1. The names of the grasps are extracted from the taxonomies.

PalmEx: Adding Palmar Force-Feedback for 3D

Manipulation with Haptic Exoskeleton Gloves Elodie Bouzbib, Marc Teyssier, Thomas Howard, Claudio Pacchierotti and Anatole Lécuyer Abstract-Haptic exoskeleton gloves are a widespread solution for providing force-feedback in Virtual Reality (VR), especially for 3D object manipulations. However, they are still lacking an important feature regarding in-hand haptic sensations: the palmar contact. In this paper, we present PalmEx, a novel approach which incorporates palmar force-feedback into exoskeleton gloves to improve the overall grasping sensations and manual haptic interactions in VR. PalmEx's concept is demonstrated through a self-contained hardware system augmenting a hand exoskeleton with an encountered palmar contact interface -physically encountering the users' palm. We build upon current taxonomies to elicit PalmEx's capabilities for both the exploration and manipulation of virtual objects. We first conduct a technical evaluation optimising the delay between the virtual interactions and their physical counterparts. We then empirically evaluate PalmEx's proposed design space in a user study (n=12) to assess the potential of a palmar contact for augmenting an exoskeleton. Results show that PalmEx offers the best rendering capabilities to perform believable grasps in VR. PalmEx highlights the importance of the palmar stimulation, and provides a low-cost solution to augment existing high-end consumer hand exoskeletons.

Index Terms-Haptics, Virtual Reality, Artefact, Exoskeleton, ETHD, Encountered-type of Haptic Device, On-demand ✦

INTRODUCTION

T HE promise of future interactive virtual environments has raised various questions around the current VR interfaces interaction capabilities. More specifically, while haptics, the sense of touch, is a major source of immersion in VR, stimulating it harmonically in virtual environments is still challenging. Indeed, interactions in VR were enabled for a long time through virtual metaphors and physical controllers, letting bare-hands interactions with believable haptic feedback out of the scope. Bare-hands interactions however enable easy-to-access virtual environments even for novice users, with interactions seamlessly resembling to the real world ones, such as direct manipulation [START_REF] Bryson | Direct Manipulation in Virtual Reality[END_REF]. In these regards, many implementations currently aim to let the user's hands unencumbered of handheld technologies while providing haptic feedback [START_REF] Bouzbib | Let's Meet and Work it Out": Understanding and Mitigating Encountered-Type of Haptic Devices Failure Modes in VR[END_REF].

In this paper, we focus on augmenting the exoskeleton class of haptic devices. Apart from controllers, they are currently a widespread solution for providing forcefeedback in VR, mainly for industry or training applications. Their feedback heavily focuses on the users' fingers, through either rigid parts [START_REF] Bouzit | The Rutgers Master II-ND force feedback glove[END_REF] or strings [START_REF] Fang | Wireality: Enabling Complex Tangible Geometries in Virtual Reality with Worn Multi-String Haptics[END_REF]. However, current haptic exoskeleton gloves are still lacking a crucial manual sensory cue: the palmar contact feedback.

According to grasp and hand gestures taxonomies [START_REF] Feix | The GRASP Taxonomy of Human Grasp Types[END_REF], [START_REF] Cutkosky | On grasp choice, grasp models, and the design of hands for manufacturing tasks[END_REF], many interactions involve the palm: these are mainly

• This project has received funding from Inria (Associated Teams programme). • E. Bouzbib and A. Lécuyer are with Inria, CNRS, Univ Rennes, IRISA -Rennes, France. E-mail: {name.surname}@inria.fr • M. Teyssier is with Léonard de Vinci Pôle Universitaire, Research Center -Paris La Défense, France. E-mail: {name.surname}@devinci.fr • C. Pacchierotti and T.Howard are with CNRS, Univ Rennes, Inria, IRISA -Rennes, France. E-mail: {name.surname}@irisa.fr.

referred to as power grasps, and are used for the exploration of planar surfaces or the manipulation of potentially large objects in 3D object manipulations [START_REF] Cutkosky | On grasp choice, grasp models, and the design of hands for manufacturing tasks[END_REF]. This paper introduces a novel haptic approach for alleviating one of current exoskeletons limitations: the lack of palmar stimulation. We show how to augment a widespread and commercially available exoskeleton (SenseGlove) with a custom-made and 3D printed on-demand handheld for palmar contact. We draw inspiration from Haptic Pivot [START_REF] Kovacs | Haptic PIVOT: On-Demand Handhelds in VR[END_REF] to improve the haptic rendering of exoskeletons through palmar stimulation.

We present PalmEx's interaction design space, and provide a technical evaluation defining its interaction latency. A novel software implementation is shown to reduce the delay between the virtual-physical interactions below 0.5s. We empirically evaluate PalmEx's proposed design space in a user experience (n= 12) and demonstrate the benefits of a palmar contact augmenting an exoskeleton. Results show that PalmEx, instantiating palmar stimulation for exoskeletons, provides a significantly increased visuo-haptic consistency for various 3D object manipulations.

RELATED WORK

Exoskeletons Gloves for Haptic Rendering

Exoskeletons show two main benefits for VR applications: (a) they enable a thorough tracking of the users' gestures while (b) letting the users' hands free for direct manipulation, "the ability for a user to control objects in a virtual environment in a direct and natural way, much as objects are manipulated in the real world" [START_REF] Bryson | Direct Manipulation in Virtual Reality[END_REF]. Their designs enable various types of grasp types. From the classic grasp taxonomies (see Section 3), they can either enable pad opposition [START_REF] Choi | Wolverine: A wearable haptic interface for grasping in virtual reality[END_REF], palm opposition [START_REF] Bouzit | The Rutgers Master II-ND force feedback glove[END_REF] or all of them [START_REF] Blake | Haptic Glove With MR Brakes for Virtual Reality[END_REF] with force feedback from 7 to over a 100 N. While these are efficient to render feedback over the fingers, none of these implementations focus on stimulating the palm. This is however a crucial area to render 3D object manipulation and their associated shape and kinesthetic force-feedback.

Palmar Stimulation in VR

Handheld devices stimulating the users' palms in VR have therefore been designed in these regards. Palmar contact enables size, shape and stiffness renderings, which can be simulated by 1.5D tangible interfaces [START_REF] Yoshida | PoCoPo: Handheld Pin-based Shape Display for Haptic Rendering in Virtual Reality[END_REF], inflatable proxies [START_REF] Teng | PuPoP: Pop-up Prop on Palm for Virtual Reality[END_REF], shape-changing handhelds [START_REF] Gonzalez | X-Rings: A Hand-mounted 360°Shape Display for Grasping in Virtual Reality[END_REF] or wearables [START_REF] Son | Tactile Sensitivity to Distributed Patterns in a Palm[END_REF], [START_REF] Minamizawa | A Palm-Worn Haptic Display for Bimanual Operations in Virtual Environments[END_REF], [START_REF] Dragusanu | Design of a Wearable Haptic Device for Hand Palm Cutaneous Feedback[END_REF]. Their main limitation is that they are required to be held continuously within the hand. A promising perspective is to let the users' hands free and to integrate palmar contact through encountered-type devices [START_REF] Bouzbib | Let's Meet and Work it Out": Understanding and Mitigating Encountered-Type of Haptic Devices Failure Modes in VR[END_REF], on-demand [START_REF] Kovacs | Haptic PIVOT: On-Demand Handhelds in VR[END_REF] or wearable interfaces [START_REF] Trinitatova | Touchvr: A wearable haptic interface for vr aimed at delivering multi-modal stimuli at the user's palm[END_REF].

Augmenting and Coupling Exoskeletons

Our approach aims to augment current exoskeletons with a tangible encountered-type interface. This augmentation of exoskeletons or wearables has already been investigated. Exoskeletons [START_REF] Nith | DextrEMS: Increasing Dexterity in Electrical Muscle Stimulation by Combining it with Brakes[END_REF] and wearables [START_REF] Lopes | Impacto: Simulating Physical Impact by Combining Tactile Stimulation with Electrical Muscle Stimulation[END_REF] were augmented using Electro-Muscle Stimulation, which was shown to improve dexterity or impact perception. Similarly, wearables and exoskeletons were coupled with grounded [START_REF] Steed | Docking Haptics: Extending the Reach of Haptics by Dynamic Combinations of Grounded and Worn Devices[END_REF] , cutaneous interfaces [START_REF] Son | Haptic Feedback to the Palm and Fingers for Improved Tactile Perception of Large Objects[END_REF], or pseudo-haptics techniques [START_REF] Achibet | Elastic-Arm: Human-scale passive haptic feedback for augmenting interaction and perception in virtual environments[END_REF].

In this paper, our focus is simple yet innovative: we focus on augmenting exoskeletons with a palmar contact, providing physical and kinesthetic shape and force-feedback.

PALMEX: DESIGN SPACE

PalmEx's principle is to augment an exoskeleton with a physical tangible contact actuator in the palm area. We build upon grasp taxonomies to explicit PalmEx's design space.

