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Free, publicly-accessible full text available May 24, 2024
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Atomically thin 2D transition metal dichalcogenides (TMDs), such as MoS2, are promising candidates for nanoscale photonics because of strong light–matter interactions. However, Fermi‐level pinning due to metal‐induced gap states (MIGS) at the metal–monolayer (1L)‐MoS2interface limits the application of optoelectronic devices based on conventional metals due to high contact resistance. On the other hand, a semimetal–TMD–semimetal device can overcome this limitation, where the MIGS are sufficiently suppressed allowing ohmic contacts. Herein, the optoelectronic performance of a bismuth–1L‐MoS2–bismuth device with ohmic electrical contacts and extraordinary optoelectronic properties is demonstrated. To address the wafer‐scale production, full coverage 1L‐MoS2grown by chemical vapor deposition. High photoresponsivity of 300 A W−1at wavelength 400 nm measured at 77 K, which translates into an external quantum efficiency (EQE) ≈1000 or 105%, is measured. The 90% rise time of the devices at 77 K is 0.1 ms, suggesting they can operate at the speed of ≈10 kHz. High‐performance broadband photodetector with spectral coverage ranging from 380 to 1000 nm is demonstrated. The combination of large‐array device fabrication, high sensitivity, and high‐speed response offers great potential for applications in photonics, including integrated optoelectronic circuits.
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Abstract Phase-change materials (PCMs) offer a compelling platform for active metaoptics, owing to their large index contrast and fast yet stable phase transition attributes. Despite recent advances in phase-change metasurfaces, a fully integrable solution that combines pronounced tuning measures, i.e., efficiency, dynamic range, speed, and power consumption, is still elusive. Here, we demonstrate an in situ electrically driven tunable metasurface by harnessing the full potential of a PCM alloy, Ge2Sb2Te5(GST), to realize non-volatile, reversible, multilevel, fast, and remarkable optical modulation in the near-infrared spectral range. Such a reprogrammable platform presents a record eleven-fold change in the reflectance (absolute reflectance contrast reaching 80%), unprecedented quasi-continuous spectral tuning over 250 nm, and switching speed that can potentially reach a few kHz. Our scalable heterostructure architecture capitalizes on the integration of a robust resistive microheater decoupled from an optically smart metasurface enabling good modal overlap with an ultrathin layer of the largest index contrast PCM to sustain high scattering efficiency even after several reversible phase transitions. We further experimentally demonstrate an electrically reconfigurable phase-change gradient metasurface capable of steering an incident light beam into different diffraction orders. This work represents a critical advance towards the development of fully integrable dynamic metasurfaces and their potential for beamforming applications.
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Abstract Translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large‐scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO2/SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high‐spatial‐resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self‐oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.