arXiv subjects

Romain Fleury

Publications and source records attributed to Romain Fleury.

At least 19 recordsLinked to original sources

arXiv2026

Beating the nonreciprocal isolation limit of integrated circulators by Floquet leakage interference

Time-modulation using semiconductor switches is a promising route to magnetless integrated nonreciprocal devices, as they can offer large modulation depths and high speeds. Yet, chip-scale devices are capped by the finite off-state capacitance of the switches, which induces detrimental leakage of the input wave to the isolated port, imposing a ceiling on the nonreciprocal isolation. Such leakage has largely constrained the development of nonreciprocal integrated systems at high frequency. Here, we beat this limit by using Floquet interference between leakages. In a time-Floquet switched-resonator circulator, two coherent leakages reach the isolated port: the release of the stored wave at the resonator's ring-down frequency, and the direct input leakage at the carrier frequency. We show that it is possible to create conditions under which the two leakages destructively interfere, and even completely cancel, yielding perfect isolation despite operating with non-ideal semiconductor switches. We experimentally confirm leakage interference in a 65-nm CMOS microwave Floquet circulator, reaching 40-dB isolation, which is more than 20 dB higher than the natural switch isolation. We also demonstrate that leakage interference has inherently fast dynamics, establishing itself within a single modulation period, allowing us to reverse the circulation chirality in 0.6~ns. Our results pave the way toward high-frequency integrated chips with ultra-high nonreciprocal isolation.

physics.app-ph↗

arXiv2026

Multi-channel Optical Vision Model

Spatial multiplexing is one of the natural strengths of optics, yet in optical neural networks, it is often used mainly as parallel throughput. Here, we show that spatial multiplexing in an optical neural network can be used not only to process multiple inputs in parallel, but also to define a trainable representational coordinate of the model. In three implemented scenarios, parallel-input processing, class-code readout and channel-mixed feature interaction, spatial channels act as independent learners, structured code dimensions, and interacting feature groups. The programmable free-space optical processor is trained through an online physical-forward/surrogate-backward scheme, where measured optical outputs define the forward pass while a differentiable surrogate estimates gradients and is continually fine-tuned during training from newly acquired optical data. We demonstrate these channel roles in image classification and regression tasks using multi-layer architectures with more than one million trainable optical phase parameters. We further implement a hybrid optical-electronic vision-language model, in which the optical neural network provides visual tokens to a digital transformer decoder for controlled image-captioning tasks. These results establish spatially multiplexed optical channels as a programmable feature and readout space for hybrid optical vision models.

physics.optics↗

arXiv2026

Towards Engineering Material Neural Networks

Structures that capture functionality in the form of animate or intelligent machines have the potential to transform modern engineering applications. Animation and embedded intelligence are typically realised by integrating advanced capabilities such as reversibility, adaptive responses and learning directly into the materials themselves. Currently, the majority of adaptive material systems rely on predefined adaptive designs combined with in-service, electronics-based computing to dynamically modify the structural behaviour. However, structural configurations with interconnected adaptable nodes are able to approximate continuous functions, providing new possibilities and opportunities than classical metamaterials and computational materials. We discuss here the potential to design load-bearing engineering materials with trainable physical parameters and neural network-inspired morphologies, embedding intelligence directly into their structure, a concept we define as Engineering Material Neural Networks (EMNNs) as a subcategory of Physical Neural Networks. In this perspective, we first establish the foundational concept of EMNNs; we then detail the mechanical and multifunctional properties required for such structural configurations. Finally, we evaluate existing and emerging engineering materials that hold promise for enabling this innovative approach. Key material candidates for realising EMNNs include composites, architected, biological and engineering living materials. We also outline future directions in materials science and structural engineering for developing EMNNs.

cond-mat.mtrl-sci↗

arXiv2026

Dynamical frustration in spacetime metamaterials enables cascading logic and synchronization

Spacetime metamaterials are engineered media whose constitutive parameters such as permittivity, permeability, stiffness, or mass density are modulated simultaneously in both space and time. These additional degrees of freedom, absent in conventional static metamaterials, unlock a cabinet of wave phenomena that cannot be achieved in time-invariant structures, e.g. compact nonreciprocal devices, topological insulators, and devices for efficient frequency conversion and mixing and pulse shaping. The vast majority of these studies, however, operate in the stable regime, where modulation parameters are chosen to yield linear wave propagation. Here, we push spacetime metamaterials into the regime of parametric instability, and discover a novel type of ``dynamically frustrated'' oscillating states, where nonlinear non-reciprocal, topologically protected phase dislocations emerge. We control these dislocations and make them stop, split, and recombine. We harness this control to create devices for cascading logic in branched networks, and synchronization in 2D metamaterials. Our findings are broadly applicable anywhere where spacetime modulation can be pushed beyond linear stability, from cold atoms and superconducting circuits to acoustics and RF circuits.

