Bahaeddine Ben Hamed

Guillaume Regnat

Guillaume Lefèvre

Cyril Buttay
email: cyril.buttay@insa-lyon.fr

Automatic Model Generation for PCB-based Power Electronics

This paper describes a workflow which allows to automatically go from the printed circuit board design of a power converter to a circuit model which includes circuit parasitics (a complete "virtual prototype"). Commercial software (Altium, Ansys Q3D, LTSpice) is used, with custom code at the interfaces. Some systematic simplifications are proposed to accelerate computations. This workflow is demonstrated on an example case and compared to experimental measurements (double pulse waveforms and impedance measurements).

I. INTRODUCTION

Designing a new power electronic converter still largely relies on expertise and hardware prototyping [START_REF] Lagier | How Good are the Design Tools in Power Electronics?[END_REF], despite a continuous drop in the cost of computing power and the availability of an increasingly large range of modelling and simulation software. This can be explained by a variety of reasons, such as the presence of strong non-linearity and multi-physics couplings, or by the large variations in time scales (from nanoseconds for switching events up to minutes or hours to reach thermal steady state). As a consequence, many research projects have focused on generating models with a reduced complexity [START_REF] Evans | Automated fast extraction of compact thermal models for power electronic modules[END_REF], [START_REF] Biela | Towards Virtual Prototyping and Comprehensive Multi-Objective Optimisation in Power Electronics[END_REF], on implementing efficient modelling techniques (such as the Partial Element Equivalent Circuit -PEEC-for the calculation of stray inductances, in Cedrat's InCa3D or FastFieldSolver's Fasthenry), or even on exploring high performance computing platforms (such as GPUs [START_REF] Yan | Large-scale nonlinear device-level power electronic circuit simulation on massively parallel graphics processing architectures[END_REF]).

One additional roadblock which prevents power electronic designers from moving to "virtual prototypes" is the lack of a unified system description: power electronics systems are usually a heterogeneous assembly of packaged devices, mechanical parts, Printed Circuit Boards (PCB). . . Such complex arrangement is not fully described in a single place (mechanical software for the geometry, circuit simulator for the components, etc.). As a result, preparing a model for computation can require a lot of manual efforts, or to exclude part of the converter [START_REF] Lagier | How Good are the Design Tools in Power Electronics?[END_REF]: a typical example is the extraction of the circuit parasitics of a power module, which often excludes any coupling with the gate driver PCB, despite both part being very close from each other. Furthermore, packaged devices are "black boxes", with all their internal details (layout, chip distribution, etc.) hidden away from the circuit designer. As a consequence, they cannot be modeled in the same way as the rest of the converter. The designer must rely on the manufacturer's devices models, if any.

Because of their low thermal performance and limited current capability, PCBs have long been limited to low power conversion (typically 10 or 100s of watts, for consumer applications). Recent improvements such as the PCB embedding technology [START_REF] Boettcher | 3D modular power electronic systems, based on embedded components[END_REF], in which power devices can be inserted within a PCB, or the availability of thick copper layers have made PCBs much more attractive for converters in the multi-kilowatt range (3.3 kW in [START_REF] Caillaud | Thermal Considerations of a Power Converter with Components Embedded in Printed Circuit Boards[END_REF], or 50 kW in [START_REF] Ostmann | Power modules with embedded components[END_REF]). As a result, a full converter (including power, control, etc.) can be built using only PCB for its interconnects, with bare-dies power semiconductor devices. One consequence of this "rationalized" assembly is that all information about the converter becomes available in the PCB design software [START_REF] Buttay | Application of the pcb-embedding technology in power electronics -state of the art and proposed development[END_REF]: complete description of the layout, bill of materials (list of components), etc. In theory, it then becomes possible to use this information to generate models (thermal, electrical, etc.) in an automated way.

In practice, generating models from PCB design software is anything but trivial: in addition to the complexity issues described above, model preparation requires a lot of user interaction. Recently, Hoffmann et al. [START_REF] Hoffmann | Pcb layout tool integrated loss and inductance estimation[END_REF] presented a solution which allows a user to select conductors in a PCB and automatically calculate parasitic inductance, resistance, and joule heating; the objective of that paper is to provide "immediate value quantification", thanks to fast computation algorithms and to the reduction of the domain to a few conductors only. In contrast, the approach we present here aims at generating models for the entire PCB (at the cost of longer computing times). Once a PCB design is complete, the parasitic elements (capacitance, resistance, inductance and their couplings) of each track are calculated, and inserted in a circuit model along with all the components of the PCB, so as to constitute a complete "virtual prototype" of the circuit.

II. MODEL GENERATION

The modeling approach presented here relies on existing pieces of software: electronic Computer-Aided Design (eCAD), Finite Elements (FE)/Method of Moments (MoM)/circuit simulators. These tools are mature and can manage a high degree of complexity in the models. Our main contribution consist in interfacing them so as to allow the automatic generation of a circuit model for a complete PCB. The workflow used to generate circuit models from the eCAD is depicted in Fig. 1. In this paper, the following pieces of software are used: Altium Designer (eCAD), Ansys suite (and in particular Q3D Extractor to compute circuit parasitics), LTSpice for circuit simulation. While most of the data import/export relies on existing file formats, these do not capture all the information required (this is described below). As a workaround, a custom Altium extension, written in C++, generates an additional file (pcb_info.txt). Model preparation in Q3D Extractor usually requires some effort (in particular, all electrical terminals must be defined manually); here, this is taken care of automatically using a script (Vbscript), based on pcb_info.txt. The circuit model of the layout (parasitic elements) is then inserted in the electrical circuit of the converter and can be simulated using LTSpice

A. File formats for PCB layout export

Once a PCB layout has been designed in Altium Designer, its geometry has to be exported as a 3D-model. Several file formats have been introduced over the years for the description of a PCB layout, from the venerable "Gerber" (RS274-D, now RS274-X), introduced in 1980 up to the more recent ODB++ or IPC-2581. Each of these file formats offers a different set of features, with variations from one revision to another (3 revisions of the IPC 2581 format have been released so far), or from one implementation to another (some file format include the possibility for proprietary extensions). Finally, the same variability can be observed regarding the "import" feature which will read the geometry description to turn it into an FE model.