Grasps

Grasping is task-and object-dependent (e.g. size, shape). Taxonomies differentiate two main types of grasps: precision and power grasps. In a precision grasp, the hand is "able to perform intrinsic movements" [START_REF] Feix | The GRASP Taxonomy of Human Grasp Types[END_REF]. This is mostly performed through the fingertips, and with small scaled objects -compared to the average hand size. Exoskeletons are ideal for these types of grasps as they constrain the fingers. A power grasp is defined as a "rigid relationship between the object and the hand" [START_REF] Feix | The GRASP Taxonomy of Human Grasp Types[END_REF], [START_REF] Cutkosky | On grasp choice, grasp models, and the design of hands for manufacturing tasks[END_REF]. This type of grasp is usually performed with the palm, and potentially large Non-Prehensile Grasp Hook Fig. 2. Types of Grasps extracted from [START_REF] Feix | The GRASP Taxonomy of Human Grasp Types[END_REF] and [START_REF] Cutkosky | On grasp choice, grasp models, and the design of hands for manufacturing tasks[END_REF] and enabled with forcefeedback with PalmEx's capabilities. PalmEx allows the manipulation of small, medium and large disks, cylinders and spheres. It also allows for non-prehensile grasps (touch of surfaces, hook).

objects. As an example, previous palmar devices Haptic

Pivot focused on the simulation of a contacting sphere, with two associated grasps (sphere 3/4 fingers). In comparison, PalmEx aims to widen this grasp design space to other objects and sizes from the taxonomies. Finally, there are also "non-prehensile" grasps defined by Cutkosky [START_REF] Cutkosky | Human Grasp Choice and Robotic Grasp Analysis[END_REF], where the hand acts as a unit, to perform "hook postures" and the touch of a surface. Our approach consists in enabling all types of grasps proposed in the literature. We show in Figure 2 some examples of grasps allowed by PalmEx 1 : we propose the manipulation and exploration of (potentially large) disks, spheres, cylinders or surfaces. The palmar interface enhances interaction in the palm, while the exoskeleton constrains the fingers.

Interactions

We differentiate PalmEx's interaction design space in two categories: static and dynamic. They relate to the way PalmEx interacts with the user. In a static interaction, PalmEx is fully engaged within the user's palm -its displacement is over (Figure 1 -c); while a dynamic interaction is enabled through PalmEx's actuation and dynamism (e.g. it bounces between Figure 1 -b andc).

Static

PalmEx encounters the user's palm when the virtual interaction occurs to provide force-feedback (see Figure 1). PalmEx enables to explore objects by touching them using the whole-hands. This exploration can be performed through non-prehensile grasps. On the contrary, prehensile grasps are enabled through grasp interactions -represented through complete taxonomies (see previous subsections). Users can also pull, push (literal displacement or push buttons) or raise objects while perceiving feedback within their palms and fingers.

Dynamic

PalmEx distance to the user's palm matches the distance between their virtual palms and the objects during the interaction. It provides different levels of force-feedbackit can touch the user's palm or push through it with a continuous force interaction contact. These variations allow for the perceptions of impacts, for instance with catching or throwing interactions (see Figure 3 -d). PalmEx also enables the users to dribble at different speeds and with balls of various weights -as the objects can be encountering the users' palm with adaptive forces at contact. PalmEx also enables the users to edit objects and mold them as play doh (see Figure 3 -c), they can modify their overall structure [START_REF] Bouzbib | Survey of Virtual Reality Interactions via Haptic Solutions[END_REF]. Similarly, users can palpate objects: they can deform them (see Figure 3 -a), and feel a palpation when the virtual objects bounce back.

APPLICATIONS

Using PalmEx's interaction design space, we envision three kinds of use-cases for PalmEx.

Medical Training

PalmEx could be used for medical training (Figure 3a). Using the dynamism of the encountered-type interface, PalmEx can be used for CardioPulmonary Resuscitation (CPR) training or medical palpation.

Social Touch

Using PalmEx, users can potentially receive or give social touch in VR. Users could either provide the interaction, by touching their friends shoulders or shaking their hands (Figure 3 -b); or receive the interaction, giving high-fives to virtual avatar friends. Some artificial skin [START_REF] Teyssier | Skin-On Interfaces: A Bio-Driven Approach for Artificial Skin Design to Cover Interactive Devices[END_REF] can be added to PalmEx's 3D printed ellipsoid, to provide users with even more believable social touch interaction.

Leisure & Sports

PalmEx can be used in a pottery scenario (Figure 3 -c)in which users could feel the deformation of the mud, and modify it using their whole hands. This use-case involves an edition task [START_REF] Bouzbib | Survey of Virtual Reality Interactions via Haptic Solutions[END_REF] -which is still underexplored in virtual environments. We finally envision sport games or training using PalmEx. PalmEx could be used for hand-ball games: users could dribble prior to throwing a ball or practice gooalkeeping. PalmEx can also be used for racket games -using force within the palm, users could therefore perceive impacts when throwing balls; or for music (saxophone, trumpet), where the instrument is held within the palm and the fingers press the pistons.

IMPLEMENTATION

PalmEx is composed of (1) an exoskeleton, constraining the fingers and (2) an encountered-type interface on the palm.

Exoskeleton

We rely on the commercially available SenseGlove DK1 exoskeleton 2 . This exoskeleton glove relies on strings attached to the fingertips and is actuated with motors and friction brakes. Coin vibration motors are also integrated in the glove's rigid mechanical parts.

2. www.senseglove.com

Hand Attachment

We adjusted the SenseGlove DK1 attachment technique and replaced the default velcro straps by a compression glove (Large size). This attachment keeps the exoskeleton over the user's hand and keeps the palm free of any contraption and available for interaction.

Tracking

We use the SenseGlove SDK to capture the user's finger phalanges positions and articulations; and we track the entire apparatus spatial location with a Vive Tracker Pro.

Similarly to other exoskeletons (e.g. [START_REF] Fang | Wireality: Enabling Complex Tangible Geometries in Virtual Reality with Worn Multi-String Haptics[END_REF]), the SenseGlove SDK relies on an inverse kinematic model, which models the finger phalanges positions based on the extension of the strings. It also provides a hand pose database capturing when a grab interaction is about to occur.

Exoskeleton Software

The exoskeleton software relies on Unity3D for visualisation and physics. We added colliders to each virtual phalanx to enable collisions with virtual objects. This upgrade allows for (non-)prehensile user interactions and game-initiated interactions, where objects encounter the users' hands.

Palmar Contact

We designed a custom palmar contact hardware interface, inspired from Haptic Pivot [START_REF] Kovacs | Haptic PIVOT: On-Demand Handhelds in VR[END_REF] and Weatavix [START_REF] De Tinguy | WeATaViX: WEarable Actuated TAngibles for VIrtual reality eXperiences[END_REF].

Interface Design Choices

We considered multiple technologies, such as pneumatic interfaces [START_REF] Teng | PuPoP: Pop-up Prop on Palm for Virtual Reality[END_REF], motor-belt haptic systems [START_REF] Aggravi | Design and Evaluation of a Wearable Haptic Device for Skin Stretch, Pressure, and Vibrotactile Stimuli[END_REF] or a crank-slider mechanisms [START_REF] Son | Haptic Feedback to the Palm and Fingers for Improved Tactile Perception of Large Objects[END_REF]. While these techniques seem efficient to provide haptic feedback, we decided upon the design of an active "on-demand" technology. We aimed for an on/off impact to highlight the interaction timestamp and balance the exoskeleton's haptic transparency [START_REF] Lawrence | Quantitative experimental analysis of transparency and stability in haptic interfaces[END_REF] -this "phantom" effect which provides a continuous feedback due to the exoskeleton inner impedance and viscosity. Indeed, as the exoskeleton is constantly worn over the fingers, a resistance can be felt even though no force-feedback is provided. Reducing this effect was investigated in the design of exoskeletons -such as in Wolverine [START_REF] Choi | Wolverine: A wearable haptic interface for grasping in virtual reality[END_REF], where mass and inertia are considered as constraints and optimised in a light-scale custom exoskeleton. However, the investigation of such custom designs is out of scope of this paper -we aim to augment current high-end commercially available ones.

Mechanical Design

We decided to design a 3D printed ellipsoid for encountering the user's palm. We tested different heights and widths (from 5 -11mm height, 70 -85mm widths, 45 -60mm length). The 3D ellipsoid is required to be ergonomic within the users' palm, and for them to be able to perceive both of flat (Figure 4 -a) or curved objects, either small (Figure 4 -b) or large (Figure 4 -c). This design prevents the perception of edges, as the curvy shape of the ellipsoid always encounters the palm first. We empirically chose a 10x55x75mm 3 interface (Figure 4). This interface is 3Dprinted, using PLA material, and weighs 11g; it is finally attached to a 3D-printed lever-arm connected to the motor. Prior to using the servomotor, we first define its working range. We define two extrema in its stroke: (1) when the interface is "out of reach" of any finger gesture; (2) when the interface is fully "engaged" (touching the SenseGlove plate). Its control is detailed in the Technical Evaluation (Section 6).

Force and Contact

We define two types of contact forces when the palmar interface touches the hand. The first contact force (≈ 0.30N) is used for simple interaction contacts, when the interface lightly touches the palm. The second contact force pushes through the palm and provides a higher force. We define it as a continuous force interaction contact (≈ 1.24N). This force is applied to simulate impacts objects within the palm. Forces can be provided between these two values, and are sufficient to render interactions.

Integration with the SenseGlove

We designed a 3D-printed holster to attach the servo and its controller board. This case is mechanically connected to the main SenseGlove plate. This plate is held within the previously discussed compression glove, which is finally sewn to the SenseGlove case (Figure 1).

TECHNICAL EVALUATION

The exoskeleton and palmar interface interact differently with the user's hand: one is continuously worn and pulls the users fingers (SenseGlove); the other one encounters and pushes on their palm (Pivot). These two stimulations occur from different directions -for them not to disrupt each other, it is hence important to have a good synchronisation at contact. We conducted a Technical Evaluation to (a) calibrate the servomotor and (b) propose a method to adjust its synchronisation as a function of the next intended interaction.

Calibration

We define two methods to calibrate the servomotor speed. The first one is based on the time to contact, the second one is based on the next intended interaction speed. We used the same experimental design for both of these methods.

6.1.1 Experimental Design 6.1.1.1 Procedure: We designed a virtual environment using Unity3D. We added a virtual object of interest -OOI (a sphere) -and assigned its position to the distance between the virtual hand and the palm. This distance represents the arc drawn by the servo during its displacement. We define a cycle to run, in which the servomotor (a) goes to being fully engaged (continuous force interaction contact position), (b) stops for 0.5s, (c) disengages itself (out of reach position), (d) waits for 0.5s (see Figure 6). The glove is not worn by users in this technical experiment.