cond-mat.soft↗

arXiv2026

Non-Hermitian fluctuations enable model-free particle manipulation

Contactless manipulation of microscopic matter is central to applications ranging from the isolation of circulating tumor cells in liquid biopsies to the removal of microplastics from environmental water. Electromagnetic approaches are particularly attractive because fields can be structured within compact microfluidic systems using either light or simple electrode architectures. However, precise manipulation requires calibrated models of the field distribution and accurate knowledge of the properties of both the object and the surrounding medium, which limits applicability to well-characterized, static systems. Here we show that energy dissipation itself provides sufficient information for deterministic particle control. Instead of relying on explicit field calibration, our approach exploits an original relationship between particle position, energy dissipation, and electromagnetic body forces, which can be accessed experimentally through variations of conductance matrices. By extracting force-shaping voltage patterns from these measurements, we demonstrate fully automated closed-loop manipulation of silica microbeads in one and two dimensions, including in the presence of other freely moving particles in a disordered background. These results establish a pathway toward deterministic force control by deliberately measuring and exploiting the non-Hermitian response of the system to engineer electromagnetic momentum transfer. This framework expands micromanipulation into realistic, dynamically evolving environments, where wave-matter interactions cannot be fully pre-characterized or eliminated through design.

physics.app-ph↗

arXiv2026

Expressivity of Programmable-Metasurface-Based Physical Neural Networks: Encoding Non-Linearity, Structural Non-Linearity, and Depth

Wave-based signal processing conventionally encodes input data into the input wavefront, making it challenging to implement non-linear operations. Programmable wave systems enable an alternative approach: encoding the input data into the scattering properties of tunable components. With such structural input encoding, two potentially non-linear mappings are involved: first, from the input data to the tunable components' scattering characteristics, and, second, from these scattering characteristics to the output wavefront. In this paper, we systematically examine the expressivity of a wave-based physical neural network (WPNN) with structural input encoding. Our analysis is based on a physics-consistent multiport-network model of a compact D-band rich-scattering cavity parametrized by a 100-element programmable metasurface. We separately control encoding non-linearity, structural non-linearity, and network depth in order to examine their interplay, considering a controlled scalar regression task. With phase encoding and strong inter-element mutual coupling (MC), both aforementioned mappings are strongly non-linear and the WPNN performs very well even with a single layer. We further observe that additional layers can partially compensate for weak inter-element MC. In addition, we demonstrate that WPNN depth can improve expressivity even when it is not associated with an increase in trainable weights. Altogether, our results provide a physics-consistent picture of how encoding choice, MC strength, and depth jointly govern the expressive power of PM-based WPNNs, informing design choices for future experimental implementations of WPNNs.

eess.SP↗

arXiv2026

Classical Analog Emulation of Quantum Circuits via Time-Averaged Dynamic States

Classical analog hardware that emulates quantum circuits at the gate level offers a route to benchmarking, prototyping, and teaching quantum algorithms. We introduce wavebits, classical wave analogs of qubits whose amplitudes are carried by physical oscillatory signals, and show that any nonseparable N-qubit state can be encoded in 2N narrowband signals that remain locally separable at every instant. The nonseparable correlations are recovered at readout by time-averaged demodulation over auxiliary carrier frequencies, referred to as nonseparability channels. We prove that any two-qubit gate unravels into the time-averaged tensor product of two local time-varying operators, and that arbitrary circuits are emulated with a base-frequency count scaling linearly with the number of entangling layers, independent of qubit number. The exponential cost of the 2^N-dimensional state reappears at readout and in the averaging time of deep circuits, not during circuit execution, as quantified by an analytic error bound that also serves as a hardware design rule. A mixed-signal prototype emulates Bell state generation, controlled-NOT gates, phase kickback, and Bloch-sphere rotations with fidelities above 0.98, and numerical benchmarks against exact state vectors validate the scheme for up to six qubits. The architecture is directly implementable in acoustic, photonic, and mixed-signal platforms.

quant-ph↗

arXiv2025

Multi-Objective Tweezers in Scattering Media

Radiation forces and torques enable the manipulation of objects with acoustic and electromagnetic waves. Yet, harnessing them in complex scattering media remains a formidable challenge, especially when multiple objects must be controlled under competing objectives. Here, we demonstrate that sound or light can be shaped to tailor momentum transfer to multiple objects simultaneously in a complex scattering medium. For a single object, our theory yields the maximal achievable force or torque; for multiple objects, it produces Pareto-optimal actuation and exact bounds on the simultaneous realization of incompatible objectives. This opens new applications for wave tweezers, enabling selective and precise manipulation of objects within complex media, ranging from the handling of cells, organoids, or microrobots, to targeted drug delivery in biological media.