As a consequence, it is difficult to predict the final quality of a layout description once it has been exported from eCAD and loaded into FEM without actually testing. Table I summarizes the results of the tests we performed using Altium Designer and the Ansys Electromagnetics suite. It shows that some file formats do not preserve the 3D geometry of a multilayer PCB (they only describe a set of 2D layouts, which must be ordered manually). Others do not describe the location of the components on the PCB. Finally, two pieces of software from the Ansys Suite can import a PCB geometry description: HFSS Layout and Siwave (Q3D Extractor does not provide an import feature for PCB geometry). Each of them can then generate a 3D model compatible with Q3D.

None of the standard file formats (Gerber, ODB++, IPC-2581) is found to allow a seamless data transfer. A fourth option, which relies on a proprietary format ("EDB", from Ansys) is found to offer better performance (most features are conserved, no error is observed). It requires the installation of an Altium plug-in provided by Ansys ("Ansys EDB Exporter", * .def file extension). Export using the EDB file format, along with a file import through HFSS Layout is therefore used in this work. To complement the geometry description, some additional data are required. In particular, some pre-processing (a custom C++ code run from Altium) is required to locate the connection points for which the circuit parasitics must be computed. Indeed, only the pads which correspond to components should be used (some geometric features of layouts are detected as pads, but are not connected to an actual component). The references of the pads to be used are exported in a text file (pcb_info.txt in Fig. 1). This file, together with the geometry description (EDB file) constitute all the data used in the Ansys Suite to generate the 3D model ( 2 in Fig. 2)

The * .def file is then imported into HFSS Layout, using a custom script. The first step A is to locate the pads (x,y,z position), as this information is available in HFSS Layout, but it is not transferred to Q3D. Then the script uses a feature of HFSS to simplify the 3D geometry, by replacing curves with faceted polygons B . This is particularly useful for copper vias, as these represent a large number of small features (thousands or tens of thousands of micro-vias can commonly be found in a multi-layer PCBs) which can be computationally intensive. Cylinders can be automatically replaced with prisms with a 4-, 12-of 24-sided base. The script produces a 3Dmodel which can then be directly processed using Q3D C .

An additional simplification is presented in Fig. 3: vias are grouped together in large "boxes", removing most of the fine features (allowing for a coarser and lighter mesh of the model).

At the moment, this grouping requires manual interaction with the user.

C. Ansys Q3D automation

In Q3D, the script defines the electrical paths for which it is necessary to calculate the circuit parasitics (resistance, inductance, capacitance, and all the mutual effects). In Q3D terms, this consists in defining "sources" and "sinks". Automating this avoids a tedious and error-prone manual entry of these sinks and sources. All objects composing the same conductor ("nets") are unified in a single part to reduce the model complexity and reduce simulation time. The automatic process also addresses two issues presented in Fig. 4: When a small surface hosts two or more connections, it is simplified to a single terminal (as it is the case for Surface 1 in Fig. 4a); on the contrary, small pads are defined on large conductor planes to accurately define connection points (by default Q3D affects entire surfaces to a single connection point), as presented in Fig. 4b.

Finally, the script runs the computation of the 3D model D and the generation of the corresponding Spice netlist E ( * .cir).

The simplifications (especially that described in Fig. 4a) result in changing the number of terminals of the layout's Spice model (Pin 1 of R2 and pin 1 of R1 end up being connected to the same terminal of the Spice model, instead of being connected to two separate terminals). A description file (spice_info.txt) is generated to describe the interface between the schematic defined in Altium Designer and the Spice netlist.

D. Ltspice schematic generation

Using the Spice model of the layout ( 3 in Fig. 2) and the schematic of the circuit as it has been designed by the user in Altium Designer 4 , one last C++ Altium program is used to generate a LTSpice schematic ( * .asc format). It reads the Altium schematic (fig. 5a) to get the connections between components , as well as the locations of the symbols on the schematic, so as to produce a readable output, in which all the connecting wires are replaced by their Q3D-calculated model (Fig. 5b). This model is too complex to be reproduced here. As an example, the data corresponding to the inductive part is presented in Tab. II.

This LTSpice schematic can then be opened in LTSpice. As a simulation is run, the software will generate the Spice netlist (exactly as it would do for a schematic that would have been entered manually). Note that the parasitic elements of the components (in particular the ESL/ESR of the capacitors) are integrated in their respective models. Here, we use the ESL and ESR values which are quoted in the datasheets of the capacitors to generate the capacitors models which are inserted automatically in the LTSpice netlist.

E. Example

To test the workflow presented above, a simple test case is used: a half-bridge structure using IGBTs and diodes, rated at 600 V and 30 A). Despite its simplicity, the PCB contains advanced features: 6 layers, vias and microvias, 4 embedded chips).

C1-2 C2-2 D1-2 C1 D1-1 D2-2 R1-1 E1 C2 C1-1 C2-1 D2-1 R2-1 E2 R1-2 G2 C1-2 1.
The layout model is calculated following the steps described in sections II-A to II-C, without manual interaction. Preprocessing of the data and preparation of the 3D model only take a few tens of seconds, and most of the computation time is taken by the computations in Q3D Extractor.

