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.

Development of a GEM Electronic Board (GEB) for triple-GEM detectors

Developed for use with triple GEM detectors, the GEM Electronic Board (GEB) forms a crucial part of the electronics readout system being developed as part of the CMS muon upgrade program. The objective of the GEB is threefold; to provide stable powering and ground for the VFAT3 front ends, to enable high-speed communication between 24 VFAT3 front ends and an optohybrid, and to shield the GEM detector from electromagnetic interference. The paper describes the concept and design of a large-size GEB in detail, highlighting the challenges in terms of design and feasibility of this deceptively difficult system component.

The direction aftereffect is driven by adaptation of local motion detectors

The processing of motion information by the visual system can be decomposed into two general stages; point-by-point local motion extraction, followed by global motion extraction through the pooling of the local motion signals. The direction aftereVect (DAE) is a well known phenomenon in which prior adaptation to a unidirectional moving pattern results in an exaggerated perceived direction diVerence between the adapted direction and a subsequently viewed stimulus moving in a diVerent direction. The experiments in this paper sought to identify where the adaptation underlying the DAE occurs within the motion processing hierarchy. We found that the DAE exhibits interocular transfer, thus demonstrating that the underlying adapted neural mechanisms are binocularly driven and must, therefore, reside in the visual cortex. The remaining experiments measured the speed tuning of the DAE, and used the derived function to test a number of local and global models of the phenomenon. Our data provide compelling evidence that the DAE is driven by the adaptation of motion-sensitive neurons at the local-processing stage of motion encoding. This is in contrast to earlier research showing that direction repulsion, which can be viewed as a simultaneous presentation counterpart to the DAE, is a global motion process. This leads us to conclude that the DAE and direction repulsion reflect interactions between motion-sensitive neural mechanisms at different levels of the motion-processing hierarchy.

Fabrication and characterisation of high resistivity SOI substrates for monolithic high energy physics detectors

Silicon on Insulator (SOI) substrates offer a promising platform for monolithic high energy physics detectors with integrated read-out electronics and pixel diodes. This paper describes the fabrication and characterisation of specially-configured SOI substrates using improved bonded wafer ion split and grind/polish technologies. The crucial interface between the high resistivity handle silicon and the SOI buried oxide has been characterised using both pixel diodes and circular geometry MOS transistors. Pixel diode breakdown voltages were typically greater than 100V and average leakage current densities at 70 V were only 55 nA/ sq cm. MOS transistors subjected to 24 GeV proton irradiation showed an increased SOI buried oxide trapped charge of only 3.45x1011cn-2 for a dose of 2.7Mrad