physics.app-ph↗

arXiv2025

Tutorial: A practical guide to the alignment of defocused spatial light modulators for fast diffractive neural networks

The conjugation of multiple spatial light modulators (SLMs) enables the construction of optical diffractive neural networks (DNNs). To accelerate training, which is limited by the low refresh rate of SLMs, spatial multiplexing of the input data across different spatial channels is possible, maximizing the number of available spatial degrees of freedom (DoFs). Precise alignment is required in order to ensure that the same physical operation is performed across each channel and thus the learning operation of the network. We present a semi-automatic procedure for this experimentally challenging alignment resulting in a pixel-level conjugation. It is scalable to any number of SLMs and may be useful in wavefront shaping setups where precise conjugation of SLMs is required, e.g. for the control of optical waves in phase and amplitude. The resulting setup functions as an optical DNN capable of processing hundreds of inputs simultaneously, thereby reducing training times and experimental noise through spatial averaging. We further present a characterization of the setup and an alignment method.

physics.optics↗

arXiv2025

Observation of amplitude-driven nonreciprocity for energy guiding

The non-Hermitian skin effect is an intriguing physical phenomenon, in which all eigen-modes of a non-Hermitian lattice become localized at boundary regions. While such an exotic behavior has been demonstrated in various physical platforms, most realizations have been so far restricted to the linear regime. Here, we explore the cooperation between nonlinearity and the non-Hermitian skin effect, revealing extraordinary amplitude-driven skin localization dynamics. By introducing an extension to the Hatano-Nelson model where couplings inherent nonlinear behavior, we demonstrate the existence of unique amplitude-driven non-Hermitian skin modes capable of concentrating the energy of a source at any point of space, depending on the power level. Our theoretical model is supported by numerical simulations and experimental realization via a highly configurable acoustic metamaterial composed of active electroacoustic resonators. In all, our findings open up exciting paths for a new generation of non-reciprocal systems, in which nonlinearities serve as a strategic tuning knob to manipulate the guiding process.

physics.app-ph↗

arXiv2025

Practical realization of chiral nonlinearity for strong topological protection

Nonlinear topology has been much less inquired compared to its linear counterpart. Existing advances have focused on nonlinearities of limited magnitudes and fairly homogeneous types. As such, the realizations have rarely been concerned with the requirements for nonlinearity. Here we explore nonlinear topological protection by determining nonlinear rules and demonstrate their relevance in real-world experiments. We take advantage of chiral symmetry and identify the condition for its continuation in general nonlinear environments. Applying it to one-dimensional topological lattices, we show possible evolution paths for zero-energy edge states that preserve topologically nontrivial phases regardless of the specifics of the chiral nonlinearities. Based on an acoustic prototype design with non-local nonlinearities, we theoretically, numerically, and experimentally implement the nonlinear topological edge states that persist in all nonlinear degrees and directions without any frequency shift. Our findings unveil a broad family of nonlinearities compatible with topological non-triviality, establishing a solid ground for future drilling in the emergent field of nonlinear topology.

cond-mat.mes-hall↗

arXiv2025

Science of a coffee cup: a physicist walks into a bar...

... and annoys everyone with unsolicited experiments. The present paper proposes a short pedagogical review of the various phenomena that can be observed in a coffee cup with little to no equipment. The physical domains spanned include acoustics, optics and, of course, fluid mechanics. The variety of experimental and theoretical techniques introduced throughout the paper makes it suitable for a broad audience. For each topic, we first propose an experimental realization before introducing a minimal model to explain the observations. We end each section by discussing more advanced works existing in the literature as well as related applications. We provide detailed experimental procedures and videos of the experiments that can be freely used for teaching purposes. The phenomena presented here also show remarkable efficiency as icebreakers for morning coffee in laboratories or conferences.

physics.ed-ph↗

arXiv2024

Motion of a floating sphere pulled by a string and induced flows

We study in this article the motion of a floating ball attached to a soft string set in circular motion through its other end. Although simple, the system exhibits rich dynamics that we investigate experimentally and theoretically. At low rotation speeds, we show that the circular trajectory of the ball shrinks when we stir faster, which challenges common intuition based on centrifugal force. For higher rotation rates, the ball is either suddenly attracted toward the center, or is repulsed away from it, depending on the string length. Experimental measurements of the generated flow show that a Magnus force must be taken into account to correctly explain all the observations. In particular, our deformable system allows to measure the ratio of the lift force over the inertial force. Interestingly, the system exhibits strong hysteretic behavior, showing that the ball can robustly trap itself at the center of the flow generated by its past motion. The present experiment also revisits the famous 'tea-leaf paradox', which refers to the unexpected migration of tea leaves toward the center of a tea cup when the latter is mixed with a rigid spoon. The ball attached to the string plays the role of a deformable spoon, and we show that there exists a maximum rotation speed above which the tea-leaves transport brutally stops.