As described in section II-B, some geometrical simplifications can be performed before computation. In particular, circular surfaces can be faceted, and groups of vias can be replaced with a "box" (Fig. 3). Fig. 6 presents the results of such simplifications on the processing time, number of elements in the mesh, memory use, and maximum error. This error is calculated by comparing the R,L and C matrices generated by Q3D Extractor, on an element per element basis, with those of the case without simplification (cylinders remain cylinders); the value in Fig. 6 (bottom right) corresponds to the largest deviation. Fig. 6 shows that systematic simplification (faceting of the circular shapes) offers a significant improvement in processing time and memory use (more than 50 % reduction for the 4- facet case over the cylinder case), without increasing error noticeably (≈2 %). Increasing the number of facets to 12 only marginally reduces the error. Further increase to 24 results in a dramatic degradation of all parameters, including error, probably because of some numerical instabilities. Box simplification offers shorter processing time and memory use as compared to the 4-facet case, at the cost of a slightly larger, but still acceptable, error (≈4 %).

III. EXPERIMENTAL VALIDATION

To validate the modelling procedure, two experiments are conducted, using the same half-bridge PCB as analyzed in section II-E: an impedance measurement, to validate the Q3D model, and a double-pulse test, to compare experimental waveforms with those predicted using the complete LTSpice model.

A. Circuit impedance

The power loop impedance of the PCB is measured using a keysight E4990 impedance analyzer and a Keysight 42941A Impedance probe kit suited to in-circuit measurements. The measurement is performed with an amplitude of 10 mV and a frequency sweep ranging from 100 kHz to 120 MHz. Compensation are performed according to the user manual (phase, open and short compensations for the probe adapter, open and short for the the tip of the probe). The embedded IGBTs are left un-controlled, with the pull-down gate resistors forcing gate-to-emitter voltage to zero. No decoupling capacitors are mounted on the PCB, and the impedance is measured across their (empty) footprint (Fig. 8).

In parallel, the experiment is reproduced in simulation, using the Spice schematic generated by the workflow described above, removing the decoupling capacitors, and replacing them with a 10 mV AC source. An AC simulation is then run, over the same frequency range as used experimentally. 2 configurations are simulated: no via simplification and "box" simplification.

The results are presented in Fig. 9. It can be seen that simulation results ("Simu via" and "Simu box") fit the exper-Adapter PCB Half-Bridge cell imental data satisfyingly, except at the resonance point (around 50 MHz). This can be corrected by adding a 1 Ω resistor in series with the voltage source in the simulation file ("Simu via +R" and "Simu box +R"). This additional resistance is probably related to the probe/PCB contact: changing the probe position resulted in changes as high as 0.2 to 0.3 Ω at the resonance point. On the contrary, changing the frequency value for which the layout parasitics are calculated in Q3D produced no noticeable change, even for values as high as 500 MHz. 4-point static characterization of the embedded IGBTs also showed that DC resistance is low (down to a few tens of mΩ, dynamic resistance of the IGBTs included), so the high resistance at resonance cannot be associated to defects in the PCB's interconnects.

In conclusion, no noticeable difference can be observed between simplified and not simplified models. Simulated results show good agreement with the measurement (for example, simulated L loop = 6.66 nH while experimental L loop = 6.89 nH). Further investigations are ongoing (using a new board allowing for 4-point impedance measurements) to validate the hypothesis that the resistance at the resonance point is only due to the probe-PCB contact.

B. Double pulse test

A standard double pulse test is built (Figs. 10 and11) and reproduced virtually using LTSpice. Measurements are performed using passive probes (TPP0500B voltage probe & TCP0030A current probe, Tektronix) and a scope with

IV. DISCUSSION

The modeling workflow presented here allows to automatically generate the SPICE model of a converter design in minutes, whereas manual model preparation would typically take days, with a strong change of introducing errors in the connections. Because it is based on established software, advanced features of PCBs are taken into account (multiple layers, microvias, embedded chips). Only a few additional scripts are required to control model preparation and exploitation. The same approach is currently under development to generate the thermal model of a PCB.

The workflow has been tested on several designs (not presented here), and has been found ton consistently produce satisfying models. For the largest models, which can contain more than 100 000 vias, the "box" simplification is mandatory. An algorithm is currently under development to remove the need for manual selections of the vias to be grouped. At the moment, no additional simplification has been considered (such as removing some of the tracks from the Q3D Extractor model), as our main goal is to allow for systematic and automatic model generation. However, as we investigate more and more complex designs, this approach may result in larger and larger models, and eventually become unpractical. It is not clear at the moment where this limit may be.

Another topic under development is the definition of "test benches" for the electrical and thermal simulation: while the PCB design in the eCAD software contains all the data needed to describe the board (part number, layout, materials, etc.), it does not provide any information regarding the "boundary conditions": input/output signals, loads, presence of a grounded heatsink, heat exchange coefficients on the surfaces, temperature. . . At the moment these must be entered manually, which can be burdensome in case of iterative design, and can cause errors (mounting the PCB on a heatsink, for example, would change the parasitic capacitances and inductances, so it should be taken into account in the Q3D Extractor simulations). The approach being pursued in our work is to describe a "test bench" (a standard practice in VHDL designs). It consists in a text file which describes the relationship between the PCB and its environment.

V. CONCLUSION

The workflow presented in section II allows to automatically go from the printed circuit board design of a power converter to a circuit model which includes circuit parasitics (a complete "virtual prototype"). Commercial software (Altium, Ansys Q3D, LTSpice) is used, with custom code at the interfaces. Some systematic simplifications of the geometry are proposed to accelerate computations, and the error they introduce is not found to be significant.

Experimental validation on a half-bridge PCB shows a good agreement between simulations results and measurements, regarding both impedance measurements and double pulse waveforms. Further investigations are ongoing, considering more complex designs and faster semiconductor devices (such as SiC MOSFETs, for which circuit parasitics cause more ringing).

Fig. 1 :Fig. 2 :

 12 Fig. 1: The workflow for model generation

Fig. 3 :

 3 Fig. 3: Advanced simplification: transforming the groups of vias into conductive boxes

Fig. 5 :

 5 Fig. 5: (a) Altium schematic of the half-bridge structure used as an example and (b) LTSpice schematic generated automatically, using the Altium schematic to define the position of the components. U1 (top right) contains the layout's spice model (parasitic elements) calculated using Q3D Extractor.

