Ultrafast Spectroscopy with Broad Probe Spectral Range Extending from Near Ultraviolet to Near Infrared

1997 ◽  
Vol 70 (6) ◽  
pp. 1211-1224 ◽  
Author(s):  
Takayoshi Kobayashi
Keyword(s):  
Spectral Range ◽  
Ultrafast Spectroscopy ◽  
Near Infrared ◽  
Near Ultraviolet

An Infrared Microscope for Use on a Focused Ion Beam for Circuit Edit and Backside Edit Applications

2015 ◽  
Author(s):  
Alexander Richards ◽  
Matthew Weschler ◽  
Michael Durller
Keyword(s):  
Focused Ion Beam ◽  
Near Infrared ◽  
Ion Beam ◽  
Visible Spectrum ◽  
Camera System ◽  
Buried Structures ◽  
Ir Camera ◽  
Area Of Interest ◽  
Near Ultraviolet ◽  
Infrared Microscope

Abstract To help solve the navigational problem, i.e., being able to successfully locate a circuit for probing or editing without destroying chip functionality, a near-infrared (NIR), near-ultraviolet (NUV), and visible spectrum camera system was developed that attaches to most focused ion beam (FIB) or scanning electron microscope vacuum chambers. This paper reviews the details of the design and implementation of the NIR/NUV camera system, as instantiated upon the FEI FIB 200, with a particular focus on its use for the visualization of buried structures, and also for non-destructive real time area of interest location and end point detection. It specifically considers the use of the micro-optical camera system for its benefit in assisting with frontside and backside circuit edit, as well as other typical FIB milling activities. The quality of the image obtained by the IR camera rivals or exceeds traditional optical based imaging microscopy techniques.

Metastructures for the Giant Enhancement of Raman Scattering in the Near Infrared Spectral Range

JETP Letters ◽  
2020 ◽  
Vol 112 (1) ◽  
pp. 31-36
Author(s):  
V. I. Kukushkin ◽  
V. E. Kirpichev ◽  
E. N. Morozova ◽  
V. V. Solov’ev ◽  
Ya. V. Fedotova ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral

scholarly journals MoS2 Based Photodetectors: A Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2758
Author(s):  
Alberto Taffelli ◽  
Sandra Dirè ◽  
Alberto Quaranta ◽  
Lucio Pancheri
Keyword(s):  
Electric Fields ◽  
Spectral Range ◽  
Performance Metrics ◽  
Transition Metal Dichalcogenides ◽  
Visible Spectral Range ◽  
Detector Performance ◽  
Strong Light ◽  
Near Ultraviolet ◽  
Passivation Layers ◽  
Low Dimensional

Photodetectors based on transition metal dichalcogenides (TMDs) have been widely reported in the literature and molybdenum disulfide (MoS2) has been the most extensively explored for photodetection applications. The properties of MoS2, such as direct band gap transition in low dimensional structures, strong light–matter interaction and good carrier mobility, combined with the possibility of fabricating thin MoS2 films, have attracted interest for this material in the field of optoelectronics. In this work, MoS2-based photodetectors are reviewed in terms of their main performance metrics, namely responsivity, detectivity, response time and dark current. Although neat MoS2-based detectors already show remarkable characteristics in the visible spectral range, MoS2 can be advantageously coupled with other materials to further improve the detector performance Nanoparticles (NPs) and quantum dots (QDs) have been exploited in combination with MoS2 to boost the response of the devices in the near ultraviolet (NUV) and infrared (IR) spectral range. Moreover, heterostructures with different materials (e.g., other TMDs, Graphene) can speed up the response of the photodetectors through the creation of built-in electric fields and the faster transport of charge carriers. Finally, in order to enhance the stability of the devices, perovskites have been exploited both as passivation layers and as electron reservoirs.

scholarly journals Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2967
Author(s):  
Nicola Acito ◽  
Marco Diani ◽  
Gregorio Procissi ◽  
Giovanni Corsini
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Random Noise ◽  
Synthetic Data ◽  
Regression Function ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Electromagnetic Spectrum ◽  
Great Opportunity ◽  
Atmospheric Compensation

Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.

scholarly journals Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

2007 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt
Keyword(s):  
Optical Absorption ◽  
Absorption Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Vegetation Type ◽  
Differential Optical Absorption Spectroscopy ◽  
Trace Gas ◽  
Satellite Monitoring ◽  
Optical Absorption Spectroscopy ◽  
Vegetation Types

Abstract. A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.

Quantum efficiency of silicon photodiodes in the near-infrared spectral range

2003 ◽  
Vol 42 (22) ◽  
pp. 4415 ◽  
Author(s):  
Chris Hicks ◽  
Mark Kalatsky ◽  
Richard A. Metzler ◽  
Alexander O. Goushcha
Keyword(s):  
Quantum Efficiency ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral

Optical properties of selected native and coagulated human brain tissues in vitro in the visible and near infrared spectral range

2002 ◽  
Vol 47 (12) ◽  
pp. 2059-2073 ◽  
Author(s):  
A N Yaroslavsky ◽  
P C Schulze ◽  
I V Yaroslavsky ◽  
R Schober ◽  
F Ulrich ◽  
...  
Keyword(s):  
Optical Properties ◽  
Human Brain ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral ◽  
Brain Tissues
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