physics.flu-dyn↗

arXiv2024

Hybrid broadband conduction and amplitude-driven topological confinement of sound via synthetic acoustic crystals

Precise wave manipulation has undoubtedly forged the technological landscape we thrive in today. Although our understanding of wave phenomena has come a long way since the earliest observations of desert dunes or ocean waves, the unimpeded development of mathematics has enabled ever more complex and exotic physical phenomena to be comprehensively described. Here, we take wave manipulation a step further by introducing an unprecedented synthetic acoustic crystal capable of realizing simultaneous linear broadband conduction and nonlinear topological insulation, depicting a robust amplitude-dependent mode localized deep within - i.e. an amplitude-driven topological confinement of sound. The latter is achieved by means of an open acoustic waveguide lined with a chain of nonlocally and nonlinearly coupled active electroacoustic resonators. Starting from a comprehensive topological model for classical waves, we demonstrate that different topological regimes can be accessed by increasing driving amplitude and that topological robustness against coupling disorder is a direct consequence of symmetric and simultaneous response between coupled resonators. Theoretical predictions are validated by a fully programmable experimental apparatus capable of realizing the real-time manipulation of metacrystal properties. In all, our results provide a solid foundation for future research in the design and manipulation of classical waves in artificial materials involving nonlinearity, nonlocality, and non-hermiticity.

physics.app-ph↗

arXiv2024

Symmetry-driven Phononic Metamaterials

Phonons are quasiparticles associated with mechanical vibrations in materials. They are at the root of the propagation of sound and elastic waves, as well as of thermal phenomena, which are pervasive in our everyday life and in many technologies. The fundamental understanding and control of phonon responses in natural and artificial media are key in the context of communications, isolation, energy harvesting and control, sensing and imaging. It has recently been realized that controlling different symmetry classes at the microscopic and mesoscopic scales in synthetic media offers a powerful tool to precisely tailor phononic responses for advanced acoustic and elastodynamic wave control. In this Review, we survey the recent progress in the design and synthesis of artificial phononic media, namely phononic crystals and metamaterials, guided by symmetry principles. Starting from tailored broken spatial symmetries, we discuss their interplay with time symmetries for non-reciprocal and non-conservative phenomena. We also address broader concepts that combine multiple symmetry classes to induce exotic phononic wave transport. We conclude with an outlook on future research directions based on symmetry engineering for the advanced control of phononic waves.

physics.app-ph↗

arXiv2024

Experimental demonstration of a space-time modulated airborne acoustic circulator

Achieving strongly nonreciprocal scattering in compact linear acoustic devices is a challenging task. One possible solution is the use of time-modulated resonators, however, their implementation in the realm of audible airborne acoustics is typically hindered by the difficulty to obtain large modulation depth and speeds while managing noise issues. Here, we propose a practical and cost-efficient route to realize simple modulated resonators and observe experimentally the strong nonreciprocal behavior of an acoustic circulator. We propose to modulate the neck cross-section areas of three coupled Helmholtz resonators using rotating circular plates actuated by an electrical motor, and control their phase difference via meshed gears, thereby implementing a modulation scheme with broken time-reversal symmetry that effectively imparts angular momentum to the system. We experimentally demonstrate tunable nonreciprocal behavior with a high nonreciprocal isolation of 34 dB and reflection as low as -9 dB, with insertion losses of 5 dB and parasitic signals below -20 dB. All the experimental results agree well with theoretical and numerical predictions.

physics.flu-dyn↗

arXiv2024

Experimental realization of an active time-modulated acoustic circulator

Reciprocity is one of the fundamental characteristics of wave propagation in linear time-invariant media with preserved time-reversal symmetry. Breaking reciprocity opens the way to numerous applications in the fields of phononics and photonics, as it allows the unidirectional transport of information and energy carried by waves. In acoustics, achieving non-reciprocal behavior remains a challenge, for which time-varying media are one of the solutions. Here, we design and experimentally demonstrate a three-port non-reciprocal acoustic scatterer that behaves as a circulator for audible sound, by actively modulating the effective mass of the acoustic membranes over time. We discuss the conception and experimental validation of such an acoustic circulator, implemented with actively controlled loudspeakers, in the realm of audible and airborne acoustics, and demonstrate its good performance in different scenarios.

physics.app-ph↗

arXiv2024

Training of Physical Neural Networks

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models.

physics.app-ph↗