6.1.1.2 Conditions: The Pololu Maestro board enables to change the servomotor speed and acceleration with units of 0.25µs/10ms (max 255 units). We tested 10 speeds and 10 accelerations: from 25 to 250 units, with an increment of 25. We run N = 10 cycles of each condition (10 SPEEDS × 10 ACCELERATIONS × 10 CYCLES).

6.1.1.3 Measures: In our virtual environment, we measured the speed of the virtual object (cm/s) and the time for the servomotor to finish its stroke (e.g. go through the full servomotor arc (≈ 90 • ).

Results

We first analysed the results per speed and accuracy, and noticed that the widest ranges (speed-wise) were reached when the acceleration matched the speed value. We therefore chose to rely on the diagonal values of our speed × acceleration matrix in the following results.

6.1.2.1 Speed (cm/s): We convert the servo speed units in cm/s (Figure 5 -Speed Calibration). This conversion is similar to a third order polynomial (R 2 = 0.99):

v virt = f (v servo ) = p 0 v 3 servo + p 1 v 2 servo + p 2 v servo + p 3 (1)
This information is used to change the servomotor speed as a function of the users' hand speed, the object's speed, or both (see Section 6.2).

6.1.2.2 Time to Contact: The servomotor takes between 0.18s to 1.9s to be fully engaged. We fitted a third order polynomial to illustrate it, similar to Equation 6.1.2.1 (R 2 = 0.98) (Figure 5 -Time to Contact). The servo speed and acceleration can then be matched using this fitted equation, to encounter the user at t c (time to contact).

Discussion

The "time to contact" calibration can be coupled with a Jerk Model [START_REF] Bratt | Minimum jerk based prediction of user actions for a ball catching task[END_REF] based on the user's hand gestures, predicting a future contact time stamp. Yet, this can create abrupt movements of the servomotor and increase the inertia of the system; ultimately altering the haptic transparency of the system (e.g. if the hand is at rest and suddenly interacts, with a predicted time to contact in 0.2s, this would activate the servomotor at almost full speed and cause some inertia). The jerk model -predicting time for contact -is more appropriately used for controlling interfaces which displacements are not altering the user experience (e.g. independent mobile platform). Similarly, while the servomotor speed can be modified as a function of the "interaction speed" (i.e. the speed between the object of interest and the user's hand), this command is always adjusted after a change of interaction speed. This calibration would hence improve the latency between the virtual and physical artefact, but a global latency would remain. We propose to intertwine the two calibrations to reduce the latency (Figure 5): we predict the time remaining to contact prior to interaction and adjust speed to optimise the servomotor displacements and reduce the time/position differences at contact.

Method: Intertwining Calibrations

According to our design space, the interaction speed depends on what/who initiates the interaction: it can either be (a) the user's hand speed, (b) the object's speed, (c) a combination of these two. We define the "virtual minimum distance", as the distance between the object's closest point from the virtual palm, and the virtual palm's closest point to the object (e.g. not between their centres but over their virtual boundaries). The interaction speed is therefore defined as the delta of virtual minimum distance during a given time (empirically defined as 30 frames, ≈ 0.6s). Fig. 5. Our method to enable the virtual/physical synchronisation. We first use the speed calibration to match the servo speed with the interaction speed. When the hand decelerates, we use the time to contact calibration for the servo to finish its stroke in the remaining time.

Real Data

We also use the "time to contact" information to adjust the servomotor displacements prior to the virtual interaction. As the hand's speed usually decreases just prior grasping an object, we capture this deceleration timestamp, and calculate t m in as the "time lapse to contact". It is used as a threshold to command the servo to finish its stroke, as per Figure 5 -Finishing Stroke. The aim is for the interface to physically encounter the user's hand simultaneously with the virtual object of interest and the avatar hand interaction.

Experimental Design

6.2.1.1 Procedure and Conditions: We designed a virtual environment and cycles where the hand is moving towards a target at a given speed. The variable is the virtual hand speed on Unity3D. We tested N = 10 cycles of each speed between 2.0 and 8.0cm/s (increment of 1.0cm/s).

6.2.1.2 Measures: We measure four key indicators: the time difference at t c (Figure 6 -a), the position difference at d c (in cm and in degrees) Figure 6 -b), the time difference when disengaging (t d ) (Figure 6 -c) and the position difference when disengaging (d d ) (Figure 6 -d). We also measure the speed at engagement (from t sH to t c ) and disengagement (from t lH to t o ).

Results

Our results are displayed in Table 1 0.71s (std = 0.3s). When the interaction speed is limited, the servomotor is synchronised correctly for the interaction to occur (∆ te is below 0.5s, d d < 4cm, e.g. at t c + 0.5s, the palmar interface is located within the palm).

Discussion

6.2.3.1 Scalability: Our approach can be applied for other types of "on-demand interfaces" and generalised for future generations of interfaces that could benefit from a palmar contact feedback: exoskeletons gloves, finger wearables etc. At a larger scale, our interface could benefit from a coupling with machine learning algorithms, to be adjusted optimally with the users' grasp gestures [START_REF] Salvato | Predicting Hand-Object Interaction for Improved Haptic Feedback in Mixed Reality[END_REF].

6.2.3.2 Simulation vs Real Users: The cycle was simulated as a virtual hand colliding with an OOI, and did not simulate the user's deceleration slope prior to interaction. We believe that the "time to contact" threshold strategy would be more effective in real user experiences. This was validated during the User Experience (Section 7) -where no synchronisation issue was noticed with real users. 6.2.3.3 Potential Speed Limitations: Using our fitted equation, we are limited to a maximum interaction speed of 29cm/s. The curve is not fitted above, as we cannot send a higher signal to the servomotor. A mitigation strategy for this potential speed failure would reduce the user's hand speed and/or the object of interest speed, to a maximum of 29cm/s. This solution ("visuo-proprioceptive illusion") is a common strategy for speed related limitations [START_REF] Bouzbib | Let's Meet and Work it Out": Understanding and Mitigating Encountered-Type of Haptic Devices Failure Modes in VR[END_REF].

USER EVALUATION

We conducted a user study to evaluate PalmEx's ability to cover grasps from Section 3. Our main hypotheses are that (1) PalmEx's approach provides more consistent feedback for (non-)prehensile grasping; (2) PalmEx is more effective for perceiving 3D manipulations than standalone exoskeleton or palmar -more effective than a control condition.

Participants

Twelve participants (6 M, 6 F), all right-handed, aged 24 to 36 (mean = 27, std = 4) volunteered for the experiment. Five participants were beginners in Virtual Reality, 4 were intermediate and 3 were experts.

Hardware

All participants wore the HTC Vive Cosmos HMD, PalmEx on their right hand (i.e. dominant hand) and a Vive controller in their left hand (to rate, change conditions etc).

Experiment Design

Procedure

Participants were first informed of the aim of the study, its duration (20 min), understanding their haptic perception while grasping. They were therefore asked to respect the objects virtual geometrical boundaries when interacting.

Tasks and Stimuli

The scene was designed on Unity3D. The tasks consisted in manipulating 3D objects and giving feedback about their physical consistency. The scenario was the following: Emma enters the virtual environment and sits by a table. At each task, four virtual objects appear around the table. Emma manipulates the first object, and answers an affirmation using a virtual slider, corresponding its grade. She releases the object and makes the table turn. She manipulates the second object, rates it, releases it; etc. Emma may keep making the table turn to adjust her grades. The simulator stops after a total of 16 tasks.

Conditions

We controlled a factor related to the physical interface in our experiments (DEVICE) and one related to the objects (SHAPE). Participants performed the same MANIPULATION. We considered four DEVICES: PALMEX, PALMAR CON-TACT, EXOSKELETON and INACTIVE. In PALMEX, both of the exoskeleton and the palmar interface are active; in PALMAR CONTACT, the exoskeleton is worn but passive, and the palmar interface is active; in EXOSKELETON, the exoskeleton is active and the palmar interface remains inactive; in INACTIVE, both of the interfaces are worn but remain inactive. All DEVICES conditions are tested without changing the apparatus. We considered 4 SHAPES: Sphere, Cylinder, Disk and Surface and 1 MANIPULATION: Grab (for prehensile objects) or Touch (for the surface). Their sizes were chosen according to the average hand size: 15cm for sphere diameters and surfaces, 8cm for cylinders heights and diameters and disks diameters, 5cm for disk height. These objects represent the basic primitives involving the whole hand, and their associated grasps (see Section 3): both precision and power disks grasps can be performed in our user experience.

Design

We used a within-subject design. The appearance of each condition was randomized within the blocks. Each object (DEVICE condition) appeared randomly on the table. Each participant performed 4 blocks, and tested 64 conditions (e.g. 4 DEVICES×4 SHAPES×1 MANIPULATION×4 BLOCKS). The global experimental design was: 12 PARTICIPANTS × 64 CONDITIONS = 768 TRIALS.

Measures

For each trial, participants had to provide feedback on their perception using a 7-point Likert-scale to rate the sentence: "I feel the SHAPE3 in my hand." (1: Completely Disagree; 7: Completely Agree). We recorded each configuration and associated rates.

Quantitative Results

We analyzed the results by conducting a 2-way repeated measures ANOVA, to define the effects of DEVICES, SHAPES and BLOCKS. Posthocs pairwise T-tests with Bonferronicorrected p-values are applied to display the results. Ninetyfive Confidence Intervals are noted as 95-CI in the following.

Global Effects

We found a significant effect for DEVICES (F (3,99) = 22.8, p < 0.001; η 2 = 0.5); no effect for SHAPES, and a small effect on BLOCKS (F (3,99) = 2.4, p < 0.1, η 2 = 0.01).

