Low noise near infra-red detection system using InGaAs pin photodiode

1993 ◽  
Vol 29 (2) ◽  
pp. 234 ◽  
Author(s):  
I. Mizumoto ◽  
S. Mashiko
Keyword(s):  
Detection System ◽  
Low Noise ◽  
Pin Photodiode ◽  
Infra Red

Charge Integrating Amplifier in the Near-Infrared Region Using an InGaAs PIN Photodiode

1993 ◽  
Vol 47 (9) ◽  
pp. 1462-1463
Author(s):  
I. Mizumoto ◽  
S. Mashiko ◽  
N. Suzuki
Keyword(s):  
Near Infrared ◽  
Detection System ◽  
Infrared Region ◽  
Low Noise ◽  
Spectroscopic Measurement ◽  
Noise Detection ◽  
Pin Photodiode ◽  
Detectable Power ◽  
Minimum Detectable Power ◽  
A Charge

A low-noise detection system using an InGaAs PIN photodiode for near-infrared spectroscopic measurement has been developed. The InGaAs PIN photodiode is more suitable than a Ge PIN photodiode for detecting low-level light in terms of dark current and quantum efficiency. The detection system consists of an InGaAs PIN photodiode with a charge integrating amplifier (InGaAs-CIA) operated at 77 K. A minimum detectable power of 10−16 W was achieved at a wavelength of 1.28 μm.

scholarly journals Towards hybrid pixel detectors for energy-dispersive or soft X-ray photon science

2016 ◽  
Vol 23 (2) ◽  
pp. 385-394 ◽  
Author(s):  
J. H. Jungmann-Smith ◽  
A. Bergamaschi ◽  
M. Brückner ◽  
S. Cartier ◽  
R. Dinapoli ◽  
...  
Keyword(s):  
Detection System ◽  
Low Noise ◽  
Energy Dispersive ◽  
Development Effort ◽  
Charge Sharing ◽  
X Ray ◽  
Pixel Detectors ◽  
Starting Point ◽  
Synchrotron Light ◽  
Photon Science

JUNGFRAU (adJUstiNg Gain detector FoR the Aramis User station) is a two-dimensional hybrid pixel detector for photon science applications at free-electron lasers and synchrotron light sources. The JUNGFRAU 0.4 prototype presented here is specifically geared towards low-noise performance and hence soft X-ray detection. The design, geometry and readout architecture of JUNGFRAU 0.4 correspond to those of other JUNGFRAU pixel detectors, which are charge-integrating detectors with 75 µm × 75 µm pixels. Main characteristics of JUNGFRAU 0.4 are its fixed gain and r.m.s. noise of as low as 27 e−electronic noise charge (<100 eV) with no active cooling. The 48 × 48 pixels JUNGFRAU 0.4 prototype can be combined with a charge-sharing suppression mask directly placed on the sensor, which keeps photons from hitting the charge-sharing regions of the pixels. The mask consists of a 150 µm tungsten sheet, in which 28 µm-diameter holes are laser-drilled. The mask is aligned with the pixels. The noise and gain characterization, and single-photon detection as low as 1.2 keV are shown. The performance of JUNGFRAU 0.4 without the mask and also in the charge-sharing suppression configuration (with the mask, with a `software mask' or a `cluster finding' algorithm) is tested, compared and evaluated, in particular with respect to the removal of the charge-sharing contribution in the spectra, the detection efficiency and the photon rate capability. Energy-dispersive and imaging experiments with fluorescence X-ray irradiation from an X-ray tube and a synchrotron light source are successfully demonstrated with an r.m.s. energy resolution of 20% (no mask) and 14% (with the mask) at 1.2 keV and of 5% at 13.3 keV. The performance evaluation of the JUNGFRAU 0.4 prototype suggests that this detection system could be the starting point for a future detector development effort for either applications in the soft X-ray energy regime or for an energy-dispersive detection system.

Design of Low-Noise, High-Density Signal Intelligent Detection System

2013 ◽  
Vol 380-384 ◽  
pp. 3882-3885
Author(s):  
Xiaoan Yang
Keyword(s):  
Phase Transition ◽  
Detection Method ◽  
Detection System ◽  
Low Noise ◽  
Weak Signal ◽  
High Effectiveness ◽  
Motion State ◽  
Intelligent Detection ◽  
Stable Performance ◽  
Engineering Practices

Using motion state of the equipment transducer to determine the presence of a weak signal is a common method of signal detection, whose core is to determine the system's phase change. There a many traditional ways to judge phase transition, but most of which have computational complexity and need a large amount of data which make them difficult to apply engineering practices. In order to solve these problems, this paper presents a detection method based on Lyapunov exponent classification with a small amount of data. This approach has some advantages such as requiring fewer observed values, small calculation amount, and able to automatically determine the phase transition without subjective factors involved etc. Experiments show that this method has stable performance, high effectiveness, strong practicality and promotion.

A compact low-noise photodiode detection system for chemiluminescence nitric oxide analyzer

2019 ◽  
Vol 90 (4) ◽  
pp. 046103 ◽  
Author(s):  
Hang Li ◽  
Wenqing Liu ◽  
Ruifeng Kan
Keyword(s):  
Nitric Oxide ◽  
Detection System ◽  
Low Noise

Low Noise Detection System Using a Silicon Photodiode with a Charge Integrating Amplifier

1995 ◽  
Vol 115 (2) ◽  
pp. 333-334
Author(s):  
Iwao Mizumoto ◽  
Shinzo Yamakawa ◽  
Shinro Mashiko ◽  
Nobumi Hagiwara
Keyword(s):  
Detection System ◽  
Low Noise ◽  
Noise Detection ◽  
Silicon Photodiode ◽  
A Charge

Design of Low-Noise and High-Density Signal Intelligent Detection System

2013 ◽  
Vol 760-762 ◽  
pp. 2229-2233
Author(s):  
Xiaoan Yang
Keyword(s):  
Phase Transition ◽  
Detection Method ◽  
Detection System ◽  
Low Noise ◽  
Weak Signal ◽  
High Effectiveness ◽  
Motion State ◽  
Intelligent Detection ◽  
Stable Performance ◽  
Engineering Practices

Using motion state of the equipment transducer to determine the presence of a weak signal is a common method of signal detection, whose core is to determine the system's phase change. There a many traditional ways to judge phase transition, but most of which have computational complexity and need a large amount of data which make them difficult to apply engineering practices. In order to solve these problems, this paper presents a detection method based on Lyapunov exponent classification with a small amount of data. This approach has some advantages such as requiring fewer observed values, small calculation amount, and able to automatically determine the phase transition without subjective factors involved etc. Experiments show that this method has stable performance, high effectiveness, strong practicality and promotion.

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