Fig. 6 :Fig. 7 :

 67 Fig.6: Effect of the geometry simplifications on Q3D Extractor calculations (processing time, number of elements in the mesh, memory used, and maximum error compared to the case without simplification -cylinder -), for the half-bridge example presented in this article (on a Core i7 workstation with 16 Go RAM.

Fig. 8 :

 8 Fig. 8: Close-up on the PCB under test (Dimensions: 35mm*45mm) and the tip of the Keysight 42941A probe placed across the decoupling capacitor footprint to measure the power loop impedance (decoupling capacitors not mounted).

Fig. 9 :

 9 Fig. 9: Experimental and simulated bode plot of the power loop impedance

Fig. 10 :

 10 Fig. 10: The half-bridge cell (with two IGBT and two diodes embedded in the PCB, and two surface-mount decoupling capacitors). An adapter board is mounted on top for easier connection and to provide more decoupling capacitance.

Fig. 11 :

 11 Fig. 11: Double pulse setup.

Fig. 12 :

 12 Fig. 12: Example of double pulse waveform

TABLE I :

 I Comparison of the data transfer quality from Altium Designer to the Ansys suite.

		Gerber	ODB++	IPC-2581	EDB
	3D-model	--	-	-	++
	Layout details	-	-	+	++
	(materials,Nets)
	Components details	-	-	+	++
	Altium→HFSS layout	Yes	Yes	Yes	Yes
	Altium→Siwave	Yes	Yes	Yes	Yes

TABLE II :

 II Inductance matrix (L AC


Rébaï Soret
email: rebai.soret@isae-supaero.fr

Pom Charras
email: pom.charras@univ-montp3.fr

Ines Khazar
email: ines.khazar@student.isae-supaero.fr

Christophe Hurter
email: christophe.hurter@enac.fr

Vsevolod Peysakhovich
email: vsevolod.peysakhovich@isae-supaero.fr

Eye-tracking and Virtual Reality in 360-degrees: exploring two ways to assess attentional orienting in rear space

Keywords: Human-centered computing → HCI attentional orienting, virtual reality, endogenous, rear space, front space, cueing, Posner task

The Posner cueing task is a classic experimental paradigm in cognitive science for measuring visual attention orienting abilities. Recently, it was suggested that this paradigm can be adapted in virtual reality (e.g. in an immersive and ecological environment) to evaluate the effectiveness of perceptual stimuli in directing attention and by extension to study the underlying cognitive processes. In this study, auditory and visual endogenous cue were used to voluntary orient attention at 360 • . Two groups of participants (N=33 and N=28) equipped with a virtual reality headset including integrated eye-tracking performed a modified version of the Posner cueing task in a 360 • immersive environment. In this task, participants had to destroy space objects, as quickly as possible, through eye interaction. Predictive visual or auditory informed participants about target location. The results show that these endogenous cues significantly improve performance even if the object to be destroyed occurred outside the visual field or through a mirror. This experiment provides one of the first demonstrations that attentional orienting mechanism can improve performances of visual information processing in an immersive and ecological 360 • environment where information can appear in rear space.

INTRODUCTION

Virtual reality technologies are currently in full swing. Due to lower production costs and to the improvement of displays and interaction, virtual reality headsets are becoming more popular every day. Furthermore, virtual reality allows the development of fully configurable, controlled, ecological, and complex environments. It allows accurate recording of targeted responses [START_REF] Eric B Larson | Virtual reality and cognitive rehabilitation: a review of current outcome research[END_REF], and, thus, offers a new way to explore the cognitive processes involved in the processing of visuospatial information. Indeed, the majority of studies conducted in laboratories use relatively simple environments and standard computer screens (e.g. [START_REF] Thiebaut | A lateralized brain network for visuo-spatial attention[END_REF][START_REF] Greenwood | Changes in visuospatial attention over the adult lifespan[END_REF][START_REF] Wang | Orienting and focusing in voluntary and involuntary visuospatial attention conditions[END_REF]), lacking, therefore, ecological validity. As a result, we are currently unsure how we select information in 360 • immersive and ecological environments with a larger stimulated field of view and 3D objects.

Attentional orienting is one of the cognitive mechanisms involved in perceptual information selection. It is a fundamental cognitive function in charge of preparing information processing located or likely to be located in a particular region of space [START_REF] Michael | Orienting of attention[END_REF]. Attentional orienting can improve the processing of the expected information (process rate and accuracy). The Posner cueing task allows to study the covert orienting mechanisms (i.e., an attentional shifting in the absence of significant ocular movement). In the classic version of this paradigm, participants are to perform a simple reaction time task in response to a target stimulus that may appear at different spatial locations (to the left or right of fixation). Attention is previously directed to one of these locations by using a cue such as a peripheral flash (to the left or right) or a central arrow (pointing to left or right) indicating the location where the target stimulus will subsequently appear. The main results show shorter response times (RT) when the cue predicts the correct location (valid) rather than when the cue is unpredictive (neutral) or predicts the wrong location (invalid) of the target [START_REF] Chica | The Spatial Orienting paradigm: how to design and interpret spatial attention experiments[END_REF], even under covert attention (without eye movement [START_REF] Michael | Orienting of attention[END_REF]). These results are attributed to a benefit of covert orienting to the location predicted by the cue. In this classic version, the cues used to direct attention are visual. But it is also possible to use auditory cues such as voice instructions or spatialized sounds [START_REF] Frassinetti | Enhancement of visual perception by crossmodal visuo-auditory interaction[END_REF][START_REF] Mcdonald | Involuntary orienting to sound improves visual perception[END_REF][START_REF] Van Der Burg | Pip and pop: nonspatial auditory signals improve spatial visual search[END_REF].