BLOCK Effect

We studied the training block effect while using the devices. We found an effect for EXOSKELETON, between the first (mean = 4.0, 95-CI = 0.49) and last block (mean = 2.9, 95-CI = 0.4) (p < 0.001). A significant effect between PALMEX (mean = 5.9, 95-CI = 0.28) and PALMAR condition (mean = 5.5, 95-CI = 0.39) occurred during the last block (p < 0.05) using Tukey pairwise tests with Bonferroni correction.

Qualitative Feedback

Consistency of Exoskeleton Rendering

Three participants spontaneously mentioned that the exoskeleton pulling their fingers made them feel like puppets -P11 mentioned that she struggled understanding how the tension in her fingers were simulating the manipulated objects. She added it was "easier to apprehend the palmar contact, as the feedback [was] consistent with [her] vision". We emphasize that participants did perceive an illusory force-feedback when the exoskeleton was inactive -yet felt the active EXOSKELETON was too sudden and gruff.

Favourite Shapes

Four participants mentioned that the cylinders were their favourite objects to grab, with no regards to their visuohaptic consistency. P6 mentioned that he could potentially see lots of use-cases using our interface, such as assembly training, or games involving swords for instance.

Summary & Discussion

We summarize our results and open a discussion on perspectives thanks to PalmEx's results.

Palmar Stimulation

Our experience validates our first hypothesis and shows that palmar stimulation for 3D object manipulation is important, no matter if the exoskeleton is active or not. From the qualitative feedback, we noted that the manipulation of cylinders (power grasps) was preferred. 7.6.2 Pseudo-Haptics Enhance Inactive Exoskeletons? Our results highlight the importance of our first hypothesis but revoke our second hypothesis. They are indeed showing only a small effect on the last block between PALMEX and PALMAR conditions and no noticeable effect between EXOSKELETON and INACTIVE conditions among blocks. We believe this is due to the internal viscosity of current haptic exoskeletons gloves. While their hand tracking is accurate, and proven to augment presence compared to classic controllers [START_REF] Moon | Hand Tracking with Vibrotactile Feedback Enhanced Presence, Engagement, Usability, and Performance in a Virtual Reality Rhythm Game[END_REF], this viscosity coupled with a "physicalized" hand (no object penetration) is here shown to be preferred for 3D object manipulation. This almost pseudo-haptics effect is usually proven to alter perception, but we did not expect it to alter the exoskeleton rendering. This opens up an interesting discussion regarding 1) the effectiveness of pseudohaptics coupled with active physical haptic interfaces, 2) current high-end exoskeletons weight, inertia, resistance and viscosity specifications.

Comparative Discussion

We highlight the advantages of the combination of palmar and exoskeleton in the following.

Exoskeleton vs Inactive

We emphasize here how the exoskeleton is required for the rendering and explain the differences between these two conditions. The fingers are receiving a force-feedback from the exoskeleton -whether it is active or not. The inner resistance and viscosity of the device coupled with visual cues provides an illusory sensation that the device actively pulls on the fingers. The exoskeleton is what enables the precision grasp to be performed, and truly enhances the rendering enabled by Haptic Pivot for instance.

Palmar vs Inactive

These two DEVICE conditions reflect on the combination of our palmar interface and the exoskeleton. The exoskeleton is inactive in both these conditions, yet participants perceived a force-feedback due to the previously-evoked pseudoeffect. We show the benefit of the palmar interface coupled with the (inactive) exoskeleton: it significantly increases the grasps consistency.

PalmEx vs Palmar

Finally, the advantages of having the exoskeleton active and the palmar interface is that it is better perceived that the inactive exoskeleton with the palmar interface. Indeed, the last block showed a significant effect between PALMEX and PALMAR. While participants qualitatively evoked the unexpected tension from EXOSKELETON compared to INACTIVE, we show that when it is correctly synchronized with simultaneous stimulation on the palm, the perception for grasp consistency and exoskeleton efficiency is thus enhanced.

PERSPECTIVES & FUTURE WORK

PalmEx highlighted the importance of the palmar contact to enhance 3D object manipulation. We envision two main perspectives in PalmEx's line of work.

Degrees of Freedom and Usability

Our interface with a degree of freedom (DoF) was sufficient to simulate most grasps from taxonomies, with large, medium and small, curved or completely flat objects. We plan on confirming this finding in an assembly training user experience with completion time and accuracy measurements, representing a viable application for PalmEx. We also believe that adding the rotation of the palmar interface as additional DoF could provide the users with more complex haptic stimulation in their palms. This subsequent DoF could potentially add an edge perception feature. In the same line of observations, interfaces with numerous DoFs such as X-rings [START_REF] Gonzalez | X-Rings: A Hand-mounted 360°Shape Display for Grasping in Virtual Reality[END_REF] could be adapted to be "on-demand" and integrated in PalmEx.

Pseudo-Haptics and Lighter Exoskeletons

Our results opened an interesting research question regarding the coupling of Pseudo-Haptics and Active Haptic Interfaces. As future work, we propose to investigate coupling lighter exoskeletons (such as [START_REF] Choi | Wolverine: A wearable haptic interface for grasping in virtual reality[END_REF]) to enable a potentially more transparent [START_REF] Lawrence | Quantitative experimental analysis of transparency and stability in haptic interfaces[END_REF] perception and verify the effectiveness of exoskeleton gloves with and without pseudo-haptics.

CONCLUSION

We proposed PalmEx, a novel approach to improve 3D object manipulation in VR. PalmEx consists in upgrading haptic exoskeleton gloves with an actuated palmar interface., and offers a wide design space, built upon current taxonomies. PalmEx was described through its ergonomic design, its control and actuation, and evaluated for its delay at interaction and virtual:physical synchronisation. We conducted a user evaluation (n = 12) demonstrating the benefits of palmar contact for augmenting exoskeletons' haptic feedback. Taken together, our results promote the use of palmar contact and pave the way to novel generations of haptic exoskeletons gloves.

Fig. 1 .

 1 Fig. 1. (a) The user's palm is unencumbered prior to interaction. (b) The palmar interface goes towards her palm while the user starts gesturing a grasp. (c) When engaging the grasp, the user perceives force-feedback from both the exoskeleton and the palmar interface.

Fig. 3 .

 3 Fig. 3. Three potential scenarios involving PalmEx. (a) Medical training: An avatar is performing CPR training on another avatar. (b) Social touch: Avatars are shaking hands. (c) Leisure: The avatar is molding a vase.

Fig. 4 .

 4 Fig. 4. Schematic of the custom-made palmar contact interface. A 10mm high half-ellipsoid is used to simulate a wide range of curvatures: (a) Flat; (b) Small curvature; (c) Large curvature.

  Calibration f(x) = p 0 x 3 + p 1 x 2 + p 2 x + p 3 Real Data Fitted Curve

Fig. 6 .

 6 Fig. 6. A Technical Evaluation cycle, and the four key phases to analyse. (a) Starting: the Object of Interest (OOI) distance to the palm goes within reach at t sH . The servo starts at t sS . t sH and t sS should be as close as possible. (b) Fully Engaged: the OOI is in the user's palm. The distance dc and ∆te, difference of time at engagement (time of contact tc -time of engagement te) are to minimise. (c) Leaving: the OOI leaves the palm position, at t lH , and the servo follows at t sS . Finally, (d) Fully Disengaged: the OOI is out of reach at to, followed by the servo at t d . ∆ td , difference of time at disengagement, and d d , distance between the object and the palmar interface, are to minimise.

IFig. 7 .

 7 Fig. 7. Boxplot of our experiment results: "I feel the SHAPE in my hand" on a 7-point Likert scale, 1: Completely Disagree; 7: Completely Agree. Stars show the significance of the results (** p < 0.01, *** p < 0.001).

7. 4 . 2

 42 DEVICE Effect (Figure7) Our results show that participants felt the virtual object in their hand using either PALMEX (mean = 5.67, 95-CI = 0.15), or PALMAR (mean = 5.67, 95-CI = 0.16) compared to EXOSKELETON (mean = 3.44, 95-CI = 0.23) or INACTIVE condition (mean = 3.46, 95-CI = 0.24). Effects were found between PALMEX or PALMAR and EXOSKELETON (p < 0.001) or INACTIVE (p < 0.01), but no effect was found between PALMEX and PALMAR or EXOSKELETON and INACTIVE.

Elodie

  Bouzbib is a postdoctorate researcher at Inria Rennes, France. She obtained a PhD from Sorbonne Universit é in Computer Science/Human Computer Interaction in 2021, for which she received the prize of 2022 Best Multidisciplinary PhD thesis from AFIHM. Her main research areas are HCI, Virtual Reality, Haptics and Robotics. She mainly prototypes artefacts to stimulate haptics in VR. Marc Teyssier is Principal Investigator of the "Resilient Futures" research group at the De Vinci Innovation Center (Paris, France). His research in HCI and Design focuses on how technology impacts human interactions and behaviour and proposes alternatives to traditional devices using radical and critical design or Low-Tech and Bio-Fabrication methodologies. Thomas Howard is a post-doctoral fellow with INSA Rennes at IRISA and Inria Rennes, France. He defended his PhD in robotics at Sorbonne Universit é in Paris in 2016, on the topic of haptic feedback for assisting in minimally invasive surgical procedures. His main research interests haptic feedback technologies for rich multisensory virtual reality interactions. Claudio Pacchierotti is a tenured researcher at CNRS-IRISA in Rennes, France, since 2016. He was previously a postdoctoral researcher at the Italian Institute of Technology, Genova, Italy. Pacchierotti earned his PhD at the University of Siena in 2014. Pacchierotti received the 2014 EuroHaptics Best PhD Thesis Award and the 2022 CNRS Bronze Medal. He is Senior Chair of the IEEE Technical Committee on Haptics and Secretary of the Eurohaptics Society. Anatole L écuyer is director of research and head of Hybrid team at Inria Rennes, France. His resarch interests are in virtual/augmented reality, haptic interaction, 3D user interfaces, and braincomputer interfaces. Anatole L écuyer obtained the IEEE VGTC Technical Achievement Award in Virtual/Augmented Reality in 2019, and was inducted in the IEEE Virtual Reality Academy in 2022.

