Noise characteristics improvement of submicron InP/InGaAs avalanche photodiode for laser detection system

2020 ◽  
Vol 455 ◽  
pp. 124561
Author(s):  
Shahram MohammadNejad ◽  
Faeze Aghaei
Keyword(s):  
Detection System ◽  
Avalanche Photodiode ◽  
Laser Detection ◽  
Noise Characteristics

scholarly journals Innovative Techniques for X-ray Calorimetry

1990 ◽  
Vol 115 ◽  
pp. 357-360
Author(s):  
S. Labov ◽  
E. Silver ◽  
D. Landis ◽  
N. Madden ◽  
F. Goulding ◽  
...  
Keyword(s):  
Detection System ◽  
Light Emitting Diode ◽  
Thermal Response ◽  
Diagnostic Procedures ◽  
Resolving Power ◽  
Infrared Light ◽  
Temperature Control System ◽  
Thermal Detector ◽  
X Ray ◽  
Noise Characteristics

AbstractIn our x-ray calorimetry effort, we have developed several techniques which may be helpful to other groups working in this field. We are studying several different monolithic and composite calorimeter designs. In our readout configuration, the preamplifier circuit employs negative voltage feedback which allows us to accurately measure the temporal profile of the thermal pulse produced by an x-ray absorbed in a micro-calorimeter. Rise times of less than two microseconds have been observed in monolithic devices operating at .3 K. Furthermore, the feedback preamplifier can be configured for either positive or negative electro-thermal feedback. This preamplifier system is followed by an analog pulse shaping amplifier with a frequency response that can be adjusted to yield the maximum signal to noise ratio for a given thermal response of the calorimeter. In addition, we have developed several diagnostic procedures which have been useful in determining the operating and noise characteristics of our devices. These include an infrared light-emitting diode which flashes a discrete amount of energy on to the calorimeter, and a capacitively coupled test input to the preamplifier which allows us to directly determine the total noise in the thermal detection system. Finally, we are developing an adiabatic demagnetization refrigerator with a temperature control system that is designed to stabilize the 0.1 K cold stage to better than 8 μK. This is required for a resistive thermal detector with resolving power of 1000.

scholarly journals Design and Research of a Color Discrimination Method for Polycrystalline Silicon Cells Based on Laser Detection System

2019 ◽  
Vol 9 (20) ◽  
pp. 4468
Author(s):  
Zijian Chen ◽  
Shiyu Wang ◽  
Lian Zhang ◽  
Zenghong Ma
Keyword(s):  
Polycrystalline Silicon ◽  
Detection System ◽  
Measurement Data ◽  
Optical Power ◽  
Color Discrimination ◽  
Laser Detection ◽  
Coaxial System ◽  
Power Meter ◽  
Optical Shutters ◽  
Silicon Cell

In this paper, a method of color discrimination based on sample sensitivity to light wavelength is proposed based on the reflection spectra of a large number of samples and the statistical calculation of the measurement data. A laser detection system is designed to realize the color discrimination. For the color discrimination of polycrystalline silicon cells, the most sensitive wavelength, 434 nm, and the least sensitive wavelength, 645 nm, of polycrystalline silicon cells is obtained according to this method. A laser detection system was built to measure the polycrystalline silicon cells. This system consists of two lasers, optical shutters, collimating beam expanding systems, an optical coaxial system, sample platform, collecting lens, and optical power meter or optical sensor. Two laser beams of different wavelengths are beamed coaxially through the optical coaxial system onto a polycrystalline silicon cell and are reflected or scattered. The reflected or scattered lights are collected through a lens with a high number aperture and received separately by the optical power meter. Then the color value of the polycrystalline silicon cell in this system is characterized by the ratio of light intensity data received. The system measured a large number of previous polycrystalline silicon cells to form the different color categories of polycrystalline silicon cells of this system in the computer database. When a new polycrystalline silicon cell is measured, the color discrimination system can automatically classify the new polycrystalline silicon cell to a certain color category in order to achieve color discrimination.

The Application of Structure Laser Detection System in Medium and Low Speed Maglev F-Rail Detection

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Xingtai He ◽  
Guofeng Zeng ◽  
GuoQiang Wang ◽  
Feng Ye ◽  
Yihong Yuan
Keyword(s):  
Detection System ◽  
Low Speed ◽  
Laser Detection ◽  
Structure Laser

Knitting needle fault detection system for hosiery machine based on laser detection and machine vision

2020 ◽  
Vol 91 (1-2) ◽  
pp. 143-151
Author(s):  
Zhouqiang Zhang ◽  
Sihao Bai ◽  
Guang-shen Xu ◽  
Xuejing Liu ◽  
Jiangtao Jia ◽  
...  
Keyword(s):  
Fault Detection ◽  
Machine Vision ◽  
Detection System ◽  
Production Quality ◽  
Image Region ◽  
Charge Coupled Device ◽  
Laser Detection ◽  
Laser Signal ◽  
A Charge ◽  
Size Classifier

The knitting needle cylinder is one of the core parts of a hosiery machine. The operation of its needles can directly affect the production quality and efficiency of the hosiery machine. To reduce the production loss of a hosiery machine caused by knitting needle faults, a knitting needle fault detection system for hosiery machines based on a synergistic combination of laser detection and machine vision is proposed in this paper. When the system was operating normally, a photoelectric detector collected the laser signal reflected by the knitting needle and the system monitored the operation of the knitting needle using the ratio of adjacent peak-to-peak distances of the signals. When a fault signal was detected, the hosiery machine was stopped by the system immediately, and a charge-coupled device camera was used to take an image of the faulty knitting needle. After image preprocessing, the faulty knitting needle could be identified quickly and accurately using an image region size classifier based on a decision tree. The experimental results showed that a single image classification by the classifier could be performed in as little as 0.002 s.

Underwater laser detection system

2015 ◽  
Author(s):  
Walid Gomaa ◽  
Ashraf F. El-Sherif ◽  
Yasser H. El-Sharkawy
Keyword(s):  
Detection System ◽  
Laser Detection ◽  
Underwater Laser
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