There are two modes of orienting: the first mode refers to the automatic capture of attention that is an involuntary reaction towards the cue, such as a flash or a sound coming from some region of space (exogenous mode of orienting); the second mode refers to orienting that requires information decoding and the deployment of a voluntary orienting strategy to the region predicted by the cues. Endogenous orienting implies top-down processing, which is used, for instance, when you read a caution "wet floor" sign and then pay attention to the wet surface. Endogenous cueing requires cues to be predictive (i.e. more valid than invalid cues) of a target location to create expectations. When the percentage of valid cues is high (e.g. 80%) participants can elaborate endogenous guidance strategies. In this study, we focus on endogenous orienting.

In everyday life, people tend to focus their attention on a relatively small area of the frontal visual field (in front of the person). As a result, some studies on attentional orienting have largely focused on frontal space orienting in hearing and vision [START_REF] Driver | Spatial synergies between auditory and visual attention. Attention and performance XV: Conscious and nonconcious information processing[END_REF][START_REF] John | Spatial relevance determines facilitatory and inhibitory effects of auditory covert spatial orienting[END_REF][START_REF] Todd | Shifting and focusing auditory spatial attention[END_REF][START_REF] Michael | Orienting of attention[END_REF][START_REF] Spence | Audiovisual links in exogenous covert spatial orienting[END_REF]]. Much less common are studies on rear space orienting (outside the visual field and usually in the rear space). Nevertheless, it seems that the perception and reaction to stimuli in the rear space are different from what is observed in the frontal space. For example, the study of [START_REF] Ho | Assessing the effectiveness of various auditory cues in capturing a driver's visual attention[END_REF] suggests that when a stimulus is identified a s c oming f rom the rear space, it is unrelated to the same attention mechanisms as when it's identified as coming from the front space, even if, the information is presented in front of the subject (mirror reflection). Soret and colleagues [2019] have shown that by using a modified version of the Posner cueing task, it is possible to assess the effectiveness of different perceptual stimuli (cues) to orient attention in an immersive and ecological environment through the use of a head-mounted display and eye-tracking system. In line with this research, in our study, we aimed to understand if manipulating endogenous orienting through auditory and visual cues could improve the processing of visual information in an immersive environment where information to be processed can occur at 360 • . To do so, we have designed two distinct virtual reality experiments to assess the reliability of two modified versions of the Posner cueing task for the study of 360-degree attentional orienting. We assume that all cues would improve RTs when providing reliable information about target location (valid cues) but with different effects depending on the modality used and the localization of incoming information (in front or behind the person). According to the existing literature, RTs should be shorter when information occurs in front of the participant rather than behind [START_REF] Ho | Assessing the effectiveness of various auditory cues in capturing a driver's visual attention[END_REF][START_REF] Spence | Responding to sounds from unseen locations: Crossmodal attentional orienting in response to sounds presented from the rear[END_REF]] and auditory cues should result in shorter RTs than visual cues [START_REF] Fernandez-Duque | Relating the mechanisms of orienting and alerting[END_REF]. We also expect that following the classic results of the Posner cueing task. Valid cues should produce shorter reaction times than neutral cues, which in turn should be shorter than invalid cues. Additionally, the magnitude of these differences may vary depending on target location (front/rear) and modality (visual/auditory). We assume that visual cue will be the most effective in frontal space [START_REF] Chica | Comparing intramodal and crossmodal cuing in the endogenous orienting of spatial attention[END_REF][START_REF] Schmitt | Interactions between exogenous auditory and visual spatial attention[END_REF], followed closely by auditory cues [START_REF] Driver | Cross-modal links in spatial attention[END_REF][START_REF] Tilak | Crossmodal links in attention in the driving environment: The roles of cueing modality, signal timing, and workload[END_REF]]. Auditory may be more effective than the intra-modal visual condition for the rear space [START_REF] Ho | Assessing the effectiveness of various auditory cues in capturing a driver's visual attention[END_REF][START_REF] Lee | Audiovisual crossmodal cuing effects in front and rear space[END_REF]].

FIRST EXPERIMENT 2.1 Materials and Methods

2.1.1 Participants. Thirty-three subjects (7 women, mean age±SD: 21±3), students of the French Aerospace Engineering School (ISAE-SUPAERO, Toulouse), volunteered to participate in the study. In accordance with the Declaration of Helsinki, all participants gave their written consent before the experiment. They did not receive any contributions for their participation.

2.1.2 Apparatus. We used an HTC Vive virtual reality headset with an integrated Tobii eye-tracking system. The eye-tracking system has a gaze data output frequency (binocular) of 120 Hz with an estimated accuracy of 0.5 • . The trackable field of view of the eye tracker is 110 • (full HTC Vive field of view). The experimental material consisted of a modified version of Posner cueing task developed using the Unity3D game engine supporting C# programming and the plugins needed for virtual reality (OpenVR and SteamVR) as well as the plugin needed for Tobii eye-tracking, Tobii Pro SDK.

Stimuli.

The participant was placed in a virtual spatial environment and was surrounded with asteroids. Two types of cues were used: voice instructions corresponding to endogenous auditive cues and written instructions for endogenous visual cues. Instructions were three French words indicating the position: (Front/Back) × (Top/Bottom) × (Left/Right). Information provided by visual and auditory cues were identical. All instructions lasted 1.7s. The written instructions were displayed in a white font in front of the International Space Station (ISS). The target was a space shuttle of various size, from 11 • × 9 • to 27 • × 14 • . The target consistently occurred outside the visual field and induced head movement of equivalent amplitude. The target could appear at 8 different positions according to the instructions. If we consider as a starting point a neutral head position (looking straight ahead), the frontal space extends from 90°to the left and 90°to the right. The field of view being 110°( 55°on each side), the frontal targets were presented at about 70°t o the right or left. Rear targets were presented beyond the front line at an angle of approximately 107°to the left or to the right. The top and bottom positions were perceived at a vertical angle of 30°u p or down from a neutral head position (maximum vertical angle 100°, 50°up, 50°down). The central fixing point was the radial progression bar of diameter 18 • . For valid trials, voice instruction was consistent with the effective position of the target. For invalid trials, only the vertical position of the target (up/down) was manipulated to avoid an active target search after cue occurrence.

