Ultrastrong Light-Matter Coupling in 2D Metal-Chalcogenates

Surendra B. Anantharaman; Jason Lynch; Mariya Aleksich; Christopher E. Stevens; Christopher Munley; Bongjun Choi; Sridhar Shenoy; Thomas Darlington; Arka Majumdar; P. James Shuck; Joshua Hendrickson; J. Nathan Hohman; Deep Jariwala

arXiv · 2308.11108

Ultrastrong Light-Matter Coupling in 2D Metal-Chalcogenates

Surendra B. Anantharaman·Jason Lynch·Mariya Aleksich·Christopher E. Stevens·Christopher Munley·Bongjun Choi·Sridhar Shenoy·Thomas Darlington·Arka Majumdar·P. James Shuck·Joshua Hendrickson·J. Nathan Hohman·Deep Jariwala

View original source ↗Read source document ↗Export metadata ↓

Abstract

Hybridization of excitons with photons to form hybrid quasiparticles, exciton-polaritons (EPs), has been widely investigated in a range of semiconductor material systems coupled to photonic cavities. Self-hybridization occurs when the semiconductor itself can serve as the photonic cavity medium resulting in strongly-coupled EPs with Rabi splitting energies > 200 meV at room temperatures which recently were observed in layered two-dimensional (2D) excitonic materials. Here, we report an extreme version of this phenomenon, an ultrastrong EP coupling, in a nascent, 2D excitonic system, the metal organic chalcogenate (MOCHA) compound named mithrene. The resulting self-hybridized EPs in mithrene crystals placed on Au substrates show Rabi Splitting in the ultrastrong coupling range (> 600 meV) due to the strong oscillator strength of the excitons concurrent with the large refractive indices of mithrene. We further show bright EP emission at room temperature as well as EP dispersions at low-temperatures. Importantly, we find lower EP emission linewidth narrowing to ~1 nm when mithrene crystals are placed in closed Fabry-Perot cavities. Our results suggest that MOCHA materials are ideal for polaritonics in the deep green-blue part of the spectrum where strong excitonic materials with large optical constants are notably scarce.

Explore related subjects

physics.optics cond-mat.mes-hall

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Surendra B. Anantharaman, Jason Lynch, Mariya Aleksich, Christopher E. Stevens, Christopher Munley, Bongjun Choi, Sridhar Shenoy, Thomas Darlington, Arka Majumdar, P. James Shuck, Joshua Hendrickson, J. Nathan Hohman, Deep Jariwala. 2023-08-22. Ultrastrong Light-Matter Coupling in 2D Metal-Chalcogenates. https://arxiv.org/abs/2308.11108

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Active Fourier Spectroscopy: From fixed sampling protocols to adaptive measurement

Fourier-transform spectroscopy (FTS) is based on the Nyquist-Shannon theorem, which dictates a strict sampling protocol for the measurement of continuous signals. Here we introduce Active Fourier Spectroscopy (AFS), a generalization of FTS in which measurements are still taken in the Fourier-conjugate delay domain, but where the most informative acquisition points are selected adaptively and the spectrum is reconstructed by sequential, uncertainty-aware inference rather than by fixed-grid Fourier inversion. Our approach relies on a theoretical insight that the Nyquist condition applies only under complete ignorance about the system under investigation, and shows how to systematically improve upon it when prior knowledge exists. Because AFS recovers conventional FTS whenever the prior is uninformative, its performance never falls below that of conventional sampling. We demonstrate AFS for optical spectroscopy in three settings vital to research and medicine: clinical FTIR-based blood plasma analysis, optical metrology of structured laser beams, and hyperspectral imaging with an RGB camera. In each case AFS reaches a higher target reconstruction quality from far fewer acquisitions and provides complete uncertainty quantification in real time, propagated rigorously through downstream analysis.

physics.optics↗

arXiv2026

Memory in Integrated Photonic Neural Networks: From Physical Mechanisms to Neuromorphic Architectures

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biological neural systems inherently co-localize computation and memory through distributed, dynamical processes. Neuromorphic computing seeks to emulate this paradigm by leveraging physical substrates whose intrinsic dynamics simultaneously encode and process information. Among emerging platforms, silicon photoncis offer a compelling approach due to its high bandwidth, low-loss propagation, and inherent parallelism. This review examines the role of memory in integrated photonic neuromorphic systems, with emphasis on the physical mechanisms that provide volatile (short-term) and non-volatile (long-term) memory in silicon-on-insulator and hybrid silicon-on-insulator platforms. Drawing inspiration from digital, biological, and photonic memory architectures, we classify existing approaches based on their underlying physical principles. We cover implementations ranging from delay lines and slow-light structures to multistable dynamics and structural memory based on charge trapping and phase-change materials. We then discuss how these mechanisms support photonic neural network architectures, including feed-forward, reservoir computing, spiking and hybrid optoelectronic recurrent systems, and assess their relevance for time-dependent singal-processing tasks such as channel equalization in telecommunications. This review aims to establish a unified framework for understanding memory and learning in neuromorphic photonics and outlines key challenges and opportunities for scalable, energy-efficient neuromorphic hardware.

physics.optics↗

arXiv2026

Exoplanet Detection Using Adaptive Quantum-Optimal Measurement

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to \(10^{-8}\) -- five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.

physics.optics↗