  5.2.3 Actuation and ControlOur palmar contact implementation relies on a servomotor. We used a Hitec 5065mg servomotor, as it has robust metal gears, and provides a 1.8kg.cm torque at 4.8V . Its small size (23.6 × 11.6 × 24mm 3 , weight = 12g) facilitates its integration. Its speed specification is around 0.14"/60 deg. We use a Pololu Micro Maestro 6-channel USB Servo Controller to control it in position with a 250 Hz serial communication.

  . The time difference between contact and servomotor engagement ∆ te is under

	Distance
	to Palm				Palmar Interface
						Object of Interest
	Engaged (Servo) In Palm (OOI) Position	0	d c	b	Fully Engaged	c Engagement Leaving
	Disengaged (Servo) Out of Reach (OOI)	R 0	t c t e Δ t e t s H t s S Engagement a Starting	t l H t l S t o t d Δ t d	Time (s) Disengaged d d d Fully
							Virtual Comman
							Speed
							73

TABLE 1

 1 Results of our evaluation. As a function of the virtual speed command, we display time difference and distance at contact/fully disengagement.

SHAPE was replaced with the configuration's associated object.


Sophie Bouton

Valérian Chambon

Narly Golestani

Elia Formisano

Anne-Lise Giraud
email: anne-lise.giraud@unige.ch

Interpretability of statistical approaches in speech and language neuroscience

Keywords: Statistics, Machine Learning, General Linear Model, Interpretation, Speech, Language

Brain functions are traditionally investigated using general linear models (GLM) in controlled experiments, which allow for a straightforward interpretation of the data. However, constraints imposed by controlled experimental setups have led neuroscientists to increasingly use machine-learning inspired approaches, which allow to work with large datasets collected in relatively natural experimental settings. Given the disparity of available methods and underlying concepts, synthesizing and relating the results of both traditional and machine-learning inspired experiments represents a substantial challenge. In this review, we propose to use a taxonomy based on the spatial specificity of the available methods and the extent to which they are constrained by a priori hypotheses, to classify their interpretability. Taking speech processing as a case example, we dissect the advantages and limitations of each of these methodological approaches, and highlight the ways in which they can be fruitfully combined to facilitate data interpretation.

/main text The diversity of statistical approaches in cognitive neuroscience A common experimental paradigm in cognitive neuroscience is to measure changes in neural activity in response to controlled manipulations of stimuli, tasks or behaviours. In these experiments, measured neural responses are generally analysed using a variety of statistical tests (e.g. t-test, F-tests, ANOVA) that are special cases of the so-called General Linear Model (GLM). These statistical analyses relate, at each point in anatomical space, the variance of neural responses with a set of pre-specified variables (predictors) which model the hypothesized effects of experimental manipulations and possible confounding factors. The GLM-based approach is well suited to test pre-defined hypotheses on the relation between specific components of cognitive and psychological models and the underlying functional neuroanatomy. In GLMs, the interpretation of the model fit to the data and the contribution of individual predictors is straightforward: predictors of the model are in small number and directly relate to controlled and a priori uncorrelated crafted experimental manipulations. In language research, for example, GLM-based studies have confirmed the important notion that speech and language-related cognitive operations emerge from processing hierarchies where each step has its own anatomofunctional specificity [START_REF] Ding | Cortical tracking of hierarchical linguistic structures in connected speech[END_REF][START_REF] Hickok | The cortical organization of speech processing[END_REF][START_REF] Sheng | The Cortical Maps of Hierarchical Linguistic Structures during Speech Perception[END_REF] (Figure 1, left panel).

Contrasting with this traditional paradigm, a more "naturalistic" approach has gained considerable traction in recent years [START_REF] Poldrack | From brain maps to cognitive ontologies: Informatics and the search for mental structure[END_REF][START_REF] Varoquaux | Predictive models can overcome reductionism in cognitive neuroimaging[END_REF]. In these experiments, participants are presented with "real world" stimuli, with the goal of measuring how the brain responds to e.g. language "as it is used" [START_REF] Hamilton | The revolution will not be controlled: natural stimuli in speech neuroscience[END_REF]. Neural responses collected using e.g. movies or audiobooks are often analysed using encoding models. In language research, encoding models have for example revealed semantic-category selective organization within broadly distributed brain regions ( [START_REF] Huth | Natural speech reveals the semantic maps that tile human cerebral cortex[END_REF], Figure 1, middle panel). Similarly to GLMs, encoding models fit the neural responses univariately (i.e., voxel-by-voxel), with the goal of explaining/predicting the level of brain activity through the (linear) combination of a set of variables -typically, features of the stimuli. Interpreting the results of encoding models is therefore more difficult than interpreting those of GLMs because the influence of each feature cannot be singled out, as the number of features might be large (in relation to the dataset), and the features might be correlated among them. This implies the use of various techniques (e.g. regularization, cross-validation) that further influence the estimated contribution of individual variables to the overall fitting. The interpretation of encoding analyses can hence benefit from comparing different stimulus representation models, with the assumption that the winning model reflects the actual neural representation [START_REF] Santoro | Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex[END_REF].

Unlike encoding models, decoding methods exploit the information in spatial patterns of neural responses and can be used to predict perception or behaviour (e.g. motor, speech output) or reconstruct the stimuli using the neural activity itself [START_REF] Holdgraf | Encoding and decoding models in cognitive electrophysiology[END_REF]. Decoding analyses rely on diverse machine learning techniques and can be broken down into (1) discriminative methods (e.g. classifiers), which aim at detecting multivariate differences between predefined task conditions or stimulus features (within small sets of voxels, regions, or the whole brain), and (2) model-based methods, which aim at reconstructing new stimuli or behaviours that are not present in the training set from brain activity, by predicting features of otherwise well-specified computational models, as in encoding [START_REF] King | Encoding and Decoding Neuronal Dynamics : Methodological Framework to Uncover the Algorithms of Cognition[END_REF]. Note that encoding models can be used in conjunction with decoding models, often to optimize the performance of the latter (see [START_REF] Naselaris | Encoding and decoding in fMRI[END_REF][START_REF] Naselaris | A voxelwise encoding model for early visual areas decodes mental images of remembered scenes[END_REF], Figure 1, right panel). In language research, a recent study leveraged a model of the vocal system to decode the speech signal from the brain's response, making significant progress towards the possibility of restoring spoken communication in patients with paralysing conditions [START_REF] Anumanchipalli | Speech synthesis from neural decoding of spoken sentences[END_REF].

Several high-profile studies have recently highlighted the value of unsupervised clustering methods -e.g., dimensionality reduction techniques. Their most defining principle is to relax the constraints imposed by supervised, model-based approaches such as encoding/decoding. This allows a maximum of variance (or information) in the data to be accounted for by a combination of n non pre-specified dimensions [START_REF] Brunton | Data-driven models in human neuroscience and neuroengineering[END_REF][START_REF] Jack | Data-Driven Methods to Diversify Knowledge of Human Psychology[END_REF]. Importantly, these unsupervised, data-driven methods (e.g. principal component analysis, PCA) do not use labels or specific information regarding the experimental design. Although these methods are often applied to conduct exploratory analyses on large data sets, they are also frequently used with the ambitious goal of discovering unpredicted representational and/or coding principles in the brain. This raises a number of issues that we attempt to expose in this article (see Behavioural relevance and functional specialization). With the increasing variety of data analysis methods in speech and language neuroscience, making sense of the findings has become a priority. In this paper, we present an operational taxonomy that classifies statistical approaches according to their interpretability, which we define along two dimensions: 1) the spatial (anatomical) specificity of the approach being chosen, and 2) the extent to which data collection and analysis are constrained by pre-defined hypotheses (data driven vs. hypothesis driven analyses) (Figure 2). Relative to traditional univariate approaches, the strength of machine-learning multivariate methods lies in the fact that they can reveal novel organisational principles in large data sets, such as complex geometric or n-dimensional representational patterns [START_REF] Chartier | Encoding of Articulatory Kinematic Trajectories in Human Speech Sensorimotor Cortex[END_REF]). Yet, sensitivity to complex patterns comes at the cost of interpretability: understanding the contribution of a single voxel to distributed/multidimensional patterns of brain activity is a difficult and not yet surmounted challenge [START_REF] Hebart | Deconstructing multivariate decoding for the study of brain function[END_REF][START_REF] Ritchie | Decoding The Brain: Neural Representation And The Limits Of Multivariate Pattern Analysis In Cognitive Neuroscience[END_REF]. Moreover, the extent to which machine-learning inspired approaches are constrained by a priori hypotheses influences the level of specification of model features, which, in turn, determines how straightforward

GLMs to study functional neuroanatomy

Representational geometry to study the processing of naturalistic stimuli the interpretation of results can be. In this review, we use this taxonomy to show that the difference in spatial specificity between methods, as well as the relative absence of biologically informed hypotheses, can lead to different interpretations of findings. We then describe different existing ways in which analysis approaches can be optimised to allow for more constrained interpretations of cognitive neuroscience research in the domain of speech and language, while at the same time capitalizing on the ability to exploit multivariate differences in the real world and/or uncover unpredicted organising neural principles.

Figure 2

Figure 2. A proposed taxonomy of statistical approaches/methods in cognitive neuroscience. The classification is based on two dimensions: (i) the "hypothesis-driven" dimension, which is characterised by a continuum between hypothesis-and data-driven approaches, i.e. between analysis methods that are to a greater or lesser extent guided by predefined assumptions (note that "data-driven" does not necessarily equate to "theory-free");

(ii) the spatial (anatomical) specificity of the approach: a univariate model considers a single spatial point at a time, whereas a multivariate model considers many spatial points at once, making the anatomical parcellation/interpretation of neural patterns more difficult.