2.1.4 Procedure. After completing a consent form, the participants filled out a preliminary questionnaire and read the information sheet. The main instructions were to destroy the target as quickly as possible using the provided cue. They were aware that 25% of the trials would be invalid and 75% valid and that only the up/down location could be wrong. Finally, they were informed to focus on the fixing point (on the HUD) until debris (the target) was visible. If participants moved their eyes too early, the trial was canceled and a new one was generated. During the training phase, each cue and each target's location were displayed at least once. Three ipsilateral invalid cues were displayed to clarify this specific situation.

Once the training completed, the experimental phase started. At the beginning of each trial, participants had to fix the fixing point until the progress bar was full (1.5s required to complete it). Then, a cue appeared as a function of the experimental conditions for 1.7s. After an interstimulus interval (ISI) of 500 ms, the target appeared at one of the possible locations depending on the experimental conditions. Participants then had to destroy the target by looking at it and pressing the "trigger button" on the HTC-Vive controller. After target destruction, an interval of 1.5 seconds was respected before the next trial. This experimental phase was composed of 50 trials including 25 trials with auditory cues and 25 trials with visual cues. For each cue type, we had 20 valid trials and 5 invalid trials (75 percent of validity). In the 50 total trials, 25 trials oriented attention to the front and 25 behind. The experiment lasted 15 minutes.

2.1.5 Data Analysis. All eye-response times (Experiments 1 and 2) were obtained by using a raycast (an invisible beam going in a straight line) which follows the position and direction of the participants' eyes (determined by eye-tracking system integrated into the helmet) and which activates the recording of the RT when it collides with a predefined object in the scene (here the target). We recorded two different RTs: motor response and ocular response. The motor RT, called Shuttle-Destruction, was the time between the end of cues' occurrence and the shuttle destruction by pressing a key on the controller. The eye-response time, called Gaze-On-Shuttle, was the time interval between the end of cue occurrence and the moment when the participant looks at the target.

We performed a statistical analysis to understand the effect of cues on subjects' RTs according to the spatial location of the target, validity of the cue and cue's modality. We excluded RTs greater than 1.4s (threshold often used in selective attention studies, e.g. [START_REF] Schoeberl | Stimulusdriven attentional capture by subliminal onset cues[END_REF][START_REF] Ter Huurne | Methylphenidate alters selective attention by amplifying salience[END_REF]) or shorter than 0.05s (1% excluded). The analysis includes 49.8±2.8 trials per participant for Shuttle-Destruction RTs and 49.9±1.9 trials per participant for Gaze-On-Shuttle RTs. We used the JASP software to perform a 3-way repeated-measure analysis of variance (ANOVA) on the average RT's for each dependent variable (Shuttle-Destruction and Gaze-On-Shuttle) in order to observe the effect of our manipulated factors: Validity (valid/invalid), Modality (visual/auditory) and Location (Front/Rear). Fisher LSD test was used for post-hoc comparisons.

Results

The exact same result pattern was observed for Gaze-On-Shuttle and Shuttle-Destruction. For clarity, we present only Gaze-On-Shuttle's RTs, . As expected, the analysis revealed a significant main effect of the cue validity, F (1, 32) = 185.7, p < .001, η 2 = 0.85. Subjects' RTs were significantly shorter when the cue was valid than when the cue was invalid (MD = 387ms). In addition, there was a main effect of cue modality, F (1, 32) = 11.2, p = .002, η 2 = 0.26. As already observed, the subjects' RTs were significantly shorter when the cue was auditory than visual (MD=113ms). Finally, the analysis showed a significant main effect of cue location, F (1, 32) = 7.4, p = .01, η 2 = 0.19. Like the results obtained by Spencer et al. [2017], participants obtained significantly shorter RTs when the target appeared in front rather than behind (MD=78ms).

However, there was a significant interaction between our three manipulated factors: validity, modality and location, F (1, 32) = 11.35, p = .002. Analysis of simple effect of validity according to location and modality showed an effect of cue validity in all modalities of all other factors manipulated (all p < .0001) but the cueing effect (difference in RT between invalid and valid trials) varies depending on cue modality and target location. When the target appeared in front, the cueing effect seemed to be identical for the visual modality and auditory modality. On the other hand, when the target occurred in the rear, the cueing effect was more important for the visual modality than for the auditory modality. Besides, analysis of the simple effect of modality according to validity and location showed that RT was significantly shorter for auditory cues than visual for invalid rear trials only, see Figure 2.

Discussion and Conclusion

As expected, we observed a main effect of cue validity as in Posner's classic studies. Participants had shorter RTs when the cue indicated a correct target location compared to incorrect. However, although this effect was present independently of other conditions, its magnitude (cueing effect) varied according to the target location and cue modality as revealed by the triple interaction between cue validity, target location, and cue modality. Indeed, when the target to be destroyed appeared in front of the participants, the validity effect was similar for the visual cue compared to the auditory cue. On the other hand, when the target occurred in the rear space, the validity effect seemed to be greater for visual cue compared to auditory cue. We can assume that the presentation of visual cues in front of the subjects automatically captures attention to the front. Thus, they entail an additional disengagement cost when attention must be directed behind compared with auditory cues that do not come from a specific spatial location, which might explain the greater validity effect for visual cues. To support this assumption, neutral cues would have been required. Consequently, experiment 2 will incorporate neutral trials. Finally, note that the relatively large size of the observed validity effects (>150ms) leads us to believe that artifacts were probably due to the subject's head movement. In the second experiment, a new version of Posner's task limiting head movements effects was developed to minimize their impact.

SECOND EXPERIMENT 3.1 Materials and Methods

3.1.1 Participants and Apparatus. Twenty-eight participants (9 women, mean age±SD: 28±9) volunteered to participated in this study. The recruitment procedure was identical to Experiment 1. The same apparatus as in Experiment 1 was used.

