Array for 980nm Vertical Cavity Surface Emitting Diodes and Detectors

Both the vertical cavity surface emitting diodes and detectors are fabricated by using the epitaxial wafer with resonant cavity structure. Their characteristics are analyzed. The light emitters have high spectral purity of 4.8nm and high electroluminescence intensity of 0.7mW while injection current is 50mA. A 1*16 array of surface emitting light device is tested on line by probes and then used for module. The light detectors have wavelength selectivity and space selectivity. The required difference in input mirror reflectivity between emitters and detectors can easily be achieved though varying the numbers of top DBR period by etching.

Optoelectronic Smart Pixels Comprising Flip-Chip-Bonded GaAs/AlGaAs MQW Detectors and Modulators on Silicon CMOS Circuit

于2010-11-23批量导入

Design, simulation and testing of large area silicon drift detectors and detector array for X-ray spectroscopy

Large area (25 mm(2)) silicon drift detectors and detector arrays (5x5) have been designed, simulated, and fabricated for X-ray spectroscopy. On the anode side, the hexagonal drift detector was designed with self-biasing spiral cathode rings (p(+)) of fixed resistance between rings and with a grounded guard anode to separate surface current from the anode current. Two designs have been used for the P-side: symmetric self-biasing spiral cathode rings (p(+)) and a uniform backside p(+) implant. Only 3 to 5 electrodes are needed to bias the detector plus an anode for signal collection. With graded electrical potential, a sub-nanoamper anode current, and a very small anode capacitance, an initial FWHM of 1.3 keV, without optimization of all parameters, has been obtained for 5.9 keV Fe-55 X-ray at RT using a uniform backside detector.

Improved neutron radiation hardness for Si detectors: Application of low resistivity starting material and or manipulation of N-eff by selective filling of radiation-induced traps at low temperatures

Radiation-induced electrical changes in both space charge region (SCR) of Si detectors and bulk material (BM) have been studied for samples of diodes and resistors made on Si materials with different initial resistivities. The space charge sign inversion fluence (Phi(inv)) has been found to increase linearly with the initial doping concentration (the reciprocal of the resistivity), which gives improved radiation hardness to Si detectors fabricated from low resistivity material. The resistivity of the BM, on the other hand, has been observed to increase with the neutron fluence and approach a saturation value in the order of hundreds k Omega cm at high fluences, independent of the initial resistivity and material type. However, the fluence (Phi(s)), at which the resistivity saturation starts, increases with the initial doping concentrations and the value of Phi(s) is in the same order of that of Phi(inv) for all resistivity samples. Improved radiation hardness can also be achieved by the manipulation of the space charge concentration (N-eff) in SCR, by selective filling and/or freezing at cryogenic temperatures the charge state of radiation-induced traps, to values that will give a much smaller full depletion voltage. Models have been proposed to explain the experimental data.

First charge collection and position-precision data on the medium-resistivity silicon strip detectors before and after neutron irradiation up to 2x10(14) n/cm(2)

Test strip detectors of 125 mu m, 500 mu m, and 1 mm pitches with about 1 cm(2) areas have been made on medium-resistivity silicon wafers (1.3 and 2.7 k Ohm cm). Detectors of 500 mu m pitch have been tested for charge collection and position precision before and after neutron irradiation (up to 2 x 10(14) n/cm(2)) using 820 and 1030 nm laser lights with different beam-spot sizes. It has been found that for a bias of 250 V a strip detector made of 1.3 k Ohm cm (300 mu m thick) can be fully depleted before and after an irradiation of 2 x 10(14) n/cm(2). For a 500 mu m pitch strip detector made of 2.7 k Ohm cm tested with an 1030 nm laser light with 200 mu m spot size, the position reconstruction error is about 14 mu m before irradiation, and 17 mu m after about 1.7 x 10(13) n/cm(2) irradiation. We demonstrated in this work that medium resistivity silicon strip detectors can work just as well as the traditional high-resistivity ones, but with higher radiation tolerance. We also tested charge sharing and position reconstruction using a 1030 nm wavelength (300 mu m absorption length in Si at RT) laser, which provides a simulation of MIP particles in high-physics experiments in terms of charge collection and position reconstruction, (C) 1999 Elsevier Science B.V. All rights reserved.