Behavioural relevance and functional specialisation

Considerable efforts have been made in recent years to better understand how the brain forms concepts based on word meaning. One proposal is that cortical regions are organized in large-scale networks which both underpin semantic relations and support the emergence of higher-level concepts [START_REF] Hagoort | Nodes and networks in the neural architecture for language: Broca's region and beyond[END_REF]. Accordingly, and in parallel to classical hypothesis-testing experimental designs [START_REF] Maess | Prediction signatures in the brain: Semantic pre-activation during language comprehension[END_REF][START_REF] Teige | Dynamic semantic cognition: Characterising coherent and controlled conceptual retrieval through time using magnetoencephalography and chronometric transcranial magnetic stimulation[END_REF], studies using machine-learning inspired approaches have revealed a semantic-category

Hypothesis-driven

Model based decoding approaches

General linear model (used in factorial design)

Discriminative model (Classification, SVM)

Interpretability

Unsupervised clustering and dimensionality reduction

Encoding model (used to model natural stimuli)

Multivariate analyses Univariate analyses

Spatial specificity

High

Low

organization that is broadly distributed across the whole brain [START_REF] Huth | A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain[END_REF][START_REF] Huth | Natural speech reveals the semantic maps that tile human cerebral cortex[END_REF][START_REF] Zhang | Connecting concepts in the brain by mapping cortical representations of semantic relations[END_REF]. Here, to characterize the semantic selectivity of each voxel, neural activity was measured with fMRI while participants passively listened to long narratives or watched natural movies. Neural activity parameters were fitted to semantic features using univariate linear regression (voxel-wise modelling), and the fitted model was projected onto a low-dimensional subspace obtained by applying dimensionality reduction techniques (e.g. principal component analysis, PCA). The resulting four dimensions were those that best explained the data. Importantly, no explicit assumptions were made regarding the combination of features that these four dimensions reflected. However, this raises some concerns related to the interpretation of the results. The inclusion of many independent variables (i.e., semantic features) requires strong posthoc assumptions about the latent/underlying organisation of the resulting neural patterns, and some implicit assumptions might be incorrect. For instance, most studies implicitly assume that neuronal responses from any location in the brain can encode any representative semantic class, and that they do so based on locally specific functional recruitment patterns. Ideally, testing the specificity of these patterns would require to decode the same semantic features from neural activity at the same locations. Subsequent studies using such decoding methods, with maps based on the weight distribution across semantic labels, revealed less local semantic category specificity than expected [START_REF] Huth | Decoding the semantic content of natural movies from human brain activity[END_REF][START_REF] Pereira | Toward a universal decoder of linguistic meaning from brain activation[END_REF]. In these follow-up studies, each voxel of a given semantic map was associated with the meaning of few but semantically related words, suggesting a global 'map' of semantic categories with a broad distribution of semantic knowledge in the brain. It remains difficult, however, to reconcile these essentially descriptive conclusions with hierarchical processing where auditory input is progressively abstracted into higher-level language representations. The recent mapping of hidden units of deep architecture models with neuroimaging data speaks to such progressive abstraction process in natural language processing [START_REF] Kell | A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy[END_REF].

The multiple interpretations of data-driven dimensions

Finding out what information is contained in multivariate patterns arising from data-driven models is a necessary yet difficult task. Interpreting how neural data maps onto behavioural data or stimuli in an n-dimensional space requires dimensionality-reduction, which in turn leads to arbitrary interpretations since it relies on post-hoc interpretations regarding what each eigenvariate may represent. If no explicit hypothesis is formulated during the reduction of observed dimensions (i.e., during unsupervised procedures), the interpretation is uncertain. Each eigenvariate resulting from dimensionality reduction procedures describes a complex trajectory in a 4-dimensional (i.e., including time) statespace. Understanding what these trajectories represent and how they relate to the functional processing of specific regions is ultimately the hardest problem. If such trajectories are specific to functional regions, they might reveal the complexity/polyvalence of neural population coding, depending on task requirements [START_REF] Rubin | Revealing neural correlates of behavior without behavioral measurements[END_REF]. However, if the sampled regions are ill-defined, e.g. when they are arbitrarily determined by cortical electrode grids or arrays used in the measurements, these trajectories might reflect the mixed contribution of different computational levels resulting from multiple regions within the hierarchy [START_REF] Roussel | Observation and assessment of acoustic contamination of electrophysiological brain signals during speech production and sound perception[END_REF]. This point is well illustrated by a specific example in the speech literature. Using a data-driven approach (i.e., principal component and clustering analyses), Bouchard and colleagues identified patterns of spatially distributed neural activity in the human sensorimotor cortex during the production of consonant-vowel syllables [START_REF] Bouchard | Functional organization of human sensorimotor cortex for speech articulation[END_REF]. The authors proposed that these patterns reflect articulatory features, i.e., elementary speech motor sequences. Although this interpretation is reasonable, one may argue that an equally valid interpretation of the results is an organization that follows acoustic traits. The phonemic distribution in a 2D state-space highlighted by Bouchard (Linear Discriminate Analysis (LDA), Suppl. Fig. 10 in [START_REF] Bouchard | Functional organization of human sensorimotor cortex for speech articulation[END_REF], Figure 3A) aligns surprisingly well with the spatial distribution of acoustic features described in 1969 by Jakobson,Fant,; see also Figure 3B, where consonants and vowels cluster along low/acute (LDA1) and compact/diffuse (LDA2) continua). Given the pivotal role of the somatosensory system in the hierarchical organisation of speech production [START_REF] Franken | Opposing and following responses in sensorimotor speech control: Why responses go both ways[END_REF], an organization based on combined acoustic and articulatory features provides the most likely interpretation for the phonemic topography emerging from neural activity in the ventral sensorimotor cortex (vSMC). This example illustrates that the same data-driven analysis can lead to very different interpretations, and that the conclusions that are drawn depend on the models that the experimenters have in mind and on the context in which results are interpreted [START_REF] Bzdok | Statistics versus machine learning[END_REF]. Unlike the hypothesis testing approach, the data-driven approach used in isolation ultimately leads to untested interpretations. In summary, and although it may appear counter-intuitive, the use of data-driven models requires extremely solid background hypotheses for post-hoc interpretation.

Can the brain exploit multidimensional features?

The choice of machine learning approaches in cognitive neuroscience implicitly contains the fundamental assumption that a given brain area can represent multiple features, and that these features can be captured by multivariate patterns. Yet, multivariate patterns may also capture phenomena other than locally processed features, such as, in particular, properties that are inherited from distant regions with which the sampled brain region interacts. For example, in patients with intracortical electrodes, it has been shown that the activity pattern identified in the ventral sensorimotor cortex (vSMC) corresponds to different articulatory features when the patients produced versus when they listened to syllables [START_REF] Cheung | The auditory representation of speech sounds in human motor cortex[END_REF]. The authors argued that the neural response structure in the vSMC is organized according to "place of articulation" during speech production, but according to "manner of articulation" during speech listening, while the neural response in the STG is organized according to "manner of articulation" while both speaking and listening [START_REF] Mesgarani | Phonetic feature encoding in human superior temporal gyrus[END_REF] (Figure 3C and3D). When speech is perceived and then produced, the conversion of acoustic cues into motor routines involves the vSMC, which interfaces auditory and motor cortices [START_REF] Assaneo | The coupling between auditory and motor cortices is rate-restricted: Evidence for an intrinsic speech-motor rhythm[END_REF][START_REF] Liebenthal | An interactive model of auditory-motor speech perception[END_REF][START_REF] Pulvermüller | Active perception: sensorimotor circuits as a cortical basis for language[END_REF]. Thus, we could argue here that the fact that articulatory features can be decoded similarly within the vSMC and the STG does not unambiguously demonstrate that these regions encode or represent articulatory categories, but could equally mean that the structure of the articulatory space shares common properties with that of the acoustic space (see [START_REF] Popov | Practices and pitfalls in inferring neural representations[END_REF] for a similar point about shared variance in non-equivalent representational spaces). In principle, this isomorphism can be exploited by any "information-greedy" decoder to retrieve, in one representational space, the features that in fact only reflect meaningful dimensions in a different space. In this example, articulatory representations can be classified in a region known to encode acoustic features (the STG) simply because there is an (even partial) mapping between the acoustic and articulatory representational schemes, which causes their intrinsic representational properties to be correlated [START_REF] Assaneo | The coupling between auditory and motor cortices is rate-restricted: Evidence for an intrinsic speech-motor rhythm[END_REF]. [START_REF] Bouchard | Functional organization of human sensorimotor cortex for speech articulation[END_REF][START_REF] Bouchard | Functional organization of human sensorimotor cortex for speech articulation[END_REF] with the acoustic features of Jakobson et al., 1969[START_REF] Jakobson | Preliminaries to speech analysis: The distinctive features and their correlates[END_REF]. [START_REF] Bouchard | Functional organization of human sensorimotor cortex for speech articulation[END_REF] propose that these patterns (a) reflect articulatory features (i.e., elementary speech motor sequences), but an organization according to acoustic features (b) also provides a plausible interpretation of the findings. Images Spatial distribution of acoustic features (Jakobson, 1969) Bouchard et al., Suppl. Fig. 10, and Fig. 3b adapted from Jakobson et al., 1969, Figs. pp. 33-34. Figures 3c & 3d adapted from Cheung et al., 2016[START_REF] Cheung | The auditory representation of speech sounds in human motor cortex[END_REF], Figure 4 and Suppl. 2 Fig.