3.1.2 Stimuli. The virtual environment was similar to Experiment 1 but this time participants were inside a fighter-like "spacecraft". "Aiming assistance tools" have been added at the front of the spacecraft to control target occurrence in specific areas of the visual field. These areas were identical in size and shape. This "aiming assistance tool" consisted of a head-up display (HUD) of 4 • × 5 • size and which served as a fixing point. At the four corners of the HUD, there were two horizontally aligned transparent 6 • × 13 • size viewfinders and two horizontally aligned 6.5 • ×13.5 • size rear-view mirrors. The position of the rear-view mirrors and viewfinders was counterbalancing between the subjects.

Two types of cues were used: voice instructions corresponding to endogenous audio cues and directional arrows as endogenous visual cues. The voice instructions were the French translation of "Front left", "Back left", "Front right" and "Back right" or "Left Front", "Left Back", "Right Front", and "Left Front". Word order was reversed according to the subjects. The arrows were 0.02 • × 0.06 • size. The voice instructions had a duration of 500-600ms and the directional displayed during 300ms. The targets were "space debris" of varying size and randomly assigned according to the subjects. As in the first experiment, if we consider a head neutral position (looking straight ahead) as the starting point, the frontal space is 90°to the left and 90°to the right; the rear space extends from 90°to 180°t o the left and right. The frontal targets were presented at 45°to the left or right and the rear targets at 135°to the left or to the right. For invalid trials, the target could not appear at a directly opposite diagonal position. For neutral auditory cue, instruction was the french translation of "nearby debris". For neutral visual cue all arrows were displayed simultaneously with a size of 1.2 • × 1.7 • 3.1.3 Procedure. Similar to Experiment 1 but all actions were performed by gaze only. The same instructions as in Experiment 1 were provided except for interaction. Participants were invited to lock targets by looking at the viewfinder or rear-view mirror corresponding to the target's position. They had to focus until the target was destroyed for frontal targets (viewfinder) or to turn around and look at the target to destroy it for rear targets (rear-view mirror). Before the experimental phase, 16 trials were given to the participant as training (8 trials per cue type). After a 300ms ISI, the target appeared in one of the 4 possible locations. Once the target appeared, participants had to destroy it following instructions described above After the destruction of the target, a variable time interval of 1 to 2s was respected. The experimental phase consisted of 120 trials: 60 trials per cue type (visual/auditory), divided into 20 neutral trials, 30 valid trials (15 front and 15 behind) and 10 invalid trials (5 with target occurrence to the front and 15 to the rear). The total duration of the experiment was 35 to 45 minutes, including a minimum of 25 minutes of experimental sessions.

3.1.4 Data Analysis. We recorded two different eye-response times, both starting from the target appearance. Gaze-Initiation corresponds to the moment when the participant initiate eye movement, Target-Lockdown -when the participant gazed at the viewfinder / rear-view mirror corresponding to the target position (eye response). The same RT filter as in Experiment 1 was applied (1% excluded) The average RT of 2 participants for Target-Lockdown (M=1.975s and M=1.413s) was above the upper limit (Q3 + interquartile range × 1.5) compared to the sample. The data obtained on these participants were considered as outliers and removed from the analysis (see [START_REF] Tukey | Exploratory data analysis[END_REF]). The analysis includes 118.8±2.7 trials per participant for Gaze-Initiation RTs and 116.6±7.8 trials per participant for Target-Lockdown RTs. We used the JASP software to perform a three-way repeated-measure analysis of variance (ANOVA) on the average RTs for each dependent variable (Gaze-Initiation and Target-Lockdown) to observe the effect of our manipulated factors: Validity (valid/neutral/invalid), Modality (visual/auditory) and Location (Front/Rear). Fisher LSD test was used for post-hoc comparisons.

Results

For Gaze-Initiation, as for the first experiment, the analysis revealed a significant main effect of validity, F (2, 56) = 26.0, p < .001, η 2 = 0.48. A post-hoc test showed that subjects' gaze initiation were significantly shorter when the cue was valid compared to neutral (p < .001, MD = 69ms) or invalid cue (p = .001, MD = 52ms). Surprisingly, there was no significant difference between invalid and neutral conditions (p = .318). Besides, there was a main effect of modality with shorter RT for auditory cue than visual cue, F (1, 28) = 9.25, p = .005, η 2 = 0.25. Finally, the analysis revealed a significant main effect of cue location, F (1, 28) = 5.63, p = .025, η 2 = 0.17. Participants obtain significantly shorter RTs when the target appeared in front rather than behind them (MD = 29ms). However, we noted a significant interaction between cue validity and cue modality, F (2, 56) = 10.6, p < .001, η 2 = 0.28. Simple modality effect analysis according to validity indicated a significant difference between auditory and visual cues only for invalid trials (p < .001, MD = 169ms). Also, a post hoc test of validity according to modality indicated that when the cue modality is visual, the gaze initiation was shorter when the cue was valid than when the cue was neutral (p < .001, MD=62ms) or invalid (p < .001, MD=79ms) but there was no difference between neutral and invalid trials (p = .652). When the cue was auditory, gaze initiation remained shorter for valid cue rather than neutral (p < .001, MD=77ms) or invalid (p = .048, MD=25ms) but there was a very surprising difference between neutral and invalid trials with shorter RT for invalid cues compared with neutral cues (p = .003, MD=52ms), see Figure 4.

