Publications by Ali Momeni | EPFL Graph Search

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We propose a bianisotropic hybrid metal-dielectric structure comprising dielectric and metallic cylindrical wedges wherein the composite metacylinder enables advanced control of electric, magnetic, and magnetoelectric resonances. We establish a theoretical ...

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditiona ...

In the past few years, analog computing has experienced rapid development but mostly for a single function. Motivated by parallel space-time computing and miniaturization, we show that reconfigurable graphene-based multilayerss offer a promising path towar ...

Wave-based signal processing has witnessed a significant expansion of interest in a variety of science and engineering disciplines, as it provides new opportunities for achieving high-speed and low-power operations. Although flat optics desires integrable ...

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

We present a polarization-insensitive metasurface processor to perform spatial asymmetric filtering of an incident beam, thereby allowing for real-time parallel analog processing. To enable massive parallel processing, we introduce a multiple-input multipl ...

Recently, investigation of metasurfaces has been extended to wave control through exploiting nonlinearity. Among all of the ways to achieve tunable metasurfaces with multiplexed performances, nonlinearity is one of the promising choices. Although several p ...

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

We introduce a novel concept of Multi-Input Multi-Output (MIMO) metasurface processor with asymmetric Optical Transfer Function (OTF) which can perform spatial first-order derivation on two orthogonal distinct input signals for both TM and TE polarizations ...

Under the trends of multifunctionality, tunability, and compactness in modern wave -based signal processors, in this paper, we propose a polarization-multiplexed graphene-based metasurface to realize distinct mathematical operators on the parallel time-dom ...

Analog computing has emerged as a promising candidate for real-time and parallel continuous data processing. This paper presents a reciprocal way for realizing asymmetric optical transfer functions (OTFs) in the reflection side of the on-axis processing ch ...

Ali Momeni

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Asymmetric Metal-Dielectric Metacylinders and Their Potential Applications From Engineering Scattering Patterns to Spatial Optical Signal Processing

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Switchable and Simultaneous Spatiotemporal Analog Computing with Computational Graphene-based Multilayers

Parallel wave-based analog computing using metagratings

Backpropagation-free Training of Analog AI Accelerators

Parallel Analog Computing Based on a 2×2 Multiple-Input Multiple-Output Metasurface Processor With Asymmetric Response

Spatial wave control using a self-biased nonlinear metasurface at microwave frequencies

Backpropagation-free training of deep physical neural networks

Parallel Optical Spatial Signal Processing Based on 2x2 MIMO Computational Metasurface

Parallel temporal signal processing enabled by polarization-multiplexed programmable THz metasurfaces

Reciprocal Metasurfaces for On-axis Reflective Optical Computing