Harnessing excitons at the nanoscale -- photoelectrical platform for quantitative sensing and imaging

Excitons -- quasiparticles formed by the binding of an electron and a hole through electrostatic attraction -- hold promise in the fields of quantum light confinement and optoelectronic sensing. Atomically thin transition metal dichalcogenides (TMDs) provide a versatile platform for hosting and manipulating excitons, given their robust Coulomb interactions and exceptional sensitivity to dielectric environments. In this study, we introduce a cryogenic scanning probe photoelectrical sensing platform, termed exciton-resonant microwave impedance microscopy (ER-MIM). ER-MIM enables ultra-sensitive probing of exciton polarons and their Rydberg states at the nanoscale. Utilizing this technique, we explore the interplay between excitons and material properties, including carrier density, in-plane electric field, and dielectric screening. Furthermore, we employ deep learning for automated data analysis and quantitative extraction of electrical information, unveiling the potential of exciton-assisted nano-electrometry. Our findings establish an invaluable sensing platform and readout mechanism, advancing our understanding of exciton excitations and their applications in the quantum realm

Spatially dispersive circular photogalvanic effect in a Weyl semimetal

Weyl semimetals are gapless topological states of matter with broken inversion and/or time reversal symmetry, which can support unconventional responses to externally applied electrical, optical and magnetic fields. Here we report a new photogalvanic effect in type-II WSMs, MoTe2 and Mo0.9W0.1Te2, which are observed to support a circulating photocurrent when illuminated by circularly polarized light at normal incidence. This effect occurs exclusively in the inversion broken phase, where crucially we find that it is associated with a spatially varying beam profile via a new dispersive contribution to the circular photogalvanic effect (s-CPGE). The response functions derived for s-CPGE reveal the microscopic mechanism of this photocurrent, which are controlled by terms that are allowed in the absence of inversion symmetry, along with asymmetric carrier excitation and relaxation. By evaluating this response for a minimal model of a Weyl semimetal, we obtain the frequency dependent scaling behavior of this form of photocurrent. These results demonstrate opportunities for controlling photoresponse by patterning optical fields to store, manipulate and transmit information over a wide spectral range

Capturing dynamical correlations using implicit neural representations

The observation and description of collective excitations in solids is a fundamental issue when seeking to understand the physics of a many-body system. Analysis of these excitations is usually carried out by measuring the dynamical structure factor, S(Q, $\omega$), with inelastic neutron or x-ray scattering techniques and comparing this against a calculated dynamical model. Here, we develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. We benchmark this approach on a Linear Spin Wave Theory (LSWT) simulator and advanced inelastic neutron scattering data from the square-lattice spin-1 antiferromagnet La$_2$NiO$_4$. We find that the model predicts the unknown parameters with excellent agreement relative to analytical fitting. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data, without the need for human-guided peak finding and fitting algorithms. This prototypical approach promises a new technology for this field to automatically detect and refine more advanced models for ordered quantum systems.Comment: 12 pages, 7 figure

Nonlocal Optoelectronics In Topological Semimetals

Quantum materials - especially electronic materials that can source, detect and control light, promise to spark the next technological revolution. Recently, investigations of light-matter interactions in topological materials have attracted enormous research interest, with a major aim towards characterizing their electronic properties by exotic optical phenomena and advancing their applications in quantum devices. However, the existing optical probes have many limitations, and new techniques need to be continuously developed to uncover and utilize the quantum beauty lurking in these materials. In this thesis, we will discuss our recent efforts introducing nonlocality into optoelectronics, and our discoveries including the spatially dispersive circular photogalvanic effect, orbital photogalvanic effect and opto-twistronic responses. By combining perspectives and approaches across quantum kinetic theory, band theory calculations and our newly developed state-of-the-art angle resolved photocurrent spectroscopy, we systemically explore the unique optical signatures of topological semimetals. We then discuss how those discoveries would open a new venue for realizing phase-sensitive photodetection and topological polaritonic waveguiding utlizing quantum materials, and their implications for the next quantum renovation

Tunable geometric photocurrent in van der Waals heterostructure

Utilizing the spin or valley degree of freedom is a promising approach for realizing more energy-efficient information processing devices. Circularly polarized light can be used to generate spin/valley current in monolayer 2D transition metal dichalcogenides. We observe a geometrically dependent photocurrent in heterostructure MoS2/WSe2, where light with a different circular polarization generates photocurrents in opposite directions. Furthermore, we show that this photocurrent persists even at room temperature, and it can be controlled using an in-plane electric field and back gating. We explain the observed phenomena via valley-dependent valence band shift and the valley optical selection rule. This finding may facilitate the use of 2D heterostructures as a platform for opto-valleytronics and opto-spintronics devices.Ministry of Education (MOE)National Research Foundation (NRF)Published versionChina Scholarship Council (No. 201709345003); National Natural Science Foundation of China (No. 61974075, No. 61704121); Agency for Science, Technology and Research (QTE); National Research Foundation Singapore (QEP, NRF-CRP21-2018-0007); Ministry of Education - Singapore (MOE2016-T2-1-163, MOE2016-T2-2-077, MOE2016-T3-1-006 (S)

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems

Capturing dynamical correlations using implicit neural representations

Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems

Optically Triggered Emergent Mesostructures in Monolayer WS₂

The ultrahigh surface area of two-dimensional materials can drive multimodal coupling between optical, electrical, and mechanical properties that leads to emergent dynamical responses not possible in three-dimensional systems. We observed that optical excitation of the WS2 monolayer above the exciton energy creates symmetrically patterned mechanical protrusions which can be controlled by laser intensity and wavelength. This observed photostrictive behavior is attributed to lattice expansion due to the formation of polarons, which are charge carriers dressed by lattice vibrations. Scanning Kelvin probe force microscopy measurements and density functional theory calculations reveal unconventional charge transport properties such as the spatially and optical intensity-dependent conversion in the WS2 monolayer from apparent n- to p-type and the subsequent formation of effective p–n junctions at the boundaries between regions with different defect densities. The strong opto-electrical-mechanical coupling in the WS2 monolayer reveals previously unexplored properties, which can lead to new applications in optically driven ultrathin microactuators