Optimizing multivariate analysis methods

Given the exploratory potential and also the interpretability issues of multivariate, machine-learning inspired approaches in cognitive neuroscience [START_REF] Varoquaux | How machine learning is shaping cognitive neuroimaging[END_REF], it seems important to think about ways in which such methods can be capitalized on while at the same time limiting some of their pitfalls. One way to tackle this is to prioritize the use of model-based approaches, and as such to make them relatively more hypothesis-driven [START_REF] De Angelis | Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds[END_REF][START_REF] King | Characterizing the dynamics of mental representations: the temporal generalization method[END_REF][START_REF] Moses | Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity[END_REF][START_REF] Pasley | Reconstructing speech from human auditory cortex[END_REF]. Furthermore, in particular in the domain of speech and language neuroscience, these models could be specifically tailored to the level(s) of the processing hierarchy and/or of the functional role of the region(s) being examined [START_REF] Çelik | Spatially informed voxelwise modeling for naturalistic fMRI experiments[END_REF][START_REF] Deniz | The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality[END_REF][START_REF] Rutten | Cortical encoding of speech enhances task-relevant acoustic information[END_REF][START_REF] Venezia | Hierarchy of speechdriven spectrotemporal receptive fields in human auditory cortex[END_REF]. Second, depending on the goal of the investigation, multiple models can be combined in order to question different levels of processing simultaneously [START_REF] Anumanchipalli | Speech synthesis from neural decoding of spoken sentences[END_REF][START_REF] De Heer | The hierarchical cortical organization of human speech processing[END_REF], or applied separately to different functional subregions, based on the a priori hypotheses and predictions of the study [START_REF] Kell | A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy[END_REF]. Third, again especially in speech and language neuroscience, it is primordial that modelling approaches take into account the dynamics of the signal, thereby constraining the search and thus also the interpretation space of machine learning approaches, this time in the temporal domain [START_REF] Dichter | Dynamic Structure of Neural Variability in the Cortical Representation of Speech Sounds[END_REF][START_REF] King | Characterizing the dynamics of mental representations: the temporal generalization method[END_REF][START_REF] Loula | Decoding fMRI activity in the time domain improves classification performance[END_REF][START_REF] Moses | Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity[END_REF]. We hence cautiously advocate that relating the output of multivariate analysis methods (i.e., multivariate patterns, state-space representations or classifier performance) to behaviour ought to involve the specification of tailored computation rules. For each possible task, and at each level of the processing hierarchy, there are optimal preinformed ways of mapping the neural activity according to the variable of interest [START_REF] Issa | Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals[END_REF][START_REF] Santoro | Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns[END_REF]. Known features of the functional hierarchy in the speech processing system could help to define an optimal algorithm, or a time-resolved model that varies depending on the processing stage (Figure 4). In visual perception, for instance, a geometric model can be defined whose features adapt to the known computational properties of each level of the functional hierarchy, from specific to general as one goes up towards more abstract levels [START_REF] Dicarlo | Untangling invariant object recognition[END_REF][START_REF] Grill-Spector | The functional architecture of the ventral temporal cortex and its role in categorization[END_REF][START_REF] Nunez-Elizalde | Brain activity recorded during free viewing of naturalistic short films simultaneously reveals the brain representations of multiple feature spaces[END_REF][START_REF] Van Gerven | A primer on encoding models in sensory neuroscience[END_REF]. For speech processing, to recover the function of the first two levels of the auditory hierarchy [START_REF] Jiang | Decoding speech using the timing of neural signal modulation[END_REF][START_REF] Santoro | Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns[END_REF], it seems necessary to use models that take both the acoustic spectrum and time into account, with a spectro-temporal receptive field (STRF) model being more accurate in primary auditory cortex, and an STRF model tailored to phonological cues being more accurate in auditory association cortex [START_REF] Dewitt | Phoneme and word recognition in the auditory ventral stream[END_REF][START_REF] Venezia | Hierarchy of speechdriven spectrotemporal receptive fields in human auditory cortex[END_REF]. The same strategy may apply to higher-order regions where the natural dimensions of the feature space are less well known, i.e., semantic regions. At these levels of the processing hierarchy, linguistic representations are presumably a-temporal, encoded with sparse or rate codes [START_REF] Chang | Categorical Speech Representation in the Human Superior Temporal Gyrus[END_REF][START_REF] Panzeri | Sensory neural codes using multiplexed temporal scales[END_REF][START_REF] Van Wassenhove | Minding time in an amodal representational space[END_REF][START_REF] Zhang | A hierarchical sparse coding model predicts acoustic feature encoding in both auditory midbrain and cortex[END_REF]. In this case, dimensionality reduction algorithms may leverage, together, various types of information, such as phonemic transitions, word probabilities or word transitions. time-based representations are gradually abstracted, from fine spectro-temporal decomposition in primary auditory cortex (PAC), to spectro-temporal encoding specific to phonological units in bilateral pSTG (posterior superior temporal gyrus), and phonological feature processing in the left pSTG, and finally to decisional processing in the left inferior frontal gyrus (IFG) [START_REF] Bouton | Focal versus distributed temporal cortex activity for speech sound category assignment[END_REF]. Along the hierarchy, information is encoded through hierarchical, parallel and recursive processes [START_REF] Macgregor | Ultra-rapid access to words in the brain[END_REF][START_REF] Strijkers | Can hierarchical models display parallel cortical dynamics? A nonhierarchical alternative of brain language theory[END_REF]. Representations are encoded within different time frames in the right and left pSTG; this is important to consider when using classification methods. A way to use machine-learning inspired methods within a hypothesis-driven approach would be, for example, to run multivariate-pattern and other forms of machine learning analyses on different types of measures selected based on the functional region they originate from, i.e., using STRFs at the lower levels of the auditory hierarchy [START_REF] Rutten | Cortical encoding of speech enhances task-relevant acoustic information[END_REF][START_REF] Santoro | Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns[END_REF], phonemic transitions in the left pSTG or word probabilities and transitions in the left IFG.

Such model-based decoding has been used to study how the auditory encoding of different acoustic dimensions is modified depending on whether people attend to linguistic (e.g. phonological) or para-linguistic (i.e. voice) information in pseudo-words [START_REF] Rutten | Cortical encoding of speech enhances task-relevant acoustic information[END_REF]. Although in this study only an acoustic model was used for questioning the data, extensions to models containing e.g. pitch and higher level phonemic features would allow to generate an 'embedded', or concatenated model space [START_REF] De Heer | The hierarchical cortical organization of human speech processing[END_REF]. Here, it might be expected that different features (e.g., acoustic, phonological and pitch) best explain the neural data within different functional subregions of the auditory hierarchy and/or during different tasks, allowing to gain a broader understanding of how our auditory system makes use of linguistic and paralinguistic information in speech. Two recent fMRI studies having compared the performance of models targeting different processing levels (i.e. acoustic versus pitch [START_REF] De Angelis | Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds[END_REF] and spectral, articulatory and semantic [START_REF] De Heer | The hierarchical cortical organization of human speech processing[END_REF] illustrate how model-based machine learning approaches can be used to tap into different levels of the processing hierarchy, and to complement hypothesis driven approaches in revealing the regional functional specificity of these.

Approaches that deviate from the GLM experimental approach can be more finetuned for research on the neuroscience of language not only by adapting models to assess different, regionally specific levels of processing within the language processing hierarchy, but also by fine-tuning models at different levels of temporal resolution depending on the processing level within this hierarchy. Several recent ECoG studies illustrate the importance of explicitly accounting for the dynamics of the system [START_REF] Dichter | Dynamic Structure of Neural Variability in the Cortical Representation of Speech Sounds[END_REF][START_REF] Moses | Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity[END_REF][START_REF] Pasley | Reconstructing speech from human auditory cortex[END_REF]. The challenge for future work will be to extend these approaches within the context of language neuroscience research to noninvasive, whole-brain, ideally time-resolved studies [START_REF] Gwilliams | Recurrent processes support a cascade of hierarchical decisions[END_REF][START_REF] Loula | Decoding fMRI activity in the time domain improves classification performance[END_REF]. This would allow to elaborate language processing models with respect to temporal dependencies, and to reveal how these are gradually abstracted across different levels of the processing hierarchy across the brain.

In vision, a field where data-driven methods have led to more easily interpretable results than in the auditory domain, several research groups propose to use detailed hierarchical artificial neural network models to address neural information transformation processes along the visual pathways [START_REF] Cichy | Resolving human object recognition in space and time[END_REF][START_REF] Mohsenzadeh | Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams[END_REF]. Bashivan et al. showed that images synthesized by artificial neural networks can precisely and selectively activate neural populations in specific regions of the monkey visual system [START_REF] Bashivan | Neural population control via deep image synthesis[END_REF]. Such tools can also be used in a powerful way in the field of speech and audition, as artificial neural networks can decode, with high precision, the latent structure learned from single trials, revealing a rich, hierarchical organization of linguistic features [START_REF] Livezey | Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex[END_REF]. These examples illustrate how the reverse engineering of artificial neural networks can allow to gain a deep understanding of neural processes underlying complex cognitive functions [START_REF] Kell | A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy[END_REF][START_REF] Saxe | If deep learning is the answer, what is the question[END_REF].

Can we get rid of behaviour?

Drawing on unsupervised sorting of mouse neuronal activity, Rubin and colleagues found that neurons could be classified based on their internal tuning properties rather than on external variables such as a pre-defined stimulus [START_REF] Rubin | Revealing neural correlates of behavior without behavioral measurements[END_REF]. The technique used by the authors, i.e., non-linear dimensionality reduction, has some definite advantages since it can reveal informative patterns of neural activity that are not tied to a particular model of the stimulus set [START_REF] Zylberberg | The role of untuned neurons in sensory information coding[END_REF]. This study suggests that one could investigate brain circuits for which little is known about the identity of the variables they encode, in the same way that dimensionality reduction can be leveraged for probing the representational space of brain regions for which the natural dimensions of the feature space are unknown, these primarily being high-order associative regions. Whether we can truly get rid of any behavioural benchmark using multivariate pattern analyses or other machine learning methods, and whether we can elicit or modify specific behaviours by artificially inducing multivariate patterns (e.g. using stimulation techniques or brain computer interfaces [START_REF] Ezzyat | Direct Brain Stimulation Modulates Encoding States Article Direct Brain Stimulation Modulates Encoding States and Memory Performance in Humans[END_REF][START_REF] Shibata | Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback[END_REF]) are primordial issues for future research [START_REF] Grootswagers | Finding decodable information that can be read out in behaviour[END_REF][START_REF] Krakauer | Neuroscience Needs Behavior: Correcting a Reductionist Bias[END_REF][START_REF] Panzeri | Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior[END_REF].