For Target-Lockdown, the analysis revealed a similar result pattern. The principal effect of validity was significant, F (2, 52) = 38.8, p < .001, η 2 = 0.60, with significantly shorter eye response for valid compared with neutral (p < .001, MD=80ms) and invalid condition (p < .001, MD=73ms). There was no significant difference between the invalid and neutral conditions. The modality effect also still significant, F (1, 26) = 6.1, p = .021, η 2 = 0.19, but the location effect was no longer significant F (1, 26) = 0.86, p = .364, η 2 = 0.03. However, the analysis revealed a significant interaction between cue validity and location. An analysis of the simple validity effects as a function of the location showed that RTs were shorter in the rear space than in the front space only for valid cue (p = .05, MD=17ms). No difference was found for neutral (p = .38) nor invalid cue (p = .218), see Fig. 6. Finally, the interaction between modality and validity was significant, F (2, 52) = 7.2, p = .002, η 2 = 0.22. Simple modality effects analysis according to validity indicated a significant difference between auditory and visual cues only for invalid trials (p < .001, MD=168ms). Also, post-hoc test of validity according to modality indicated that for the visual modality, there was a statistical difference between all validity condition with shorter RT for valid cues than neutral cues (p < .001, MD=68ms), for valid cues than invalid (p < .001, MD=98ms) and for neutral than invalid (p = .026, MD=30ms). For the auditory modality, participants' eye responses were shorter for valid cues than neutral (p < .001, MD=92ms) and for valid cues than invalid (p = .018, MD=47ms). we also found that RTs for invalid cues tended to be shorter than for neutral cues (p = .054, MD=45ms), see Figure 6.

Discussion and Conclusion

As expected, we observed a main effect of cue validity as in Experiment 1. While participants react more quickly for a valid cue, there is no significant difference between neutral and invalid trials. This is true for Gaze-Initiation and for Target-Lockdown. A significant interaction between modality and cue validity sheds further light on these unexpected results. It would seem that all the cues can be used to improve information processing regardless of its position in space but their costs seem to vary depending on modality and metrics. For visual cues, no significant cost was observed for gaze initiation. But, for target lockdown, our results replicate those usually encountered in the literature. Indeed, we observe shorter RTs when the cue indicates the correct position of the target compared to neutral cue (which does not provide information about target location). In turn, neutral cues triggered shorter RTs than invalid cues. For auditory cue, the results suggest a benefit when the cue indicates a wrong location of target occurrence rather than when the cue provides no information. This benefit seems to be significant for gaze initiation and marginal for target lockdown. This result is unexpected and determining its precise origins is rather difficult. Further study is needed to replicate and understand this effect. Finally, the main effect of cue location is significant for gaze initiation and shows shorter RTs for targets appearing in front rather than in the rear. This result suggests that participants processed information more efficiently when it appeared in front (viewfinder) rather than behind (rear-view mirrors mirror) even if all the information to be processed was provided in front. As in the experience of [START_REF] Ho | Assessing the effectiveness of various auditory cues in capturing a driver's visual attention[END_REF] and hypotheses suggested by [START_REF] Spence | Responding to sounds from unseen locations: Crossmodal attentional orienting in response to sounds presented from the rear[END_REF], it would seem that information that exists outside the visual field but is seen by reflection requires different cognitive processes than those involved in information processing that is actually present in front of us. Note that this effect is no longer significant for target lockdown. But a significant interaction between cue validity and location shows that RTs are shorter when the target appears in the rear rather than in the front when the cue provides correct information about target location, which could explain the absence of the main effect of location for this metric.

GENERAL DISCUSSION

The Posner cueing task is a classic experimental paradigm in cognitive science for measuring attentionnal orienting. Recently, it has been suggested that this paradigm can be adapted in virtual reality (i.e. in an immersive and ecological environment) to evaluate the effectiveness of perceptual stimuli in directing attention [START_REF] Soret | Attentional orienting in virtual reality using endogenous and exogenous cues in auditory and visual modalities[END_REF]. The objective of this study was to understand whether these mechanisms could improve the processing of visual information when this information appear outside the direct visual field and especially in the rear space. To do this, we assessed visual information processing performance (reaction time and oculometric response) in two spatial tasks when endogenous cues are provided to participants to trigger a voluntary attentional orienting in a specific region of the visual space (FOR of 360 • ). The two experiments replicated the classical results of the literature on Posner task which shows that it can be adapted for the investigation of the effects of 360-degree attentional orienting. Besides, our study suggested that a modification of this paradigm could allow us to study the orienting mechanisms involved in the treatment of rear space. In particular through the use of reflecting devices, such as a mirror, to simulate rear space which does not seem to involve the same mechanisms as those involved in the treatment of frontal space. Indeed, in experiment 2, even if all the information to be processed (the targets) was provided in front of the participants, as in a classic Posner task, it could be perceived through a window (viewfinder) or by reflection (rear-view mirrors mirror). This difference is not perceptual (the information being perceptually identical in both cases) but cognitive, i.e., the subjects were aware that the information seen in the mirror existed in the immersive environment behind them while the information seen through the window only existed in front of them, leading to statistically different response times between these two conditions. This result suggests that the real spatial location of the information to be processed (inside or outside the visual field of the subject) will influence the processing even if it is perceived in the frontal visual field. However, further studies are needed to understand and measure the difference between orienting in front and orienting in rear space but this exploratory study lays the foundation for a new method of studying the processing of spatial information outside the visual field.

Figure 1 :

 1 Figure 1: The time course of a trial (from top to bottom) of the first experiment.

Figure 2 :

 2 Figure 2: Mean RTs for Gaze-On-Shuttle according to validity, modality and location. * = p < .05, * * = p < .01, * * * = p < .001

Figure 3 :

 3 Figure 3: The time course of a trial (from top to bottom) of the second experiment.

Figure 4 :

 4 Figure 4: Mean RTs for Gaze-Initiation according to validity, and modality. * = p < .05, * * = p < .01, * * * = p < .001

Figure 5 :

 5 Figure 5: Mean RTs for Target-Lockdown according to validity and Location. * = p < .05, * * = p < .01, * * * = p < .001

Figure 6 :

 6 Figure 6: Mean RTs for Target-Lockdown according to validity and modality. . = p < .10, * = p < .05, * * = p < .01, * * * = p < .001

ACKNOWLEDGMENTS

This work was supported by the ANR ASTRID program (Grant no. ANR-18-ASTR-0026 -ELOCANS project).