBICS's NMOS bulk nodes (see fig. 3 as an illustration).

Conclusion

We propose an improved BBICS dedicated to optimal fault detection. We use an unconventional approach to increase the detection sensitivity by in-troducing an asymmetry in the flipping abilities of the BBICS' core latch. This behaviour was obtained thanks to the use of low and high threshold voltage transistors. The corresponding area overhead was estimated to 30%, a figure similar to a previous architecture [START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF]. The proposed principles are general enough to be easily adapted to any design implemented with recent CMOS submicron processes while guaranteeing low area and power overheads. Furthermore, the chosen technology allows to keep unmodified the standard cells, which simplified the design work necessary to use BBICS.

Figure 1 :

 1 Figure 1: Single event transient mechanism: cross section view of an inverter.

Figure 2 :

 2 Figure 2: SET mechanism: inverter schematic outline.

Figure 3 :

 3 Figure 3: PMOS sensitivity monitoring with a pBBICS.

Figure 4 :

 4 Figure 4: Tbulk BICS architecture for PMOS monitoring [2].

Figure 5 :

 5 Figure 5: PMOS BBICS with low-power sleep-mode [3].

  Fig. 6 illustrates such a scenario in which the bulk current (i.e. Ipmos bulk), the currents passing through M1 and M2 (i.e. Ids M1 and Ids M2), and the voltages of FLAG b and FLAG are drawn.

Figure 6 :

 6 Figure 6: A 65-nm CMOS technology-based case study of Fig. 5 scheme showing the insufficient discharge of FLAG node to register the occurrence of a SET.

Figure 7 :

 7 Figure 7: CMOS 65-nm improved pBBICS.

Figure 8 :

 8 Figure 8: Improved pBBICS waveforms during SEE detection.

Figure 9 :

 9 Figure 9: Standard cell biasing filler (a) and BBICS biasing filler (b)

Table 1 :

 1 Area and detection capability of BBICS cells: comparison between low-power sleep-mode[START_REF] Bastos | Novel transient-fault detection circuit featuring enhanced bulk built-in current sensor with low-power sleep-mode[END_REF] and improved BBICS

		improved BBICS
	Type	Area	SEE detection (65-nm process)
	pBBICS 12.1µm 2	yes
	nBBICS 11.3µm 2	yes
		low-power sleep-mode BBICS [3]
	Type	Area	SEE detection (65-nm process)
	pBBICS 10.5µm 2	no
	nBBICS 10.5µm 2	no

Table 2 :

 2 Sensitivity and SET occurrence thresholds of the improved pBBICS and nBBICS versus the length of the monitored inverters chains -Simulation results for the TT corner and a pulse duration of 50ps

		pBBICS
	# of inverters	10	30	50
	Sensitivity threshold 76µA 113µA 148µA
	SET threshold	130µA 133µA 135µA
		nBBICS
	# of inverters	10	30	50
	Sensitivity threshold 87µA 93µA 118µA
	SET threshold	99µA 101µA 102µA

Table 3 :

 3 Minimum Detectable Transient-Fault Profile

		Corner	I peak (µA) Q min (f C)
		FS 80 • C	84.5	14.3
	nBBICS	FS 0 • C TT 27 • C	109.4 112.1	18.5 18.9
		SF 0 • C	131.7	22.2
		FF 80 • C	53.7	9.2
		Corner	I peak (µA) Q min (f C)
		FS 80 • C	121.3	20.9
	pBBICS	FS 0 • C TT 27 • C	148.7 126.6	25.3 21.5
		SF 0 • C	119.5	20.3
		FF 80 • C	67.3	11.6