Conclusion

With examples from the speech and language literature, we illustrate strengths and weaknesses of using machine-learning inspired methods as alternatives to classical GLM methods. The fact that machine-learning inspired methods impose little constraint on how information is selected and classified, and on how dimensions are extracted, allows to reveal unexpected structure in the data, for example in the form of multidimensional, spatially distributed representations. But this higher sensitivity to non-predicted dimensions may come at the cost of not seeing the true structure that the underlying neuronal populations represent, and hence, of missing dimensions in the data that truly relate to the observed behaviour [START_REF] Carlson | Ghosts in machine learning for cognitive neuroscience: Moving from data to theory[END_REF]. Addressing these specific concerns may require the use of adjusted methods, which take advantage of biologically informed priors so as to functionally limit the size of the model space [START_REF] Ahlheim | Estimating the functional dimensionality of neural representations[END_REF][START_REF] Snoek | How to control for confounds in decoding analyses of neuroimaging data[END_REF].

In the era of big data, it is now commonplace to say that scientists need theory more than ever. Two decades ago, science-fiction writer Ted Chiang wrote a premonitory article in Nature purporting to celebrate the 25th anniversary of the last human scientific publication [START_REF] Chiang | Catching crumbs from the table[END_REF]. In this provocative, futuristic short-story, science is now exclusively conducted by genetically improved humans, called "metahumans", and all that remains for human scientists is the interpretation of metahuman data. "In the face of metahuman science, humans have become metascientists". It is of course tempting to see metahuman science as a metaphor for the use of artificial intelligence methods, whose inner functioning and scientific output sometimes appear to lie beyond the reach of our understanding. But data are rarely, if ever, self-explanatory. They make sense to the extent that they validate or falsify theoretically informed predictions. When it comes to interpreting data-driven findings, possessing a biologically informed theory beforehand retains some usefulness. "Human researchers", says Chiang, "may discern applications overlooked by metahumans, whose advantages tend to make them unaware of our concerns". The same surely applies to data-driven methods, whose appeal and advantages often make us oblivious to theoretical concerns.

Figure 1 Figure 1 .

 11 Figure 1

Figure 3 Figure 3 .

 33 Figure3

  3a & 3b courtesy of Michel Hoen and Willy Serniclaes. (c) In the STG (superior temporal gyrus), decoding patterns arising from machine learning algorithms show that the dimensions distinguishing the neural responses during listening and speaking are organized according to 'manner of articulation' cues. (d) In the vSMC (ventral sensorimotor cortex), decoding patterns show that neural responses are organized according to 'manner of articulation' cues during speech listening and according to 'place of articulation' cues when speaking. Fig. 3a adapted from /k/ & /a

Figure 4 Figure 4 .

 44 Figure 4

Acknowledgments

We wish to thank Willy Serniclaes and Michel Hoen for their contribution to the critical review of some of the work discussed here, and Timothée Proix for his helpful comments and suggestions on an earlier draft of this article. This work was funded by the Swiss National Science Foundation (SNF 320030_149319 and SNF 320030_163040 to A-L.G., SNF P300P1_167591 to S.B., and SNF 100014_182381 to N.G.), by the Fondation pour l'Audition (RD-2016-5 to S.B.), by the Fyssen Foundation (to S.B.), by the Agence Nationale pour la Recherche (ANR, Grants ANR-17-EURE-0017 FrontCog, ANR-10-IDEX-0001-02 PSL* and ANR-16-CE37-0012-01 to V.C.), and by the University of Geneva Language and Communication Research Network (FID-LC-EXPERTSE to N.G.). EF is partially supported by the Dutch Province of Limburg.


domain: spi chim phys sde

text:
Clothilde Péra

Jean-Baptiste Doucet
email: jbdoucet@laas.fr

Franck Carcenac

Emmanuelle Daran

Towards greener nanoimprint processes

CONTEXT

Surfaces nanostructuration is commonly used in biomedical applications and also in renewable energy applications. For bio-systems, approved bio-materials have to be used with biocompatibility and/or biodegradability properties depending on their utility. For components dedicated to renewable energy applications, the use of greener polymers makes sense compared to petrochemicals materials. Moreover, reducing the use of polluting and harmful materials in micro and nano-technology processes is of great interest in terms of health and environmental issues.

The purpose of this work is to develop nanoimprint processes using "greener" polymers: Poly Lactic Acid (PLA) and Poly Vinyl Alcohol (PVA). PLA, a bio-sourced, bio-degradable and bio-compatible polymer, will be directly nano-structured via a thermal nanoimprint process (T-NIL). PVA, a synthetic bio-compatible and bio-degradable [1] polymer, will be used as water-soluble sacrificial layer in a lift-off process.

Soft nanoimprint lithography is a simple, very fast and cheap 2-step process. Patterns are first replicated in a polymer film by thermal nanoimprint. Then the structured film is used to pattern the substrate with photosensitive resist.

MATERIALS & METHODS

RESULTS

PVA water-soluble sacrificial layer in a lift-off process Lift-off process with a PVA underlayer has several advantages : its water-solubility and the benefit of a wet etching. Wet etching is an isotropic process, this makes it possible to create an interesting underetching profile for the lift-off step, called a cap profile. This profile implies a discontinuity of the metal deposit therefore avoiding the creation of metal bridges.

PVA (13-23 kg/mol) is dissolved overnight in water (concentration 5%) in a 70°C water bath (+/-10°C). To obtain 70 nm thick layer , PVA is deposited on a silicon substrate at 4000 rpm for 30 seconds. Sample is annealed 2 min at 110°C. The photoresist NILUV135 [2] is coated at 4000 rpm for 30 seconds to obtain a 130 nm thick layer and annealed 1 min at 95°C. Sample is shaped using nanoimpint process explained just above.

Resist pattern residual layer is removed by dry oxygen plasma etching. Etching speed is 20 nm/min, plasma lasts 3 min.

Then, to remove the PVA pattern residual layer, a wet etching is carried out. 2 structured samples are immersed during 1 min and 5 min respectively, in a mixture of 44% water / 56% ethanol under vacuum degassing. Samples are rinsed with pure ethanol and annealed for 2 minutes at 90°C.

PLA films structuration

COP films usually used are replaced with polylactic acid films.

Commercial films (Goodfellow) and home calendered films (based on Total Corbion granules) were introduced into nanoimprint process presented above with following parameters.

• 5 min thermal nanoimprint using a pressure of 450 PSI (30 bars) and a temperature of 70°C.

• 3 min UV nanoimprinting using a pressure of 200 PSI (14 bar) at room temperature.

• PLA anti-adhesive surface treatment for unmolding is composed of a few nanometers SiO2 deposit followed by a per fluoro decyl trichloro silane self-assembled monolayer grafting. This process allows patterns replication with good resolution and same depth as the initial mold. Total mask thickness (PVA + resist) is 200 nm (+/-2 nm measured by atomic force microscopy).

Total mask thickness after dry etching is measured at 150 nm (+/-2 nm), the PVA layer has been opened.

• 5 min immersion time dissolves too strongly PVA layer and suspends the resist.

• 1 min immersion time is not sufficient and does not allow to dissolve PVA in the patterns. The mask is measured at 115 nm (+/-2 nm) in height and the patterns at 110 nm (+/-2 nm) in depth.

Both structured PLA films contain bumps defects probably coming from water condensation during calendering.

These films are used to structure a layer of resist by UV nanoimprinting.

Patterns of 20 nm wide and 100 nm pitch are perfectly replicated up to defects (holes).

CONCLUSION

• To carry out lift-off step, wet etch step duration should be adjusted to obtain the desired profile.

• We demonstrate that it is possible to use PLA films for nanoimprint process to replicate patterns with very good resolution.

o Ready to use in biology applications o For microelectronics field use, PLA film fabrication process should be improved to remove film defects. This work was supported by the LAAS-CNRS micro and nanotechnologies platform, a member of the french Renatech network.

Optical image of patterns consisting of 500 nm, 200 nm or 100 nm wide squares and lines with variable pitch replicated in UV-NIL resist / PVA bilayer system. SEM image of patterns consisting of 500 nm wide lines with 2 µm pitch replicated in UV-NIL resist / PVA bilayer system after 5 min wet etchnig SEM image of patterns consisting of 50 nm or 20 nm wide squares and lines with variable pitch replicated in PLA film SEM image of patterns consisting of 20 nm wide lines with 100 nm pitch replicated in UV NIL resist

  process flow (1) Dry polymer film nano patterning by T-NIL (2) UV-NIL resist nano patterning with the polymer soft mold (3) Dry etching (4) Wet etching of sacrificial PVA layer with water/ethanol solution (5) Undercut profile of PVA open window (6) Metal deposition (7) Dissolution of PVA sacrificial layer in water (8) Metal patterns

[ 1 ]

 1 Chiellini, E., Corti, A., D'Antone, S., & Solaro, R. (2003). Biodegradation of poly (vinyl alcohol) based materials. In Progress in Polymer Science (Oxford) doi.org/10.1016/S0079-6700(02)00149-1 [2] Jean Baptiste Doucet, 'A UV-NIL resist developed for a transitional soft mold process and the subsequent dry etch transfer into hard materials', 43rd International Conference on Micro-and Nano-engineering (MNE 2017), Braga, Portugal, Sep. 2017.