Structural and photoelectric studies on double barrier quantum well infrared detectors

GaAs/AlAs/GaAlAs double barrier quantum well (DBQW) structures are employed for making the 3 similar to 5 mu m photovoltaic infrared (IR) detectors with a peak detectivity of 5x10(11) cmHz(1/2)/W at 80K. The double crystal x-ray diffraction is combined with synchrotron radiation x-ray analysis to determine the exact thickness of GaAs, AlAs and GaAlAs sublayers. The interband photovoltaic (PV) spect ra of the DBQW sample and the spectral response of the IR photocurrent of the devices are measured directly by edge excitation method, providing the information about spatial separation processes of photogenerated carriers in the multiquantum wells and the distribution of built-in field in the active region.

双面二维硅微条探测器的沾污失效分析及修复；Contamination failure analysis and repairing for double side two dimensional silicon microstrip detectors

硅微条探测器通过微电子工艺制作,易因沾污导致性能下降甚至失效;裸露的键合引线,也易因机械力形成隐性或显性失效。对上述现象的研究可用于修复、维护探测器并在设计和工艺流程中改进其性能。本文通过光学、电气手段分析其结构和制作工艺流程,根据沾污性质在不同条件下清洗探测器,中测后根据芯片图形、封装方式和电气要求修复探测器,最后采用同位素α能谱测试修复效果。对一块沾污后失效(无法加载偏压)的硅微条清洗后在大气环境,N面接地,P面加载负偏压条件下进行了测试,结果显示:170 V全耗尽,平均漏电流2.94μA,5.486 MeV的α峰能量分辨率约1.28%。失效键合所在条的另一面各条能谱观测到假峰,键合修复后消除。因沾污失效的硅微条探测器经过合适的清洗、修复,部分可以恢复性能,但清洗对表面和结构有损伤,须谨慎。另外,键合失效后,因信号不能引出导致的电荷积累会通过电容效应影响其它灵敏区。文章提示,探测器应存放于洁净,恒温,低湿度,避光,避强电磁干扰的环境,以提高能量和位置分辨率,并增加工作稳定性,延长使用寿命。

Segmented HPGe平面型位置灵敏探测器阵列的γ成像分析；Gamma-ray imaging analysis by using array of segmented planar HPGe detectors

探测器位置分辨能力的高低是实现γ成像的一个重要指标.Segmented HPGe平面型位置灵敏探测器能够很好地给出γ射线与探测器晶体相互作用的作用点位置信息.利用由这种探测器组成的探测器阵列对~(22)Na标准源进行了γ成像实验.结果能够区分出标准源两个不同的摆放位置的细微差别,并与实际情况符合得很好.从而检验了Segmented HPGe平面型位置灵敏探测器的位置分辨能力.

Improvements in the selectivity of electrochemical detectors for liquid chromatography and flow injection analysis using the self-assembled n-alkanethiol monolayer-modified au electrode

4-Aminophenol (4-AP), paracetamol (PRCT), norepinephrine (NE), and dopamine (DA) (all somewhat hydrophobic compounds) were HPLC electrochemically detected while the signals from uric acid (UA) and ascorbic acid (AA) (both hydrophilic compounds at the pH studied) were minimized, taking advantage of the permselectivity of the self-assembled n-alkanethiol monolayer (C-10-SAM)-modified Au electrodes based on solute polarity, The effects of various factors, such as the chain length of the n-alkanethiol modifier, modifying time, and pH value, on the permeability of C-10-SAM coatings were examined, The calibration curves, linear response ranges, detection limits, and reproducibilities of the EC detector for 4-AP, PRCT, NE, and DA were obtained, The result shows that the EC detector can be applied in the chromatographic detection of 4-AP, PRCT, NE, and DA in urine, effectively removing the influence of UA and AA in high concentrations existing in biological samples. As a result, a great improvement in the selectivity of EC detectors has been achieved by using Au electrodes coated with neutral n-alkanethiol monolayer.

Parameter Sensitive Detectors

Object detection can be challenging when the object class exhibits large variations. One commonly-used strategy is to first partition the space of possible object variations and then train separate classifiers for each portion. However, with continuous spaces the partitions tend to be arbitrary since there are no natural boundaries (for example, consider the continuous range of human body poses). In this paper, a new formulation is proposed, where the detectors themselves are associated with continuous parameters, and reside in a parameterized function space. There are two advantages of this strategy. First, a-priori partitioning of the parameter space is not needed; the detectors themselves are in a parameterized space. Second, the underlying parameters for object variations can be learned from training data in an unsupervised manner. In profile face detection experiments, at a fixed false alarm number of 90, our method attains a detection rate of 75% vs. 70% for the method of Viola-Jones. In hand shape detection, at a false positive rate of 0.1%, our method achieves a detection rate of 99.5% vs. 98% for partition based methods. In pedestrian detection, our method reduces the miss detection rate by a factor of three at a false positive rate of 1%, compared with the method of Dalal-Triggs.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.